INVENTOR(S)

SAMIR VARMA, MICHAEL WAYNE SHORE

INCORPORATION BY REFERENCE

[0001] This provisional application incorporates by reference and in their entirety: U.S. Provisional Application 61/410,237 filed November 4, 2010; U. S. Patent Application No. 13/288,660 filed November 3, 2011; U. S. Patent Application No. 13/942,145 filed July 15, 2013; U. S. Pat. No. 8,606,672 filed December 10, 2013; U. S. Patent Application No. 15/097,855 filed April 13, 2016; and U.S. Provisional Application No. 62/550,156 filed August 25, 2017.

FIELD OF THE INVENTION

[0002] This invention relates generally to methods and systems for analyzing sports-based information.

BACKGROUND OF THE INVENTION

[0003] Analysis to predict outcomes of sporting events in progress and future sporting events yet to be undertaken often rely on linear thinking and pattems associated with linear thinking. An example of this scenario is when a coach could thinks "you know, the team with the higher yards per pass attempt wins 75% of the time, but we aren't that good at passing, so perhaps we should run the ball." Oftentimes, though, the predicted outcomes do not match the expectations derived from linear thinking patterns.

[0004] Accordingly, there is a need to address sporting events having scenarios that exhibit patterns more complicated than those arising from linear thinking.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Fig. 1 is a schematic depiction of a sports information analysis system in accord with exemplary embodiments of the present invention;

[0006] Fig. 2 is a schematic depiction of a sports information processing apparatus according to an exemplary embodiment of the present invention; [0007] Fig. 3 is a schematic depiction of a sports processing system block schematic operating within the mobile device 8 according to an exemplary embodiment of the present invention;

[0008] Fig. 4 is a functional depiction of the sports information analysis system in an exemplary embodiment of the present invention; and

[0009] Fig. 5 schematically depicts a microprocessor-executable algorithmic flow chart configured to analyze sports information utilizing the exemplary embodiments described in Figs. 1-4.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0010] Disclosure herein describe systems and methods to generate advice regarding sporting event outcomes based upon microprocessor-executable information- containing vectors having associated with it definable sports-performance variables that are configured to automatically handle nonlinear patterns, either separately or in combination with linear patterns also being exhibited, thereby allowing as many variables as needed to be simultaneously considered from which to make a sports event prediction or provide advice to a sporting event in progress. Such predictions and advisory pronouncement can automatically be drawn from past experience. Embodiments of the present invention are to find the situations, or variables, between any present and/or past sporting events that present "similar" characteristics to a sporting event under study. Thereafter, the situations can be ranked by their similarity. Using exemplary embodiments of the systems and methods described below, one can then hypothesize that the future that followed those past situations is likely to follow the current sporting event situation in progress or a future sporting event yet to be undertaken.

[0011] More specifically, described are systems and methods that allow a user to make better and more informed decisions related to sport events based on an analysis of sports information that can be scaled with additional variables that can be fitted into selected patterns between any two or more sports events undertaken for analysis by the particular embodiments discussed below.

[0012] In general terms described are particular embodiments of an information processing apparatus and associated method for analyzing a distribution of possible future sports outcomes. An input receives a possible future sports outcome distribution request identifying sports outcome distribution information relating to a possible future sports outcome distribution, and one or more different types of sports performance variables relating to at least one of the possible future sports outcomes and a vector unit that generates at least two vectors containing the one or more sports performance variables based on the possible future sports outcome distribution request. A similarity value is then calculated comparing the at least two vectors and output information is generated of possible future sports outcome advice for a user.

[0013] An embodiment includes an information processing apparatus for analyzing a distribution of possible sports outcomes. The apparatus includes a microprocessor able to receive programmable instructions to carry out a series of tasks. Among the tasks includes the ability to receive a first sports-related future outcome distribution request and a second sports-related outcome distribution request, the first and second sports-related distribution requests having one or more sports performance variables having the potential to be linearly and/or non-linearly distributed. Upon receiving the first and second sports-related distribution request, the microprocessor further implements instructions that generate at least two vectors containing the one or more sports performance variables and then calculates a similarity value and/or one or more similarity values between the at least two vectors. Thereafter, the microprocessor implements instructions to analyze the similarity value and/or the one or more similarity values, and based on the similarity value and/or similarity values, predict a sports outcome arising from the first and second sports-related distributions so that the rendering or presenting of at least one advisory opinion concerning the predicted outcome can be made from the remaining instructions programmed and executed by the microprocessor.

[0014] Another embodiment includes a microprocessor executable method allowing for the analysis of distributions of possible sports outcomes. The microprocessor executable methods include programmable instructions to carry out a series of tasks. Among the tasks includes the ability to impart receiving a first sports- related future outcome distribution request and a second sports-related outcome distribution request, the first and second sports-related distribution requests having one or more performance sports variables having the potential to be linearly and/or non- linearly distributed. Upon receiving the first and second sports-related distribution request, the microprocessor further implements instructions for generating at least two vectors containing the one or more sports performance variables and then calculating a similarity value and/or one or more similarity values between the at least two vectors. Thereafter, the microprocessor executable instructions implements instructions for analyzing the similarity value and/or the one or more similarity values, and based on the similarity value and/or similarity values, predicting a sports outcome arising from the first and second sports-related distributions so that the rendering or presenting of at least one advisory opinion concerning the predicted outcome can be made from the remaining instructions executed by the microprocessor.

[0015] A particular embodiment describes an information processing apparatus for analyzing a distribution of possible future sports outcomes, in which the information processing apparatus includes a processor programmed to receive a first future sports outcome distribution request. The first future sports outcome distribution request identifies sports outcome distribution information relating to a possible future sports outcome distribution, and one or more different types of sports performance variables relating to at least one of the possible future sports outcomes and a benchmark.

[0016] In other embodiments the processor is further programmed to generate at least two vectors containing the one or more sports performance variables based on the first sports outcome distribution request, calculate a similarity value by calculating a distance between at least two vectors, the at least two vectors including a first vector relating to the first sports outcome distribution request, and a second vector relating to a second sports outcome distribution to which the first sports outcome distribution request is compared. The first vector and the second vector defined to include at least one to two past sporting events, a current sporting event in progress and a past sporting event, a future sporting event and a past sporting event, a first sporting event in progress and a second sporting event in progress, and a future first sporting event and a second future sporting event. Thereafter, the processor is programmed to engage analysis of the vectors to determine a similarity value for any of the at least two vectors and output information from the analysis as a distribution of outcomes expressed as advice to a user. In the case of future possible outcomes of sporting events, a possible future sports outcome distribution request and similarity values are correlated and provided to the user.

