JP5839765B1 - Information processing apparatus, information processing method, and information processing program - Google Patents

Abstract

It is an object of the present invention to disperse the processing load of a server device that accepts purchase of a voting ticket by predicting odds fluctuation and providing the predicted fluctuation. The information processing apparatus receives selected race identification information for identifying the selected race. The information processing apparatus acquires from the purchase history storage unit that stores the purchase history of the voting ticket, the purchase type corresponding to the purchaser identification information indicating the anticipator and the race identification information indicating the past race. Based on the acquired purchase type, the information processing apparatus predicts the purchase of the voting ticket that the predictor purchases in the selected race. The information processing apparatus predicts a change in odds of the voting ticket of the selected race based on the predicted purchase. The information processing apparatus displays variation information indicating the predicted variation in odds.

Description

  The present invention relates to the technical field of a system for providing information on odds of race voting tickets.

  In Patent Document 1, in a race such as a horse race, a bicycle race, and a boat race, all the voting data is totaled at predetermined time intervals, the odds data is calculated from the total information, and the odds data selected from the odds data is displayed. The technique to be disclosed is disclosed.

JP 09-106426 A

  The user may determine the purchase eye or purchase price of the voting ticket to be purchased based on the presented odds. However, the odds that can be referred to in order to determine the purchase eye or the purchase price are the unfixed odds up to the time of purchase of the voting ticket. Odds can fluctuate significantly between the time of purchase and the final odds. Therefore, even if the purchased voting ticket is won, the user may not be able to increase the profit calculated from the odds at the time of purchase. In order to avoid large fluctuations in odds, the user must purchase a voting ticket just before the deadline for voting. Then, since the purchase of voting tickets is concentrated just before the deadline of voting, the processing load of the server device that accepts the purchase of voting tickets increases just before the deadline of voting.

  The present invention has been made in view of the above points, and by predicting odds fluctuations and providing the predicted fluctuations, it is possible to distribute the processing load of a server device that accepts purchases of voting tickets. An information processing apparatus, an information processing method, and the like are provided.

  In order to solve the above-mentioned problem, the invention according to claim 1 is characterized in that a receiving means for receiving selected race identification information for identifying a selected race among a plurality of races, and the selected race identification received by the receiving means. A specifying means for specifying a predictor who has predicted winning of any race body among a plurality of race bodies participating in the race indicated by the information, and a purchaser who has purchased a voting ticket for any race of the plurality of races. Purchase history storage means for associating the purchaser identification information to be identified, the type of purchase of the purchased voting ticket, and the race identification information for identifying the race from which the voting ticket is purchased, in association with each other as a purchase history From the purchaser identification information indicating the anticipation person specified by the specifying means, the acquisition means for acquiring the type of purchase eye corresponding to the race identification information indicating the past race, Based on the type of the buy eye acquired by the acquisition means, the predictor specified by the specifying means predicts the purchase of a voting ticket to be purchased in the selected race; Based on the buying eye predicted by the buying eye prediction means, a fluctuation prediction means for predicting fluctuations in odds of the selected race's voting ticket, and a fluctuation indicating the fluctuations in odds predicted by the fluctuation prediction means And control means for displaying information.

  According to the present invention, the information processing apparatus purchases the voting ticket purchased by the predictor in the selected race based on the purchase of the voting ticket purchased in the past race by the predictor who has predicted the selected race. Predict. A conjecture who has made a prediction about the selected race is likely to purchase a voting ticket for the selected race. When the prospective person actually purchases a voting ticket, the odds change. That is, the information processing apparatus can predict fluctuations in odds by using the purchase of a voting ticket that is likely to be purchased. Then, the information processing apparatus displays variation information indicating the predicted variation. Based on the predicted odds indicated by the variation information, the user can determine the purchase price or purchase price of the voting ticket to be purchased. Therefore, the user can determine the purchase eye or the purchase price without looking at the odds just before the voting deadline. Accordingly, since the concentration of voting ticket purchases just before the deadline is eased, it is possible to distribute the processing load of the server device that accepts purchases of voting tickets.

  According to a second aspect of the present invention, in the information processing apparatus according to the first aspect, selection information receiving means for receiving selection information indicating a selected race body among a plurality of race bodies participating in the selected race. The specifying means specifies a predictor who predicted that the race body indicated by the selection information received by the selection information receiving means wins the selected race, and the fluctuation prediction means includes the selection Among the voting tickets of the races that have been made, it is possible to predict a change in odds of the voting ticket of the purchase including the race body indicated by the selection information received by the selection information receiving means.

  According to the present invention, the information processing apparatus predicts a purchase based on the purchase history of only the predictor who predicted that the selected race body would win among the predictors who predicted the selected race. . In addition, the information processing apparatus predicts fluctuations in odds of the purchase voting ticket including the selected race body. The odds of a voting ticket for a purchase that includes the selected race body when a purchase that includes the selected race body is purchased, rather than when a purchase that does not include the selected race body is purchased. The fluctuation of is large. The processing load of the information processing device is suppressed while the information processing device predicts a purchase based on the purchase history of only the predictor who is expected to win the selected race body, while suppressing a decrease in the prediction accuracy of the odds fluctuation. Can be reduced.

  According to a third aspect of the present invention, in the information processing apparatus according to the first or second aspect, for each of the plurality of races, race identification information for identifying a race is associated with race information regarding the race. Race information corresponding to race identification information indicating a past race in which the anticipation person specified by the specifying means has purchased a voting ticket from the stored race information storage means, and the selected race identification received by the receiving means Race information acquisition means for acquiring race information corresponding to the information, and the buy eye prediction means includes the type of the buy eye acquired by the acquisition means and the race acquired by the race information acquisition means. Based on the information, the predictor predicts the purchase of a voting ticket to be purchased in the selected race.

  According to the present invention, the information processing apparatus can more accurately predict a purchase for a predictor who changes the purchase of a voting ticket to be purchased, according to information about the race.

  According to a fourth aspect of the present invention, in the information processing apparatus according to any one of the first to third aspects, the voting ticket of the selected race among the predicted persons identified by the identifying means The system further comprises second specifying means for specifying a predictor who is predicted not to be purchased thereafter, wherein the buy eye predicting means is the prediction specified by the second specifying means among the predictors specified by the specifying means. An anticipation person other than a person predicts the purchase of a voting ticket purchased in the selected race.

  According to this invention, the information processing apparatus predicts fluctuations in odds without taking into account the information of a predictor who is predicted not to purchase a voting ticket for the selected race after the current time. Therefore, prediction accuracy can be increased.

  The invention according to claim 5 is the information processing apparatus according to any one of claims 1 to 4, wherein the purchaser identification information, the type of the purchase, the race identification information, and the purchase of the voting ticket Purchase price acquisition means for acquiring a purchase price corresponding to purchaser identification information indicating the anticipation person specified by the specifying means from the purchase history storage means that associates and stores the amount of money as the purchase history; The fluctuation prediction means predicts the fluctuation based on the purchase eye predicted by the purchase eye prediction means and the purchase price acquired by the purchase price acquisition means.

  According to this invention, the information processing apparatus can predict fluctuations in odds more accurately by using the purchase price of a voting ticket by a predictor in a past race.

  A sixth aspect of the present invention is the information processing apparatus according to any one of the first to fifth aspects, wherein race identification information for identifying a race and a voting ticket for the race are provided for each of the plurality of races. Odds acquisition means for acquiring the odds corresponding to the race identification information indicating the past race in which the anticipation person specified by the specifying means purchased the voting ticket from the odds storage means for storing the odds in association with the odds. The buying eye prediction means is based on the type of the buying eye obtained by the obtaining means, the odds obtained by the odds obtaining means, and the odds at a predetermined time point of the voting ticket of the selected race. The predictor predicts the purchase of a voting ticket to be purchased in the selected race, and the fluctuation prediction means is based on the purchase predicted by the purchase eye prediction means. Predicting the odds after the change of the voting ticket of the selected race, the buying eye prediction means, the type of the buying eye obtained by the obtaining means, the odds obtained by the odds obtaining means, Based on the odds predicted by the fluctuation prediction means, the predictor re-predicts the purchase of a voting ticket to be purchased in the selected race, and the fluctuation prediction means re-predicts the purchase eye prediction means. Based on the predicted purchase, the odds after change of the voting ticket of the selected race are re-predicted.

  According to the present invention, the information processing apparatus feeds back the predicted odds to the prediction of the buying eye and predicts the fluctuation of the odds again. Therefore, it is possible to predict the odds fluctuation more accurately.

  According to a seventh aspect of the present invention, in the information processing apparatus according to any one of the first to sixth aspects, the specifying means is a plurality of race bodies participating in any one of the plurality of races. Distribute prediction information including predicted race body information indicating a race body that is expected to win a prize, predictor identification information that identifies a predictor expected to win the race body, and expected race identification information that identifies the race The anticipation person identification information corresponding to the anticipation race identification information indicating the selected race is acquired from the website through the network, and the anticipation person identification information acquired by the specifying means and the past race are indicated. Anticipation information acquisition means for acquiring anticipation information including anticipation race body information corresponding to anticipation race identification information from the website via the network; and the purchase Acquired by the anticipation information acquisition means from the purchase history storage means for associating identification information, the type of purchase, the race identification information, and voting race body information indicating the voted race bodies as the purchase history. Voting race body information acquisition means for acquiring voting race body information corresponding to race identification information indicating a race indicated by the predicted race identification information included in the predicted information, and the prediction information acquired by the prediction information acquisition means Race identification indicating the race indicated by the expected race identification information included in the prediction information based on the degree of coincidence between the predicted race body information included in the voting race body information acquisition means and the voting race body information acquisition means From the purchaser identification information stored in the purchase history storage means in association with the information, the preliminarily acquired by the specifying means. Purchaser specifying means for specifying purchaser identification information associated with the purchaser identification information, wherein the acquisition means selects a type of purchase corresponding to the purchaser identification information specified by the purchaser specifying means. It is characterized by acquiring.

  According to the present invention, even if the anticipator identification information of the anticipator is different from the purchaser identification information of the same person as the anticipator, the type of purchase of the voting ticket purchased by the anticipator in the past race Can be obtained.

  The invention according to claim 8 is an information processing method executed by a computer, the reception step receiving selection race identification information for identifying a selected race among a plurality of races, and the reception step receiving the selection race identification information A step of identifying a predictor who anticipates winning of any race body among a plurality of race bodies participating in the race indicated by the selected race identification information, and purchasing a voting ticket for any race of the plurality of races The purchaser identification information for identifying the purchased buyer, the type of purchase of the purchased voting ticket, and the race identification information for identifying the race for which the voting ticket is purchased are associated with each other and stored as a purchase history. From purchase history storage means, purchaser identification information indicating the anticipation specified by the specifying step and race identification information indicating a past race. An obtaining step for obtaining a type of the corresponding purchase, and a voting that the predictor identified in the identification step purchases in the selected race based on the type of the purchase obtained in the acquisition step A buying eye prediction step for predicting the purchase of a ticket, a fluctuation prediction step for predicting a change in odds of the voting ticket of the selected race based on the buying eye predicted by the buying eye prediction step, And a control step for displaying fluctuation information indicating the fluctuation of the odds predicted by the fluctuation prediction step.

  According to the ninth aspect of the present invention, the computer participates in the race indicated by the selected race identification information received by the receiving means, receiving means for receiving the selected race identification information for identifying the selected race among the plurality of races. Specifying means for specifying a predictor who has predicted winning of any race body among a plurality of race bodies, purchaser identification information for identifying a purchaser who has purchased a voting ticket for any race of the plurality of races; Specifying from the purchase history storage means for associating and storing the purchase type of the purchased voting ticket and the race identification information for identifying the race from which the voting ticket is purchased, as a purchase history. Acquisition means for acquiring a purchase type corresponding to purchaser identification information indicating the predicted person and race identification information indicating a past race; The buy eye prediction means for predicting the purchase of a voting ticket purchased by the specifier identified by the specifying means based on the type of the purchase eye obtained in the selected race, and the buy eye prediction Based on the buy eye predicted by the means, the fluctuation prediction means for predicting the fluctuation of the odds of the voting ticket of the selected race, and the fluctuation information indicating the fluctuation of the odds predicted by the fluctuation prediction means are displayed. It is characterized by functioning as control means.

  According to the present invention, the information processing apparatus is configured to purchase a voting ticket purchased by a predictor in a selected race based on a purchase of a voting ticket purchased in a past race by a predictor who has predicted the selected race. Predict. A conjecture who has made a prediction about the selected race is likely to purchase a voting ticket for the selected race. When the prospective person actually purchases a voting ticket, the odds change. That is, the information processing apparatus can predict fluctuations in odds by using the purchase of a voting ticket that is likely to be purchased. Then, the information processing apparatus displays variation information indicating the predicted variation. Based on the predicted odds indicated by the variation information, the user can determine the purchase price or purchase price of the voting ticket to be purchased. Therefore, the user can determine the purchase eye or the purchase price without looking at the odds just before the voting deadline. Accordingly, since the concentration of voting ticket purchases just before the deadline is eased, it is possible to distribute the processing load of the server device that accepts purchases of voting tickets.

It is a figure which shows an example of schematic structure of information processing system SA which concerns on one Embodiment. It is a block diagram which shows an example of schematic structure of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a figure which shows an example of the functional block of the system control part 14 of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a figure which shows an example of the content registered into the database constructed | assembled in the memory | storage part 12 of the betting ticket sale agency server 1. FIG. It is a figure which shows the example of the process of anticipation of a purchase eye. It is a figure which shows an example of the fluctuation | variation of odds. It is a figure which shows an example of an odds comparison page. It is a flowchart which shows an example of the odds fluctuation information provision process by the system control part 14 of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of the buying eye prediction process by the system control part 14 of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of the odds fluctuation | variation prediction process by the system control part 14 of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a figure which shows an example of prediction of a purchase eye. It is a flowchart which shows an example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows the 1st example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows the 2nd example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows the 3rd example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a figure which shows an example of an odds comparison page. It is a flowchart which shows an example of the odds fluctuation information provision process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of the odds fluctuation | variation prediction process by the system control part 14 of the betting ticket sale agency server 1 which concerns on one Embodiment. It is a figure which shows the process of the fluctuation | variation of odds in one Embodiment. It is a flowchart which shows an example of the odds fluctuation information provision process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of the buying eye prediction process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a figure which shows an example of schematic structure of information processing system SB which concerns on one Embodiment. It is a figure which shows an example of a race prediction list page. It is a figure which shows an example of an anticipation person prediction list page. It is a figure which shows an example of the anticipation of the anticipation person X of the past race. It is a figure which shows an example of the purchase eye of the user's past race. It is a figure which shows an example of the purchase eye of the user's past race. It is a flowchart which shows an example of the odds fluctuation information provision process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment. It is a flowchart which shows an example of an expected contribution site corresponding | compatible process by the system control part 14 of the betting ticket issue agency server 1 which concerns on one Embodiment.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The embodiment described below is an embodiment when the present invention is applied to an information processing system for a user to purchase a betting ticket (winning betting ticket) by Internet voting in horse racing. A racehorse participating in a race is an example of a race body in the present invention. A betting ticket is an example of a voting ticket in the present invention. The race to which the present invention is applicable is not limited to horse racing. For example, the present invention is applicable to public competitions such as bicycle races, boat races, and auto races.

[1. First Embodiment]
[1-1. Overview of information processing system configuration and functions]
First, the configuration of the information processing system SA according to the present embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a schematic configuration of an information processing system SA according to the present embodiment.

  As shown in FIG. 1, the information processing system SA is configured to include a betting ticket sales agency server 1, a sponsor server 2, and a plurality of user terminals 3. The betting ticket sale agency server 1, the sponsor server 2, and each user terminal 3 can transmit and receive data to and from each other using, for example, TCP / IP as a communication protocol via the network NW. The network NW is constructed by, for example, the Internet, a dedicated communication line (for example, a CATV (Community Antenna Television) line), a mobile communication network (including a base station), a gateway, and the like.

  The betting ticket sale agency server 1 is a server device that performs various processes related to a horse racing site for selling betting tickets on behalf of a horse race organizer. The betting ticket sale agency server 1 is an example of an information processing apparatus according to the present invention. The betting ticket sales agency server 1 acquires information on the race announced by the organizer from the organizer server 2. The betting ticket sale agency server 1 transmits a web page in response to a request from the user terminal 3. Thereby, the betting ticket sale agency server 1 provides information on the race. Information provided includes, for example, race tables, odds, predictions, race results, and the like. In addition, the betting ticket sale agency server 1 performs, for example, a process of accepting purchase of a betting ticket or a process of refunding a hit betting ticket.

  The sponsoring server 2 is a server device that is installed in order for the horse racing organizer to provide race information or issue a betting ticket. The sponsoring server 2 transmits information on the race to the betting ticket sales agency server 1.

  The user terminal 3 is a terminal device of a user who uses a horse racing site managed by the betting ticket sale agency server 1. The user terminal 3 receives and displays a web page from the betting ticket sales agency server 1 by accessing the betting ticket sales agency server 1 based on an operation from the user. Software such as a browser and an e-mail client is incorporated in the user terminal 3. As the user terminal 3, for example, a personal computer, a PDA (Personal Digital Assistant), a portable information terminal such as a smartphone, a mobile phone, or the like is used.

