JP4326286B2 - Game machine analysis system - Google Patents

Description

  The present invention relates to a system for analyzing a game appearance of a gaming machine, and more particularly to a gaming machine analysis system capable of analyzing a future prediction based on the game appearance.

In game halls (halls) where game machines such as pachinko machines and slot machines are installed, hundreds and thousands of game machines are managed and operated.
It is extremely difficult to check profitability while keeping track of the operating status of these gaming machines, and to maintain profitability while maintaining customer attraction, and it requires a great deal of labor from employees. In addition, although the design values on the software such as jackpot probability and probability variation probability that control the game machine's appearance are set by probability, they occur in random situations and cause variations, so the probability of jackpot and probability variation occurrence A certain amount of variation must always be taken into consideration for the rate of appearance and the appearance rate.
In addition, the sensitivity of the hall customers to play is high, and in recent years, the increase or decrease in the number of visitors often changes instantly with the development of information transmission means, which has had a great impact on the management of the hall.

Therefore, a hall management system, a game tendency analysis system, and the like have been developed so far, and the game machine management is analyzed by analyzing the game appearance of the game machine.
For example, Patent Document 1 discloses, as a “pachinko hall management system”, detection means for detecting the occurrence of winning in a pachinko machine installed in the pachinko hall, the gaming state such as the number of balls lent and the number of prize balls, for each pachinko machine. In addition, there is disclosed a management system including an analysis unit that analyzes a gaming state at predetermined time intervals and an output unit. Analyzing means of this pachinko hall management system is provided with evaluation value calculating means and index calculating means, calculating profit evaluation values for evaluating pachinko hall profits and ease of winning every predetermined time, We provide pachinko halls that can appropriately adjust parameters such as starting prizes and base without relying on intuition and improve operating profit.

In addition, in Patent Document 2, a management system for managing information such as game machines and a store equipped with a hall computer and terminal means can be connected to the Internet as a “gaming machine type management system”. A system is disclosed in which an external server provided with model-specific management software capable of managing and analyzing pachinko and slot game machines is provided to manage game machines by model.
Furthermore, in Patent Document 3, as a “playing tendency analysis system”, a player is registered as a member, and the playing tendency of the gaming machine used by the member is compared with the individual playing tendency when playing the game. A system for individually providing a member's specific game tendency is disclosed.
Japanese Patent Laid-Open No. 9-295977 Japanese Patent Laid-Open No. 2002-210184 JP 2002-78939 A

However, in the invention described in Patent Document 1, counting of the number of safe balls and out balls, winning at the starting opening and winning at the large winning opening are detected, and data such as the starting winning number and base are created. However, they are not based on the state of the game machine but on the whole analysis. That is, it is only an analysis of how many safe balls are in the total amount, how many are out balls, and how many start winnings are made, and how many are the base as a whole.
In actual amusement machines, the number of balls is different depending on the situation where the amusement machine is placed, such as a big win (big prize), a chance change, and a normal time. Since the nail for adjusting the ball is different for each event, it is difficult to independently adjust the nail that generates each situation unless detailed analysis is performed for each event. It is.
Moreover, if the nail is adjusted according to the overall analysis, only a rough adjustment can be made after all, and the accuracy of the business forecast will be lowered.

  For Patent Document 2, the number of starts for each amusement machine at each store, the number of out-of-special prizes, the number of out-of-special prizes, the number of start-ups between special prizes, and the percentage of appearance are analyzed. There is no analysis on the issue, nor is there any analysis on the probability of occurrence or base on these events, and there is no link between analysis of individual events and analysis for making overall business forecasts.

Further, in Patent Document 3, a game tendency for each member is analyzed with a player as a member, and analysis of a game situation for each game machine or analysis of operating profit on the hall side is not performed.
The present invention has been made in response to such a conventional situation, and provides a game machine analysis system capable of analyzing a ball, an occurrence probability, a base, etc. for each event during a game of a game machine. The purpose is to provide an amusement machine analysis system that enables business prediction based on the analysis results.

In order to achieve the above object, a gaming machine analysis system according to the first aspect of the present invention provides a pachinko machine that can recognize a pachinko gaming machine from a pachinko gaming machine that has at least a bonus state and a normal state as a gaming state. A data input unit for reading out gaming state information including information on gaming states in time series of gaming machines and information on the number of gaming states, a ball count information for each gaming state, and a big start number and storing them in a first database; The probability of reading the number of hit states included in the game state information corresponding to the recognition information of the pachinko gaming machine from the first database and the number of hits of the start, calculating the hit probability, and storing it in the second database The game state information and the ball count information are read from the analysis unit and the first database using the recognition information as a key, and the big base and the normal base corresponding to the game state are read. And based analysis unit to be stored in the third database and calculates the scan, immediately after clearing the game state information of pachinko machines stored in the substrate installed in pachinko machines (hereinafter, "Pachinko stored in the substrate A sixth database that stores a large probability (hereinafter referred to as a clear large probability) of “just after clearing the gaming state information of the gaming machine” is cleared from the sixth database. A first prediction analysis unit that reads out the big probability and reads out the big probability from the second database using the recognition information as a key, calculates the difference, calculates the predicted big probability, and stores it in the seventh database ; is there.

The gaming machine analysis system according to claim 2 is based on a pachinko gaming machine having at least a bonus state and a normal state as a gaming state, recognition information that can recognize the pachinko gaming machine, and a time series of the pachinko gaming machine. A data input unit that reads out the game state information including information on the game state and the number of game states, the game count information for each game state, and the big start number and stores them in the first database; and from the first database From the third database, a base analysis unit that reads the gaming state information and the ball count information using the recognition information as a key, calculates at least a big base and a normal base corresponding to the gaming state, and stores them in the third database; Using the recognition information as a key, calculate the big start coefficient obtained by dividing the big base corresponding to the gaming state by the number of big starts. A start coefficient analyzing section for storing a large person start coefficient immediately clear pachinko machine and the eighth database for storing (hereinafter. Referred to clear large person start coefficient), the pachinko game machine of a large person start nail gauge A ninth database that stores information corresponding to the recognition information, and a second prediction that reads the gauge information from the ninth database using the recognition information as a key, calculates the expected number of big starts and stores it in the tenth database The analysis unit reads the clear big start coefficient from the eighth database, reads the big start coefficient from the fifth database using the recognition information as a key, and calculates the expected number of big start starts from the tenth database using the recognition information as the key To calculate the expected big base and store it in the eleventh database. Those having an analysis unit.

The gaming machine analysis system according to claim 3 is the gaming machine analysis system according to claim 2 , wherein the clear big start coefficient is read from the eighth database, and the expected big start number is recognized from the tenth database. And a fourth predictive analysis unit for calculating the expected big base immediately after clearing and storing it in the twelfth database.

The gaming machine analysis system according to claim 4 is the gaming machine analysis system according to claim 3, which is read from the normal base and the twelfth database read from the third database using the recognition information as a key. Statistically in the hall where the pachinko machine is installed, and the sixth predictive analysis unit that calculates the expected payout rate immediately after clearing and stores it in the fourteenth database. The thirteenth database for storing the hole ball unit coefficient and the assumed unit price that are obtained in advance, the clear expected yield rate read from the fourteenth database, the hole unit price coefficient and the assumed unit unit price read from the thirteenth database. And a seventh predictive analysis unit for calculating the expected divisor immediately after clearing.