[0017] In other embodiments the at least two vectors includes one and/or more of the following types: a sports performance reward vector containing one or more sports performance variables which the user selects as related to an expected possible future outcome distribution of the sports performance variables, a sports performance risk vector containing one or more sports performance variables which the user selects as related to a level of risk of the sports performance outcome distribution, a benchmark reward vector containing the one or more sports performance variables which the user selects as related to the expected possible future outcome distribution of the sports performance, the one or more variables having values identified by the benchmark, or a benchmark risk vector containing the one or more sports performance variables which the user selects as related to the level of risk of the expected possible future outcome distribution of the sports performance, the one or more variables having values identified by the benchmark.

[0018] Another particular embodiment describes an information processing apparatus for analyzing a distribution of possible future sports outcomes, in which the information processing apparatus includes a processor programmed to receive a possible future sports outcome distribution request. The possible future sports outcome distribution request identifies sports outcome distribution information relating to a possible future sports outcome distribution, and one or more different types of sports performance variables relating to at least one of the possible future sports outcomes and a benchmark.

[0019] In other embodiments the processor is further programmed to generate at least two vectors containing the one or more sports performance variables based on the possible future sports outcome distribution request, calculate a similarity value by calculating a distance between at least two vectors, and analyze the similarity value and the possible future sports outcome request. Thereafter, the processor is programmed to determine output information as a distribution of possible future sports outcome advice for a user, correlating to the possible future sports outcome distribution request and the similarity values based on results of the analysis. [0020] The at least two vectors includes one and/or more of the following types: a sports performance reward vector containing one or more sports performance variables which the user selects as related to an expected possible future outcome distribution of the sports performance variables, a sports performance risk vector containing one or more sports performance variables which the user selects as related to a level of risk of the sports performance outcome distribution, a benchmark reward vector containing the one or more sports performance variables which the user selects as related to the expected possible future outcome distribution of the sports performance, the one or more variables having values identified by the benchmark, or a benchmark risk vector containing the one or more sports performance variables which the user selects as related to the level of risk of the expected possible future outcome distribution of the sports performance, the one or more variables having values identified by the benchmark.

[0021] Particular embodiments of the systems and methods to analyze sports- based information are described in the figures:

[0022] Fig. 1 is a schematic depiction of a sports information analysis system in accord with exemplary embodiments of the present invention.

[0023] Fig. 2 is a schematic depiction of a sports information processing apparatus according to an exemplary embodiment of the present invention.

[0024] Fig. 3 is a schematic depiction of a sports processing system block schematic operating within the mobile device 8 according to an exemplary embodiment of the present invention.

[0025] Fig. 4 is a functional depiction of the sports information analysis system in an exemplary embodiment of the present invention.

[0026] Fig. 5 schematically depicts a microprocessor-executable algorithmic flow chart configured to analyze sports information utilizing the exemplary embodiments described in Figs. 1-4.

[0027] FIG. 1 is a schematic diagram of a sports information analysis system according to an exemplary embodiment of the present embodiment. In FIG. 1, a computer 2 is connected to a server 4, a database 6 and a mobile device 8 via a network 10. The server 4 represents one or more servers connected to the computer 2, the database 6 and the mobile device 8 via the network 10. The database 6 represents one or more databases connected to the computer 2, the server 4 and the mobile device 8 via network 10. The mobile device 8 represents one or more mobile devices, such as a cell phone, connected to the computer 2, the server 4 and the database 6 via the network 10. The network 10 represents one or more networks, such as the Internet, connecting the computer 2, the server 4, the database 6 and the mobile device 8.

[0028] The server 4, the computer 6 and/or the mobile device 8 can be utilized as a central hosting site for the sport analysis system. Users of the computer 6 and mobile device 8 can also access the sport analysis system on the server 4 via network 10. Accordingly, the server 4 may notify users of the sport information through the network 10 to the computer 6 or mobile device 8.

[0029] Next, a hardware description of the sports information processing apparatus for analyzing sports information according to exemplary embodiments is described with reference to FIG. 2. In FIG. 2, the sports information processing apparatus includes a CPU 200 which performs the processes described above. The process data and instructions may be stored in memory 202. These processes and instructions may also be stored on a storage medium disk 204 such as a hard drive (HDD) or portable storage medium or may be stored remotely. Further, the claimed embodiments are not limited by the form of the computer-readable media on which the instructions of the inventive process are stored. For example, the instructions may be stored on CDs, BLU-RAY, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other sports information processing device with which the sports information processing device communicates, such as a server or computer.

[0030] Further, the claimed invention embodiments may be provided as a utility application, background daemon, or component of an operating system, or combination thereof, executing in conjunction with CPU 200 and an operating system such as Microsoft Windows 7, UNIX, Solaris, LINUX, Apple MAC-OS and other systems known to those skilled in the art.

[0031] The sports information processing apparatus in FIG. 2 also includes a network controller 208, such as an Intel Ethernet PRO network interface card from Intel

Corporation of America, for interfacing with network 10. As can be appreciated, the network 10 can be a public network, such as the Internet, or a private network such as an

LAN or WAN network, or any combination thereof and can also include PSTN or ISDN sub-networks. The network 10 can also be wired, such as an Ethernet network, or can be wireless such as a cellular network including EDGE, 3G and 4G wireless cellular systems. The wireless network can also be WiFi, Bluetooth, or any other wireless form of communication that is known.

[0032] The sports information processing apparatus further includes a display controller 210, such as a NVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation of America for interfacing with display 212, such as a Hewlett Packard HPL2445w LCD monitor. A general purpose I/O interface 214 interfaces with a keyboard and/or mouse 216 as well as a touch screen panel 218 on or separate from display 212. General purpose I/O interface also connects to a variety of peripherals 220 including printers and scanners, such as an OfficeJet or DeskJet from Hewlett Packard.

[0033] A sound controller 226 is also provided in the information processing apparatus, such as Sound Blaster X-Fi Titanium from Creative, to interface with speakers/microphone 228 thereby providing sounds and/or music.

[0034] The general purpose storage controller 222 connects the storage medium disk 204 with communication bus 224, which may be an ISA, EISA, VESA, PCI, or similar, for interconnecting all of the components of the information processing apparatus. A description of the general features and functionality of the display 212, keyboard and/or mouse 216, as well as the display controller 210, storage controller 222, network controller 208, sound controller 226, and general purpose I/O interface 214 is omitted herein for brevity as these features are known.

[0035] FIG. 3 is a sports processing system block schematic operating within the mobile device 8. The mobile device 8, which may be laptop or a smart phone such as an iPhone from Apple, Inc. of America, includes a processor 320 used to control the functions of the mobile device 8 and to run applications thereon, such as an electronic address book, internet browser, etc. Processor 320 may be an ARM processor or a processor such as a Core 2 Duo from Intel Corporation of America. Alternatively, Processor 320 may be implemented on an FPGA, ASIC or using discrete logic circuits, as would be recognized by one of ordinary skill in the art.