  The user of the horse racing site can post a prediction of the result of the race on the horse racing site. When posting the prediction, the user can select the favorite, the rival, the rival, the dark horse, and the lower (or lower) as the racehorse that is expected to win or the racehorse that is expected to be in the top order. level). A racehorse selected as one of a favorite, a challenge, a single hole, or a descendant is referred to as a predicted horse. The degree of expectation or the degree to which the user expects that each racehorse of the favorite, confrontation, single hole, and consecutive races will win is called the expectation. Expectations are high in the order of the favorite, the challenge, the single hole, and the descendants. In response to a request from the user terminal 3, the betting ticket sale agency server 1 transmits a web page including the posted prediction information to the user terminal 3.

  In addition, when a user of a horse race site wants to purchase a betting ticket, he selects a race to vote, a purchase eye, a purchase amount (or the number of purchases), and the like. The race to be voted, the purchase item, the purchase amount, etc. indicate the purchase contents. The buy eye includes a racehorse or a frame selected by the user (voting method) and selected by the user as a voting target. The racehorse or frame to be voted is specified by a horse number or a frame number. Each type is the type of purchase. The expression type indicates whether the purchase number is indicated by the horse number or the frame number, the number of the horse number and the frame number, and whether to specify the arrival order. In the case of each expression that specifies not only the combination of horse numbers but also the order of arrival, the purchase order also includes the order of arrival for the racehorses to be voted on.

  The user operates the user terminal 3 to request the betting ticket sale agency server 1 for a web page of information on odds. At this time, the user selects a race for displaying odds. This race is called a selection race. The betting ticket release agency server 1 predicts the fluctuation of odds in the selected race. Then, the betting ticket sale agency server 1 transmits a web page including information indicating the predicted fluctuation to the user terminal 3.

[1-2. Configuration of betting ticket sales agency server]
Next, the configuration of the betting ticket sale agency server 1 will be described with reference to FIGS. 2A to 3.

  FIG. 2A is a block diagram illustrating an example of a schematic configuration of the betting ticket sale agency server 1 according to the present embodiment. As shown in FIG. 2A, the betting ticket release agency server 1 includes a communication unit 11, a storage unit 12, an input / output interface 13, and a system control unit 14. The system control unit 14 and the input / output interface 13 are connected via a system bus 15.

  The communication unit 11 is connected to the network NW and controls the communication state with the host server 2, the user terminal 3, and the like.

  The storage unit 12 is an example of a purchase history storage unit, a race information storage unit, and an odds storage unit in the present invention. The purchase history storage means, the race detailed information storage means, and the odds storage means may be configured by the same device or may be configured by different devices. The storage unit 12 is configured by, for example, a hard disk drive. In the storage unit 12, databases such as a member information DB 12a, a race information DB 12b, a betting ticket information DB 12c, an odds information DB 12d, a browsing history DB 12e, an anticipation information DB 12f, a purchase information DB 12g, and a fund history DB 12h are constructed. “DB” is an abbreviation for database. FIG. 3 is a diagram illustrating an example of contents registered in the database constructed in the storage unit 12 of the betting ticket sale agency server 1.

  In the member information DB 12a, member information related to users registered as members of the horse racing site is registered in association with each user. Specifically, the member information DB 12a is associated with user attributes such as a user ID, password, nickname, name, date of birth, gender, postal code, address, telephone number, e-mail address, credit card information, and the like. Registered as information. The user ID is user identification information. The user ID is an example of purchaser identification information in the present invention.

  In the race information DB 12b, race detailed information regarding the race is registered for each race. Specifically, the race information DB 12b is associated with information such as a race ID, a race name, a grade, a racetrack ID, a date, a sales deadline time, a start time, a race number, a racetrack information, a racehorse information, and jockey information. Registered as race details. The race ID is race identification information. The race ID is an example of race identification information and selected race identification information in the present invention. The racetrack ID is identification information of the racetrack where the race is held. The sales deadline time is the time when sales of betting tickets are closed. That is, the betting ticket can be purchased until the sales deadline. The start time is the time when the race starts. The Baba information includes, for example, a Baba state. The racehorse information is information relating to the racehorse participating in the race. For example, the horse number, frame number, horse name, horse weight, horse weight increase / decrease from the previous race, race time, number of wins, winning rate, etc. of the previous race are registered in the racehorse information. The jockey information is information about a jockey riding a racehorse. For example, in the jockey information, the horse number of the racehorse to ride, the name of the jockey, the past results of the jockey, whether or not the jockey has been changed, and the like are registered for each racehorse.

  In the betting ticket information DB 12c, betting ticket information related to betting tickets to be sold is registered for each betting ticket. Specifically, in the betting ticket information DB 12c, a race ID, purchase information, and the like are associated and registered as betting ticket information. The race ID indicates which race the betting ticket indicated by the purchase information is. The buy-out information includes the betting ticket ID, the expression type, and the voting target number. The betting ticket ID is identification information of the betting ticket. By formula, for example, single win, double win, frame number double win double win (frame double), frame number double win single win (frame single), horse number double win single win (horse double), horse number double win single win (horse) Single), extended horse number double winning double (wide), horse number triple winning double (triple double), horse number triple winning single (triple single), etc. The number to be voted is a horse number or a frame number. The voting target number is registered in one or more purchase information according to the formula. The number of horse numbers or frame numbers included in the purchase information is referred to as a run number. When the type is a winning type or a winning type, the purchase information includes one horse number or frame number. That is, the number of consecutive wins and wins is one. When the type is “double frame”, “single frame”, “double horse”, “single horse”, or “wide”, the purchase information includes two horse numbers or frame numbers. In other words, the number of stations of double frame, single frame, double horse, single horse and wide is two. When the type is triple or triple, the purchase information includes three horse numbers or frame numbers. That is, the number of triplets and triplets is three. When the type is a single frame, a single horse, or a triple, each horse number or frame number is registered in the purchase information in association with a number indicating the arrival order. A combination of a formula and a voting target number, or a combination of a formula and a voting target number and a number indicating the order of arrival indicates a purchase.

  In the odds information DB 12d, odds information related to betting ticket odds is registered for each betting ticket at predetermined time intervals. Specifically, in the odds information DB 12d, a race ID, a betting ticket ID, an update date, odds, and the like are associated and registered as odds information. The odds information is transmitted from the sponsoring server 2 to the betting ticket sales agency server 1 at predetermined time intervals (for example, at intervals of 1 minute). The system control unit 14 registers the received odds information in the odds information DB 12d. The update date and time indicates the date and time when the betting ticket odds indicated by the betting ticket ID are updated by the sponsor server 2.

  In the browsing history DB 12e, a browsing history of web pages by the user in the horse racing site is registered. Specifically, in the browsing history DB 12e, a user ID, a browsing date, a URL (Uniform Resource Locator), and the like are associated and registered as a browsing history. The user ID indicates the user who browsed the web page. The browsing date indicates the date when the web page was browsed. The URL is identification information of the browsed web page. This URL includes, for example, a race ID, a page type, and the like. The race ID indicates which race the browsed web page is related to. The page type indicates the type of the browsed web page. The types of web pages include, for example, a racetrack page, an odds page, a race analysis page, and a prediction list page.

  Every time the betting ticket sale agency server 1 receives a request for a web page from the user terminal 3, the system control unit 14 registers the browsing history in the browsing history DB 12e. The request for the web page includes, for example, the user ID of the user who uses the user terminal 3, the URL of the web page, and the like. The system control unit 14 registers a history including the user ID and URL included in the request and the current date and time as the browsing date in the browsing history DB 12e.

  In the prediction information DB 12f, prediction information indicating the prediction of the race result by the user is registered every time the betting ticket release agency server 1 receives the prediction. Specifically, in the prediction information DB 12f, a user ID, posting date / time, race ID, prediction horse information, and the like are associated and registered as prediction information. The user ID indicates the user who made the prediction. The posting date / time indicates the date / time when the prediction was posted. The race ID indicates which race the accepted prediction is for. The predicted horse information indicates the predicted horse selected by the user. Specifically, the predicted horse information includes the horse number of the favorite horse, the horse number of the opposing horse, the horse number of the single hole horse, and the horse number of the consecutive horses.

  In the purchase information DB 12g, betting ticket purchase information indicating the purchase contents of a betting ticket at a horse racing site by a user is registered every time a betting ticket is purchased. The betting ticket purchase information is an example of a betting ticket purchase history. Specifically, in the purchase information DB 12g, a user ID, purchase date / time, race ID, purchase information, purchase price, and the like are associated and registered as betting ticket purchase information. The user ID indicates the user who purchased the betting ticket. The purchase date / time indicates the date / time when the purchase of the betting ticket is accepted. The buy eye information indicates a betting ticket purchased by the user and a racehorse selected by the user as a voting target. In the case of different expressions that specify frame numbers, one or more racehorses are specified by the frame number. The horse number or the frame number included in the purchase eye information is an example of voting race body information of the present invention. The combination of the purchase information and the purchase amount is registered for each betting ticket ID of the betting ticket purchased by the user at one time.

  Note that the system control unit 14 may periodically calculate the odds of each purchase of each race based on the purchase information and purchase price included in the purchase history registered in the purchase information DB 12g. Then, the system control unit 14 may register the odds information including the calculated odds in the odds information DB 12d instead of the odds received from the sponsor server 2.

  In the fund history DB 12h, a fund purchase fund change history indicating a change history of a betting ticket purchase fund is registered every time the balance changes. The betting ticket purchase funds are deposited in a betting ticket purchase account provided for each user at a horse racing site, for example. The user can purchase a betting ticket within the range of purchase funds. Specifically, in the fund history DB 12h, the user ID, the date and time of change, the reason for change, the balance before change, the balance after change, and the like are associated and registered as a change history of funds purchase funds. The user ID indicates a user who has changed the balance of purchase funds. The reason for change indicates the reason why the balance of purchased funds has changed. Reasons for the change include, for example, payment to an account for purchasing a betting ticket, appropriation of funds from an account for purchasing betting tickets to a purchase price for betting tickets, and the like. The balance before change is the balance of purchase funds immediately before change. The balance after change is the balance of purchased funds immediately after change.

  Next, other information stored in the storage unit 12 will be described. The storage unit 12 stores various data such as an HTML document for displaying a web page, an XML (Extensible Markup Language) document, image data, text data, and an electronic document. The storage unit 12 stores various set values, threshold values, reference values, specified values, constants, and the like.

  The storage unit 12 stores various programs such as an operating system, a WWW (World Wide Web) server program, a DBMS (Database Management System), and a betting ticket issuing agency management program. The betting ticket issuance agency management program is a program for executing processing such as acceptance of betting ticket purchase, refund, acceptance of prediction, prediction of fluctuations in odds, and the like. The betting ticket issuing agency management program is an example of an information processing program in the present invention. Various programs may be acquired from other server devices via a network NW, for example, or may be recorded on a recording medium such as an optical disk, a magnetic tape, or a memory card and read via a drive device. You may be made to do. Further, the betting ticket issuing agency management program may be a program product.

  The input / output interface 13 performs interface processing between the communication unit 11 and the storage unit 12 and the system control unit 14.

  The system control unit 14 includes a CPU 14a, a ROM (Read Only Memory) 14b, a RAM (Random Access Memory) 14c, and the like. The CPU 14a is an example of a processor. The present invention can also be applied to various processors different from the CPU. Each of the storage unit 12, the ROM 14b, and the RAM 14c is an example of a memory. The present invention can also be applied to various memories different from the hard disk, ROM, and RAM.

  The betting ticket sale agency server 1 may be configured by a plurality of server devices. For example, a server device that performs processing such as betting ticket purchase acceptance and refund processing, a server device that performs processing such as prediction reception and prediction display control, a server device that performs odds fluctuation prediction, and requests from user terminals 3 Accordingly, a server device that transmits a web page and a server device that manages a database may be connected to each other via a LAN or the like.

[1-3. Details of system control function]
Next, functions of the system control unit 14 will be described with reference to FIGS. 2B and 4A to 5. FIG. 2B is a diagram illustrating an example of functional blocks of the system control unit 14 of the betting ticket release agency server 1 according to the present embodiment. As shown in FIG. 2B, the system control unit 14 reads and executes a program such as a betting ticket issuance agency management program by the CPU 14a, so that a selection receiving unit 141, a predictor specifying unit 142, a purchase history obtaining unit 143, It functions as a prediction unit 144, an odds fluctuation prediction unit 145, an odds fluctuation information provision unit 146, and the like.

  The selection receiving unit 141 is an example of each of a receiving unit and a selection information receiving unit in the present invention. Anticipation person specific part 142 is an example of each of a specific means and the 2nd specific means in the present invention. The purchase history acquisition unit 143 is an example of each of acquisition means, prediction information acquisition means, voting race body information acquisition means, and purchaser specification means in the present invention. The buying eye prediction unit 144 is an example of each of buying eye prediction means, race detailed information acquisition means, and odds acquisition means in the present invention. The odds fluctuation prediction unit 145 is an example of each of a fluctuation prediction unit and a purchase price acquisition unit in the present invention. The odds fluctuation information providing unit 146 is an example of a control unit in the present invention.

  The system control unit 14 predicts a change in odds of the betting ticket of the selected race. Then, the system control unit 14 provides information indicating the predicted fluctuation to the user terminal 3. The change in odds is based at least on the betting ticket formula purchased by the user who submitted the prediction for the selected race in the past race. A user who has posted a prediction about the selected race is called a prior predictor. Predictors are likely to purchase betting tickets for selected races. When the prior anticipator purchases an actual betting ticket, the odds change depending on the purchase. The system control unit 14 determines, based on the betting ticket type purchased in the past race of the prior anticipation person and the prediction in the selection race of the prior anticipation person, the betting ticket type and purchase item purchased by the prior anticipation person in the selection race. Predict. And the system control part 14 estimates the fluctuation | variation of the odds of the betting ticket of a selection race based on the predicted purchase.

  The selection receiving unit 141 receives a race ID of a selected race selected by the user from among a plurality of races. For example, when a user requests some information about a race, the user selects a race for which the information is requested as a selected race. A user who requests information is called a requesting user. The selection receiving unit 141 acquires the race ID of the selected race from the request received from the user terminal 3 via the communication unit 11. In the present embodiment, the information regarding the race is information on odds for each purchase. For example, the selection receiving unit 141 may receive only race IDs of races for which sales of betting tickets are not closed as race IDs of selected races.

  Moreover, the selection reception part 141 may receive the horse number which shows the racehorse selected by the request | requirement user among the some racehorses which participate in a selection race, and does not need to accept it. This racehorse is called a selected horse. For example, when requesting information on any racehorse in the selected race, the racehorse requesting that information is selected as the selected horse. The selection receiving unit 141 acquires the horse number of the selected horse from the request received from the user terminal 3 via the communication unit 11. The number of selected horses may be one or more. The number of selected horses may be determined in advance, or at least one of the minimum value and the maximum value of the number of selected horses may be determined in advance. In this embodiment, the information regarding the racehorse is information on odds of purchase including the selected horse. When the selection receiving unit 141 does not receive the selected horse, the system control unit 14 may process all racehorses that participate in the selected race. For example, the system control unit 14 may provide information regarding all racehorses.

  The anticipation person specific | specification part 142 specifies the user who anticipated the winning of any racehorse of the some racehorse participating in a selection race as a prior anticipation person. For example, anticipation person specific part 142 acquires anticipation information corresponding to user ID of a selection race from anticipation information DB12f. And anticipation person specific part 142 acquires user ID contained in acquired anticipation information as a user ID of a prior anticipation person of a selection race.

  When the horse number of the selected horse is received by the selection receiving unit 141, the predictor specifying unit 142 may specify all the users who have predicted the result of the selected race as prior predictors, or the result of the selected race Of the users who have made predictions, only users who have predicted that the selected horse will win may be identified as prior predictors. For example, the selection reception unit 141 may acquire the user ID included in the prediction information including the horse number of the selected horse among the prediction information corresponding to the user ID of the selected race as the user ID of the prior predictor. For example, assume that the horse numbers of the selected horses are 2 and 6. The favorite, counter, single hole, and consecutive horse numbers in the prediction information of the user A are 2, 6, 5 and 1, and the favorite, counter, single hole, and consecutive horse numbers in the prediction information of the user B are 5, 2, Assume that 6 and 1. In this case, since the selected horse is included in any of the predicted horses of the users A and B, the predictor specifying unit 142 may specify the users A and B as prior predictors. Moreover, the selection reception part 141 makes only the user who performed the prediction that the expectation degree of the selected horse is higher than the expectation degree of the racehorse that was not selected as the prior predictor in the main battle, the counter, the single hole, and the descendants. You may specify. For example, in the prediction of the user A, since the racehorses of the horse numbers 2 and 6 of the selected horse are the favorite and the opponent, the predictor specifying unit 142 may specify the user A as a prior predictor. On the other hand, in the expectation of the user B, the racehorse of the horse number 5 that is not the selected horse is the favorite, and the selected horse is the opposing and single hole. Opposition and single holes are less expected than their favorite. Therefore, anticipation person specific part 142 does not need to specify user B as prior anticipation person.