A gaming machine analysis system according to claim 5 is the gaming machine analysis system according to claim 4 , wherein the pachinko gaming machine has at least a win state, a probable change state, and a normal state as gaming states. The base analysis unit calculates at least the big base, the probability variation base, and the normal base corresponding to the gaming state and stores them in the third database, and the eighth database is the clear big start coefficient of the pachinko machine. The 9th database stores the gauge information of the big start nails and the probability change start nails of the pachinko machine corresponding to the recognition information, and the second predictive analysis unit is the gauge information. Is read from the 9th database using the recognition information as a key, and the expected number of expected start times and the expected number of expected variation start times are calculated and stored in the 10th database. The fourth prediction analysis unit reads the clear big start coefficient from the eighth database, reads the predicted big start number from the tenth database using the recognition information as a key, takes the product, , Read the clear probability variation start coefficient from the eighth database, read the number of predicted probability variation start times from the tenth database, take the product, calculate the expected probability variation base immediately after clearing, store it in the twelfth database, The prediction analysis unit calculates the clear prediction by taking the sum of the normal base read from the third database using the recognition information as a key, the clear prediction big base read from the twelfth database, and the clear prediction probability variation base. The ball ratio is calculated and stored in the 14th database, and the seventh predictive analysis unit reads the data read from the 14th database. Is intended for calculating the number of clearing expected split using assumptions ball bid with holes ball bid coefficient read out from the database of A expected payout rate and the 13th.

The gaming machine analysis system according to claim 6 is the gaming machine analysis system according to claim 3 , wherein the gaming machine analysis system is connected to the third prediction analysis unit and the fourth prediction analysis unit, and is based on a prediction jackpot base and a clear prediction jackpot. A data communication unit is provided that reads the base and displays the expected jackpot base and the clear predicted jackpot base on the display screen provided in the portable terminal.

Gaming machine analysis system is a seventh aspect of the invention, in the gaming machine analysis system according to claim 4, the fifth predicted analyzer and the seventh expected is connected to the analysis unit expected dividing number and clear expected split of A data communication unit is provided that reads the number and displays the expected split and the expected clear split on a display screen provided in the mobile terminal.

In the gaming machine analysis system according to any one of claims 1 to 3, at least the output ball, the occurrence probability, the base, the output rate and the bonus start coefficient of a gaming machine having at least a bonus state and a normal state are determined for each state. It is possible to analyze the situation accurately and to carry out highly accurate situation analysis and business forecasting. In addition, for a game machine having at least a big game state and a normal state, it is affected by the occurrence state of the big game state that is stored as it is in the memory of the big game state stored on the board of the game machine. When the probability of future occurrence is assumed, and when the probability of occurrence of the design emergency state stored in advance by clearing the memory of the emergency state stored on the board is assumed as the future occurrence probability By taking the difference in the occurrence probability, it is possible to analyze the predicted probability and the predicted base of the future appropriate state.
Furthermore, it is possible to analyze by comparing the prediction base of the jackpot when the memory of the stored state of the stored bonus is cleared and when it is not. Therefore, the hall manager can determine whether or not to clear the memory stored in the board while judging the status of the hall customers.

In the gaming machine analysis system according to claim 4 , for a gaming machine having at least a big win state and a normal state, an expected divide when the memory stored on the board of the gaming machine is cleared or not is calculated. It can be analyzed and compared.

In the gaming machine analysis system according to claim 5, for a gaming machine having at least a jackpot state and a normal state and a probabilistic state, a case where the memory accumulated on the board of the gaming machine is cleared or not predicted Divide by number and compare.

In the gaming machine analysis system according to the sixth aspect , the expected jackpot base and the clear predicted jackpot base can be compared at any place.

In the gaming machine analysis system according to the seventh aspect , the expected split and the clear expected split can be compared at any place.

The best mode for carrying out the gaming machine analysis system according to the present invention will be described below with reference to FIGS.
Before describing this embodiment, definitions of terms used in the claims and specification of this application are summarized in Table 1.

  The words and phrases shown in Table 1 mean the contents described in the right column, and are those described in the right column as variables used in this specification. In addition, those variables that have a certain relationship among the variables and can be expressed by expressions are shown in the right column.

  FIG. 1 is a conceptual diagram of a configuration of a gaming machine analysis system according to the present embodiment, and FIG. 2 is a conceptual diagram of a gaming machine that is a target for installation of the gaming machine analysis system. FIG. 3 is a conceptual diagram showing a pachinko machine base for each game state, and FIG. 4 is a conceptual diagram showing the relationship between the gauge of the big start nail and the number of big hits. Before describing the present embodiment with reference to FIG. 1, a gaming method and its mechanism will first be described with reference to FIGS. 2 to 4 and Table 1 above.

  In FIG. 2, the game table 2 is provided with an operation handle 40 on the front surface thereof so that a pachinko ball 41 can be automatically fired. As shown in Table 1, this driving interval is about 100 shots per minute. The inserted pachinko ball 41 hits a nail placed on the game table 2, wins a prize while passing between the nails, or is sucked into the collection port 42. As a prize, the jackpot 46 is open for a certain period of time, so that a big hit can be obtained, and the probability that this jackpot will occur is about five times larger. For example, there is a normal winning that can earn about 10 balls when the pachinko ball 41 enters the normal winning opening 47 or the like.

When described in order from the left side with reference to FIG. 3, the normal state is initially the normal base 52, but it becomes a big hit state while playing. This big hit state ball is the big hit base 50 and is discharged most. Thereafter, the probability variation state is obtained, the probability variation base 51 is obtained, and the normal state 52 is returned to the normal state.
Next, when a big hit is made at the time of probability change, big hits are continuously generated, and the big hit base 53 and the probability change base 54 are alternately generated. Thereafter, it becomes the normal base 52 again, and once becomes the big base 50 and the probability variation base 51, but there is no big hit in the probability variation state, and it returns to the normal base 52. Usually, although there are some time differences in each state, such a change in gaming state can be enjoyed.

Returning to FIG. 2, in order to reach the big hit state, first, the pachinko ball 41 needs to win the big hit start opening 43. When the pachinko ball 41 wins the big hit start opening 43, the big win display screen 45 rotates. For example, when numbers, characters or designs coincide with each other like 777, the big hit opening 46 is opened. In this way, the probability that numbers and the like match is about 1/300, which is very small. However, once a big hit is made, it is easy to make a big hit again, or when the pachinko ball 41 wins the probability variation start port 44 When winning at the big hit start opening 43, the probability of winning big hits may be about five times larger, so the player's gambling feelings are very popular.
Big hit start nails 43a and 43b are provided above the big hit start opening 43 for the big hit, and the pachinko balls 41 can easily enter the big hit start opening 43 due to the wideness of the gauge between the nails. It becomes difficult to enter. The probability of a big hit once entering the big hit start opening 43 is irrelevant to this gauge and is constant, but the number of rotations of the big hit display screen 45 can be adjusted by adjusting the width of the gauge. After all, the probability of jackpot can be adjusted by this gauge after all.

In addition, the probability variation in which the probability of winning a big hit is increased and the opening time of the normal winning opening 47 is extended, the probability variation start nails 44a and 44b are provided above the probability variation start port 44. By adjusting the gauge between the nails, the number of probability variation starts can be adjusted.
Of course, the normal winning rate varies by adjusting the normal winning nails 47a and 47b provided above the normal winning port 47.
Here, the relationship between the gauge between nails and the number of start times will be described with reference to FIG.