[0036] A user interface 310, which may include a microphone, speaker, touch screen, keyboard or any combination thereof, allows the processor 320 to receive input from a user of the mobile device 300, and a display 315 provides feedback to the user. Display 315 may be a color or monochrome LCD display or any other display that would be recognized by one of ordinary skill in the art.

[0037] Mobile device 8 also includes a communication interface 305 and antenna 335 to communicate with other devices, such as the server 4 or another mobile device 8. Further, mobile device 8 may communicate with these other devices wirelessly using the cellular network (including EDGE, 3G, 4G, etc.,) a WiFi connection, a Bluetooth connection or any other wireless form of communication that is known. Mobile device 8 may also communicate through a wired connection to communication interface 305, such as a USB connection.

[0038] In some embodiments, mobile device 8 includes read-only memory, ROM 330, to store low-level functions and processes necessary to support core functionality, and re-writable memory 325, which stores an operating system, drivers, applications, application data and user data. Re-writable memory 325 may be dynamic or static random access memory (RAM), FLASH memory, EEPROM memory, and the like. Further, portions of the re-writable memory 325 may be removable.

[0039] For example, re-writable memory 325 may store an application for communicating with server 4 to identify mobile device 8 and to provide the server 4 with access to data stored therein. Such application may be downloaded, via any of the communication methods described above, from the server 4, from a software repository or from an online store, such as the App Store from Apple, Inc. of America. Further, such an application may be an electronic address book, or electronic personal information manager (PIM). However, the application may also be a plug-in for an electronic address book or PIM already installed on mobile device 8, a background application, driver and the like.

[0040] As one of skill in the art would recognize, the above descriptions of the server 4 and mobile device 8 are merely examples and other server configurations and mobile devices may be used without departing from the scope of the present embodiments.

[0041] According to some embodiments, a "module" refers to hardware architecture or one or more programming architectures, or a combination of both, configured to perform one or more designated functions. [0042] FIG. 4 describes the functionality of the sports analysis system according to one embodiment of the present embodiment. FIG. 4 illustrates sports variables and a sports request 400, the database 6, the server 4, the sports advice 410, a sports vector description unit 402, a normalizing unit 404, sports vector similarity description unit 406 and an sports analysis unit 408. The sports vector description unit 402, normalizing unit 404, sports vector similarity description unit 406 and the sports analysis unit 408 are illustrated within the server 4 but in other embodiments could be included within the computer 6 or the mobile device 8. Sports variables and a sports request 400 relating to a sporting game or event, or a sports-based indicator, are received from a user depending on the sporting information the user wants to obtain.

[0043] Partial examples of sports variables 400, as occurs in the sport of baseball, would include statistics concerning batting, base running, pitching, fielding, overall player values, and general statistics.

[0044] A partial example of batting statistics related sports variables 400 include IB-single: hits on which the batter reaches first base safely without the contribution of a fielding error; 2B-double: hits on which the batter reaches second base safely without the contribution of a fielding error; 3B-Triple: hits on which the batter reaches second base safely without the contribution of a fielding error; At bat: Plate appearances, not including bases on balls, being hit by pitch, sacrifices, interference, or obstruction; AB/HR - At bats per home run: at bats divided by home runs; BA - Batting average (also abbreviated A VG): hits divided by at bats (H/AB); GS - Grand Slam: a home run with the bases loaded, resulting in four runs scoring, and four RBI or runs batted in credited to the batter; BB - Base on balls (also called a "walk"): hitter not swinging at four pitches called out of the strike zone and awarded first base; HBP - Hit by pitch: times touched by a pitch and awarded first base as a result; SF -Sacrifice fly:

Fly balls hit to the outfield which although caught for an out, allow a base runner to advance; RBI - Run batted in: number of runners who score due to a batters' action, except when batter grounded into double play or reached on an error; K - Strike out (also abbreviated SO): number of times that a third strike is taken or swung at and missed, or bunted foul, in which the catcher must catch the third strike or batter may attempt to run to first base; RISP - Runner in scoring position: a breakdown of the batter's baiting average with runners in scoring position, which include runners at second and third bases; LOB - Left on base: number of runners neither out nor scored at the end of an inning; and OBP - On-base percentage: times reached base (H + BB + HBP) divided by at bats plus walks plus hit by pitch plus sacrifice flies (AB BB + HBP SF); and HR - Home runs: hits on which the batter successfully touched all four bases, without the contribution of a fielding error.

[0045] A partial example of base running statistics related sports variables 400 include SB - Stolen base: number of bases advanced by the runner while the ball is in the possession of the defense; CS - Caught stealing: times tagged out while attempting to steal a base; SBA/ATT - Stolen base attempts: total number of times the player has attempted to steal a base (SB+CS); SB% - Stolen base percentage: the percentage of bases stolen successfully. (SB) divided by (SBA) (stolen bases attempted); Di - Defensive Indifference: if the catcher does not attempt to throw out a mnner (usually because the base would be insignificant), the runner is not awarded a steal. Scored as a fielder's choice; R - Runs scored: times reached home plate legally and safely; and L ^! BR - Ultimate base running: a metric that assigns linear weights to every individual base running event in order to measure the impact of a player's base running skill.

[0046] A partial example of pitching statistics related sports variables 400 include ER - Earned run: number of runs that did not occur as a result of errors or passed balls; ERA - Earned run average: total number of earned runs (see "ER" above), multiplied by 9, divided by innings pitched; K (or SO) - Strikeout: number of batters who received strike three; HB - Hit batsman: times hit a batter with pitch, allowing runner to advance to first base; IP/GS - Average number of innings pitched per game started; IR - Inherited runners: number of runners on base when the pitcher enters the game; K/9 (or SO/9) - Strikeouts per 9 innings pitched: strikeouts times nine divided by innings pitched; PIT (or NP) - Pitches thrown (Pitch count); QS - Quality start: a game in which a starting pitcher completes at least six innings and permits no more than three earned runs; RA - Run average: umber of runs allowed times nine divided by innings pitched; SHO - Shutout: number of complete games pitched with no runs allowed; SV -

Save: number of games where the pitcher enters a game led by the pitcher's team, finishes the game without surrendering the lead, is not the winning pitcher, and either (a) the lead was three runs or fewer when the pitcher entered the game, (b) the potential tying run was on base, at bat, or on deck, or (c) the pitcher pitched three or more innings; WHIP - Walks and hits per inning pitched: average number of walks and hits allowed by the pitcher per inning; and WP - Wild pitches: charged when a pitch is too high, low, or wide of home plate for the catcher to field, thereby allowing one or more runners to advance or score.