  The purchase history acquisition unit 143 specifies the purchase history corresponding to the race ID of the past race from the purchase history DB 12e out of the purchase history corresponding to the user ID of the prior anticipation person specified by the anticipation person specifying unit 142. Then, the purchase history acquisition unit 143 acquires at least formulas from the specified purchase history. The purchase history acquisition unit 143 may acquire the specified purchase history. The past race may be, for example, a race that has already ended, a race that has already started, or a race in which sales of betting tickets have been closed. The purchase history acquisition unit 143 can identify a past race based on the date of the event, the sales deadline time, the start time, etc. registered in the race information DB 12b. A race whose sales deadline has passed is a race in which sales of betting tickets are closed. A race whose start time has passed is a race that has already started. Further, the purchase history acquisition unit 143 may determine that a race that has passed a predetermined time from the start time is a race that has already ended.

  The buy eye prediction unit 144 selects the prior predictor specified by the predictor specifying unit 142 based on at least the formula acquired by the purchase history acquisition unit 143 and the prediction information in the selection race of the prior purchaser. Predict the purchase of betting tickets to be purchased in the race for each prospective person. This purchase is called a predicted purchase.

  For example, the purchase eye prediction unit 144 first predicts the betting ticket type purchased by the prior anticipation person. This formula is referred to as a prediction formula. For example, the purchase eye prediction unit 144 may calculate the purchase ratio of the betting ticket that the prior predictor purchased in the past race for each formula based on the formula acquired by the purchase history acquisition unit 143. The purchase ratio for each formula is the ratio of the number of betting tickets purchased for each formula to the total number of betting tickets purchased for each formula of the prior anticipation person. Then, the purchase eye prediction unit 144 may specify, as a prediction formula, a formula according to the highest purchase ratio or a formula whose purchase ratio is equal to or greater than a predetermined value. Further, when the horse number of the selected horse is received by the selection receiving unit 141, the buying eye prediction unit 144 sets the formula according to the highest purchase ratio among the formulas according to the number of selected horses as the prediction formula. It may be specified, or a formula with a relatively high purchase ratio compared to other formulas may be specified as a prediction formula. For example, when the number of selected horses is 1, the buying eye prediction unit 144 may determine the prediction formula by type from among the formulas having a run number of 1. In addition, for example, when the number of selected horses is 2, the buying eye predicting unit 144 may determine the prediction formula from among the formulas having the run number of 2. Further, for example, when the number of selected horses is 3, the buying eye prediction unit 144 may determine the prediction formula from among the formulas having the run number of 3. Further, for example, the buying eye prediction unit 144 may determine the prediction formula by the number of stations specified by the requesting user. In addition, for example, the buying eye prediction unit 144 may determine the prediction formula according to the formula selected by the requesting user.

  FIG. 4A is a diagram illustrating an example of a process of forecasting a purchase eye. For example, it is assumed that the prior anticipation person A selects racehorses having horse numbers of 6, 2, 5, and 1 as a favorite, a challenge, a single hole, and a descendant. It is assumed that the prior anticipation person has purchased a horse ticket, a horse ticket, a horse frame, a frame ticket and a frame ticket in a past race. As shown in FIG. 4A, among these formulas, the formula with the highest purchase ratio is simple. Therefore, the buying eye prediction unit 144 determines a single horse for each prediction formula.

  When predicting by formula, the buying eye prediction unit 144 predicts a racehorse to be selected when, for example, a prior predictor purchases a betting ticket by prediction formula. For example, the buy eye prediction unit 144 specifies race horses corresponding to the number of heads corresponding to each prediction formula in the descending order of expectation from a favorite, a challenge, a single hole, and a descendant in the prediction information of a selection race of a prior predictor. May be. In this case, the prediction buying eye is indicated by the specified racehorse by prediction formula. When each prediction formula is a formula that specifies the arrival order, the prediction buy eye is indicated in the order of the racehorse specified as the prediction formula and the degree of expectation. In the example shown in FIG. 4A, the prediction formula is simply a single. The number of horses is 2. Therefore, the buying eye forecasting unit 144 acquires the horse number of the favorite 6 and the opposing 2 horse number, and determines 6-2 of the horse as the forecast buying eye.

  Further, for example, the buying eye prediction unit 144 may predict a plurality of buying eyes. For example, the buying eye prediction unit 144 may predict a plurality of buying eyes with different combinations of horse numbers or frame numbers. The user may purchase a plurality of betting tickets for the same type. As a combination of a racehorse in a plurality of buying eyes, for example, there are a combination of a favorite and a challenge, a favorite and a single hole, a favorite and a descendant, a challenge and a single hole. The buying eye prediction unit 144 may determine the number of buying eyes to be predicted based on, for example, a purchase history of a prior anticipation person in a past race. For example, the buying eye prediction unit 144 may count the number of betting tickets of different racehorses by the same formula as the number of betting tickets purchased by the prior predictor for each past race. The buying eye prediction unit 144 counts the number of races for which the number of betting tickets has been purchased for each number of betting tickets purchased. Then, the purchase eye prediction unit 144 may determine the number of betting tickets purchased in the most races or the number of betting tickets purchased in races with a predetermined frequency or more. Then, the buying eye prediction unit 144 may determine the determined number of buying eyes. For example, the buying eye prediction unit 144 may determine a plurality of buying eyes including a combination of predicted horses having a relatively high degree of expectation in the prediction information of the selection race of the prior anticipation person. For example, when the number of consecutive stations by prediction formula is 2 and the number of buy-outs is 3, the buy-out predicting unit 144 buys the combination of the favorite and the opposing combination, the purchase of the combination of the favorite and the single hole, the favorite The purchase price of the combination of and may be determined as the predicted purchase price.

  By the way, when purchasing a betting ticket according to a formula that specifies the arrival order, there are a plurality of betting tickets that can be purchased with the same combination of racehorses. For example, there are two types of purchases for single frames and single horses, and there are six types of purchases for triple units. Among the users, there are users who purchase a betting ticket for only some of the purchases among a plurality of purchases, and there are users who purchase all the purchases. Therefore, the buy eye prediction unit 144, for example, based on the purchase history purchase information acquired by the purchase history acquisition unit 143, the purchase ratio of only a part of the purchase tickets in the past race of the prior anticipation person and The purchase ratio of all purchases may be calculated. Then, when the purchase ratio of all purchases is higher than the purchase ratio of betting tickets for only some purchases, the purchase prediction unit 144 determines that all purchases are predicted purchases. May be. In the example shown in FIG. 4A, in the past race, when the prospective person A has the same purchase ratio of the racehorses and the purchase ratio of all the purchases is higher than the purchase ratio of only some purchase eyes, The eye forecasting unit 144 determines the horse single 2-6 and the horse single 6-2 as the prediction buying eyes.

  The odds fluctuation prediction unit 145 predicts the odds fluctuation of the betting ticket of the selected race based on the purchase predicted by the purchase prediction unit 144. Basically, the odds of the betting ticket of the predicted purchase are lowered, and the odds of the betting ticket other than the predicted buying are increased. The degree to which the odds of the betting ticket for the predicted purchase are lowered is greater than the degree to which the odds for the betting ticket other than the predicted buying is increased. FIG. 4B is a diagram illustrating an example of fluctuations in odds. If the uni of 6-2 is the predicted purchase, the odds of the uni of 6-2 will drop and the odds of 2-6 of the uni will rise.

  For example, the odds fluctuation prediction unit 145 counts the number of prior predictors who are predicted to purchase the betting ticket for each purchase, and predicts the odds fluctuation based on the counted number. Good. For example, the odds fluctuation prediction unit 145 may calculate the degree of fluctuation of the odds of each purchase. The fluctuation prediction unit 145 multiplies the number of prior predictors predicted to purchase the target betting ticket by the lowering coefficient to calculate the lowering degree. The descending coefficient is a positive predetermined value. In addition, the fluctuation prediction unit 145 calculates the degree of increase by multiplying the number of prior predictors who are predicted to purchase a betting ticket other than the target purchase by a predetermined increase coefficient. The increase coefficient is a positive predetermined value smaller than the decrease coefficient. The odds fluctuation prediction unit 145 calculates the degree of fluctuation of the odds of the target purchase by subtracting the degree of increase from the degree of decrease. For example, the odds fluctuation prediction unit 145 may predict that the odds of the purchase whose absolute value of the degree of fluctuation is equal to or less than a predetermined value will not fluctuate. Further, the odds fluctuation prediction unit 145 may predict that, for example, the odds of a purchase having a degree of fluctuation larger than a predetermined value will rise. Further, the odds fluctuation prediction unit 145 may predict, for example, that the odds of a purchase with a degree of fluctuation smaller than a predetermined value having a minus value are lowered. Note that the odds fluctuation prediction unit 145 may predict that the odds will increase when the degree of fluctuation is greater than 0, and may predict that the odds will decrease when the degree of fluctuation is 0 or less.

  When the horse number of the selected horse is received by the selection receiving unit 141, the odds fluctuation prediction unit 145 may determine the formula for predicting the odds fluctuation based on the number of the selected horses. For example, when the number of heads of the selected horse is 1, the odds fluctuation prediction unit 145 may predict only the fluctuation according to the formula whose run number is 1. Further, for example, when the number of selected horses is 2, the odds fluctuation prediction unit 145 may predict only the fluctuation according to the formula whose run number is 1 or 2. Further, for example, when the number of selected horses is 3 or more, the odds fluctuation prediction unit 145 may predict only the fluctuation according to the formula whose run number is any one of 1 to 3. Alternatively, the odds fluctuation prediction unit 145 may predict only the fluctuation of each station specified by the user, for example. Alternatively, the odds fluctuation prediction unit 145 may predict only the fluctuation for each formula designated by the user, for example.

  When the horse number of the selected horse is received by the selection receiving unit 141, the odds fluctuation prediction unit 145 may predict the odds change of only the buy eye including either the selected horse or the frame of the selected horse, or all The odds of buying the odds may be predicted.

  An anticipation person who has predicted that the selected horse will win is likely to purchase a betting ticket including the selected horse. When a buy eye including a selected horse is purchased, the degree to which the odds of the buy eye including the selected horse fluctuate depends on the odds of the buy eye including the selected horse when a buy eye not including the selected horse is purchased. Greater than the degree of variation. That is, the prior anticipation person who predicted that the selected horse will win will have a greater influence on the odds of the purchase including the selected horse than the user who predicted that the selected horse will not win. The buy eye prediction unit 144 predicts the purchase of only the prior predictor that the selected horse is expected to win, and based on the purchase, the odds fluctuation prediction unit 145 calculates the odds change of the purchase eye including the selected horse. By predicting, it is possible to reduce the processing load of the system control unit 14 while suppressing a decrease in prediction accuracy.

  The odds fluctuation information providing unit 146 displays information indicating the fluctuation of the odds predicted by the odds fluctuation prediction unit 145 on the user terminal 3 of the requesting user. The information indicating the change in odds may be, for example, a character, an image, a sign, or the like indicating the change in odds. When the horse number of the selected horse is received by the selection receiving unit 141, the odds change information providing unit 146 may cause the user terminal 3 to display the change in odds only for the buying eye including the selected horse or the frame of the selected horse.

  For example, the odds fluctuation information providing unit 146 may transmit an odds comparison page including information indicating fluctuations in odds to the user terminal 3. As a result, the odds fluctuation prediction unit 145 causes the user terminal 3 to display information indicating the odds fluctuation. The odds comparison page is a web page for comparing odds between buying eyes in the selected race. FIG. 5 is a diagram illustrating an example of the odds comparison page. As shown in FIG. 5, the odds comparison page includes at least a duplex button 101, a triple button 102, a racehorse list 110, and a comparison button 120. The double button 101 is a button for comparing odds of purchases according to formulas having two stations. The triple button 102 is a button for comparing odds of purchases according to formulas having two stations. The user can select the double button 101 or the triple button 102. In the example of FIG. 5, the double button 101 is selected. The racehorse list 110 is a list of racehorses participating in the selected race. The racehorse list 110 includes frame numbers, horse numbers, horse names, win odds, multiple odds, selection buttons 111 to 113, and the like for each racehorse. The selection buttons 111 to 113 are buttons for selecting a racehorse corresponding to the button as a selection horse. When the double button 101 is selected, the selection buttons 111 and 112 can be selected. In this case, the requesting user selects two or more racehorses. When the triple button 102 is selected, the selection buttons 111 to 113 can be selected. In this case, the requesting user selects three or more racehorses. In the example of FIG. 5, racehorses with horse numbers 2, 6, and 7 are selected.

  The racehorse list 110 may include information indicating changes in odds of winning and winning of each racehorse. In this case, the buy eye prediction unit 144 predicts the purchase of the prior predictor in the selected race by setting all users who have predicted the result of the selected race as the prior predictors, and the odds fluctuation prediction unit 145 Predict the odds of winning single and double wins.

  When the comparison button 120 is selected by the user, the odds comparison table 130 is displayed in the odds comparison page. The odds comparison table 130 is a table including odds and arrows 131 for each purchase including the selected horse. An arrow 131 indicates the predicted odds variation. An upward arrow 131 indicates an increase in odds, a horizontal arrow 131 indicates that the odds do not fluctuate, and a downward arrow 131 indicates a decrease in odds. In the example of FIG. 5, the odds and arrow 131 of each buy horse corresponding to horses 2 and 6 and horses corresponding to horses 2 and 7, and horse horses corresponding to horses 2 and 7 The odds and arrows 131 for each purchase of a single horse, double frame and single frame, and odds and arrows 131 for each double buy, horse single, double frame and single frame corresponding to horses 6 and 7 And are displayed. When the triple button 102 is selected, the odds comparison table 130 displays triple and triple odds and arrows 131.

[1-4. Operation of information processing system]
Next, the operation of the information processing system SA will be described with reference to FIGS. In the example described below, the system control unit 14 predicts the purchase of a prior anticipation person who has predicted that the selected horse will win. Further, the system control unit 14 transmits an odds comparison page as shown in FIG.

  FIG. 6 is a flowchart showing an example of odds variation information provision processing by the system control unit 14 of the betting ticket release agency server 1 according to the present embodiment. The odds variation information providing process is a process for transmitting an odds comparison page and displaying an odds comparison table on the odds comparison page. For example, the requesting user selects a desired race as the selected race. Then, the user terminal 3 transmits an odds comparison page request including the user ID of the selected race to the betting ticket issuing proxy server 1. When the betting ticket sale agency server 1 receives the request for the odds comparison page, the system control unit 14 executes the odds fluctuation information providing process.

  As illustrated in FIG. 6, the selection receiving unit 141 acquires the odds comparison page request received from the user terminal 3 via the communication unit 14 (step S1). Next, the selection receiving unit 141 acquires the race ID of the selected race from the odds comparison page request (step S2). Next, the selection receiving unit 141 transmits an odds comparison page that does not include an odds comparison table to the user terminal 3 as an odds comparison page corresponding to the race ID of the selected race (step S3).

  The user terminal 3 displays the received odds comparison page on the display. This odds comparison page includes a double button 101, a triple button 102, a running horse list 110, and a comparison button 120. The user selects the double button 101 or the triple button 102 and selects two or more racehorses from the racehorse list 110. When the user selects the comparison button 120, the user terminal 3 transmits an odds comparison table request including the selected station number and the horse number of the selected horse to the betting ticket issuing agency server 1. When the double button 101 is selected, the selected station number is 2. When the triple button 102 is selected, the selected station number is 3. After step S3, the selection receiving unit 141 determines whether an odds comparison table request has been received from the user terminal 3 (step S4). At this time, if the selection receiving unit 141 determines that an odds comparison table request has not been received (step S4: NO), the selection receiving unit 141 executes the determination in step S4 again. On the other hand, if the selection receiving unit 141 determines that the odds comparison table request has been received (step S4: YES), the selection receiving unit 141 proceeds to step S5. In step S <b> 5, the selection receiving unit 141 acquires the run number and the horse number of the selected horse from the odds comparison table request.

  Subsequently, anticipation person specific part 142 searches anticipation information including race ID of a selection race, and a horse number of selection horse from anticipation information DB12f, and acquires user ID of an anticipation person from anticipation information (Step S6). For example, anticipation person specific part 142 extracts all the combinations of the horse number of the selection horse of the number which a run number shows from the horse number of the selection horse. Subsequently, the anticipation person specific | specification part 142 specifies the horse number of the anticipation horse of the number which a run number shows from the anticipation person information corresponding to race ID of a selection race in order with high expectation from a favorite. Next, the anticipation person specifying unit 142 acquires, from the prediction information DB 12f, prediction information in which the horse number of the specified predicted horse matches the horse number of the extracted combination for each combination of the extracted horse numbers of the selected horse. And anticipation person specific part 142 acquires a user ID of prior anticipation person from acquired anticipation information. In Step S6, anticipation person specific part 142 may acquire anticipation information including race ID of a selection race regardless of whether a horse number of a selection horse is included. In this case, anticipation person specific part 142 can specify all users who are predicting in a selection race as an anticipation person.

  Next, the odds fluctuation prediction unit 145 sets the number of predicted purchasers of all purchases corresponding to the horse number combination of the selected horses to 0 for all the horse number combinations of the selected horses extracted in step S6 (step S7). ). When the number of stations is 2, the corresponding buy-up type is, for example, Uma, Uma, Uo, and Uo. When the number of stations is 3, the corresponding buy-up type is, for example, triple duplex or triple simple.