In FIG. 4, the horizontal axis shows the gauge of the big start nail in mm, and the vertical axis shows the number of start times. The relationship shown in FIG. 4 is an example, but if the gauge is 11 mm, the number of starts is 0. This is because the pachinko balls 41 cannot pass between the big start nails 43a and 43b. Also, you can see how the number of big hit starts increases as the gauge gets wider. In FIG. 4, a straight line that is substantially proportional is drawn from empirical data, but depending on the pachinko machine or pachinko nails, there may be a steep or slow curve. However, in general, it can be expressed as a straight line or a curve with the gauge as a variable, and can be expressed using an expression.
For such a game machine, in this embodiment, an analysis system is introduced to enable the game play and the business prediction while analyzing the game play status in each state, that is, jackpot, probability change, and normal condition. It is.

In FIG. 1, a gaming machine analysis system 1 according to the present embodiment is connected to a hall computer 3 connected to a gaming table 2. As described above, the game console 2 in recent years has a mechanism in which jackpots and the like are generated by probability / statistical calculation, and electronic parts are frequently used for that purpose. For example, the gaming board substrate 30 provided in the gaming machine 2 stores the number of times that each state has occurred in the gaming machine 2 in time series, or the jackpot setting probability, probability variation setting probability, and the like.
In addition, the counter 31 counts out balls and recovered balls in each state. These pieces of information are collected in the hall computer 3, and this information is further input into the gaming machine analysis system 1 to analyze the probability of occurrence, the appearance, the base, etc. for each state of the gaming table 2, and further accumulated. Analyze future expectations based on the data.
These analysis results can be viewed at various places through the display unit 7 and the display screen 32 of the portable terminal 4.
The structure of the gaming machine analysis system 1 will be described. Data from the hall computer 3 is read out by the data input unit 5, and the read out data is stored in a store / base / model data storage unit 20, a sales data storage unit 21, a gauge data storage unit 22, It is stored in any one of the event data storage unit 23, the ball count data storage unit 24, and the analysis data storage unit 25. The data input unit 5 is also used for data input by a user using the game machine analysis system and for setting conditions for analysis.
The classification of the data storage units such as the store / base / model data storage unit 20, the sales data storage unit 21, the gauge data storage unit 22, the event data storage unit 23, the ball count data storage unit 24, and the analysis data storage unit 25 is as follows. This is not a limitation, and the data storage unit may be further divided or integrated and provided.

Specifically, data stored in each data storage unit will be described.
First, the store / base / model data storage unit 20 manages data for each store, for example, in the halls that belong to franchise, in addition to the hall number, the store name and address, the telephone number and other data related to the store, Stored are recognition information such as numbers and codes assigned to the tables installed in the hall, model names, data specific to those models, hall ball unit cost coefficient (to be described later), assumed unit price, and the like. Furthermore, the clearing time of the game board substrate is stored for each table. This clear time will be described later.
The sales data storage unit 21 stores sales data for each unit or for each group in which a plurality of units are set in advance.
Further, although the gauge data storage unit 22 stores at least the gauge data of the big hit start opening and the gauge data of the probability changing start opening for each unit, it is also possible to store the data of the normal winning opening.
The event data storage unit 23 is input from the gaming board substrate 30 provided in the gaming table 2 via the hall computer 3 and the data input unit 5, and the gaming state along the time series for each gaming table 2 is displayed. It is stored as data. Therefore, the number of big hits and the number of probability changes are also stored as data.
The ball counting data storage unit 24 stores the data of the ball that is counted (safe) and the operation (out) that is counted by the counter 31 of the game table 2. Since the appearance and operation of this game are linked to the game state stored in the event data storage unit 23, how many items have been played in what game state or the operation is performed along the time series for each unit. It is possible to grasp whether it has occurred.
Since the data stored in these data storage units is stored for each table using the recognition information of the table as a key, it is possible to read and analyze the data for each table using the recognition information as a key. . Therefore, it is possible to analyze the gaming state and the business state for each unit, and to make predictions about these units for each unit.

Analysis is performed by the analysis unit 6 using the data stored in these data storage units. The analysis data storage unit 25 stores the analysis results. The analysis result stored in the analysis data storage unit 25 will be described later while describing details of the analysis.
The analysis unit 6 is roughly classified into a probability analysis unit 9, a base analysis unit 10, a start coefficient analysis unit 11, a payout rate analysis unit 12, and a prediction analysis unit 13. The result of the analysis performed by the analysis unit 6 is displayed on the display unit 7 or transmitted to the mobile terminal 4 through the data communication unit 8 as described above.
The manager or manager of the hall can grasp the state of the hall for each gaming table and for each state by referring to the analysis result, and at the same time analyze the gaming state and the sales state that are expected to occur in the future. can do.

The content specifically analyzed in the analysis part 6 is demonstrated referring FIG. In FIG. 5, step S <b> 1 indicates a condition capturing process. This condition means the setting of the type of analysis described as the contents of the subsequent process, and the selection of the pachinko machine model or machine in the hall. In this condition fetching process, data relating to the type of analysis, pachinko machine type and machine setting is received via the data input unit 5. After the conditions for analysis are set, the following steps S2 to S7 are appropriately executed.
First, in step S2, a probability analysis process is performed. The probability analysis in this process will be described with reference to FIG.

FIG. 6 is a flowchart showing the details of step S2 in detail. Each process included in step S2 is executed by the probability analysis unit 9 of the analysis unit 6.
First, in step S21, the number of bonuses, the number of probability changes, the number of bonus starts, and the number of probability changes start are read from the event data storage unit 23 and the ball count data storage unit 24. In the figure, for example, a large number of ON (Dk, m), ON is as defined in Table 1, Dk means the kth base of the D model of the pachinko machine, m is the date, etc. A variable with a temporal element.
In the case of reading a large number of times, first, k = 1 is read in order from the first of the units included in the model. Since the event data storage unit 23 and the ball count data storage unit 24 store the game state data and the game data and operation data for each model, the data of the designated date is stored for each unit. Read out.

  Next, step S22 is a step of calculating a jackpot probability and a probability variation probability. The jackpot probability and the probability variation probability are as defined in Table 1, but are generally the reciprocals of the probabilities. For example, the jackpot probability is the number of jackpot start times required to make one jackpot. To tell. Although this number varies depending on the model as described above, it is almost 300.

  Step S23 is a step of incrementing the table number k and re-reading necessary data, and sequentially calculating the probability and probability change probability for each table. If the last of the platforms included in the model is nth, the analysis is executed up to the nth. Of course, since it is possible to select a pachinko machine model and machine number in step S1, it is not always limited from k = 1 to k = n, but by setting an initial value of k. It is possible to analyze the desired platform. The same applies to the following analysis.

The results of the calculated big probability OP and probability variation probability KP are stored in the analysis data storage unit 25 in step S24. In step S24, the calculation result is stored in the analysis data storage unit 25 after waiting for k = n. After calculating the probability in step S22, k is incremented while storing in the analysis data storage unit 25 one by one. Needless to say. In this embodiment, the analysis flow is such that only the unit number k is incremented. However, since it is common to provide a plurality of models in one hall, the models also span a plurality of models. In this way, the model variable may be incremented, and further, a temporal element m such as a date may be incremented. This is also common to the following analysis units.
According to this probability analysis unit 9, it is possible to know the probability of the day and the probability of probability change after business, and how much the occurrence of the jackpot and probability of the day compared to the design specification value of the pachinko machine Every vehicle can be recognized.