[0047] A partial example of fielding statistics related sports variables 400 include A - Assists: number of outs recorded on a play where a fielder touched the ball, except if such touching is the putout; CI - Catcher's Interference (e.g., catcher makes contact w ith bat); DP - Double plays: one for each double play during which the fielder recorded a putout or an assist; E - Errors: number of times a fielder fails to make a play he should have made with common effort, and the offense benefits as a result; FP - Fielding percentage: total plays (chances minus errors) divided by the number of total chances; INN - Innings: number of innings that a player is at one certain position; PB - Passed ball: charged to the catcher when the ball is dropped and one or more runners advance; PO - Putout: number of times the fielder tags, forces, or appeals a runner and he is called out as a result; RF - Range factor: 9*(putouts + assists)/innings played. Used to determine the amount of field that the player can cover; TC - Total chances: assists plus putouts plus errors; TP - Triple play: one for each triple play during which the fielder recorded a putout or an assist; and UZR- Ultimate zone rating: the abilit ' of a player to defend an assigned "zone" of the field compared to an average defensive player at his position.

[0048] A partial example of statistics related sports variables 400 for Overall player value include VORP Value over replacement player: a statistic that calculates a player's overall value in comparison to a "replacement-level" player. There are separate formulas for players and pitchers; Win shares: a complex metric that gauges a player's overall contribution to his team's wins; WAR - Wins above replacement: a non-standard formula to calculate the number of wms a player contributes to his team over a "replacement-level player"; PWA - Player Win Average: performance of players is shown by how much they increase or decrease their team's chances of winning a specific game; and PGP - Player Game Percentage: defined as, "the sum of changes in the probability of winning the game for each play in which the player has participated".

[0049] A partial example of general statistics related sports variables 400 include G - Games pla ed: umber of games where the pla er played, in whole or in part; GS - Games started: number of games a player starts: GB - Games behind: number of games a team is behind the division leader; and Pythagorean expectation: estimates a team's expected winning percentage based on runs scored and runs allowed.

[0050] The sports variables and sport requests 400 are then transmitted to the server 4 via the network 10 as described in FIG. 1. The description vector unit 402 of the server 4 defines a description vector based on the sports variables and the sports request 400. The sports description vector can represent a multidimensional point in multidimensional space and contains the sports variables 400. The sports description vector defines what type of sports information, such as game risk or game reward information, the user is seeking to obtain for his or her sports portfolio needs. More information with respect to the sports description vector is provided later with respect to FIGS. 5 and 6.

[0051] The normalizing unit 404 of the server 4 normalizes all of the sport variables 400 of the description vector defined by the description vector unit 402. Normalization is required so that all of the sports variables 400 have an equal weight with respect to each other when sport calculations are performed. One type of normalization is 1/N, where N is the number of sports variables 400. Another type of normalization is to use the standard deviation of all the information in database 6. For this type of normalization, the sports information analyzing system calculates the standard deviation for each sport variable 400 contained in the database 6 and then divides the sports variable 400 of the particular sport being studied by the standard deviation. For example, if the user selects baseball as a particular sport and chooses the AB/HR as one of the sports variables 400, the sports analysis system locates all of the AB HR ratios in the database 6, calculates the standard deviation, and then divides the AB/HR ratio of the baseball sport by the standard deviation to obtain a normalized value.

[0052] The present embodiment, however, does not require the input sport variables to be equally weighted. As long as the sum of all the normalization weights is 1 , different sports variables 400 could have different weights. Any additional description vector being compared to the description vector defined by the description vector unit 204 would also have to be normalized to keep calculations consistent. More information with respect to the normalizing unit 404 is provided later with respect to FIG. 5.

[0053] The description vector similarity unit 406 calculates the similarities between description vectors created by the description vector unit 402 and normalized by the normalizing unit 404. As described later, at least one way to determine the similarity between description vectors is to calculate the Euclidean distance between the description vectors. However, another metric for determining the similarity is to use an absolute value metric or any other metric defined by the user of the sports analysis system. The results of the description vectors similarity unit are then transmitted to the sports analysis unit 408.

[0054] The sports unit 408 analyzes the results of the description vector similarity unit 406 to provide the user with sports advice based on the sports variables and the sports request 400 defined by the user. To do this, the sports analysis unit 408 compares similarity information from the description vector similarity unit 406 based on what type of sports information the user is interested in obtaining for his or her portfolio. Non-limiting examples of sports information include a list of best or worst sporting events, a comparison of sporting games, and game risk and game reward information. More information on the sports information determined by the sport analysis unit 408 is described later with respect to FIG. 5 discussed below.

[0055] The sports advice 410 of the sports analysis unit 408 are provided to the database 6 to be stored for possible later analysis and are provided to the user via at least a computer 2 or mobile device 8 as described in FIG. 1.

[0056] Fig. 5 schematically depicts a microprocessor-executable algorithmic flow chart configured to analyze sports information utilizing the exemplary embodiments described in Figs. 1-4. The process of analyzing sports information starts at step S500 by having a user input a customized ranking of factors, or sports variables 400, as for example in the sport of baseball, certain sports variables 400 such as IB, 3B, AB/HR, SB, R, RBI, BB, BB/9, E, ERA, G, GS, K, A, CI, DP, UZR, VORP, WHIP, and a sports request 400. The system then determines whether all the variables have been received at step S502. If "No" at step S502, the system continues to accept sport variables 400 until the user has finished. If "Yes" at step S502, the system proceeds to step S504 to generate a description vector based on the sports variables and the sports request 400.

[0057] The description sports vector SDV generated by the description vector unit 402 is defined as follows: SDV= (si, s2, s3, . . . , sn), where si through sn are sports variables 400 that describe a sports variable or sports benchmark in question. Therefore, if the user wanted to measure the risk of a single game between two teams and determines that the most important factors are K, A, CI, DP, UZR exhibited by each team's members then the historical resampled risk of player performances for the respective K, A, CI, DP , UZR sports variable 400 are determined. Then the sports description vector for this particular game, i.e. for this particular calculation between the two opposing teams, would be an "Sports Risk Vector" defined as SRV= (K, A, CI, DP , UZR, historical resampled risk of player performances of these selected sports variable 400 are determined). A corresponding "Sports Benchmark Risk Vector" would be then defined as SBRV= (K, A, CI, DP , UZR historical resampled risk of player performances of these selected sports variable 400 are determined). While both vectors contain the same sports variables 400, the values of the sports variables 400 for the respective K, A, CI, DP , UZR variables will likely be different based on the corresponding value of each sports variable 400 in the database 6 with respect to the baseball sport or benchmark of the two opposing teams. Once the sports description vector is defined, the process proceeds to step S506 to normalize each of the sports variables 400.

[0058] The sports variables 400 must be normalized to ensure they each provide the appropriate amount of weight to the sport analysis calculations. For example, the absolute value of K, A, CI, DP, UZR is determined for each team. If the two variables were not normalized, then any future calculation would be almost entirely dominated by the respective K, A, CI, DP, and UZR statistics values.