  Next, the buying eye prediction unit 144 sets the number i to 1 (step S8). Next, the purchase history acquisition unit 143 acquires the purchase history of the past race corresponding to the user ID of the prior anticipation person (i) from the purchase history acquisition unit 143 (step S9). Prior predictor (i) is the i-th prior predictor among the prior predictors indicated by the user ID acquired in step S6. For example, the purchase history acquisition unit 143 acquires the race ID from the purchase history corresponding to the user ID of the prior anticipation person (i) among the purchase histories registered in the purchase history acquisition unit 143. Next, the purchase history acquisition unit 143 acquires race detailed information corresponding to the acquired race ID from the race information DB 12b. Next, the purchase history acquisition unit 143 determines whether or not the race indicated by the acquired race ID is a past race based on the date, sales deadline time, start time, and the like included in the race detailed information. And purchase history acquisition part 143 acquires purchase history corresponding to race ID of a past race from purchase history acquisition part 143 among purchase history corresponding to user ID of prior anticipation person (i).

  Next, the buying eye prediction unit 144 executes a buying eye prediction process (step S10). FIG. 7 is a flowchart showing an example of a buy-ahead prediction process by the system control unit 14 of the betting ticket release agency server 1 according to the present embodiment. As illustrated in FIG. 7, the buying eye prediction unit 144 acquires formulas from each of the purchase histories of the prior anticipation person (i) acquired in step S9 of the odds fluctuation information providing process (step S21).

  Next, the buying eye prediction unit 144 calculates the purchase ratio for each formula by counting the number of the acquired formulas for each formula (step S22). Next, the purchase eye prediction unit 144 determines, for each prediction formula, a formula with the highest purchase ratio among the formulas acquired from the purchase history (step S23). At this time, the buying eye prediction unit 144 may determine the prediction formula from among the formulas having the same number of stations as the selected station.

  Next, the buying eye prediction unit 144 determines a prediction buying eye based on the prediction information of the selected race of the prior anticipation person (i) and the prediction formula (Step S24). For example, the buying eye prediction unit 144 acquires the number of horse numbers corresponding to the number of consecutive stations by prediction formula from the predictor information in descending order of expectation. When the prediction formula is a formula specified by horse number, the buy eye prediction unit 144 determines the combination of the acquired horse number and prediction formula by the prediction purchase. When the prediction formula type is a type specified by a frame number, the buying eye prediction unit 144 acquires a frame number corresponding to the acquired horse number from the detailed race information of the selected race. Then, the buying eye prediction unit 144 determines a combination of the acquired frame number and the prediction formula by prediction eye. Note that, as described above, the buying eye prediction unit 144 may determine a plurality of prediction buying eyes. For example, the number of predicted purchases may be determined based on the number of betting tickets purchased by the prior predictor (i) in the past race indicated by the purchase history of the prior predictor (i). Then, the buying eye prediction unit 144 may determine the determined number of prediction buying eyes. In this case, the buying eye prediction unit 144 may acquire a plurality of combinations of horse numbers of the predicted horses from the prediction information of the prior anticipation person (i). Then, the buying eye prediction unit 144 may determine a buying eye including a horse number included in the combination or an expected horse frame number indicated by the horse number for each acquired combination. Further, the buying eye prediction unit 144 may determine a plurality of buying eyes with the same combination of horse number or frame number. For example, in a past race, if the purchase ratio of all the purchases including the same racehorse combination by the prior predictor (i) is higher than the purchase ratio of the betting ticket for only some purchases, The eye prediction unit 144 may determine all purchases.

  Next, the odds fluctuation prediction unit 145 increments the predicted buyer number of the determined predicted purchase by 1 (step S25), and ends the buy eye prediction process. When a plurality of predicted purchases are determined, the odds fluctuation prediction unit 145 increases the number of predicted purchasers by 1 for each determined predicted purchase. In addition, when the selected station number is 2 and the prediction formula is not any of multiple, single, multi-frame, and single-frame, the system control unit 14 does not have to execute steps S24 and S25. Further, when the number of selected stations is 3, and the prediction formula is not either triple or triple, the system control unit 14 does not have to execute steps S24 and S25.

  When the purchase eye prediction process ends, the purchase eye prediction unit 144 determines whether the number i is less than the number of prior anticipation persons as shown in FIG. 6 (step S11). At this time, if it is determined that the number i is less than the number of prior predictors (step S11: YES), the purchase eye prediction unit 144 proceeds to step S12. In step S12, the buying eye prediction unit 144 increments the number i by 1, and proceeds to step S9. On the other hand, if it is determined that the number i is not less than the number of prior predictors (step S11: NO), the purchase eye prediction unit 144 proceeds to step S13.

  In step S13, the odds fluctuation prediction unit 145 executes odds fluctuation prediction processing. FIG. 8 is a flowchart showing an example of odds fluctuation prediction processing by the system control unit 14 of the betting ticket release agency server 1 according to the present embodiment. As shown in FIG. 8, the odds fluctuation prediction unit 145 sets the number j to 1 (step S31). Next, the odds fluctuation prediction unit 145 multiplies the predicted purchase number of the purchase item (j) by the decrease coefficient stored in the storage unit 12 to calculate the decrease degree (step S32). The buying eye (j) is the j-th buying eye among all buying eyes corresponding to all combinations of the horse numbers of the selected horses. Next, the odds fluctuation prediction unit 145 multiplies the total number of predicted purchasers of other purchases by the same formula as the purchase item (j) by the increase coefficient stored in the storage unit 12 to increase The degree is calculated (step S33). Next, the odds fluctuation prediction unit 145 calculates the degree of fluctuation by subtracting the descent coefficient from the rise coefficient (step S34). Next, the odds fluctuation prediction unit 145 determines the prediction fluctuation information of the buying eye (j) according to the degree of fluctuation (step S35). The predicted fluctuation information indicates the predicted odds fluctuation. For example, the odds fluctuation prediction unit 145 sets the prediction fluctuation information to “rise” when the degree of fluctuation is larger than the rise threshold value stored in the storage unit 12. The rising threshold is, for example, a positive constant. The odds fluctuation prediction unit 145 sets the prediction fluctuation information to “down” when the degree of fluctuation is smaller than the lowering threshold value stored in the storage unit 12. The falling threshold is, for example, a negative constant. Also, the odds fluctuation prediction unit 145 sets the predicted fluctuation information to “no fluctuation” when the degree of fluctuation is not less than the falling threshold and not more than the rising threshold. Next, the odds fluctuation prediction unit 145 determines whether or not the number j is less than the number of all purchases corresponding to all combinations of the horse numbers of the selected horses (step S36). At this time, if the odds fluctuation prediction unit 145 determines that the number j is less than the number of all purchases corresponding to all combinations of the horse numbers of the selected horses (step S36: YES), the process proceeds to step S37. move on. In step S37, the odds fluctuation prediction unit 145 increments the number j by 1 and proceeds to step S32. On the other hand, if the odds fluctuation prediction unit 145 determines that the number j is not less than the number of all purchases corresponding to all combinations of the horse numbers of the selected horses (step S36: NO), the odds fluctuation prediction process End.

  When the odds fluctuation prediction process ends, the odds fluctuation information providing unit 146 generates data of the odds comparison table (step S14). Specifically, the odds fluctuation prediction unit 145 acquires the latest odds corresponding to the race ID of the selected race from the odds information DB 12d. The odds fluctuation prediction unit 145 includes, for each purchase, a combination of the horse number or frame number of the purchase, the odds of the purchase, and the URL of the arrow image corresponding to the prediction fluctuation information of the purchase. Are generated as data of the odds comparison table. Next, the odds fluctuation prediction unit 145 transmits the data of the generated odds comparison table to the user terminal 3 (step S15), and ends the odds fluctuation information provision process. The user terminal 3 displays an odds comparison table in the odds comparison page based on the data received from the betting ticket issuing agency server 1. At this time, the user terminal 3 transmits the URL included in the data of the odds comparison table to the betting ticket sale agency server 1, and the odds variation information providing unit 146 transmits the image of the arrow corresponding to the URL to the user terminal 3. The user terminal 3 displays the received arrow image in the odds comparison table.

  As described above, according to the present embodiment, the system control unit 14 receives the race ID of the selected race. In addition, the system control unit 14 identifies a prior anticipation person who has predicted a winning of any racehorse among a plurality of racehorses participating in the selected race. In addition, the system control unit 14 acquires from the storage unit 12 formulas corresponding to the user ID indicating the specified prior anticipation person and the race ID indicating the past race. Further, the system control unit 14 predicts the purchase of the betting ticket that the prior predictor purchases in the selected race based on the acquired formula. Further, the system control unit 14 predicts a change in odds of the betting ticket of the selected race based on the predicted purchase. In addition, the system control unit 14 displays an odds comparison page that includes information indicating predicted fluctuations in odds. Therefore, the user can determine the purchase price or the purchase price without looking at the odds just before the deadline for selling the betting ticket. Accordingly, since the concentration of betting ticket purchases just before the deadline is eased, the processing load of the betting ticket issuing agency server 1 that accepts the purchase of betting tickets can be distributed.

  Further, the system control unit 14 may accept the selected horse number. Next, the system control unit 14 may specify a prior anticipation person who has predicted that the racehorse indicated by the selected horse number will win the selected race. And the system control part 14 may estimate the fluctuation | variation of the odds of the betting ticket of the purchase including the racehorse which a selection horse number shows among the betting tickets of a selection race. In this case, it is possible to reduce the processing load of the betting ticket issuing proxy server 1 while suppressing a decrease in prediction accuracy.

[2. Second Embodiment]
[2-1. Functional overview of betting ticket sales agency server 1]
Next, an outline of functions in the second embodiment will be described with reference to FIG. In the present embodiment, the buy eye prediction unit 144 includes at least a formula of the purchase history acquired by the purchase history acquisition unit, race detailed information or odds of a past race in which the prior anticipation person purchased the betting ticket, and the selected race. Based on the race detailed information or the odds, the prior anticipation person specified by the anticipation person specifying unit 142 predicts the purchase of the betting ticket to be purchased in the selected race. Race detailed information and odds are examples of race information according to the present invention. In other respects, the second embodiment may be the same as the first embodiment.

  For example, the buy eye prediction unit 144 may use the odds ratio or difference between the buy eyes according to a plurality of different formulas including the horse number or the frame number of the same racehorse. Then, the buying eye prediction unit 144 may determine the prediction formula by a plurality of different formulas based on the odds ratio or difference. A user may determine which betting ticket for each formula is to be purchased by comparing the odds of purchases by a plurality of formulas. In the following, a case where the odds ratio is used will be described as an example.

  For example, the buying eye prediction unit 144 may determine a plurality of formulas having the same number of consecutive stations as candidates for each prediction formula. In addition, for example, the buying eye prediction unit 144 may determine a plurality of formulas whose purchase ratio in the past race is equal to or greater than a predetermined value as candidates for each prediction formula. Candidates by prediction formula are referred to as candidate formulas.

  The buying eye prediction unit 144 obtains odds of the buying eye including the predicted horse of the prior anticipation person in the selected race for each candidate formula. Then, the buying eye prediction unit 144 calculates the odds ratio between formulas. When calculating the ratio between the odds of the buy-by-expression specifying the order of arrival and the odds of the buy-by-expression not hitting the order of arrival, the buy-out predicting unit 144, for example, You may divide the odds of the purchase by the odds of the purchase of each expression that does not match the order of arrival. Further, when calculating the ratio between the odds of the buying eyes for each expression that specifies the horse number and the odds of the buying eyes for each expression that specifies the frame number, the buying eye prediction unit 144, for example, for each expression that specifies the horse number The odds of the purchase may be divided by the odds of the purchase according to the expression specifying the frame number. The buying eye forecasting unit 144 calculates the odds ratio by dividing the odds of the formula with the higher odds of the two formulas by the odds of the formula with the lower odds in general. To do. The number of purchases that can be purchased with the same racehorse combination may differ between formulas. For example, the number of purchases that can be purchased with the same combination of racehorses is different between the formula specifying the arrival order and the expression not specifying the arrival order. Assuming that the prior predictor purchases a formula that specifies the order of arrival, he / she may purchase a betting ticket for only a part of the purchase, or may purchase a betting ticket for the entire purchase. Therefore, if it is assumed that the prior predictor purchases only part of the betting tickets for the formulas that specify the order of arrival, the buy-ahead prediction unit 144, for example, purchases each purchase item for each formula that specifies the order of arrival. The ratio between the odds of the purchase and the odds of the purchase by the formula that does not specify the order of arrival may be calculated. When it is assumed that the prior anticipation person purchases all betting tickets for a formula that specifies the order of arrival, the buy-ahead prediction unit 144, for example, odds of a plurality of purchase eyes for each formula that specifies the order of arrival. The odds ratio may be calculated using the representative value of. The representative value may be, for example, an average value, a minimum value, or the like.

  Next, the buying eye prediction unit 144 determines a threshold value of the odds ratio. For example, the buy eye prediction unit 144 acquires a horse number or a frame number from the purchase information of the purchase history of the prior anticipation person for each past race in which the prior predictor has purchased a betting ticket for each candidate formula, and the acquired horse number Alternatively, the ratio of odds between buying eyes by a plurality of candidate formulas including the racehorse of the frame number is calculated. Then, the buying eye prediction unit 144 may calculate a representative value of the ratio of odds when the betting ticket for each candidate formula is purchased, for example, for each candidate formula. The representative value in this case may be, for example, an average value, a minimum value, a maximum value, a median value, or the like. Then, the buying eye prediction unit 144 may determine the threshold based on, for example, the representative value of the odds ratio. For example, the buying eye prediction unit 144 may determine the threshold value between the representative value of the odds ratio for one candidate formula and the representative value of the odds ratio for another candidate formula. For example, the buying eye prediction unit 144 may determine a representative value of the odds ratio for each candidate formula and a further representative value of the representative value of the odds ratio for another candidate formula as the threshold value. The representative value in this case may be, for example, an average value, a minimum value, a maximum value, or the like.

  The buying eye prediction unit 144 determines the prediction formula from among the plurality of candidate formulas based on the threshold and the odds ratio in the selected race. For example, when the odds ratio in the selected race is equal to or less than the threshold value, the buying eye prediction unit 144 may determine the candidate formula with the lower odds for each prediction formula. Then, when the odds ratio in the selected race is larger than the threshold, the buying eye prediction unit 144 may determine the candidate formula with the higher odds for each prediction formula.

  The buy eye prediction unit 144 sets the first threshold of the ratio of odds when the prior predictor purchases all the betting tickets of the same combination of horse number or frame number in the past race according to the formula specifying the arrival order. The second threshold value of the ratio of odds when purchasing some betting tickets may be calculated. In this case, the buying eye prediction unit 144 may compare, for example, the first threshold value with the representative value of the odds ratio in the selected race. Then, when the representative value of the odds ratio in the selected race is equal to or less than the first threshold, the buying eye prediction unit 144 may determine the formula for not specifying the arrival order for each prediction formula. On the other hand, when the representative value of the odds ratio in the selected race is larger than the first threshold, the buying eye prediction unit 144 may determine the formula for specifying the arrival order for each prediction formula. In this case, the buying eye prediction unit 144 may determine all the buying eyes including the predicted horse according to the formula for specifying the arrival order as the prediction buying eyes. Further, the buying eye prediction unit 144 may compare, for example, the second threshold value with the ratio of odds calculated for each buying eye according to the formula that specifies the arrival order. Then, when the odds ratio of all purchases according to the formula for specifying the order of arrival is equal to or less than the second threshold, the purchase forecast unit 144 predicts the formula for which the order of specification is not specified. You may decide according to a formula. On the other hand, if the ratio of the odds of at least one purchase among the formulas for specifying the order of arrival is greater than the second threshold, the buy eye prediction unit 144 determines the formula for specifying the order of arrival. Alternatively, it may be determined for each prediction formula. In this case, the buying eye prediction unit 144 may determine a buying eye having a ratio of odds larger than the second threshold value as the predicting buying eye according to the formula for specifying the arrival order.

  When there is a buy eye with a representative value of the odds ratio larger than the first threshold and an odds ratio larger than the second threshold, the buy eye prediction unit 144 predicts the type of the order specifying the arrival order. Determine by formula. In this case, the buy eye prediction unit 144 may determine all purchases including the predicted horse as the predicted purchase, or determine a purchase having an odds ratio larger than the second threshold as the predicted purchase. May be.

  FIG. 9 is a diagram illustrating an example of prediction of purchase. For example, it is assumed that the horse numbers of the predictor A's favorite and opposing horse numbers are 2 and 6. In the selected race, the odds of 2-6 for horses are 2.9, the odds of 2-6 for horses are 6.5, and the odds of horses 6-2 are 4.8. The ratio of the odd 2-6 odds to the odd 2-6 odds is 2.24, and the ratio of the odd 6-2 odds to the odd 2-6 odds is 1. 66. In terms of the relationship between Uma and Uma, if the odds of Uma are more than twice the odds of Uma, the two of the Uta's betting tickets will be purchased one by one rather than purchasing 2 The expected value is higher if purchased. For this reason, it is conceivable that the odds ratio between the horse and the horse is doubled. However, the ratio at which the horse ticket or horse ticket is purchased may differ depending on the user. It is assumed that the first threshold value calculated based on the past race purchase history is 1.5 and the second threshold value is 3.0. As a representative value of the odds ratio of the selected race, for example, the minimum value of 1.66 is larger than the first threshold value of 1.5. On the other hand, the odds ratios 2.24 and 1.66 are smaller than the second threshold value 3.0. Therefore, the buy-ahead prediction unit 144 determines a simple unit for each prediction formula, and determines a predicted purchase unit of 2-6 and 6-2 for a single unit.