Next, step S3 in FIG. 5 is executed. This step S3 will be described in detail with reference to FIG.
FIG. 7 is a flowchart showing the details of step S3 in detail. Each process included in step S3 is executed by the base analysis unit 10 of the analysis unit 6.
First, step S31 combines the information regarding the gaming state stored in the event data storage unit 23 with the information on the outgoing and out stored in the ball count data storage unit 24, so that It is a process of reading out the normal ball and out. At the time of reading, k = 1 as in step S21, and reading is performed sequentially from the first base in the D model.

Step S32 is a step of calculating the big base, the probability variation base, and the normal base by dividing each ball by the whole out O, and step S33 increments the value of k until k = n. Returning to step S31, each base is calculated.
Finally, step S34 is a step of storing the big bonus base OB, the probability variation base KB, and the normal base NB in the analysis data storage unit 25.
According to this base analysis unit 10, for example, after the business day, the base of the day, the base at the time of probability change and the base at the normal time can be known, and the status of the pachinko machine can be grasped for each gaming state instead of the whole. it can.
Here, the concept of the base will be explained. The base is defined in Table 1, but in this embodiment, there are three types of base base OB, probability variation base KB, and normal base NB. This is a numerical value that is calculated by dividing the total number of balls in the jackpot, the total number of balls at the time of probability change, and the total number of balls in the normal time by the total out, but it is not out in each game state. By dividing by a common numerical value of the overall out, it is possible to quantitatively determine how much the contribution in each game state contributes from the whole. And the sum of these three bases is the payout rate.

Next, step S4 of FIG. 5 is executed. This step S4 will be described in detail with reference to FIG.
FIG. 8 is a flowchart showing the details of step S4 in detail. Each process included in step S4 is executed by the start coefficient analysis unit 11 of the analysis unit 6.
First, step S41 is a step of reading the big hit start count and the probability variation start count from the event data storage unit 23, and reading the big hit base and the probability variation base calculated in the previous step S3 from the analysis data storage unit 25. At the time of reading, k = 1 as in step S21, and reading is performed sequentially from the first base in the D model. In FIG. 8, the base-related data is read from the analysis data storage unit 25. However, for example, when the analysis conditions are set so that step S4 is not performed but step S3 is performed, the analysis data is stored. Data is read not from the storage unit 25 but from the event data storage unit 23 and the ball count data storage unit 24 as shown in FIG.
That is, in the present embodiment, the description is based on the premise that step S2 to step S7 are sequentially executed, and therefore there is a numerical value that has already been analyzed and stored in the analysis data storage unit 25. When used for analysis, the data is read from the analysis data storage unit 25. However, depending on the content of the analysis to be executed, necessary data may not be stored in the analysis data storage unit 25. In that case, it is assumed that the data read for the analysis in which the data is obtained as a result is directly read.

Step S42 is a step of calculating a significant start coefficient and a probability variation start coefficient by dividing each base by each start number.
The big start coefficient and the probability variation start coefficient are a big hit base and a probability variation base per start prize, and a probability variation start prize, respectively. This coefficient is calculated when the start start nail or the probability variation start nail gauge is adjusted, and the occurrence of the number of starts for each gaming state varies. This is a convenient coefficient that can easily calculate the base by multiplying these coefficients. A base prediction analysis is performed using these coefficients, which will be described later.
Step S43 is a step of calculating each start coefficient by returning to step S41 while incrementing the value of k until k = n.
Finally, step S44 is a step of storing the big start coefficient K1 and the probability variation start coefficient K2 in the analysis data storage unit 25.
Also in the start coefficient analysis unit 11, for example, the big start coefficient and probability variation start coefficient of the day after business can be known, and the status of the pachinko machine can be grasped for each gaming state instead of the whole.

Step S5 in FIG. 5 is a process of payout rate analysis, which will be described in detail with reference to FIG. FIG. 9 is a flowchart showing the details of step S4 in detail.
In FIG. 9, each step included in step S <b> 5 is executed by the payout rate analysis unit 12 of the analysis unit 6. Step S51 is a step of reading the big base, probability variation base, and normal base calculated in step S3 from the analysis data storage unit 25. At the time of reading, k = 1 as in step S21, and reading is performed sequentially from the first base in the D model.
Step S52 is a step of calculating the payout rate by taking the sum of each base. The payout rate, which has already been defined in Table 1, is obtained by dividing all the payouts (safe) by all the operations (out). As described above, the base in each gaming state is obtained by dividing the number of balls in each gaming state by the total out O, so the sum of the bases is the total number of balls played.
Step S53 is a step of returning to step S51 and calculating the payout rate of each stand while incrementing the value of k until k = n.
Step S54 is a step of storing the calculation result of the payout rate in the analysis data storage unit 25 as in the previous analysis steps.
In the payout rate analysis unit 12, it is possible to analyze the payout rate over the entire game time using the base in each gaming state, so that the overall performance of each car is more than a detailed analysis. It is possible to grasp operating profit indicators.

  Tables 2 to 7 show examples of matrixes of data to be used and data obtained as analysis results when executing the analysis from step S2 to step S5.

  In these tables, the model name is specifically AA for one model, the unit numbers are D1 to D7 for seven pachinko machines, and the period of data acquisition and analysis from Table 2 Table 6 shows 5 days from July 11th to 15th. These tables are formed by reading data from each data storage unit by the data input unit 5 shown in FIG. 1 and storing the analysis result and the used data in the analysis data storage unit 25 by the analysis unit 6. It is a matrix of data. In the embodiments described so far, the used data is not specifically stated to be stored in the analysis data storage unit 25, but may be stored, of course, stored in the analysis data storage unit 25. As a result, a matrix of data as shown in Tables 2 to 7 is formed.

The matrix of this data will be described with reference to Table 2. First, Table 2 is composed of a two-stage table, but it does not need to be constructed separately as described above, and is composed of one table with a two-stage structure taking into account the size of the specification. Also good.
In the left column of the upper table, the model names of pachinko machines are sorted, and then the machine number is displayed. The next BUP clear is an abbreviation for backup clear, and means that the data stored on the board is cleared by turning on the switch provided on the game board board. The impact of this clearing will be explained in the prediction analysis.
In the next column, the raw data of “out”, “safe”, “start count”, and “probability start count” are sorted in order. From the next column, the data as a result of analysis by the analysis unit 6 is sorted from the large payout rate to the payout rate in the lower table. Finally, sales are the raw data sorted.
Table 2 is formed based on the data acquired on July 11, but the column of the machine number D2 is blank. Such a state can be seen in Tables 4 to 6, but since it is cleared, the data immediately after that is shown in another Table 7 because it is the data in the reset state, and the part of Table 2 is blank. Clearing on the 11th means that it was cleared just before the business day of the 11th, and data will be accumulated from the 11th. Clearing is not made on July 12 in Table 3, but on July 13 in Table 4, the numbers D1 and D2 are cleared, and on July 14 in Table 5, the numbers D6 and July in Table 6 are cleared. On the 15th, the machine numbers D1, D6 and D7 are cleared. Table 7 summarizes information about the cleared tables.