[0059] One option is for the user to specify how to normalize each variable based on the user's needs. However, a natural, default, normalization for each piece of data is supplied for the ease of the user. As noted above, one such natural normalization is to use the standard deviation of all such pieces of information in the database 6. So, for example, if the user requested analysis on a particular game, and the user's choices were

K, A, CI, DP, UZR player statistics, their standard deviation would be calculated, and then that particular game's K, A, CI, DP, UZR would be divided by this standard deviation. Then all the K, A, CI, DP, UZR values would be located in the database 6, their standard deviation would be calculated, and this games K, A, CI, DP, UZR values would be divided by that standard deviation. This process is repeated until all the sports variables 400 of the description vector are normalized.

[0060] Returning to the description vector, non-limiting examples of the description vector include a Sports Risk Vector, a Sports Benchmark Risk Vector, a Sports Reward Vector and a Sports Benchmark Reward Vector. The description vector can also be any other vector chosen by the user. As such, the names "Sports Benchmark Risk Vector", "Sports Benchmark Reward Vector", "Sports Risk Vector" and "Sports Reward Vector" are for convenience and the user is free to specify any number of vectors with customized naming conventions so as to make whatever comparison the user sees fit between whatever games, game collections or player groups the user wants. The Sports Risk Vector SRisk is defined as follows: SRisk=(SRiskl, SRisk2, SRisk3, . . . , SRiskn), where SRiskl through SRiskn are the normalized sports variables 400 that describe the sport in question. For example, SRiskl= K, A, CI, DP, UZR values, SRisk2= historical period #1 for K, A, CI, DP, UZR values resampled risk, SRisk3= historical period #2 for K, A, CI, DP, UZR values resampled risk, etc, to SRiskn = historical period #N for K, A, CI, DP, UZR values resampled risk.

[0061] The Sports Benchmark Risk Vector Brisk is defined as follows: SBRisk=(SBRisk 1, SBRisk2, SBRisk3, . . . , SBRiskn) where SBRiskl through SBRiskn are the same sports variables 400 as SIRiskl through SRiskn, but for the Sports Benchmark rather than the values exhibited for the selected Sports variables 400 if the Sports Benchmark Risk Vector SBRisk is used in comparison to the Sports Risk Vector. A Sports Benchmark is a standard against which the performance of a team member or team members of a particular sports team that can be measured. Generally, widely available sporting statistics can be use for comparative purposes. For example, the Sports Benchmark could be a recognized benchmark like for the selected K, A, CI, DP, UZR stats, or could be other stats described above for the batting, base running, pitching, fielding, overall player values, and general statistics discussed above.

[0062] The Sports Sport Reward Vector SIReward is defined as follows.

SIReward=(SIRewardl, SIReward2, SIReward3, SIRewardn), where the sports variables 400 are variables that the user thinks affect the outcome of a particular game competiton. The sports variables 400 could be for an individual team player, a group of team players, a player league of multiple teams, or a combination thereof. It should be noted that the Sports Sport Reward Vector should not be the same vector as Sports Risk Vector SIRisk. The Sports Sport Risk Vector SIRisk represents what the user thinks of as the sports information that goes wrong, or might go wrong, or that which indicates an elevated level of risk. SIReward represents that sports information which the user thinks might go right. Thereby providing a favorable sporting outcome for the user.

[0063] The Sports Benchmark Reward Vector is defined as follows. SBReward=(SBRewardl, SBReward2, SBReward3, . . . , SBRewardn) where the sporting variables 400 correspond to the sporting variables 400 defined in the Sports Sport Reward Vector SIReward if the Sports Benchmark and the Sports Sport Reward Vector SIReward are being compared. It should be noted that SBReward represents the "ideal" sports competition outcome according to the user. For example, if the user selected K, A, CI, DP, UZR stats, or could have selected other stats described above for the batting, base running, pitching, fielding, overall player values, and general statistics discussed above. Thereafter, the sports variables 400 and normalized. A comparison of proposed sporting scenarios to this "ideal" sport will be described later.

[0064] Returning to FIG. 5, after the sports variables 400 have been normalized at step S506, the similarities between the description vectors are calculated at step S508. There are different ways of defining a similarity between two sports description vectors. One option is to take the Euclidean distance between two multidimensional vectors generated by the description vector unit 402. This would be represented by, for example, || SIRisk— SBRiskll or HSIReward-SBRewardll, which represents taking each normalized sports variable 400 from each sports description vector generated by the sports description vector unit 402, subtract one from the other, square the result, sum them together, and take the square root. For example, to calculate the risk of a sport, the following calculation is performed: (SIRiskl-SBRiskl) ^A 2+(SIRisk2-SBRisk2) ^A 2+(SIRisk3-SBRisk3) ^A 2+... +(SIRiskN-S BRiskN) ^A 2. The square root of the resulting sum is then calculated to obtain the similarity value.

[0065] Another method of calculating the similarity is by using an absolute value similarity metric. Using the absolute value metric, each normalized sport variable from each description vector generated by the description vector unit 402 is subtracted from one another and the resultant values are added to obtain a distance between the sports description vectors. The absolute value of the distance is then determined to obtain a sports similarity value. For example, to calculate the risk of a sporting competition, the following calculation is performed:

|(SIRiskl-SBRiskl)|+|(SIRisk2-SBRisk2)|+|(SIRisk3-SBRisk3 )|+

+|(SIRiskN-SBRiskN)|.

[0066] It should be noted that when more variables are added, the absolute value of the result will increase. While the results for each calculation will be unaffected by this, it might cause some confusion to a user. Therefore, one non-limiting example to keep intuition correct from one similarity calculation to the next for the user, is to divide each sport "similarity" result by VN, where N is the number of sport variables in the sports vector.

[0067] Sports advice is then determined by the sports analysis unit 408 based on the results of step S508 and the types of sports description vectors compared. Provided below are non-limiting examples of sports advice determined by the sports analysis unit 408 based on a variety of sports requests 400 received that relate to sports information. For example, upon receiving a sports request to identify the risk of achieving a sporting outcome, the sports analysis system calculates the similarity between the sports risk vector and the sport benchmark risk vector. For this analysis, the greater the number calculated from the similarity between the sport risk vector and the sport benchmark risk vector, the higher the risk of the achieving a desired sporting outcome.

[0068] A sport comparison can also be calculated to provide the user with information on how the current sporting event or game compares to an ideal sporting event or game. An ideal sports game or sporting event is a predefined sport selected by the user based on the user's sporting requirements. This is determined by calculating the similarity between the sports reward vector and the sports benchmark reward vector. For this analysis, the smaller the number calculated from the similarity between the sports reward vector and the sport benchmark reward vector, the closer the current game or sports event is to the ideal game or sports event and therefore the better the current game or sports event. [0069] For a given group of sports event, a list of the best and worst sport competitions or games out of the group can be calculated. This list is determined by calculating the risk and reward for each sports game or competition in the database 6. Next, for each game, the ratio "(l/reward)/(risk)" is calculated and the sports game or competition can be ranked by this number. Accordingly, the sports events with the highest ranks are the best and the sports events with the lowest ranks are the worst. The best or worst sports events can be any type of sport-based meeting predetermined by constraints set by the user. For example, a best sport game could be one which makes the shortest term gain in the score whereas the worst sports competition could be one with the quickest loss in the short term.