  Also in the relationship between the double frame and the single frame, and the relationship between the triple double and the single triple, it is possible to determine the prediction formula by the same method as described above. In addition, for example, even in the relationship between a win and a win, a relationship between a horse and a frame, and a relationship between a horse and a frame, it is possible to determine the prediction formula by the same method as described above. .

  The odds of the selected race used for calculating the odds ratio may be, for example, current odds, or future odds or final odds predicted by the betting ticket release agency server 1 or the like by some method. This method is different from, for example, the above-described method of predicting the fluctuation of odds based on the purchase history of the past predictor's past race. For example, the system control unit 14 may specify the fluctuation amount or trend of the odds per unit time based on the fluctuation of the odds up to the present time according to the passage of time. Then, the system control unit 14 may predict the odds after a predetermined time based on the fluctuation amount or trend of the odds per unit time, or predict the odds at the sales deadline time of the betting ticket of the selected race. Also good.

  The purchase eye prediction unit 144 predicts the purchase eye of the prior anticipation person's selection race based on the purchase history of only the races having the same condition as the race condition of the selection race among the past races in which the prior anticipation person purchased the betting ticket. May be. This is because the user may change his / her purchase according to the race conditions. For example, in the case of a heavy Baba, the user may purchase a winning betting ticket, and in the case of a good Baba, the user may purchase a winning betting ticket. Each of Shiba and Raba is an example of a race condition.

  The buying eye prediction unit 144 can specify the race condition of the selected race based on the detailed race information or odds of the selected race. Further, the buying eye prediction unit 144 can specify the race conditions of the past race based on the detailed race information or odds of the past race. The race condition items include, for example, the racetrack state, course type, racetrack, race distance, grade, popularity trend of racehorse, position of the racehorse frame of the expected horse or the purchase of the purchased betting ticket, and the expected horse. Or there are odds of buying a purchased betting ticket. As a popular tendency, for example, it is possible to specify whether the race has a major favorite, a race in which a melee race is expected, or an intermediate race between them. A popular tendency can be identified from, for example, winning odds. The position of the racehorse frame is specified based on the racehorse frame number. The position of the frame is, for example, an inner frame or an outer frame.

  The item of the race condition may be determined in advance, for example. Further, for example, the buying eye prediction unit 144 may determine the race condition item from among a plurality of items. For example, the buying eye prediction unit 144 may calculate, for each item, a difference in past purchase ratios for each formula among a plurality of race conditions corresponding to the item. Then, the buying eye prediction unit 144 may determine an item having the largest purchase ratio difference or an item having a purchase ratio difference equal to or greater than a predetermined value as the race condition item of the selected race. Items with a large difference in the purchase ratio are likely to have an influence on the decision of the betting ticket to be purchased. For example, in the past races, the ratio of purchases of heavy Baba and single frames is 0.8: 0.2, and the ratio of purchases of good Baba and single frames is 0.8: 0.2. In this case, the difference between the purchase ratios of the multiple frames and the difference between the purchase ratios of the single frames are 0 respectively. In this case, it is considered that the racetrack state does not affect the determination of the type of betting ticket to be purchased. On the other hand, if the purchase ratio between the inner frame and the single frame is 0.6: 0.4, and the purchase ratio between the outer frame and the single frame is 0.3: 0.7, The difference in the purchase ratio and the difference in the purchase ratio for each frame are 0.3. In this case, the position of the racehorse frame may have an influence on the determination of the type of betting ticket to be purchased.

  Based on the purchase history and odds of races with the same conditions as the race conditions of the selected race among the past races in which the prior anticipator purchased the betting ticket, the buy eye prediction unit 144 calculates the ratio or difference of odds between formulas. A threshold may be determined. Then, the buying eye prediction unit 144 may predict the buying eye based on the determined threshold and the ratio of odds of buying eyes according to a plurality of different formulas including the predicted horse of the selected race. This is because the odds ratio for changing the type of betting ticket purchased by the user may change depending on the race conditions. Also in this case, the item of the race condition may be determined in advance, for example. Further, for example, the buying eye prediction unit 144 may determine the race condition item from among a plurality of items. For example, the buying eye prediction unit 144 may calculate, for each item, a difference in the odds ratio threshold value between a plurality of race conditions corresponding to the item. Then, the buying eye prediction unit 144 may determine an item having the largest threshold difference or an item having a threshold difference greater than or equal to a predetermined value as the race condition item of the selected race.

[2-2. Operation of information processing system]
Next, the operation of the information processing system SA will be described with reference to FIG. The odds fluctuation information providing process and the odds fluctuation prediction process may be the same as in the first embodiment. FIG. 10 is a flowchart illustrating an example of a buy-ahead prediction process by the system control unit 14 of the betting ticket issuing proxy server 1 according to the present embodiment.

  As illustrated in FIG. 10, the purchase eye prediction unit 144 acquires a race ID from each purchase history of the prior anticipation person (i) acquired in step S9 of the odds fluctuation information provision process. Then, the buying eye prediction unit 144 acquires race detailed information corresponding to the acquired race ID from the race information DB 12b, and acquires the latest odds corresponding to the acquired race ID from the odds information DB 12d (step S41). ).

  Next, the purchase eye prediction unit 144 obtains a formula type from each purchase history of the prior anticipation person (i) (step S42). Next, the buying eye prediction unit 144 calculates the purchase ratio for each formula by counting the number of the acquired formulas for each formula (step S43). Next, the purchase eye prediction unit 144 determines, for each candidate formula, one or more formulas whose purchase ratio is greater than or equal to a predetermined value among the formulas acquired from the purchase history (step S44). Next, the purchase history acquisition unit 143 determines whether or not there is only one candidate formula whose purchase ratio is equal to or greater than a predetermined value (step S45). At this time, if the buying eye prediction unit 144 determines that there is only one candidate formula (YES in step S45), the process proceeds to step S46. On the other hand, if it is determined that there are a plurality of candidate formulas (step S45: NO), the buying eye prediction unit 144 proceeds to step S48.

  In step S46, the buying eye prediction unit 144 determines candidate formulas by prediction formulas. Next, the buying eye prediction unit 144 determines a prediction buying eye based on the prediction information of the selected race of the prior anticipation person (i) and the prediction formula (Step S47). The method for determining the predicted purchase is the same as in step 24 of FIG. Next, the buying eye prediction unit 144 proceeds to step S54.

  In step S48, the buying eye prediction unit 144 determines, for each of the plurality of items that are candidates for the race condition, for each candidate formula including the racehorse of the betting ticket that the prior predictor (i) purchased in the past race. A threshold for the odds of buying is calculated for each race condition. For example, the purchase eye prediction unit 144 specifies, for each of a plurality of items, a purchase history of a race that matches each race condition corresponding to the item from the purchase history of the prior predictor (i). The buying eye prediction unit 144 can specify the race conditions of each past race based on the detailed race information and odds acquired in step S41. Next, the buy eye prediction unit 144 extracts purchase histories including candidate formulas from the purchase history of the horse number prior anticipation person (i) included in the purchase information of the specified purchase history for each race condition. The purchase eye prediction unit 144 specifies the racehorse of the purchase of the purchased betting ticket based on the horse number or frame number included in the purchase information of the specified purchase history for each race condition. The buying eye prediction unit 144 obtains the odds of the buying eye including the specified racehorse, and the buying eye according to the candidate formula. The buying eye prediction unit 144 calculates, for each combination of race conditions and candidate formulas, a ratio of odds by a plurality of candidate formulas when a betting ticket by the candidate formula is purchased. Next, the buying eye prediction unit 144 calculates an odds ratio threshold for each race condition based on the calculated ratio. In addition, the buy eye prediction unit 144 may determine the first threshold value and the second threshold value as described above when the formulas specifying the arrival order and the formulas not specifying the arrival order are candidate formulas. .

  Next, the buying eye prediction unit 144 determines the race condition item (step S49). For example, the buying eye prediction unit 144 calculates, for each item, a threshold difference between a plurality of race conditions corresponding to the item. Then, the buying eye prediction unit 144 determines the item with the largest threshold difference as the race condition item.

  Next, the buying eye prediction unit 144 acquires race detailed information corresponding to the race ID of the selected race from the race information DB 12b, and acquires the latest odds corresponding to the race ID of the selected race from the odds information DB 12d (step S50). ). Next, the buying eye prediction unit 144 specifies the race condition in the determined item based on the acquired detailed race information or odds (step S51). Next, the buy eye prediction unit 144 acquires the odds of the buy eye including the predicted horse of the prior anticipation person (i) in the selected race, and the buy eye according to the candidate formula. Then, the buying eye prediction unit 144 calculates the ratio of the acquired odds (step S52).

  Next, the buying eye prediction unit 144 determines the prediction buying eye based on the threshold value determined in step S48, the threshold value corresponding to the race condition of the selected race, and the odds ratio in the selected race (step S53). ). For example, when the odds ratio is less than or equal to the threshold, the buying eye prediction unit 144 determines the formula that does not specify the arrival order for each prediction formula, and specifies the arrival order when the odds ratio is greater than the threshold. You may determine each type | formula according to a prediction type | formula. Further, for example, when the odds ratio is less than or equal to the threshold, the buying eye prediction unit 144 determines the formula for specifying the frame number for each prediction formula, and when the odds ratio is greater than the threshold, The formula may be determined for each prediction formula. The buying eye prediction unit 144 may determine the prediction buying eye based on the prediction information of the selected race of the prior predictor (i) and the prediction formula. In addition, when the formulas specifying the arrival order and the formulas not specifying the arrival order are candidate formulas, the buying eye prediction unit 144 uses the first threshold value and the second threshold value to determine the prediction formula and the predicted buying eye. May be determined. For example, when the representative value of the odds ratio in the selected race is equal to or less than the first threshold, the buying eye prediction unit 144 may determine a formula for which the arrival order is not specified for each prediction formula. Then, the buying eye prediction unit 144 may determine the buying eye including the imaginary horse as the predicting buying eye for each prediction formula. Further, for example, when the representative value of the odds ratio in the selected race is larger than the first threshold, the buying eye prediction unit 144 may determine for each prediction formula, a formula for specifying the arrival order. Then, the purchase eye prediction unit 144 may determine all purchases including the predicted horse as prediction purchases for each prediction formula. Further, the buy eye prediction unit 144 designates the odds and the order of arrival of any one of the buy-by-buy according to the formulas in which the representative value of the odds ratio in the selected race is equal to or less than the first threshold and specifies the order of arrival. In the case where the ratio of odds for different formulas is greater than the second threshold, the formulas for specifying the arrival order may be determined for each prediction formula. Then, the buy eye prediction unit 144 may determine a buy eye having an odds ratio equal to or greater than the second threshold value for each prediction formula. Next, the buying eye prediction unit 144 proceeds to step S54.

  In step S <b> 54, the odds fluctuation prediction unit 145 increments the determined number of predicted buyers of the predicted purchase by 1 and ends the purchase prediction process.

  As described above, according to the present embodiment, the system control unit 14 selects, from the storage unit 12, the race detailed information or odds corresponding to the race ID indicating the past race in which the prior predictor has purchased the betting ticket, and the selection. The detailed race information or odds corresponding to the race ID of the race is acquired. Further, the system control unit 14 predicts the purchase of the betting ticket that the prior anticipation person purchases in the selected race based on the obtained formula and the detailed race information or odds. Therefore, it is possible to more accurately predict the purchase of a prior anticipation person who changes the purchase of the betting ticket to be purchased in accordance with the information related to the race.

[3. Third Embodiment]
[3-1. Functional overview of betting ticket sales agency server 1]
Next, an outline of functions in the third embodiment will be described. In this embodiment, anticipation person specific part 142 specifies an anticipation person who is predicted not to purchase a betting ticket of a selection race after the present time among exclusion anticipation persons as an exclusion anticipation person. And anticipation person specific part 142 predicts the purchase of a betting ticket which anticipation persons other than an exclusion anticipation person among prior anticipation persons purchase in a selection race. Even if the result of the selected race is predicted, there are actually users who do not purchase a betting ticket. By excluding such users, the accuracy of odds fluctuation prediction can be improved. In other respects, the third embodiment may be the same as the first embodiment or the second embodiment.

  For example, the anticipation person specific | specification part 142 may specify the prior anticipation person who has already purchased the betting ticket of a selection race as an exclusion anticipation person. A user who has already purchased a betting ticket for a selected race has a low probability of purchasing a betting ticket after the present time. The predictor specifying unit 142 determines whether or not the prior predictor has already purchased the betting ticket of the selected race by determining whether or not the purchase history of the selected race of the prior predictor is registered in the purchase history DB 12g. Can be determined.

  In addition, for example, the anticipation person specifying unit 142 may include an anticipation person who has a probability of not purchasing a betting ticket even though it has been predicted in a past race, and an anticipation person who has an anticipation of not purchasing a betting ticket as expected. At least one of them may be specified as an exclusion expectant. For example, the anticipation person specific | specification part 142 acquires race ID from the anticipation information in the past race of a prior anticipation person. Anticipation person specific part 142 judges whether the purchase history corresponding to acquired user ID is registered into purchase history DB12g. When the purchase history is not registered in the purchase history DB 12g, the anticipation person specifying unit 142 determines that the prior anticipation person has not purchased the betting ticket. Anticipation person specific part 142 acquires buy eye information from a purchase history registered in purchase history DB12g. The anticipation person specifying unit 142 determines whether or not any horse number of one or a plurality of racehorses indicated by the horse number or the frame number included in the purchase information is included in the prediction information. If any horse number of one or a plurality of racehorses indicated by the horse number or frame number included in the purchase eye information is not included in the prediction information, the anticipation person specifying unit 142 determines that the prior anticipation person is the purchase eye as expected. It is determined that the betting ticket was not purchased. The anticipation person specific | specification part 142 calculates the ratio of the race which the prior anticipation person purchased the betting ticket with the purchase eyes as expected among the races which the prior anticipation person predicted. This ratio is called the expected purchase ratio. And anticipation person specific part 142 determines a prior anticipation person whose calculated anticipation corresponding purchase ratio is less than a predetermined value as an exclusion anticipation person.

  Here, the anticipation person specifying unit 142 predicts the anticipation person based on the prediction information and the purchase history of only the races having the same condition as the race condition of the selected race among the past races that the anticipation person has made an anticipation. It may be determined whether or not a betting ticket has been purchased with a street purchase. This is because the user may change whether he / she purchases a betting ticket or purchases a betting ticket as expected according to the race condition. The item of the race condition may be determined in advance, for example. For example, anticipation person specific part 142 may determine an item of race conditions from a plurality of items. For example, anticipation person specific part 142 may calculate the difference of the past anticipation correspondence purchase ratio for every formula between a plurality of race conditions corresponding to the item for every item. And anticipation person specific part 142 may determine an item with the largest difference of an anticipation corresponding purchase ratio, or an item whose difference of an anticipation corresponding purchase ratio is more than a predetermined value as an item of race conditions of a selection race.

  In addition, the anticipation person specifying unit 142 determines whether or not there is a probability that the prior anticipation person seriously purchases the betting ticket in the selection race based on the browsing count or the browsing time of the web page of the predetermined type of information regarding the selection race. You may judge. The more frequently viewed or the longer the viewing time, the higher the probability that a prior predictor will seriously purchase a betting ticket in the selected race. For example, anticipation person specific part 142 calculates anticipation person specific part 142, the number of times of the inspection of the web page of the information about a race, or inspection time about each past race where prior anticipation persons purchased a betting ticket based on browsing history. To do. At this time, anticipation person specific part 142 may calculate the remaining time from the present time to the sales deadline time of the betting ticket of the selection race. And anticipation person specific part 142 may calculate the number of times of inspection before the time before the calculated remaining time or browsing time from the sales deadline time of the betting ticket of the past race about the past race. The anticipation person specifying unit 142 counts the number of browsing history including the user ID of the prior anticipation person, the race ID of the target race, and the predetermined page type, thereby determining the number of times the web page of the target race is viewed. Can be calculated. Moreover, in the case of browsing time, anticipation person specific | specification part 142 searches the browsing history DB12e for the browsing history containing the user ID of a prior anticipation person. The anticipation person specific | specification part 142 sorts the searched browsing history in order of browsing date. The anticipation person specific part 142 is from the browsing date of the browsing history including the race ID of the target race and the predetermined page type in the sorted browsing history to the browsing date of the browsing history next to the browsing history. The time may be calculated as the browsing time of one web page of the subject race. And the anticipation person specific | specification part 142 may calculate the sum total of the browsing time of an object race and a predetermined page classification. The anticipation person specific | specification part 142 determines the threshold value of the frequency | count of browsing or browsing time based on the calculated frequency | count of browsing or browsing time. For example, the anticipation person specific | specification part 142 may determine the minimum value, average, etc. of the calculated browsing frequency or browsing time as a threshold value. And anticipation person specific part 142 judges that there is no probability that a prior anticipation person will seriously purchase a betting ticket in selection race, when the number of times of browsing or the time of browsing of a web page of information about selection race is less than a threshold. Anticipation person specific part 142 determines this prior anticipation person as an exclusion anticipation person.