Returning to Table 2, the lowermost column of both the upper and lower tables sorts the average values of the values of the entire table shown in the table or a plurality of previously selected tables. The calculation of the average value is not specifically described with reference to the drawing, but is executed in the analysis unit 6. This portion may be provided as an average value calculation unit in the analysis unit 6 of FIG. The average value calculation unit reads out the data shown in Tables 2 to 7 from the data storage unit in which each data is stored, calculates the average value, and stores it in the analysis data storage unit 25. In Tables 2 to 6, the average value for each vehicle in the business day when it is not cleared is calculated and the result data is sorted. In Table 7, the average value immediately after clearing is calculated and the result data is calculated. It is sorted.
In particular, the average value data after being cleared is also used for prediction analysis described later.

Next, the prediction analysis process in step S6 of FIG. 5 will be described. The prediction analysis process includes an analysis process (FIG. 10) regarding the expected bonus probability and the expected probability change probability, an analysis process (FIG. 11) regarding the expected bonus start count and the expected probability change start count, and the expected bonus base and the expected probability change base. The analysis process (FIG. 12), the analysis process for the predicted clear big base and the predicted clear probability change base (FIG. 13), the analysis process for the expected split (FIG. 14), and the analysis process for the predicted clear split (FIG. 15) are included. .
First, an analysis process relating to an expected hit probability and an expected probability change probability will be described with reference to FIG. FIG. 10 is a flowchart showing in detail the process of step S61 included in step S6. In FIG. 10, in step S <b> 611, the big hit clear average probability and the probability variation clear average probability are read from the store / base / model data storage unit 20, and the big hit probability and the probability variation probability are read from the analysis data storage unit 25. Similarly, at the time of reading, k = 1 is read in order from the first base in the D model, but the big clear average probability and the probability variation clear average probability are constant values for each model and are common for the base. Therefore, the information for recognizing the platform is irrelevant.
In this figure, the big clear average probability and the probability variation clear average probability are read out from the store / base / model data storage unit 20, but as shown in Table 7, the analysis data storage unit 25 stores them as described above. Since it is stored, it may be read from here. In this figure, it is assumed that the clear average value has already converged to a constant value over a long period of time and is stored in the store / base / model data storage unit 20.

Here, the jackpot clear average probability and the probability variation clear average probability will be described in comparison with the jackpot probability and the probability variation probability.
First, the jackpot probability and the probability variation probability are as defined in Table 1 and described above, but the number of bonus starts for making one big hit and the number of probability variation starting times for making one probability change. . This probability is given in advance as a specification at the design stage for each model and remains unchanged.
On the other hand, the game table board provided in the game table shown in FIG. 1 generates a win state or a probability change state based on this probability. In addition, the game board substrate stores the number of start times and the number of bonuses, the number of probability variation starts, and the number of probability variations.
Therefore, for example, it is a random phenomenon as to whether or not it will be a big hit for one big start, and although it is impossible to predict how many big starts will be a big hit, it is accumulated by then If the information such as the number of jackpots or the number of probability changes is used as a base, it is possible to predict the probability of occurrence of subsequent jackpots or probability changes.

  Here, for the sake of simplicity, the prediction of this probability will be described using a dice. The probability of each eye appearing in the dice is 1/6, but this probability does not vary in the probability that 1 appears in the second time even if one eye appears in the first time. However, for example, if you roll the dice 60 times, and if you are programmed to roll 10 times each, then if you roll the dice 30 times, the 1st roll will be 2 times. In the case where only one has appeared, the first eye will appear eight times in the following 30 times, so the probability that the first eye will appear will be higher than 1/6.

  Since the gaming board substrate installed in the gaming machine stores the big probability, the probability variation probability, the number of big hits, and the probability variation number, the gaming machine analysis system according to the present embodiment reads these data. By doing so, the subsequent prediction is made. In addition, the game board can also clear data stored by itself using a dedicated switch. If it is cleared, the subsequent probability and probability change probability will be the specified values, so if the game state up to that point is larger than the design probability, that is, if the probability or probability probability is large. If you do not clear probabilistically, future balls will be suppressed.

In the gaming machine analysis system according to the present embodiment, the jackpot probability and the probability variation probability are made to be able to be grasped by the hall manager and the like by comparing the case of clearing with the data accumulated so far and the case of not clearing. , It will be possible to control the appearance of the ball stochastically.
In FIG. 10, first, an expected probability and an expected probability change probability are calculated. These probabilities both predict future probabilities based on the data accumulated so far. It is not something to clear.
The big clear average probability read from the store / table / model data storage unit 20 in step S611 is the average big probability after clearing the game board substrate as described with reference to Table 7, Is the same as the spec probability, but some game machine manufacturers do not publish the spec value of the probabilities, so there may be an average over a long period of time that the probability is almost constant. In the present embodiment, assuming that case, the letters “average” are used to determine the big clear average probability. The probability variation clear average probability is the same.
If the specification value can be read from the gaming board substrate, the data from the gaming board substrate 30 shown in FIG. 1 is stored in the store / table / model data storage section 20 via the hall computer 3 and the data input section 5. In the case of obtaining by a statistical method as in the present embodiment, the value may be stored in the store / base / model data storage unit 20. Of course, if there is variation among the tables, the information recognized for each table may be stored as a key. In this case, the big clear average probability and the probability variation clear average probability are substituted for each table in step S612. Need to be corrected.

In step S612, the expected correct probability POP and the expected probability change probability PKP are calculated. The calculation is obtained by subtracting the integrated value of the hit probability so far from a value obtained by multiplying the allowance clear average probability of that stand by (m + 1). As described above, m is a variable having a temporal element such as a date, but this starting point is the point in time when information stored on the game board substrate is cleared.
For example, if it is the number of days that have passed since the day when m was cleared, and it is today that is being analyzed, (m + 1) will be tomorrow. The POP and PKP equations in step S612 are integrated with the number of days from the time when the big probability obtained by data accumulation up to today is cleared, and the integration when the big probability is always generated until tomorrow on average. By subtracting from the value, the probabilities for tomorrow are obtained. In other words, according to this equation, it is assumed that the distortion of probability that has occurred so far will be repaired in one day of tomorrow. Specifically, when the big clear average probability is 300, it is assumed that 5 days have passed since the completion. Further, when the probability varies from 270, 300, 280, 310, 290 from the first day to the fifth day after clearing, the expected probability POP expected on the sixth day is shown in FIG. From the equation in step S612, POP = 300 * (5 + 1) − (270 + 300 + 280 + 310 + 290) = 350.
The previous big probability is lower than the average probability, so that it becomes a large probability of 350 to repair it in the next day. However, since the probability here is the number of big start times required to generate one big hit as described above, it is necessary to pay attention to this point. In addition, the number of jackpots and probability changes related to these probabilities are recorded on the game board substrate, and game states such as big hits and probability changes are generated based on those information. As described above, the occurrence probability related to the state fluctuates.

The expected probability change probability PKP is calculated in the same manner using the probability variation probability instead of the probability probability POP of the expected probability probability POP and the probability variation clear average probability instead of the probability clear average probability.
Next, step S613 is a step in which the value of k is incremented until k = n and the process returns to step S611 to calculate the expected probability of each unit.
Step S614 is a step of storing the calculation results of the predicted big probability and the predicted probability variation probability in the analysis data storage unit 25 as in the previous analysis steps.