[0070] Alternative sports that have a similar risk profile to the current sports competition can also be calculated. This sports analysis is performed by calculating the risk for all the a selected sports competition in the database 6 and finding those sporting events with a risk value that is closest to the risk value of the current sports competition selected for examination. Similarly, altemative games or sports competitions that have a similar reward profile to the current sports competition can also be calculated. This sports analysis is performed by calculating the reward for all the sporting competitions in the database 6 and finding those sports competitions with a reward value that is closest to the reward value of the current sports competition selected for examination.

[0071] For a user interested in one sports competition, better competitions with similar risk profiles can be calculated. First, and as explained above, all other games that have a similar risk profile to the one competition under examination are located from the database 6 and their reward values are calculated. These competitions are then ranked by the equation "(1/reward value)/(risk value)" to determine the better sports competition or team.

[0072] A user can also build a portfolio of game competitions having an optimal risk/reward. In this case, an optimization routine would find the sports portfolio of games that maximized the "(l/reward)/risk" ratio subject to user defined constraints.

The user defined constraints can represent certain filters within the portfolio with respect to the sports information. For example, the user could define constraints such that no single baseball competition can have more than IB, 3B, AB/HR, SB, R, RBI, BB, BB/9,

E, ERA, G, GS, K, A, CI, DP, UZR, VORP, WHIP sports variables 400 in the sports game portfolio. To define best portfolio, similarity to a benchmark is used for optimization, say when the benchmark includes professional players.

[0073] In other words, for a sports portfolio, there would be a weight (wi) for each sport. Each variable that went into either the sport risk vector or the sport reward vector would be weighted by that weight (wi) as follows:SIReward=wl*SIRewardVl+w2*SIRewardV2+w3*SIRewardV3 and so on, with the sum of the w's=l, where each of the SIRewardV's is a vector. SBReward is set to the user's idea of the ideal sports competition. SIRisk=wl*SIRiskVl+w2*SIRiskV2+w3*SIRiskV3 and so on, while having the same w's as the SIReward. An optimization routine to maximize (l/||SIReward-SBReward||)/(||SIRisk-SBRisk||) by varying the w's, subject to constraints, would then be used to rank the various sports based on the user constraints.

[0074] In another example according to the present embodiment, when trying to choose amongst 10 sport competitions that have been identified via a standard screen or some other associated method, it is important to know which of sport variables 400 among a group of sports variables 400 in a given sport competition gives the most return with respect to the risk. If, for example, ten sports variables 400 are examined for their respective effects on prediction and advice, the ten distances, or similarity values are calculated as SdRewardl through SdRewardlO to obtain the reward similarity values between SIRewardl and BRewardl, IReward2 and BReward2 and so forth through IRewardlO and BRewardlO.

[0075] These ten distances, or similarity values, are then calculated as sdRiskl through sdRisklO by obtaining the risk similarity values between SIRiskl and SBRiskl, SIRisk2 and SBRisk2 and so forth through SIRisklO and SBRisklO.

[0076] Next, the ten Reward/Risk statistics are calculated as "(1/Reward Similarity Value[i])/Risk Similarity Value[i]", with i going from 1 to 10. Given the above-noted considerations and based on the results, the highest ranked sport competition would be the best and the lowest ranked sport competition would be the worst. For example, the highest ranked sport competition has the lowest calculated value and the worst sport competition has the highest calculated value based on the equation: "(1/Reward[i])/Risk[i]." [0077] In another example according to the present embodiment, sometimes the user is not worried about risk but is instead interested in sport competitions that best match the high level skills tournament competitions. In other words, the user may be interested in a particular sport competition that best matches the skill levels exhibited by team members' competition in championship games.

[0078] A problem with this scenario is that a user will typically adjust the values of a selected group of sports variables 400 until a certain number of sport competitions that come up yet do not match the selected sports variables 400. On occasion that presents challenges because often the user is looking for a "common- sense" or "close-enough" match, but not providing a substantial difference between game groups.

[0079] Next, all the sport competition in the database 6 are processed and for each one the SIReward is calculated in which the selected sports variables 400 criteria are specified as "greater than a given value" or "less than a given value" and may be replaced by any numbers that exceed (are less than) the threshold for the selected criteria. For example, if there are N sport competitions, N distances N[i] are calculated between SBReward[i] and SIReward[i]. When the N distances N[i] are ranked in ascending order, the lowest N's have the best match to the selected value criteria of variables 400. Therefore, the present embodiment provides a more useful and reliable ability to generate meaningful sports game advice.

[0080] Referring back to FIG. 5, once the sport advice is returned, it is determined whether there are any additional sports requests at step S512. If YES at step S512, then the process returns to step S500 to receive different sport variables and sports requests 400. If NO at step S512, the analysis process is completed and the process ends.

[0081] According to one embodiment of the present embodiment, any sport information determined by the analysis sport system is organized and stored virtually, for example, sports analysis results can be stored on a recording medium of a PC or in the database 6 connected to the network 10. A user can then use his computer 2 or connect to the network 10 to obtain previously calculated sport information based on previously customized sport description vectors.

[0082] Websites hosted by servers 4 can also provide users with online access to the features afforded by the analysis sport system. These websites provide general access features to all users as well as increased access and functionality when a user registers with the website. A user who is registered could access prior sport information based on previously customized description vectors via a search of the virtual folders. The prior sport information could then be compared against current values obtained by running the previously customized description vectors. A user can also use customized description vectors to determine sport information based on prior sport outcomes at a specific date or automatically update the old sport information based on current sporting event in progress.

[0083] Numerous modifications and variations of the present embodiments are possible in light of the above teachings. In particular, while the application of the present embodiment has been described with respect to events such as conventions, sports and concerts, other applications are within the scope of the appended claims. For example, without limitation, the present embodiment may be applied to video games, TV, cell phones, tablets, web applications, and any other platform as would be understood by one of ordinary skill in the art. It is therefore to be understood that within the scope of the appended claims, the present embodiments may be practiced otherwise than as specifically described herein.

[0084] Though the above discussion has been made with reference to the sport competition market, other sports markets or sport structures may also be used without departing from the spirit of this embodiment.

[0085] Any processes, descriptions or blocks in flowcharts described herein should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the exemplary embodiment of the present embodiments in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order depending upon the functionality involved.

[0086] The aforementioned baseball-based sporting events in which the sporting variables 400 are processed to generating sports advice is also applicable in other sports that similarly exhibit situations having non-linear patterns. For example, say when a coach considers that "the other team's pass defense is really bad, we are at home, and they're a cold weather team coming to a hot part of the country, so maybe it would be good for us to game plan for a lot of passes." In the past, this coach has had experiences that have informed his decision making and he recognizes the similarities between the current situation and his past experience, which he is using to reason via induction.