  Here, anticipation person specific part 142 is based on the inspection frequency or inspection time of only the race of the same conditions as the race conditions of selection race among the past races where the prior anticipation person purchased the betting ticket, or the inspection frequency or inspection time. The threshold value may be determined. This is because, depending on the race conditions, the user may change the number of times or time to view race information before purchasing a betting ticket. Also in this case, the item of the race condition may be determined in advance, for example. For example, anticipation person specific part 142 may determine an item of race conditions from a plurality of items. For example, the anticipation person specific | specification part 142 may calculate the difference of the frequency | count of browsing or the browsing time between several race conditions corresponding to the item for every item. And anticipation person specific part 142 may determine the item with the most difference of the number of times of inspection or inspection time, or the item whose difference of the number of times of inspection or inspection time is more than a predetermined value as the item of race conditions.

  Moreover, the anticipation person specific | specification part 142 may determine whether there is a probability that an anticipation person will seriously purchase a betting ticket by a selection race based on the balance of the purchase fund of an anticipation person's betting ticket. For example, anticipation person specific part 142 acquires the balance of purchase funds when a prior anticipation person purchases a betting ticket from purchase fund change history. Anticipation person specific part 142 may determine the threshold of the balance of purchase funds based on the balance acquired from purchase fund change history. The threshold value may be, for example, the minimum value, average value, or median value of the balance of purchased funds. And anticipation person specific part 142 may judge that there is no probability that an anticipation person seriously purchases a betting ticket in a selection race, when the balance of the present purchase fund of an anticipation person is less than a threshold. Anticipation person specific part 142 determines this prior anticipation person as an exclusion anticipation person.

  Even if the anticipation person specifying part 142 determines whether there is a probability that the anticipation person seriously purchases the betting ticket in the selected race, based on the history of the deposit to the account of the fund of purchase of the anticipation person's betting ticket. Good. For example, the anticipation person specifying unit 142 may calculate the elapsed time from the time when the prior anticipation person has deposited money to the start time of the race where the prior anticipation person has purchased the betting ticket. When a plurality of deposits are made, the anticipation person specifying unit 142 may calculate the elapsed time using the deposit time closest to the betting ticket purchase time. The anticipation person specific | specification part 142 may determine the threshold time of payment based on the calculated elapsed time. The threshold time for depositing is the latest time that the prior predictor may deposit when the prior predictor purchases the selected race betting ticket. For example, the anticipation person specific | specification part 142 may calculate the representative value of the calculated elapsed time. The representative value may be, for example, a minimum value, an average value, or the like. The anticipation person specific | specification part 142 may subtract the representative value of elapsed time from the starting time of a selection race, and may calculate the threshold time of payment. If the current time is later than the threshold time, the anticipation person specifying unit 142 may determine that the prior anticipation person is not likely to purchase the betting ticket seriously in the selected race. Anticipation person specific part 142 determines this prior anticipation person as an exclusion anticipation person.

  The predictor identifying unit 142 may determine that the prior predictor is an excluded predictor only when the balance of the current purchase funds of the prior predictor is less than the threshold and the current time is later than the threshold time. Good.

[3-2. Operation of information processing system]
Next, the operation of the information processing system SA will be described with reference to FIGS. The odds fluctuation information providing process and the odds fluctuation prediction process may be the same as in the first embodiment. FIG. 11 is a flowchart showing a first example of a buy-ahead prediction process by the system control unit 14 of the betting ticket issuing proxy server 1 according to the present embodiment. In FIG. 11, processes similar to those in FIG. The example shown in FIG. 11 is a processing example in the case of specifying an exclusion prospector based on the purchase history.

  As shown in FIG. 11, the anticipation person specifying unit 142 searches the purchase history DB 12g for a purchase history corresponding to the race ID of the selected race and the user ID of the prior anticipation person (i) (step S61). Subsequently, anticipation person specific part 142 judges whether a purchase history was found as a result of search (Step S62). At this time, when it is determined that the purchase history has been found (step S62: YES), the anticipation person specifying unit 142 ends the purchase eye prediction process. That is, anticipation person specific part 142 determines prior anticipation person (i) as an exclusion anticipation person. On the other hand, if it is determined that the purchase history has not been found (step S62: NO), the anticipator specifying unit 142 proceeds to step S63.

  In Step S63, anticipation person specific part 142 acquires race ID from each purchase history of prior anticipation person (i) acquired at Step S9 of odds change information offer processing. Then, the buy eye prediction unit 144 acquires race detailed information corresponding to the acquired race ID from the race information DB 12b, and acquires the latest odds corresponding to the acquired race ID from the odds information DB 12d. Subsequently, anticipation person specific part 142 acquires the anticipation information of the past race among anticipation information corresponding to user ID of prior anticipation person (i) from anticipation information DB12f (Step S64).

  Subsequently, anticipation person specific part 142 computes an anticipation corresponding purchase ratio for every race condition about each of a plurality of items used as a candidate of race conditions (Step S65). For example, anticipation person specific part 142 specifies anticipation information of a race which meets each race condition corresponding to an item from anticipation information of prior anticipation person (i) about each of a plurality of items, respectively. Anticipation person specific part 142 judges whether there is any purchase history of advance anticipation person (i) of the race for every race in which anticipation information of prior anticipation person (i) was registered. Anticipation person specific part 142 judges whether a purchase history contains a horse number or a frame number of an anticipation horse which a horse number contained in anticipation information shows about a race with purchase history. Subsequently, anticipation person specific part 142 and anticipation person specific part 142 compute the number of anticipation information in which purchase history contains the horse number or frame number of an anticipation horse for every race condition. And anticipation person specific part 142 calculates an anticipation corresponding purchase ratio for every race condition based on the number of calculated anticipation information.

  Subsequently, anticipation person specific part 142 determines the item of race conditions (Step S66). For example, the anticipation person specific | specification part 142 calculates the difference of the anticipation corresponding | compatible purchase ratio between the some race conditions corresponding to the item for every item. Then, the purchase eye prediction unit 144 determines the item having the largest difference in the prediction corresponding purchase ratio as the race condition item.

  Subsequently, anticipation person specific part 142 acquires race detailed information corresponding to race ID of a selection race from race information DB12b, and acquires the newest odds corresponding to race ID of selection race from odds information DB12d (Step S67). ). Subsequently, anticipation person specific part 142 specifies race conditions of a selection race in a determined item based on acquired race detailed information or odds (Step S68). Subsequently, anticipation person specific part 142 judges whether anticipation correspondence purchase ratio corresponding to race conditions of a selection race is more than a predetermined value among prediction correspondence purchase ratios calculated at Step S65 (Step S69). . At this time, if the anticipation person specifying unit 142 determines that the anticipation-compatible purchase ratio corresponding to the race condition of the selected race is greater than or equal to a predetermined value (step S69: YES), the process proceeds to step S21. The buying eye prediction unit 144 executes steps S21 to S25 as in the case of FIG. 7 and ends the buying eye prediction process. On the other hand, when it is determined that the predicted corresponding purchase ratio corresponding to the race condition of the selected race is less than the predetermined value (step S69: NO), the anticipator specifying unit 142 ends the buy eye prediction process. That is, anticipation person specific part 142 determines prior anticipation person (i) as an exclusion anticipation person.

  FIG. 12 is a flowchart showing a second example of a buy-ahead prediction process by the system control unit 14 of the betting ticket issuing proxy server 1 according to this embodiment. 12, processes similar to those in FIG. 11 are given the same reference numerals. The example shown in FIG. 12 is an example of a process in the case where an expected exclusion person is identified based on the number of times a race web page is viewed.

  As shown in FIG. 12, the anticipation person specific | specification part 142 performs step S61-S63 similarly to the case of FIG. Subsequently, anticipation person specific part 142 acquires race ID of a past race from each purchase history of prior anticipation person (i). Subsequently, anticipation person specific part 142 acquires a browsing history corresponding to race ID of an acquired past race from browsing history DB12e among browsing history corresponding to user ID of prior anticipation person (i) (Step S81). ). At this time, anticipation person specific part 142 acquires the browsing history of a predetermined type of web page based on URL contained in browsing history. Next, the anticipation person specifying unit 142 calculates the remaining time from the present time to the sales deadline time of the betting ticket of the selected race. Subsequently, anticipation person specific part 142 extracts the browsing history before the time before the calculated remaining time from the sales deadline time of the betting ticket of the past race for every past race (Step S82). Subsequently, anticipation person specific part 142 calculates the number of times of browsing by counting the number of browsing history extracted for every past race (Step S83).

  Subsequently, anticipation person specific part 142 computes the threshold of the number of times of browsing for every race condition about each of a plurality of items which become a candidate of race conditions (Step S84). For example, anticipation person specific part 142 specifies the frequency | count of browsing of the web page of a race which meets each race condition corresponding to an item from the number of times of browsing calculated about each of a plurality of items. The anticipation person specific part 142 calculates the representative value of the frequency | count of browsing as a threshold value for every race condition. The representative value may be, for example, a minimum value.

  Subsequently, anticipation person specific part 142 determines the item of race conditions (Step S85). For example, the anticipation person specific part 142 calculates the difference of the representative value of the frequency | count of browsing between the some race conditions corresponding to the item for every item. Then, the buying eye prediction unit 144 determines the item having the largest difference in the representative value of the number of browsing times as the race condition item.

  Subsequently, the anticipation person specific | specification part 142 performs step S67 and S68 similarly to the case of FIG. Subsequently, anticipation person specific part 142 acquires browsing history corresponding to race ID of a selection race, and user ID of prior anticipation person (i) from browsing history DB12e (Step S86). At this time, anticipation person specific part 142 acquires the browsing history of a predetermined kind of web page. Subsequently, anticipation person specific part 142 calculates the number of times of browsing of a web page of a selection race by counting the number of acquired browsing history (Step S87). Subsequently, anticipation person specific part 142 specifies a threshold corresponding to race conditions of a selection race among thresholds computed at specific step S84. And anticipation person specific part 142 judges whether the number of times of inspection of selection race is more than a threshold corresponding to race conditions of selection race (Step S88). At this time, if the anticipation person specifying unit 142 determines that the number of browsing times is equal to or greater than the threshold (step S88: YES), the process proceeds to step S21. The buying eye prediction unit 144 executes steps S21 to S25 as in the case of FIG. 7 and ends the buying eye prediction process. On the other hand, the anticipation person specific | specification part 142 complete | finishes a purchase eye prediction process, when it determines with the frequency | count of browsing being less than a threshold value (step S88: NO). That is, anticipation person specific part 142 determines prior anticipation person (i) as an exclusion anticipation person.

  FIG. 13 is a flowchart showing a third example of a buy-ahead prediction process by the system control unit 14 of the betting ticket issuing proxy server 1 according to the present embodiment. In FIG. 13, processes similar to those in FIG. The example shown in FIG. 13 is a processing example in the case where an expected exclusion person is specified based on the balance of purchase funds.

  As shown in FIG. 13, the anticipation person specific | specification part 142 performs step S61 and S62 similarly to the case of FIG. Subsequently, anticipation person specific part 142 acquires purchase fund change history corresponding to user ID of prior anticipation person (i) (Step S91). Subsequently, anticipation person specific part 142 determines the threshold of the balance of purchase funds (Step S92). For example, the anticipation person specifying unit 142 determines the minimum value of the purchase funds before the change included in the purchase fund change history whose change reason is the appropriation of funds to the purchase price of the betting ticket among the acquired purchase fund change histories, etc. May be determined as a threshold value. Subsequently, anticipation person specific part 142 acquires the balance after change as the balance of the present purchase fund from the latest purchase fund change history among the acquired purchase fund change histories. And anticipation person specific part 142 judges whether the present balance is more than a threshold (Step S93). At this time, if the predictor identification unit 142 determines that the current balance is equal to or greater than the threshold (step S93: YES), the process proceeds to step S21. The buying eye prediction unit 144 executes steps S21 to S25 as in the case of FIG. 7 and ends the buying eye prediction process. On the other hand, if the predictor identification unit 142 determines that the current balance is less than the threshold (step S93: NO), the process proceeds to step S94.

  In Step S94, anticipation person specific part 142 determines the threshold time of payment. For example, anticipation person specific part 142 acquires race ID and purchase date and time of a past race from each purchase history of prior anticipation person (i). Subsequently, anticipation person specific part 142 acquires race detailed information corresponding to acquired race ID from race information DB12b. Subsequently, anticipation person specific part 142 acquires the sales deadline time of a betting ticket from race detailed information. Subsequently, anticipation person specific part 142 acquires a change date from a purchase fund change history whose change reason is payment among purchase fund change history acquired at Step S91. Subsequently, anticipation person specific part 142 acquires a change date and time nearest to a purchase date and time among change dates and times before purchase date and time for every past race. And anticipation person specific part 142 calculates the remaining time from the specified change date to the sales deadline time for every past race. And anticipation person specific part 142 subtracts the minimum value of remaining time, etc. from the sales deadline time of the betting ticket of a selection race, for example. Calculate the threshold time.

  Subsequently, anticipation person specific part 142 judges whether the present time is later than threshold time (Step S95). At this time, when it is determined that the current time is later than the threshold time (step S95: YES), the predictor specifying unit 142 ends the buy eye prediction process. That is, anticipation person specific part 142 determines prior anticipation person (i) as an exclusion anticipation person. On the other hand, if the predictor identification unit 142 determines that the current time is not later than the threshold time (step S95: NO), the process proceeds to step S21.

  As described above, according to the present embodiment, the system control unit 14 specifies an excluded predictor among the prior predictors. In addition, the system control unit 14 predicts the purchase of a betting ticket purchased by the prior predictors other than the excluded predictors among the prior predictors in the selected race. Therefore, the prediction accuracy of the odds fluctuation can be increased.

[4. Fourth Embodiment]
[4-1. Functional overview of betting ticket sales agency server 1]
Next, an outline of functions in the fourth embodiment will be described with reference to FIG. In the present embodiment, the odds fluctuation prediction unit 145 acquires the purchase price of the betting ticket from the purchase history of the previous anticipation race in the past. Then, the odds fluctuation prediction unit 145 predicts fluctuations in the odds based on the acquired purchase amount. Thereby, the prediction precision of the fluctuation | variation of odds can be raised. In other respects, the fourth embodiment may be the same as the first to third embodiments.

  For example, the odds fluctuation prediction unit 145 may predict the purchase price of the betting ticket in the selection race of the prior anticipation person based on the purchase price acquired from the purchase history. This purchase price is referred to as the predicted purchase price. The predicted purchase price may be, for example, a representative value of the purchase price of a past race. The representative value in this case may be, for example, an average value, a median value, or the like.

  Here, the odds fluctuation prediction unit 145 may determine the predicted purchase price based on the purchase price of only the race having the same condition as the race condition of the selected race among the past races in which the prior predictor purchased the betting ticket. Good. This is because the user may change the purchase price depending on the race conditions. The item of the race condition may be determined in advance, for example. Further, for example, the odds fluctuation prediction unit 145 may determine a race condition item from a plurality of items. For example, the odds fluctuation prediction unit 145 may calculate, for each item, a purchase price difference or ratio between a plurality of race conditions corresponding to the item. And anticipation person specific part 142 may determine the item with the largest difference or ratio of purchase amount, or the item whose difference or ratio of purchase amount is more than a predetermined value as an item of race conditions.

  The anticipation person specific part 142 calculates odds based on the prediction purchase eye and the prediction purchase price. This odds is called predictive odds. The predicted odds are the odds after the predicted fluctuation. Specifically, the odds fluctuation prediction unit 145 acquires the purchase eye and the purchase price from all purchase histories of all users who have purchased betting tickets in the selected race, for example. The odds fluctuation prediction unit 145 calculates the prediction odds for each formula using the purchase item and purchase price acquired from the purchase history, and the prediction purchase item and prediction purchase amount of each prior anticipation person. Since the calculation method of odds is well-known, detailed description of the calculation method of odds is abbreviate | omitted.

  The odds fluctuation information providing unit 146 may display the odds predicted by the odds fluctuation prediction unit 145 on the user terminal 3 of the requesting user. For example, the odds fluctuation information providing unit 146 may transmit an odds comparison page including predicted odds to the user terminal 3. FIG. 14 is a diagram illustrating an example of the odds comparison page. FIG. 14 differs from FIG. 5 in that in FIG. 14, prediction odds 132 are displayed in the odds comparison table 130 instead of the arrow 131.

  Note that the odds fluctuation information providing unit 146 may cause the user terminal 3 to display characters, images, indicia, and the like indicating ascending, descending, no change, and the like as information indicating the fluctuation of the odds based on the predicted odds. For example, the odds fluctuation information providing unit 146 can determine ascending, descending, no change, or the like based on the difference or ratio between the current odds and the predicted odds.