Next, with reference to FIG. 11, step S62, which is an analysis step relating to the expected number of expected start times and the expected probability variable start number included in step S6 of FIG. 5, will be described. FIG. 11 is a flowchart showing in detail the process of step S62.
In FIG. 11, in step S621, the big start gauge, the probability variation start gauge, and the calculation coefficients a1, b1, a2, and b2 are read from the gauge data storage unit 22. The big hit start gauge and the positive change start gauge mean the gauge between the big hit start nails 43a and 43b and the gauge between the positive change start nails 44a and 44b described with reference to FIG.
There is a certain empirical relationship between these gauges and the number of big hits and the number of probability variation starts, and this analysis step calculates this.

Specifically, in step S622, the start gauge and the probability variation start gauge are assigned to each formula for each table. Among these formulas, x1 in the formula for the expected number of start times indicates the number of start times that increases for each unit gauge, and means the slope of the straight line shown in FIG. In this equation, when the gauge is a1, the number of start times per unit time is b1. For example, when the gauge is 11.5 mm and the number of big starts is 6 times per minute, and the gauge is increased 3 times per 1 mm, the STN equation is STN = (y1-11.5 ) * 3 + 6. Therefore, if the gauge y1 is set to 12.5 mm, the number of successful start increases from 6 to 9 times.
The same is true for the number of expected start of change.
As described above, the relationship between STN and KTN and each gauge is not necessarily linearly approximated as shown in FIG. 11, but may be any function that better approximates the relationship such as a quadratic function or an exponential function. It does not limit the shape of the function.
In step S623, the value of k is incremented until k = n, and the process returns to step S621 to calculate the expected number of expected start times and the expected probability variation start number of each unit.

Step S624 is a step of storing the calculation results of the expected big start number and the expected probability variable start number in the analysis data storage unit 25 as in the previous analysis steps. In this embodiment, in step S621, a time element is set as m, a gauge is selected, and the prediction for the next time element m + 1 is executed on the basis of the time element m being analyzed. That is, although the time on the left side of the STN and KTN expressions in step S622 is m + 1 and indicates an expectation, the time of the variable y1 used in the calculation is m and is based on the time when the analysis is performed. Yes. However, for example, when the gauge has already been adjusted for the next day, or when there are some candidates for the gauge of the next day, the gauge data storage unit 22 is adjusted in advance from the data input unit 5. These gauges or gauge candidates may be input, and these gauges may be read in step S621.
By doing in this way, it is also possible to compare the expected number of expected start times and the expected number of probable start times when the gauge is changed and when the gauge is changed.

Next, with reference to FIG. 12, step S63, which is an analysis process related to the expected bonus base and the expected probability variation base included in step S6 of FIG. 5, will be described. FIG. 12 is a flowchart showing in detail the process of step S63. The expected bonus base and the expected probability change base are processes for predicting the bonus base and the probability change base without clearing the data relating to the gaming state accumulated so far.
In FIG. 12, in step S631, the big hit start coefficient, the probability variation start coefficient, the predicted big hit start number analyzed in step S62, and the expected big change start number are read from the analysis data storage unit 25, and the big clear start coefficient and the certain variation clear start coefficient Is read from the store / base / model data storage unit 20.
In addition, although described in the big clear average probability OP (AA, CL) in FIG. 10, AA included in K1 (AA, CL) and K2 (AA, CL) means an average, and CL is Means clear. The numerical values of K1 (AA, CL) and K2 (AA, CL) are obtained empirically and are stored in the store / base / model data storage unit 20 in advance. Since these numerical values are affected by the gauge, it is preferable to store statistics in the store / base / model data storage unit 20 for each gauge and select them according to the gauge. Of course, as shown in Table 7, it may be read from the analysis data storage unit 25 as in the case described with reference to FIG.

In step S632, the expected bonus base POB and the predicted probability variation base PKB are calculated for each vehicle. The concept of the calculation formula of POB and PKB is the same as the calculation of the expected probability described with reference to FIG. Also in this base calculation, the calculation formula is such that distortion up to m is repaired at m + 1. Further, as shown in Table 1 or as described above, K1 or K2 is expressed by the ratio of the base and the number of start times, so that the equation shown in step S632 is obtained.
Next, in step S633, the value of k is incremented until k = n, and the process returns to step S631 to calculate the expected big base and the expected probability variation base of each unit.
Step S634 is a step of storing the calculation results of the expected bonus base and the predicted probability variation base in the analysis data storage unit 25.

Next, with reference to FIG. 13, step S64, which is an analysis process related to the expected big clear base and the expected probability variation clear base, included in step S6 of FIG. 5 will be described. FIG. 13 is a flowchart showing in detail the process of step S64.
In contrast to the expected big base and the predicted probability variation base described with reference to FIG. 12 based on the data accumulated so far, in this analysis process, the accumulated data is cleared. It predicts a big base and a probabilistic base.
In FIG. 13, in step S641, the big clear start coefficient and the probability variation clear start coefficient are read from the store / base / model data storage unit 20, but it may be read from the analysis data storage unit 25 with reference to FIG. This is the same as the case. Further, the predicted big start number and the predicted probability variation start number analyzed in step S62 are read from the analysis data storage unit 25.
In step S642, the predicted clear big base POB and the predicted clear probability variation base PKB are calculated according to the formulas shown.
In the expression shown in step S642, STN and KTN have m + 1 as a time element, and this is the start count at the time advanced from the time of analysis already analyzed in step S62. It can be understood that this is a clear-based forecast at the next point in time.

In step S643, the value of k is incremented until k = n, and the process returns to step S641 to read the expected big start number and the expected probability variable start number of each unit, and in step S642, each predicted clear base calculation is performed. Execute.
Step S <b> 644 is a step of storing the calculation results of the predicted clear bige base and the predicted clear probability variation base in the analysis data storage unit 25.

Next, with reference to FIG. 14, step S65, which is an analysis step regarding the expected division calculation included in step S6 of FIG. 5, will be described. FIG. 14 is a flowchart showing in detail the process of step S65.
In FIG. 14, in step S <b> 651, the hall ball unit price coefficient HYK and the assumed ball unit price KA are read from the store / base / model data storage unit 20. These are numerical values that are obtained empirically or statistically in advance by the hall, and are stored in the store / base / model data storage unit 20.
Here, the meanings of the hole ball unit price coefficient HYK and the assumed ball unit price KA will be described. In this explanation, first, consider again the mechanism of the game on the game table. As described with reference to FIG. 2, the gaming table 2 usually has a gaming state called a normal state in addition to a big hit state or a probability variation state. A player must first buy a ball at a specified unit price (this unit price is α yen / ball) and play it, and during that time, increase or decrease the ball by jackpot or probability change, and take the ball out of the hall. Eventually, the game ends either by exchanging the remaining balls for prizes or losing all the balls.

  Let's express this state with numbers. Now, assuming that the time required for one big hit is the time required for one game, the time t1 required for one game is expressed by equation (1).

By dividing the jackpot probability by the number of times the jackpot start occurs per minute, first, how many minutes it takes to win is calculated. Further, if it takes 2 minutes to finish the big hit, it will take t1 (minutes) from the start of the game to the end of the game. However, these two minutes are not particularly limited to that time.
The number of balls that are obtained within this time is the number of balls that can be obtained by one big hit, and the time t2 until all the balls are out is expressed by equation (2).

  The coefficient β in the equation (2) is about 1 to 1.5, although it varies depending on the results of the hole. The meaning of this number is a factor of how many times the player sticks to a single big hit, and the larger the hole, the thicker the credit of the hole, and the player believes in the ball and plays Means to continue.