[0087] One of the major issues in statistics is the issue of nonlinearity. What this means is that quite often, there will be a relationship between one or more variables that traditional analysis, which essentially assumes linearity, doesn't pick up.

[0088] A similar problem occurs in sports that are confined to linear thinking patterns in which non-linear patterns may overlap. For example, an NFL coach could linearly think: "you know, the team with the higher yards per pass attempt wins 75% of the time, but we aren't that good at passing, so perhaps we should run the ball". However, as an example of overlapping non-linear thinking patterns, the Coach could also consider that "the other team's pass defense is really bad, we are at home, and they're a cold weather team coming to a hot part of the country, so maybe it would be good for us to game plan for a lot of passes." In the past, this coach has had experiences that have informed his decision making and he recognizes the similarities between the current situation and his past experience, which he is using to reason via induction.

[0089] The difference between the present invention, and what is currently done, is that the state of the art in sports analysis is to find "same" situations or patterns and presume that they will recur. Examples of the state of the art include asking questions such as, how many yards will an NFL team gain offensively, if it has a top ranked QB, and he's facing a bottom ranked defense. How often does a team win the game when its yards/pass attempt are greater than 7? The problem with this is that the current technique cannot scale to additional variables, because as a user adds more variables, there are less and less past examples that fit into the selected linear or nonlinear pattern.

[0090] Embodiments of the present invention, various embodiments of which are described above, as further discussed below, solve the problems of non-linear patterns operating independently or concurrently with situations presenting linear patterns, as often happens in any sport and complements the inductive reasoning of users having a past experience in sporting matters. Further, embodiments of the present invention may provide numerical support for each decision for any number of input sporting variables 400 for any sport.

[0091] Sports statistics by and large help with linear problems. For example, let's think about Football some more. The side with more yards/pass attempt wins 75% of the time; a side with a tumover differential of +1 wins 68% of the time, a side with a tumover differential of +2 wins 82% of the time, a side with a tumover differential of +3 or more wins 93% of the time; a side with a net field position advantage of 2 yards wins one extra game per season, and so on.

[0092] Coaches, however, tend to consider tens of variables. What's the weather? What kind of field is it? What's the time difference? How statistically strong/weak is their defense/offense? What is the other team's points per scoring opportunity? How many penalties do we have in a game versus them? How many personal fouls? A coach can use their past experience to adjust their base expectations to the current game and matchup.

[0093] The same process is generally undertaken by a user playing fantasy sports. For example, a user may wonder which running back to play today? Factors could include: highest yards/carry, the most outside runs, best on grass fields, the lowest number of fumbles/carry, etc. In order to make this decision, a fantasy player takes all these variables, as well as his or her past experience, and tries to pick that lineup that he thinks maximizes his points for the upcoming matchup.

[0094] The goal of the present invention is to find all the situations, or variables, in the past data that are "similar" to the current situation. Thereafter, the situations can be ranked by their similarity. Using this method, one can then hypothesize that the future that followed those past situations is likely to follow the current situation.

[0095] For example, in one embodiment, the user picks those statistics he or she thinks are relevant to his or her question. Alternatively, in another embodiment, they can be pre-picked— different statistics for different questions— or, most likely, both. For example, say yards/pass attempt, temperature at the start of the game, the type of field, the rank of the defense in points/game allowed, the number of defensive penalties, the starting personnel at each position and the number of offensive personal fouls. This is 7 variables. In one embodiment, for example, using a method or system similar to the one described in U.S. Pat. No. 8,606,672, which is incorporated by reference in its entirety, the computer creates a vector with these 7 variables. According to this embodiment, the computer may then go into all past games and finds those games that most closely match the vector: that is, their total distance from 7 variables is the smallest(i.e., they're the best matches). Using the method according to the present invention, it is then possible to hypothesize that the results of those past matches are likely outcomes for the current game. Some possible benefits to this approach include: i) is analogous to what skilled players and coaches do: they use their experience; ii) fast to implement on a computer; iii) automatically handles as many variables as a user desires to throw at it; and iv) gives a sensible answer even when (as is likely if many variables are being considered) there are no past situations that are the same.

[0096] Importantly, in various embodiments of the present invention, the two comparisons do not need to be current versus past situations: they could be past versus past or present versus present, for example.

[0097] Utilizing the present invention, the method can answer any question that the user poses, a non-limiting and purely exemplary list could include: who might win, which side might run more, how will the temperature affect the passing game, which defense will score more points, etc. Utilizing the teachings of the present invention would be of immense use both to coaches, general managers, and fantasy fans.

[0098] Additional embodiments of the present invention allow for additional uses. For example, this procedure may also be used as an in game decision support system for a coach. A coach, or other interested user, may wonder: Should I call a run or pass? How well does play action work versus a cover 2 on a grass field when the opposition has flown in from California but they're playing one second string safety? What pattern of plays should I call so that a future flea-flicker has the best chance of success? Should I kick a field goal now that it's 4 ^th and 2 on the 6 with 5 minutes remaining in the fourth quarter, I'm down by 4, the other side is on its second string quarterback, but this is the third away game for me in a row? The present method would allow the user to make real time determinations that have the best chance of affecting the desired outcome.

[0099] For example, going back to baseball, suppose that it is the bottom of the 5 ^th inning, there are runners on first and second, there are two outs and you are up by one. Your starting pitcher has pitched 5 2/3rds of an inning. He is right handed, there is a left handed batter coming up, and you have to decide i) whether to bring in a relief pitcher; and ii) decide if to bring in a not so good but young left handed relief pitcher but who's had 4 days of rest or an older, more experienced, and excellent right handed relief pitcher, but who's only had 3 days of rest. It's a relatively smaller field, the temperature is 70F, and the umpires have not been friendly with their ball and strike calls all night. What should you do? According to an embodiment of the presently described invention, the system can go back through a database of all past games (or a subset if you so choose) and find those games that most closely match the current situation, and tell the decision maker what happened in the past in similar situations.

[00100] Similarly, various embodiments of the present invention may allow a user to decide on the best lineup and batting order before the game even begins. Quandaries presented by a user and answerable by the present invention may include: Is it really best to put your home run hitters at positions 3 and 4 in the batting order? Another question could be whether the conditions that exist at the start of the game suggest one option over another. Conditions could include any number of variables that the user thinks are important: from size of field and temperature to the ERA of the opposing pitcher and the on base percentage of the opposing team's leadoff hitter. Understanding these variables, using the present invention, could affect your starting lineup and batting order, for example.

[00101] The range of questions that can be answered is limited only by the imagination. In much the same manner, the invention is not limited to any particular sport. Uses can be found throughout the sporting universe, wherever decisions need to be made, or questions answered.