[4-2. Operation of information processing system]
Next, the operation of the information processing system SA will be described with reference to FIGS. FIG. 15 is a flowchart showing an example of odds variation information provision processing by the system control unit 14 of the betting ticket issuing agency server 1 according to the present embodiment. In FIG. 15, processes similar to those in FIG. 6 are denoted by the same reference numerals.

  As illustrated in FIG. 15, the system control unit 14 executes Steps S <b> 1 to S <b> 6 as in the case of FIG. 6. Next, the odds fluctuation prediction unit 145 initializes the prediction list (step S101). The prediction list is a list of prediction buying eyes and prediction amounts. Next, the system control unit 14 executes steps S8 to S13. Next, the odds fluctuation information providing unit 146 generates data of the odds comparison table (step S102). At this time, the odds fluctuation information providing unit 146 generates data of the odds comparison table including the prediction odds calculated in the odds fluctuation prediction process. Next, the odds fluctuation information providing unit 146 transmits the data of the generated odds comparison table to the user terminal 3 (step S15), and ends the odds fluctuation information providing process.

  FIG. 16 is a flowchart illustrating an example of a buy-ahead prediction process performed by the system control unit 14 of the betting ticket issuing proxy server 1 according to the present embodiment. In FIG. 16, processes similar to those in FIG. 7 are given the same reference numerals. As illustrated in FIG. 16, the buying eye prediction unit 144 executes Steps S <b> 21 to S <b> 24 as in the case of FIG. 7. Next, the purchase eye prediction unit 144 acquires the purchase amount from each purchase history of the prior anticipation person (i) acquired in step S9 of the odds fluctuation information provision process (step S111). Next, the purchase eye prediction unit 144 determines the predicted amount based on the acquired purchase amount (step S112). For example, the purchase eye prediction unit 144 may determine the average purchase price as the predicted price. Next, the buy eye prediction unit 144 associates the determined buy eye information of the predicted purchase with the predicted amount and adds it to the prediction list (step S113), and ends the buy eye prediction process.

  FIG. 17 is a flowchart showing an example of odds fluctuation prediction processing by the system control unit 14 of the betting ticket sale agency server 1 according to the present embodiment. In FIG. 17, processes similar to those in FIG.

  As illustrated in FIG. 17, the odds fluctuation prediction unit 145 sets the number k to 1 (step S121). Next, the odds fluctuation prediction unit 145 obtains from the purchase history DB 12g including the purchase information for each formula (k) among all purchase histories corresponding to the race ID of the selected race (step S122). Formula (k) is the kth formula among the formulas corresponding to the selected station number. Next, the odds fluctuation prediction unit 145 obtains buy information and prediction amount for each formula (k) from the prediction list (step S123). Next, the purchase eye prediction unit 144 selects each purchase according to formula (k) based on the purchase information and purchase price included in the acquired purchase history, and the purchase information and prediction price acquired from the prediction list. The predicted odds of the eyes are calculated (step S124).

  Next, the odds fluctuation prediction unit 145 determines whether the number k is less than the number according to the formula corresponding to the selected station number (step S125). At this time, if the odds fluctuation prediction unit 145 determines that the number k is less than the number according to the formula corresponding to the selected station number (step S125: YES), the process proceeds to step S126. In step S126, the odds fluctuation prediction unit 145 increments the number k by 1, and proceeds to step S122. On the other hand, if the odds fluctuation prediction unit 145 determines that the number k is not less than the number according to the formula corresponding to the selected station number (step S125: NO), the odds fluctuation prediction process is terminated.

  As described above, according to the present embodiment, the system control unit 14 acquires the purchase amount corresponding to the user ID indicating the prior anticipation person from the storage unit 12. Further, the system control unit 14 predicts fluctuations based on the predicted purchase eyes and the purchase amount. Therefore, it is possible to predict the odds fluctuation more accurately.

[5. Fifth Embodiment]
[5-1. Functional overview of betting ticket sales agency server 1]
Next, an outline of functions in the fifth embodiment will be described with reference to FIG. In the present embodiment, the buying eye prediction unit 144 feeds back the odds after fluctuation predicted by the odds fluctuation prediction unit 145 to the prediction of the buying eye, and buys the betting ticket of the betting ticket that the prior anticipation person purchases in the selected race. Predict again. Then, the odds fluctuation prediction unit 145 predicts the odds after the fluctuation again based on the buy eye predicted again. The user may change the purchase price of the betting ticket to be purchased when the user sees only the current odds and when the user sees the fluctuation of the predicted odds. The prediction of the odds variation can be improved by the purchase eye prediction unit 144 predicting a purchase that is likely to be purchased by the user who sees the predicted variation in the odds. In other points, the fifth embodiment may be the same as the first to fourth embodiments.

  As a precondition, first, the buy eye prediction unit 144 selects at least the formula of the betting ticket purchased by the prior predictor in the past race, and the odds of the past race from which the prior predictor purchased the betting ticket. Based on the odds at a predetermined time of the race, the prior predictor predicts the purchase of the betting ticket to be purchased in the selected race. The method of predicting a purchase using odds may be, for example, the method of the second embodiment. The odds at the predetermined time may be, for example, current odds, future odds predicted by some method, or final odds. FIG. 18 is a diagram showing a process of odds variation in the present embodiment. As shown in FIG. 18, the input for the prediction of the first purchase is odds at a predetermined time point.

  The odds fluctuation prediction unit 145 predicts the odds after the fluctuation based on the buying eye predicted by the buying eye prediction unit 144. The method for predicting odds after fluctuation may be, for example, the method of the fourth embodiment.

  The buying eye prediction unit 144 uses the odds after fluctuation predicted by the odds fluctuation prediction unit 145 as odds at a predetermined time of the selected race to predict the buying eye again, and the odds fluctuation prediction unit 145 predicts again. Based on the purchase price, the odds after the change are predicted again. As shown in FIG. 18, the input for the prediction of the second and subsequent purchases is predicted odds.

  The number of times of performing the prediction again may be determined in advance, for example. Further, for example, the buy eye prediction unit 144 and the odds fluctuation prediction unit 145 may re-predict the buy eye and re-predict the odds until the difference or ratio between the previously predicted odds and the current predicted odds is less than a predetermined value. May be performed. That is, re-prediction is performed until the predicted odds converge.

[5-2. Operation of information processing system]
Next, the operation of the information processing system SA will be described with reference to FIGS. The odds fluctuation prediction process may be the same as in the first to fourth embodiments. FIG. 19 is a flowchart showing an example of odds variation information provision processing by the system control unit 14 of the betting ticket issuing proxy server 1 according to the present embodiment. In FIG. 19, the same reference numerals are assigned to the same processes as those in FIG.

  As illustrated in FIG. 19, the system control unit 14 executes Steps S <b> 1 to S <b> 6 as in the case of FIG. 15. Next, the buying eye prediction unit 144 acquires the latest odds corresponding to the race ID of the selected race from the odds information DB 12d as reference odds (step S131). Next, the system control unit 14 executes steps S101 and S8 to S13 as in the case of FIG. Next, the buying eye prediction unit 144 calculates the difference between the reference odds and the prediction odds calculated in the odds fluctuation prediction process for each buying eye (step S132). Then, the buying eye prediction unit 144 calculates an average value of the odds difference. Next, the buying eye prediction unit 144 determines whether or not the average value of the odds difference is greater than or equal to a predetermined value (step S133). At this time, if the buying eye prediction unit 144 determines that the average value of the odds difference is equal to or greater than the predetermined value (step S133: YES), it proceeds to step S134. In step S134, the buying eye prediction unit 144 determines the prediction odds as new reference odds, and proceeds to step S101. On the other hand, if it is determined that the average value of the odds difference is not equal to or greater than the predetermined value (step S133: NO), the buying eye prediction unit 144 proceeds to step S102. In step S <b> 102, the odds fluctuation information providing unit 146 generates data of an odds comparison table including the predicted odds calculated last in the odds fluctuation prediction process. Then, the odds fluctuation information providing unit 146 transmits the data of the odds comparison table to the user terminal 3 (step S15), and ends the odds fluctuation information providing process.

  FIG. 20 is a flowchart illustrating an example of a buy-ahead prediction process performed by the system control unit 14 of the betting ticket issuing agency server 1 according to the present embodiment. In FIG. 20, processes similar to those in FIG. 10 are given the same reference numerals.

  As illustrated in FIG. 20, the purchase eye prediction unit 144 acquires a race ID from each purchase history of the prior anticipation person (i) acquired in step S <b> 9 of the odds fluctuation information provision process. Then, the purchase eye prediction unit 144 acquires race detailed information corresponding to the acquired race ID from the race information DB 12b (step S141). Next, the buying eye prediction unit 144 executes steps S42 to S51 as in the case of FIG. When step S47 is completed, the buying eye prediction unit 144 proceeds to step S143. When step S51 is completed, the buying eye prediction unit 144 proceeds to step S142. In step S142, the buy eye prediction unit 144 acquires the reference odds of the buy eye including the expected horse of the prior anticipation person (i) in the selected race, and the buy odds for each candidate formula. Then, the buying eye prediction unit 144 calculates the ratio of the acquired reference odds (step 142). Next, the buying eye prediction unit 144 executes step S53 and proceeds to step S143.

  In step S143, the purchase eye prediction unit 144 acquires the purchase amount from each purchase history of the prior anticipation person (i). Next, the purchase eye prediction unit 144 determines the predicted amount based on the acquired purchase amount (step S144). Next, the buying eye prediction unit 144 associates the determined buying information about the predicted buying eye with the predicted amount and adds it to the prediction list (step S145), and ends the buying eye prediction process.

  As described above, according to the present embodiment, the system control unit 14 acquires, from the storage unit 12, odds corresponding to a race ID indicating a past race in which a prior predictor has purchased a betting ticket. In addition, the system control unit 14 predicts the purchase of the betting ticket that the prior anticipation person purchases in the selected race based on the acquired formula and odds and the odds at the predetermined time of the betting ticket of the selected race. Further, the system control unit 14 predicts odds after change of the betting ticket of the selected race based on the predicted purchase. Further, the system control unit 14 re-predicts the purchase of the betting ticket that the prior anticipation person purchases in the selected race based on the obtained formula and odds and the predicted odds. Further, the system control unit 14 re-predicts the odds after the change of the betting ticket of the selected race based on the re-predicted purchase. Therefore, it is possible to predict the odds fluctuation more accurately.

[6. Sixth Embodiment]
[6-1. Overview of information processing system configuration and functions]
Next, the configuration of the information processing system SB according to the sixth embodiment will be described with reference to FIG. FIG. 21 is a diagram illustrating an example of a schematic configuration of the information processing system SB according to the present embodiment. In FIG. 21, elements similar to those in FIG.

  As shown in FIG. 21, the information processing system SB includes a betting ticket sales agency server 1, a host server 2, a plurality of user terminals 3, and an expected posting site server 4. The betting ticket sale agency server 1, the sponsoring server 2, each user terminal 3, and the expected posting site server 4 can mutually transmit and receive data via the network NW.

  The expected posting site server 4 is a server device that performs various processes related to an expected posting site where a prediction of a race result from a user is posted. The posting site is a different site from the horse racing site. The posting site is an example of the website of the present invention. The expected posting site server 4 distributes the posted expected information to the user terminal 3 in response to a request from the user terminal 3. For example, the prediction posting site server 4 may transmit a web page indicating a list of predictions about the race selected by the user to the user terminal 3. This web page is called the race prediction list page. In addition, the expected posting site server 4 may transmit a web page of a list of previous predictions to the user terminal 3 by the predictor selected by the user. This web page is called an anticipator prediction list page. The race prediction list page and the predictor prediction list page are examples of prediction information of the present invention.

  FIG. 22A is a diagram illustrating an example of a race prediction list page. As shown in FIG. 22A, the race prediction list page includes prediction race identification information 210 and a prediction list 220. The expected race identification information 210 is race identification information. The predicted race identification information 210 includes, for example, a race date, a racetrack where the race is held, and a race number. The prediction list 220 is a list of predictions posted for the race identified by the prediction race identification information 210. The prediction list 220 includes a horse number of an anticipation horse, an anticipation person ID, etc. for every anticipation person who posted an anticipation. The expected horse number is the horse number of the favorite, the counter, the single hole, and the legion. The horse number of the predicted horse is an example of predicted race body information of the present invention. The anticipation person ID is an example of anticipation person identification information of the present invention. The predictor ID may be, for example, the nickname of the user who posted the prediction.

  FIG. 22B is a diagram illustrating an example of a predictor prediction list page. As shown in FIG. 22B, the predictor prediction list page includes a predictor ID 310 and a prediction list 320. The prediction list 320 is a list of predictions of the user shown by the predictor ID 310 so far. The prediction list 320 includes, for each race predicted by the predictor, the date of race, the racetrack where the race is held, the race number, and the horse number of the predicted horse.

  The expected posting site server 4 includes a predetermined storage unit. In the storage unit of the expected posting site server 4, HTML documents of web pages such as a predicted race list page and a predicted candidate list page are stored in association with URLs. The user can register as a member on both the horse racing site and the expected posting site. When the user registers as a member in the expected posting site, the user's expected person ID is registered in the predicted posting site. The user can register an anticipation person ID different from the user ID and nickname registered in the member information DB 12a of the horse racing site on the anticipation posting site.

[6-2. Functional overview of betting ticket sales agency server 1]
Among users who purchase a betting ticket at a horse racing site, there may be a user who does not post a prediction on the horse racing site. Some users who do not post predictions to the horse racing site may post users to the prediction posting site. The anticipation person ID of the anticipation posting site is not associated with the user ID registered in the member information DB 12a. In this case, the prospective buyer's purchase cannot be predicted. Therefore, the purchase history acquisition unit 143 specifies the user ID associated with the anticipation ID of the anticipation posting site based on the anticipation information of the web page of the anticipation posting site and the purchase history. Then, the purchase history acquisition unit 143 acquires the purchase history corresponding to the identified user ID as the purchase history of the prior anticipation person. In other points, the sixth embodiment may be the same as the first to fifth embodiments.

  The anticipation person specific | specification part 142 acquires anticipation person ID of the anticipation person who performed anticipation by the selection race from the prediction contribution site via network NW. Specifically, anticipation person specific part 142 generates URL of a race prediction list page of a selection race based on a date of a selection race, a racetrack, and a race number. The anticipation person specific | specification part 142 acquires the race prediction list page of a selection race from the anticipation contribution site server 4 by transmitting produced | generated URL to the anticipation contribution site server 4. FIG. The anticipation person specific | specification part 142 acquires anticipation person ID of the anticipation person contained in the acquired race prediction list page as anticipation person ID of prior anticipation person.

  The purchase history acquisition unit 143 acquires the acquired anticipation ID of the prior anticipation person and the prediction information corresponding to the past race date, racetrack, and race number from the prediction posting site via the network NW. Specifically, the purchase history acquisition unit 143 generates the URL of the predictor prediction list page of the prior predictor based on the predictor ID of the prior predictor. The purchase history acquisition unit 143 transmits the generated URL to the expected posting site server 4, so that the purchase history acquisition unit 143 acquires the expected anticipation list page of the prior predictor from the expected posting site server 4. Then, the purchase history acquisition unit 143 acquires the predicted race identification information of the past race and the horse number of the predicted horse from the predictor prediction list page.

  The purchase history acquisition unit 143 purchases the purchase history in association with the race ID of the selected race based on the degree of coincidence between the acquired horse number of the predicted horse and the purchase information included in the purchase history registered in the purchase history DB 12g. The user ID associated with the anticipation person ID of the prior anticipation person acquired by the anticipation person specifying unit 142 is specified from the user IDs registered in the DB 12g. The degree of coincidence is such that the expected horse and the buying eye coincide. For example, the purchase history acquisition unit 143 acquires the race ID of the past race based on the date of the event, the racetrack name, and the race number included in the acquired anticipation prediction list page. The purchase history acquisition unit 143 acquires a purchase history corresponding to the race ID of the past race from the purchase history DB 12g for each past race. The purchase history DB 12g groups the acquired purchase history by user ID included in the purchase history. The purchase history acquisition unit 143 acquires a horse number or a frame number from the purchase information included in the purchase history. When all of the horse numbers acquired from the purchase eye information are included in the horse numbers of the predicted horses, the purchase history acquisition unit 143 may determine that the predicted horses and the purchase eyes match. In addition, for all the frame numbers acquired from the buy eye information, when the horse number of any race horse among the race horses of the frame number is included in the horse number of the predicted horse, the purchase history acquisition unit 143 determines that the expected horse It may be determined that the purchase eye matches. The purchase history acquisition unit 143 may calculate, for each user, the number of purchase histories in which the predicted horse matches the purchase eye as the degree of coincidence. Then, the purchase history acquisition unit 143 may specify the user ID having the highest degree of matching as the user ID associated with the predictor ID of the prior predictor.

  The purchase history acquisition unit 143 may determine the degree of coincidence for each past race. This degree of coincidence is called intermediate coincidence. Then, the purchase history acquisition unit 143 may calculate the sum of the intermediate matching degrees of all the races as the final matching degree for each user ID. For example, when all the racehorses of the purchase are included in the anticipation horse of the prior anticipation person, the purchase history acquisition unit 143 includes a part of the purchase horses in the anticipation horse of the anticipation person. The intermediate matching degree may be made higher than the case. Further, for example, the purchase history acquisition unit 143 may increase the intermediate matching degree as the racehorse of the purchase has a higher expectation degree among the predicted horses of the prior predictors. In addition, for example, in the case of identification specifying the arrival order, the purchase history acquisition unit 143 matches when the order of arrival of the racehorse of the purchase matches the order of expectation in the predicted horse of the prior predictor. The intermediate coincidence may be higher than the case where it is not.