In this case, since it is the time until all the balls are out as they are, β = 1 is considered. Also, 100 is the number of pachinko balls fired per minute, and the normal base is subtracted, although there is no big hit or a certain change in this one-minute launch, but normal winning is considered, and due to that winning This is because a prize ball is added to the possession ball, and there is substantially no loss of 100 balls per minute, and the number is usually reduced except for the base.
When t1 and t2 are considered in this way, for example, if a ball is obtained at t1, and if it is in a state where there is no big hit and there is no certain change, the ball will be lost at t2. Here, the ratio t2 / t1 is referred to as a suction rate s.

  By the way, the ball unit price on the game stand means a value obtained by dividing the sales amount of one game by the time required for one game. The thing divided by. The sales u in one game are expressed by Expression (3).

This equation (3) lends considering that the number of pachinko balls fired in the time until the big hit comes out, actually there are balls on a regular basis, and the decrease in holding balls is suppressed. I'm spending the unit price of jade.
Further, the operation (out) o of one game is expressed by Expression (4).

  In Equation (4), 200 is 100 pachinko balls fired per minute, and the time for jackpot is 2 minutes, so the total number of outs until the jackpot ends is counted. .

Therefore, the unit price of a single exchange game machine is expressed in u / o. However, since there is actually a suction time as described above, it is necessary to consider the unit price by subtracting the suction rate s from the viewpoint that the player spends money.
Specifically, the actual ball unit price p is expressed by Expression (5).

  The actual unit price expressed in this way is considered to be close to the actual state in consideration of the suction rate s, etc., but the actual actual unit price of the ball is the actual operation (out) of the actual sales. Divided by. If this actual true ball unit price is pr, it is expressed as in equation (6).

The true ball unit price calculated in consideration of operation based on the sales paid by the player to the hall is called pr and the theoretically calculated actual ball unit price is called p. HYK.
In addition, although p existing in Formula (5) and Formula (6) has been described as an actual ball unit price, it can be said that it is a provisional ball unit price based on the actual ball unit price pr. That is, the previous assumed unit price KA corresponds to this p.

To summarize the above, the assumed ball unit price is the ball unit price that is theoretically determined as the actual unit price in consideration of the suction rate s, and the ratio between this unit price and the actual true unit price is It will be the hole ball unit cost coefficient HYK.
Since KA and pr are data obtained empirically and statistically by the hole, HYK is empirically obtained. Therefore, the HYK and KA can be stored in the store / stand / model data storage unit 20 via the data input unit 5 in advance. Since HYK and KA show different values for each hall and for each type of game machine, it is necessary to acquire data in advance.

After reading these coefficients, the expected average payout rate DR (AA, m + 1) is read from the analysis data storage unit 25. This expected average payout rate DR (AA, m + 1) represents the average of DR values at m + 1 of the same model and D model in the present embodiment.
Next, in step S652, an expected division calculation is performed as an expression as shown.
Step S653 is a step of storing the calculation result of the expected division in the analysis data storage unit 25 as in the previous analysis steps.

Finally, with reference to FIG. 15, step S66, which is an analysis step regarding the expected clear divisor included in step S6 of FIG. 5, will be described. FIG. 15 is a flowchart showing in detail the process of step S66. This expected clear divide analysis analyzes the divide when the game board is cleared while the divide calculated in step S65 is a divide calculated based on the gaming state so far. Is what you expect.
In FIG. 15, in step S <b> 661, the hall ball unit price coefficient and the assumed ball unit price are read from the store / base / model data storage unit 20, and the predicted clear payout rate is read from the analysis data storage unit 25. Of course, the expected clear payout rate has converged, and when it is stored in the store / base / model data storage unit 20 in advance, it may be read from the store / base / model data storage unit 20.
In step S662, an expected clear divisor is calculated using the same formula as in step S652. In step S663, the expected clear divisor that is the operation result is similarly stored in the analysis data storage unit 25.

  Table 8 shows an example of a matrix of data to be used and data obtained as an analysis result when executing the analysis of step S6 described above with reference to FIGS.

Although Table 8 has a three-stage configuration, this is configured in consideration of the size of the specification, and it goes without saying that it may be summarized in one table.
In Table 8, the items of the sorted data are almost the same as those in Tables 2 to 6, except that the date column is 16 days. This analysis was performed after business on the 15th, and is a table in which data is sorted for use as information for predicting the gaming state and business state on the 16th. In addition, there are two date columns. This indicates the result of analysis based on data accumulated between the date in the left column and the date in the right column. The table number D4 in the upper part of Table 8 is “Clear” in the clear column, and since it was cleared after business on the 15th, the date column is blank. Since D4 has been cleared, for example, the correct probability is the same as the expected clear value OP (AA, CL).

Next, as a result of the prediction probability analysis described with reference to FIG. 10, for example, the POP (D2, 16) obtained for the table number D2 is shown in the upper table of Table 8. This POP (D2, 16) is based on data accumulated from July 14 to July 15.
In the column displaying the predicted value, the average value of the predicted values for each vehicle is sorted. In the column of predicted clear values, predicted values immediately after clear are sorted.
With reference to Table 8, the manager or manager of the hall, for example, for the model number D5 of the AA model, the expected large-scale base POB (D5 expected from the data accumulated from July 12 to 15 , 16) and the predicted clear big base POB (AA, CL) which is the clear predicted value. If these two bases are compared and the expected bonus base is very small, it can be considered that the four-day bonus is very large. In such a case, it is necessary to check the size of the gauge, but it is expected that the base will decrease after tomorrow (this is nothing but the expected base is small) and the campaign period. If you are aiming to increase the number of customers, such as inside, it is judged that you should clear it.
Of course, it is considered that the whole hall cannot be judged only by the stand number D5, but the average value from the stand numbers D1 to D7 is sorted in the column of the expected value, and by referring to this, the whole model It is possible to devise a policy and a strategy for controlling the appearance by selecting a desired base while predicting the appearance of the appearance.

As described above, according to the gaming machine analysis system according to the present embodiment, data is stored and read for each table, using the recognition information for each table as a key, and the game state and the sales state are displayed. It can be managed for each model and also for each hall. Furthermore, while using the data recorded on the board of the game stand, the game state and the business state can be predicted separately when the data is cleared or not. It is possible to control and manage the appearance and profit status of the hall.
The matrixes shown in Tables 2 to 8 may be displayed on the display screen 32 of the portable terminal 4 via the display unit 7 and the data communication unit 8 shown in FIG. In particular, the display on the portable terminal 4 can simultaneously adjust the nail while moving to the site in the hall, that is, in front of the platform, thereby shortening the working time and labor.

  According to the present invention, it is possible to easily analyze a gaming state and a business state at a level of each unit or the whole hall in a hall where a plurality of models are mixed and a large number of models are installed per model. Various game machines can be used as long as the game machine stores information related to the game state.