[00102] For example, another possible question posed by a user could be: In a given game situation, should I use a pinch hitter? And if so, which one should I use and why? In the past, how have game win percentages improved (or got worse) when a pinch hitter has been used in some given situation? All of these questions can be answered using the present invention, which then provides the user with the best chance of making an informed and statistically correct decision.

[00103] Additional embodiments could be used in basketball, for example. If the other team has really tall players playing, is it really true that I should play my best outside shooters? Does being home or away affect this? Does the size of the crowd (generally assumed to operate via the extra noise subconsciously affecting refereeing decisions) change this? Does it change if we are talking about the end of the game versus the middle? Again, all of these questions can be answered using the present invention, which then provides the user with the best chance of making an informed and statistically correct decision.

[00104] Another possible embodiment of the present invention would allow you to answer other questions such as: suppose you are the coach of a football program that has made controversial decisions in the past and the athletic director and alumni are unhappy with you. The system according to an embodiment of the present invention can make it possible for you to show that your decisions were in accord with past experience. For example, that in similar situations in the past, the decisions you made were the right ones. In much the same way, the present invention could also assist users who are the athletic director trying to decide if to fire a losing coach: did he not have enough talent, or did he make bad decisions during games? The system according to the present invention allows the user to determine the answers to these questions.

[00105] Additional embodiments may be useful in cricket, the second most popular sport in the world. For example, a user may wonder if it is true that you should put your best batsman in at #3? Is that true whether you bat first or second? Is that still true if you are chasing a total or setting at total? What about if it's a short form match versus a long form? Is your highest average batsman really your best or does he mostly make his runs in low pressure situations, and if so, should you send him higher up the order or lower? Is it a good tactic for a right handed bowler to bowl "around the wicket" to a left handed batsman who is well set, and on a score of 33? Do batsmen bat differently when getting close to a score of 50 or 100 because those are big psychological milestones? If so, how can you bowl or set your field to take advantage of this difference? When should the wicket-keeper stand up to a fast bowler and what's the success rate of this tactic? Is it really true that in a limited overs match you should have your faster bowlers bowl the final overs? If so, which types are most successful: swing, seam, or fast? Does the size of the ground change whether or not you should think about using your slower bowlers at the end? What about temperature and condition of the pitch? The system according to the present invention allows the user to determine the answers to these questions. [00106] The present invention allows a user to get as detailed or as high level as they like and in whatever sport they like, and the system can automatically adjust and give sensible answers that accord with human intuition and expertise.

[00107] Preferred embodiments of the present invention produce a precise, quantifiable result such that a user can rely on them to reinforce their decisions and/or to make a decision in the first place. Further, embodiments of the present invention can quantify and support making one decision versus another with a precise measurement.

[00108] In yet another example from football, a user may wonder: how often does a team win the game when its yards/pass attempt is greater than 7, it's yards/rush is less than 3.5, its defense is in the bottom half of the league rankings, the temperature is below 45F, the game is at home, and the other team is using its second string safety? Under this hypothetical example, it is unlikely that there are any past situations to draw from. But, using the present invention a user can create a similarity metric that compared this situation to every past situation and found those that are similar. So, for example, there may be past situations where all the criteria are met but the temperature was 5 IF. Or, the criteria were met but the game was away, not home. Or it was a first string safety, the temperature was 70F but otherwise everything was the same.

[00109] Using the similarity metric, embodiments of the present invention can rank all past situations by analyzing their overall similarity to the situation being examined. A shorter distance defines a higher similarity. Conversely, a longer distance defines a lower similarity. The system could then hypothesize that the future sporting outcome that followed those past similarities may be the future that follows the situation the user is comparing against. This can then be used as desired. For example, a weighted sum of all past game outcomes could be used to make a forward prediction. Or a distribution of possible game outcomes could be output along with their probabilities of occurrence. A "fair value" for a bet could be computed and under and overvalued bets for a gaming outcome could be found. The possible game or sports outcomes would be of interest to the announcers and commentators because they would greatly inform their opinions and commentary. The possible sports game outcomes would also be of great interest to coaches that were game planning and/or calling the game in real time.

[00110] One embodiment of the present invention may show an "expected outcome" in which you take the best matching pasts, and average together their result. Another embodiment may actually show all the best matching pasts, in the form of a distribution.

[00111] For example, if there is a matchup between two teams, that are 3000 miles apart, where one is 4-0 and the other is 3-1 , both teams are playing their starting QB, the temperature is 50F, the 4-0 team is ranked 3 ^rd on Offence and 15 ^th on Defense, and the 3-1 team is ranked 7 ^th on Offence and 23 ^rd on Defense. According to some embodiments of the present invention, the system can go back into the database of all past NFL games and find a number, call it N, of those that best match the above scenario. Then, you take an average (weighted or otherwise) of the various outcomes of all those past games to predict future performance: how many yards might the 4-0 team's QB throw for, how many interceptions will the 3-1 team's Defense get, who will win the game, etc. These would be point predictions: i.e., a type of (possibly weighted) average. The most natural such weighting is the inverse of the distance: the closer the match, the smaller the distance and the higher the weight it should have in the average. Using the other method, for example the system would show all N outcomes that occurred in the past for the best matching N games. This would show the entire distribution of outcomes for the 4-0 team's QB, the 3-1 team's interceptions, and which team won the game. The system may (or may not) show the probability of those outcomes and may or may not show the distance measure for each of those outcomes. Of course the two methods here are simply two examples of many possible methods that can be used across a wide array of systems and should not be considered limiting.

[00112] An additional feature of the present system or method, according to certain embodiments, may be that it can find players to fit any "ideal" that a coach or other user has in mind. Suppose that you want to find a quarterback to fit your offensive scheme. A quarterback that is similar to John Doe would be perfect. How do you draft a quarterback that is most similar to John Doe? One method of doing that is to collect the college statistics of all college quarterbacks: where they played, in what kind of system

(pro style or college style), the height, weight and grades (ie personal characteristics), the statistics of their supporting players (better receivers for instance make QBs look better), their passing statistics, games played, the record of the opponents they played, and so on.

Compare these collected statistics to those of John Doe and find those that are most similar. Rank by similarity. By construction, the highest ranked players are those that are most similar. A "scout" does this qualitatively already by using his years of experience in trying to find QBs that "look good" to him: in effect, he has an archetype in mind. This method would allow quantification of that intuition, as well as allowing a much broader and more systematic search potentially unearthing many currently undiscovered players.

[00113] Coaches could use this embodiment of the present invention to find players that fit their schemes, fantasy sports players could use this to set their lineups, high school and college coaches could use it for recruiting. In each case, the present method allows for a quantification of the similarity of a player to any arbitrary set of desired criteria.

[00114] While the preferred embodiment of the invention has been illustrated and described, as noted above, many changes can be made without departing from the spirit and scope of the invention. Accordingly, the scope of the invention is not limited by the disclosure of any particular embodiments.