  FIG. 23A is a diagram illustrating an example of prediction of a past race by the predictor X. The horse number of the anticipation horse X in the race R1 is 6, 2, 5 and 1 in descending order of expectation. The horse numbers of the anticipation horse X's race R2 are 7, 1, 9, and 11 in descending order of expectation. The horse numbers of the anticipation horse X's race R3 are 8, 3, 12, and 2 in descending order of expectation. FIG. 23B is a diagram illustrating an example of purchase eyes of the past race of the user B. The purchase price of the race B1 of the user B is just 6-2. The purchase price of the race B2 of the user B is 1-7. User B's purchase of race R3 is a single 3-8. For user B, the racehorses of all the purchases of the three races are included in the anticipation horse of anticipation person X. FIG. 23C is a diagram illustrating an example of purchase eyes of the past race of the user C. The purchase price of race R1 of user C is 4-3. The purchase price of the race R2 of the user C is 7-1. User C has not purchased a betting ticket for race R3. For the user C, the racehorse of the purchase of one race is included in the anticipation horse of the anticipation person X. Therefore, the purchase history acquisition unit 143 may determine the user ID of the user B among the users B and C as the user ID associated with the predictor ID of the predictor X.

  The purchase history acquisition unit 143 may specify a plurality of user IDs having the same degree of matching. In that case, the purchase history acquisition unit 143 may determine, for example, all of the plurality of user IDs as user IDs associated with the predictor ID of the prior predictor.

[6-3. Operation of information processing system]
Next, the operation of the information processing system SB will be described with reference to FIGS. The buy eye prediction process and the odds fluctuation prediction process may be the same as those in the first to fifth embodiments. FIG. 24 is a flowchart showing an example of odds fluctuation information provision processing by the system control unit 14 of the betting ticket issuing agency server 1 according to the present embodiment. In FIG. 24, the same reference numerals are assigned to the processes similar to those in FIG.

  As shown in FIG. 24, the system control unit 14 executes steps S1 to S12 as in the case of FIG. In step S11, when it is determined that the number i is not less than the number of prior predictors (step S11: NO), the purchase eye prediction unit 144 proceeds to step S151. In step S151, the buying eye prediction unit 144 executes a predicted posting site correspondence process.

  FIG. 25 is a flowchart showing an example of the expected posting site handling process according to the present embodiment. As shown in FIG. 25, the anticipation person specific | specification part 142 acquires the HTML document of the race prediction list page of selection race from the prediction contribution site server 4 (step S161). Specifically, anticipation person specific part 142 specifies the date of a selection race, a racetrack, and a race number based on race detailed information on a selection race. The anticipation person specific | specification part 142 produces | generates URL of the race prediction list page of a selection race based on a date, a racetrack, and a race number. Anticipation person specific part 142 transmits generated URL to anticipation contribution site server 4. The expected posting site server 4 transmits an HTML document of a predicted race list page corresponding to the received URL to the betting ticket issuing agency server 1, and the expected posting site server 4 receives the HTML document.

  Subsequently, anticipation person specific part 142 acquires anticipation person ID from an acquired HTML document (Step S162). Subsequently, anticipation person specific part 142 sets number i to 1 (Step S163). Next, the purchase history acquisition unit 143 acquires the HTML document of the anticipation person prediction page of the prior anticipation person (i) from the anticipation posting site server 4 (step S164). The prior predictor (i) is the i-th prior predictor among the prior predictors indicated by the acquired predictor ID. Specifically, the purchase history acquisition unit 143 generates the URL of the predictor prediction list page of the predictor (i) based on the predictor ID of the predictor (i). The purchase history acquisition unit 143 transmits the generated URL to the expected posting site server 4. The expected posting site server 4 transmits the HTML document of the expected person prediction list page corresponding to the received URL to the betting ticket issuing proxy server 1, and the purchase history acquisition unit 143 receives the HTML document. Next, the purchase history acquisition unit 143 specifies the race ID of the past race in which the prior anticipation person (i) posted the prediction based on the HTML document of the acquired anticipation person prediction list page. Specifically, the purchase history acquisition unit 143 acquires an event date, a racetrack name, and a race number for each race from the HTML document. The purchase history acquisition unit 143 determines a racetrack ID corresponding to the racetrack name. The purchase history acquisition unit 143 acquires detailed race information including the date of the race, the racetrack ID, and the race number from the race information DB 12b. And purchase history acquisition part 143 acquires race ID from race detailed information. Purchase history acquisition part 143 specifies race ID of the past race among acquired race ID. How to identify past races has already been explained.

  Next, the purchase history acquisition unit 143 acquires a purchase history corresponding to the race ID from the purchase history DB 12g for each identified race ID of the past race (step S166). Next, the purchase history acquisition unit 143 calculates the degree of coincidence between the prediction of the prior anticipation person (i) and the purchase eye for each user who has purchased a betting ticket for at least one of the past races (step S167). For example, the purchase history acquisition unit 143 acquires the horse number of the predicted horse of each past race from the HTML document of the predictor prediction list page. Further, the purchase history acquisition unit 143 acquires a user ID from the purchase history. Next, the purchase history acquisition unit 143 determines, for each acquired user ID, whether or not the prediction information corresponding to the user ID is registered in the prediction information DB 12f. The purchase history acquisition unit 143 determines only user IDs whose prediction information is not registered in the prediction information DB 12f as candidate user IDs associated with the anticipation ID of the prior anticipation person (i). That is, the purchase history acquisition unit 143 excludes users who have posted predictions on the horse racing site. Next, the purchase history acquisition unit 143 groups the acquired purchase history by candidate user IDs. Next, for example, for each candidate user ID, the purchase history acquisition unit 143 determines that all the horse numbers of the racehorses indicated by the horse numbers or frame numbers included in the purchase information of the purchase history are the predicted horses of the prior predictor (i). The number of past races included is calculated as the degree of coincidence.

  Next, the purchase history acquisition unit 143 determines whether there is a user ID having a calculated matching degree equal to or greater than a predetermined value among the candidate user IDs (step S167). At this time, if the purchase history acquisition unit 143 determines that there is no user ID having a matching degree equal to or greater than a predetermined value (step S167: NO), the purchase history acquisition unit 143 proceeds to step S171. On the other hand, if the purchase history acquisition unit 143 determines that there is a user ID with a matching degree equal to or greater than a predetermined value (step S167: YES), the purchase history acquisition unit 143 proceeds to step S168.

  In step S168, the purchase history acquisition unit 143 determines the user ID having the highest degree of coincidence among the user IDs acquired from the purchase history as the user ID of the prior anticipation person (i). Next, the purchase history acquisition unit 143 acquires the purchase history of the past race corresponding to the user ID determined for the prior anticipation person (i) from the purchase history acquisition unit 143 (step S169). Next, the purchase eye prediction unit 144 performs a purchase eye prediction process based on the purchase history acquired in step S169 (step S170).

  Next, the buying eye prediction unit 144 determines whether or not the number i is less than the number of prior predictors (step S171). At this time, if it is determined that the number i is less than the number of prior predictors (step S171: YES), the buying eye prediction unit 144 proceeds to step S172. In step S172, the buying eye prediction unit 144 increments the number i by 1, and proceeds to step S164. On the other hand, when it is determined that the number i is not less than the number of prior predictors (step S171: NO), the purchase eye prediction unit 144 ends the expected posting site correspondence process.

  When the expected posting site correspondence processing is completed, as shown in FIG. 14, the odds fluctuation prediction unit 145 performs the buying eye predicted in the buying eye prediction processing executed in step S10 and the predicted posting site correspondence processing in step S170. The odds prediction process is executed using the buy eye predicted by the executed buy eye prediction process (step S13). Then, the odds fluctuation information providing unit 146 executes steps S14 and S15 and ends the odds fluctuation information providing process.

  As described above, according to the present embodiment, the system control unit 14 obtains the predictor ID corresponding to the date indicating the selected race, the racetrack name, and the race number from the expected posting site via the network NW. To do. In addition, the system control unit 14 acquires the predicted horse ID corresponding to the predicted person ID, the date indicating the past race, the racetrack name, and the race number from the predicted posting site via the network NW. Further, the system control unit 14 acquires the horse number or frame number of the purchase history information of the purchase history corresponding to the race ID indicating the past race from the storage unit 12. Further, the system control unit 14 is included in the purchase history corresponding to the race ID indicating the past race based on the degree of coincidence between the acquired horse number of the predicted horse and the horse number or frame number of the purchase information. The user ID associated with the anticipation ID is specified from the user ID. Therefore, even if the predictor ID of the prior predictor is different from the user ID of the same person as that prior predictor, it is possible to acquire the type of betting ticket purchased by the prior predictor in the past race.

1 betting ticket sale agency server 2 sponsor server 3 user terminal 4 expected posting site 11 communication unit 12 storage unit 12a member information DB
12b Race information DB
12c betting ticket information DB
12d odds information DB
12e Browsing history DB
12f anticipation information DB
12g Purchase information DB
12h Fund history DB
13 Input / output interface 14 System controller 14a CPU
14b ROM
14c RAM
15 System Bus 141 Selection Accepting Unit 142 Predictor Identification Unit 143 Purchase History Acquisition Unit 144 Purchase Eye Prediction Unit 145 Odds Fluctuation Prediction Unit 146 Odds Fluctuation Information Providing Unit NW Network SA, SB Information Processing System

Claims (9)

Receiving means for receiving selected race identification information for identifying a selected race among a plurality of races;
A specifying means for specifying a predictor who anticipates winning of any race body among a plurality of race bodies participating in the race indicated by the selected race identification information received by the receiving means;
Purchaser identifying information for identifying a purchaser who has purchased a voting ticket of any of the plurality of races, a type of purchase of the purchased voting ticket, and identifying the race in which the voting ticket is purchased Corresponding to purchaser identification information indicating the anticipation person specified by the specifying means and race identification information indicating a past race, from the purchase history storage means for associating and storing the race identification information to be stored as a purchase history An acquisition means for acquiring the type of purchase;
Based on the type of the buy eye acquired by the acquisition means, the predictor specified by the specifying means predicts the purchase of a voting ticket to be purchased in the selected race;
Fluctuation prediction means for predicting fluctuations in odds of the voting ticket of the selected race based on the purchase eye predicted by the purchase eye prediction means;
Control means for displaying fluctuation information indicating fluctuations in odds predicted by the fluctuation prediction means;
An information processing apparatus comprising: The information processing apparatus according to claim 1,
A selection information receiving means for receiving selection information indicating a selected race body among the plurality of race bodies participating in the selected race;
The specifying means specifies a predictor who has predicted that the race body indicated by the selection information received by the selection information receiving means will win the selected race,
The fluctuation prediction means predicts fluctuations in odds of the voting ticket of the purchase including the race body indicated by the selection information received by the selection information reception means among the voting tickets of the selected race. Information processing apparatus. The information processing apparatus according to claim 1 or 2,
For each of the plurality of races, from the race information storage means for storing the race identification information for identifying the race and the race information relating to the race in association with each other, the predictor specified by the specifying means receives the voting ticket. Race information acquisition means for acquiring race information corresponding to race identification information indicating a purchased past race and race information corresponding to the selected race identification information received by the reception means,
The buy eye predicting unit purchases the predictor in the selected race based on the type of the buy eye acquired by the acquiring unit and the race information acquired by the race information acquiring unit. An information processing apparatus for predicting purchase of a voting ticket. The information processing apparatus according to any one of claims 1 to 3,
Of the predictors specified by the specifying means, further comprising second specifying means for specifying a predictor who is predicted not to purchase a voting ticket for the selected race after the present time,
The buying eye prediction means predicts the purchase of a voting ticket purchased by the predictor other than the predictor specified by the second specifying means among the predictors specified by the specifying means. An information processing apparatus characterized by: The information processing apparatus according to any one of claims 1 to 4,
The prediction specified by the specifying means from the purchase history storage means for associating and storing the purchaser identification information, the type of purchase, the race identification information, and the purchase price of the voting ticket as the purchase history Purchase price acquisition means for acquiring the purchase price corresponding to the purchaser identification information indicating the buyer,
The fluctuation prediction means predicts the fluctuation based on the purchase eye predicted by the purchase eye prediction means and the purchase price acquired by the purchase price acquisition means. . The information processing apparatus according to any one of claims 1 to 5,
For each of the plurality of races, the predictor identified by the identifying means is a voting ticket from the odds storage means for storing the race identification information for identifying the race and the odds of the voting ticket for the race in association with each other. Further comprising odds acquisition means for acquiring odds corresponding to race identification information indicating a past race where the
The buy eye prediction means is based on the type of the buy eye acquired by the acquisition means, the odds acquired by the odds acquisition means, and the odds at a predetermined time of the selected race voting ticket, Predicting the purchase of a voting ticket that the anticipator will purchase in the selected race,
The fluctuation prediction means predicts odds after fluctuation of the voting ticket of the selected race based on the buy eye predicted by the buy eye prediction means,
The buy eye prediction means is based on the buy eye type acquired by the acquisition means, the odds acquired by the odds acquisition means, and the odds predicted by the fluctuation prediction means. Re-predicts the purchase of voting tickets purchased in the selected race,
The information processing apparatus according to claim 1, wherein the fluctuation prediction unit re-predicts odds after fluctuation of the voting ticket of the selected race based on the buy eye re-predicted by the buy eye prediction means. The information processing apparatus according to any one of claims 1 to 6,
The specifying means identifies predicted race body information indicating a race body expected to win among a plurality of race bodies participating in any one of the plurality of races, and a predictor predicted to win the race body Predictor identification information corresponding to the expected race identification information indicating the selected race is transmitted via a network from a website that distributes the prediction information including the expected race identification information for identifying the race and the expected race identification information for identifying the race. And get
Prediction information for acquiring prediction information including prediction race body information corresponding to the prediction person identification information acquired by the specifying means and prediction race identification information indicating a past race from the website via the network Acquisition means;
Obtaining the expected information from the purchase history storage means for associating and storing the purchaser identification information, the type of purchase, the race identification information, and the voting race body information indicating the voted race body as the purchase history Voting race body information acquisition means for acquiring voting race body information corresponding to race identification information indicating a race indicated by the predicted race identification information included in the prediction information acquired by the means;
Included in the prediction information based on the degree of coincidence between the predicted race body information included in the prediction information acquired by the prediction information acquisition means and the voting race body information acquired by the voting race body information acquisition means Purchase associated with the anticipator identification information acquired by the specifying means from among the purchaser identification information stored in the purchase history storage means in association with the race identification information indicating the race indicated by the expected race identification information. Purchaser identifying means for identifying the buyer identification information;
Further comprising
The information processing apparatus according to claim 1, wherein the acquisition unit acquires a type of purchase corresponding to the purchaser identification information specified by the purchaser specifying unit. An information processing method executed by a computer,
A reception step for receiving selected race identification information for identifying a selected race among the plurality of races;
A specifying step of specifying a predictor who has predicted a winning of any race body among a plurality of race bodies participating in the race indicated by the selected race identification information received by the receiving step;
Purchaser identifying information for identifying a purchaser who has purchased a voting ticket of any of the plurality of races, a type of purchase of the purchased voting ticket, and identifying the race in which the voting ticket is purchased Corresponding to purchaser identification information indicating the predictor specified in the specifying step and race identification information indicating a past race, from the purchase history storage means for storing the race identification information to be associated and stored as a purchase history An acquisition step for acquiring the type of purchase;
Based on the type of the buy eye acquired by the acquisition step, the predictor specified by the specifying step predicts the purchase of a voting ticket to be purchased in the selected race;
A fluctuation prediction step for predicting fluctuations in odds of the voting ticket of the selected race based on the buy eye predicted by the buy eye prediction step;
A control step for displaying fluctuation information indicating fluctuations in odds predicted by the fluctuation prediction step;
An information processing method comprising: Computer
Receiving means for receiving selected race identification information for identifying a selected race among a plurality of races;
A specifying means for specifying a predictor who has predicted a winning of any race body among a plurality of race bodies participating in the race indicated by the selected race identification information received by the receiving means;
Purchaser identifying information for identifying a purchaser who has purchased a voting ticket of any of the plurality of races, a type of purchase of the purchased voting ticket, and identifying the race in which the voting ticket is purchased Corresponding to purchaser identification information indicating the anticipation person specified by the specifying means and race identification information indicating a past race, from the purchase history storage means for associating and storing the race identification information to be stored as a purchase history An acquisition means for acquiring the type of purchase,
Buy eye prediction means for predicting the purchase of a voting ticket purchased by the predictor specified by the specifying means based on the type of the purchase eye acquired by the acquisition means, in the selected race,
Fluctuation prediction means for predicting fluctuations in odds of the voting ticket of the selected race based on the buy eye predicted by the buy eye prediction means; and
Control means for displaying fluctuation information indicating fluctuations in odds predicted by the fluctuation prediction means;
An information processing program that functions as a computer program.

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