It is a composition conceptual diagram of the game machine analysis system concerning this embodiment. It is a conceptual diagram of the game machine used as the object of installation of a gaming machine analysis system. It is a conceptual diagram which shows the appearance base of a pachinko machine for every gaming state. It is a conceptual diagram which shows the relationship between the gauge of a big hit start nail and the number of big hits start. It is a flowchart which shows the analysis process performed in the analysis part which concerns on this Embodiment. FIG. 6 is a flowchart showing the details of step S2 in FIG. 5 in detail. FIG. 6 is a flowchart showing the details of step S3 in FIG. 5 in detail. It is a flowchart which decomposes | disassembles and shows the content of step S4 in FIG. 5 in detail. FIG. 6 is a flowchart showing the details of step S5 in FIG. 5 in detail. It is a flowchart which shows the process of step S61 contained in step S6 in FIG. 5 in detail. It is a flowchart which shows the process of step S62 included in step S6 in FIG. 5 in detail. It is a flowchart which shows the process of step S63 contained in step S6 in FIG. 5 in detail. It is a flowchart which shows in detail the process of step S64 included in step S6 in FIG. It is a flowchart which shows the process of step S65 contained in step S6 in FIG. 5 in detail. It is a flowchart which shows the process of step S66 contained in step S6 in FIG. 5 in detail.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Game machine analysis system 2 ... Game stand 3 ... Hall computer 4 ... Portable terminal 5 ... Data input part 6 ... Analysis part 7 ... Display part 8 ... Data communication part 9 ... Probability analysis part 10 ... Base analysis part 11 ... Start coefficient Analyzing section 12 ... Departure rate analyzing section 13 ... Predictive analyzing section 20 ... Store / base / model data storing section 21 ... Sales data storing section 22 ... Gauge data storing section 23 ... Event data storing section 24 ... Ball count data storing section 25 ... analysis data storage unit 30 ... game table board 31 ... counter 32 ... display screen 40 ... operation handle 41 ... pachinko ball 42 ... recovery port 43 ... hit hit start port 43a, 43b ... big hit start nail 44 ... probability start port 44a, 44b ... Probabilistic start nail 45 ... Bonus display screen 46 ... Bonus outlet 47 ... Normal prize opening 47a, 47b ... Normal prize nail 5 ... Large those base 51 ... probability variation base 52 ... normal base 53 ... probability variation at atmospheric those base 54 ... probability variation based

Claims (7)

A game that includes recognition information capable of recognizing the pachinko gaming machine from the pachinko gaming machine having at least a bonus state and a normal state as a gaming state, information on the gaming state along the time series of the pachinko gaming machine and the number of gaming states. A data input unit that reads out the state information, the ball count information for each gaming state and the number of big starts and stores it in the first database, and the game corresponding to the recognition information of the pachinko gaming machine from the first database A probability analysis unit that reads out the number of hit states included in the state information, the number of hit start times, calculates a hit probability, and stores it in a second database; and the recognition information from the first database as a key The game state information and the ball count information are read out, and the big base and the normal base corresponding to the game state are calculated and stored in the third database. And based analysis unit that, after clearing the game state information of the pachinko machines stored in the substrate installed in the pachinko game machine (hereinafter, clears the game state information of pachinko machines stored in the "substrate “Immediately after” is simply referred to as “immediately after clearing”), a sixth database for storing the probability of success (hereinafter referred to as “clearance probability”), and reading the clearance probability from this sixth database. And a first predictive analysis unit that reads the big probability from the database and uses the recognition information as a key, calculates a difference of the big probability, and stores it in a seventh database. Analysis system. A game including at least a pachinko machine having at least a big game state and a normal state as a game state, recognition information capable of recognizing the pachinko machine, information on a game state along the time series of the pachinko machine, and information on the number of game states. A data input unit for reading out the state information, the game play count information for each gaming state and the number of bonus starts and storing it in the first database; and the gaming state information from the first database using the recognition information as a key The base analysis unit that reads out the ball count information, calculates at least the big base and the normal base corresponding to the gaming state, and stores them in a third database, and the recognition information from the third database as a key The big start coefficient obtained by dividing the big hit base corresponding to the gaming state by the number of big hit starts is calculated and stored in the fifth database. A start coefficient analyzer for the large person start coefficient immediately clear pachinko machine and the eighth database for storing (hereinafter. Referred to clear large person start coefficient), a large person start nail gauge of the pachinko machines A ninth database that stores information corresponding to the recognition information; and a gauge that reads the gauge information from the ninth database using the recognition information as a key, calculates an expected number of successful start times, and stores it in the tenth database. 2 prediction analysis units, and the clear big start coefficient is read from the eighth database, the big start coefficient is read from the fifth database using the recognition information as a key, and the difference information is obtained from the tenth database. Using the key as the key, read the expected number of start times and take the product to calculate the expected number The third game machine analysis systems; and a forecast analysis unit that stores the database. The clear big start coefficient is read from the eighth database, the expected big start number is read from the tenth database using the recognition information as a key, the product is taken, and the expected big base immediately after clearing (hereinafter, clear) The gaming machine analysis system according to claim 2 , further comprising a fourth prediction analysis unit that calculates and stores in a twelfth database. The expected yield rate immediately after clearing by taking the sum of the normal base read from the third database using the recognition information as a key and the clear expected big base read from the twelfth database ( Hereinafter, the sixth predicted analysis unit that calculates and stores in the fourteenth database by calculating the clear expected appearance rate, and the ball unit price coefficient statistically obtained in advance in the hall where the pachinko gaming machine is installed. The 13th database that stores the assumed ball unit price, the clear predicted expected ball rate read from the 14th database, the hole unit price coefficient read from the 13th database, and the assumed ball unit price immediately after the predicted split number (hereinafter, clear expectation that the split number.) the seventh expected analyzer and gaming machine according to claim 3, characterized by having a for calculating the Analysis system. The pachinko gaming machine is a pachinko gaming machine having at least a bonus state, a probability variation state, and a normal state as a gaming state, and the base analysis unit corresponds to the gaming state at least a bonus base, a probability variation base, and a normal base. Is calculated and stored in the third database, and the eighth database stores the clear large start coefficient of the pachinko machine and the probability variation start coefficient immediately after the clear (hereinafter referred to as the clear probability variation start coefficient). The ninth database stores the gauge information of the big start nail and the probability variation start nail of the pachinko machine in correspondence with the recognition information, and the second predictive analysis unit recognizes the gauge information from the ninth database. Read the information as a key, calculate the expected number of expected start times and the expected number of probable start times and store it in the 10th database. The fourth prediction analysis unit reads the clear big start coefficient from the eighth database, reads the predicted big start number from the tenth database using the recognition information as a key, and takes the product to obtain the clear prediction big This base and the expected probability variation base immediately after clearing (hereinafter referred to as a clear prediction probability variation base) are read out from the eighth database, read out the clear probability variation start coefficient from the eighth database, and read out the predicted probability variation start number from the tenth database. Is calculated and stored in the twelfth database, and the sixth anticipation analysis unit reads from the normal base and the twelfth database read from the third database using the recognition information as a key. The clear expected payout rate is calculated by taking the sum of the clear expected big hit base and the clear expected probability base Calculated and stored in the fourteenth database, and the seventh predictive analysis unit reads the clear expected betting rate read from the fourteenth database, the hall ball unit price coefficient read from the thirteenth database, and the assumption. 5. The gaming machine analysis system according to claim 4, wherein a clear expected division is calculated using a unit price of balls. The predicted jackpot base and the clear predicted jackpot base are connected to the third forecast analysis section and the fourth forecast analysis section, and the predicted jackpot base and the clear forecast jackpot base are displayed on the display screen provided in the portable terminal. 4. The gaming machine analysis system according to claim 3, further comprising a data communication unit that performs the operation. Connected to the fifth predictive analysis unit and the seventh predictive analysis unit, reads the expected split and clear expected split, and displays the expected split and clear expected split on the display screen provided in the mobile terminal The gaming machine analysis system according to claim 4, further comprising a data communication unit that performs the operation.