Patent Publication Number: US-2015065214-A1

Title: Systems and Methods for Providing Statistical and Crowd Sourced Predictions

Description:
BACKGROUND 
     1. Field 
     Embodiments disclosed herein generally relate to providing statistical and crowd sourced predictions, and particularly to providing accurate predictions of sporting and other events. 
     2. Technical Background 
     As sports and other events have increased in popularity, various fan-based activities have developed to add to the game experience. As an example, many sports now have a “fantasy league” associated therewith. Fantasy leagues are generally created to provide fantasy league players the ability to draft athletes from a predetermined sports league onto their fantasy team. Based on those athletes&#39; actual performance during the season, the fantasy players&#39; team may perform better or worse. Similarly, many wagering opportunities are now being provided with these events. Wagering players may place a wager on a team, for a player, or on other outcomes of the event. As a consequence of those developments, there is now an increased desire for accurate predicting of the outcome of the events to perform better at these fan-based activities. 
     SUMMARY 
     Included are embodiments for providing statistical and crowd sourced predictions that includes a memory component that stores logic that causes the system to determine default player ratings for a plurality of players based on statistical data, receive user player rankings from a plurality of users, and convert the user player rankings into user ratings. In some embodiments, the logic causes the system to determine team data for a plurality of teams, where each of the plurality of teams includes a player that has been rated and simulate a game between at least two of the plurality of teams, and where the simulation is made based on the default player ratings, the user ratings, and the team data. In some embodiments, the logic causes the system to determine an outcome of the game from the simulation and provide the outcome to the plurality of users for display 
     In another embodiment, a method for providing statistical and crowd sourced predictions may include determining default player ratings based on statistical data, receiving player rankings from a plurality of users, and converting the player rankings into user ratings. In some embodiments the method includes determining a rating for a first subset of a first team and a second subset of a second team, determining a first play strategy for the first subset and a second play strategy for the second subset. In some embodiments, the method includes simulating a game between the first subset and the second subset based on the first play strategy, the second play strategy, the default player ratings, and the user ratings, determining an outcome of the game from the simulation, and providing the outcome to the plurality of users for display. 
     In yet another embodiment, a non-transitory computer-readable medium for providing statistical and crowd sourced predictions may include logic that causes a computing device to determine a first rating for a first team and a second rating for a second team, simulate a game between the first team and the second team, and determine an outcome from the simulation. In some embodiments, the logic causes the computing device to determine a predicted wagering outcome of the game between the first team and the second team, compare the predicted wagering outcome with the simulation to determine a wagering strategy for the game, determine a confidence level of the wagering strategy, and provide the wagering strategy and the confidence level to a user for display. 
     These and additional features provided by the embodiments described herein will be more fully understood in view of the following detailed description, in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which: 
         FIG. 1  depicts a computing environment for providing statistical and crowd sourced predictions, according to embodiments disclosed herein; 
         FIG. 2  depicts a remote computing device for providing statistical and crowd sourced predictions, according to one or more embodiments shown and described herein; 
         FIG. 3  depicts a user interface for providing default player ratings, according to one or more embodiments shown and described herein; 
         FIG. 4  depicts a user interface for providing user ranking options for players, according to one or more embodiments shown and described herein; 
         FIG. 5  depicts a user interface for providing a prediction of a specific player, according to one or more embodiments shown and described herein; 
         FIG. 6  depicts a user interface for providing a player performance variance, according to one or more embodiments shown and described herein; 
         FIG. 7  depicts a user interface for providing actual performance information of a player, according to one or more embodiments shown and described herein; 
         FIG. 8  depicts a user interface for providing a user scorecard for player and team prediction, according to one or more embodiments shown and described herein; 
         FIG. 9  depicts a user interface for simulating a game based on user rankings, according to one or more embodiments shown and described herein; 
         FIG. 10  depicts a user interface for simulating a game based on crowd sourcing, according to one or more embodiments shown and described herein; 
         FIG. 11  depicts a user interface for simulating a game based on statistical analysis, according to one or more embodiments shown and described herein; 
         FIG. 12  depicts a user interface for simulating a game based on user predicted strategies, according to one or more embodiments shown and described herein; 
         FIG. 13  depicts a user interface for providing wagering predictions for a game, according to one or more embodiments shown and described herein; 
         FIG. 14  depicts a user interface for providing wagering results for a past game, according to one or more embodiments shown and described herein; 
         FIG. 15  depicts a flowchart for simulating a game based on statistical data and crowd sourcing data, according to one or more embodiments shown and described herein; 
         FIG. 16  depicts a flowchart for simulating a portion of a game, based on performance in that game, according to one or more embodiments shown and described herein; and 
         FIG. 17  depicts a flowchart for determining a wagering strategy for a game, according to one or more embodiments shown and described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments disclosed herein relate to an online event prediction system that utilizes historical statistical data and/or crowd sourcing data to make predictions. As an example, professional sports, such as professional football may have “fantasy football leagues” that fans may join to add to the enjoyment of the games. A fantasy football league may allow fantasy players to draft and trade actual professional football players as part of the fantasy league rules. Based on the professional football players&#39; performances, the fantasy players may score points and/or achieve rankings. Accordingly, the ability to accurately predict which professional football players will perform well during a game or season is of value to the fantasy players. 
     Similarly, when wagering on outcomes of events such as football games, a bettor desires to know, not only how a team or player will perform, but the outcome of a game in relation to “the spread,” which represented a book maker&#39;s prediction of the outcome of a game. Accordingly, embodiments disclosed herein utilize statistical data, as well as crowd sourcing data to predict an outcome to a game relative to the spread, as well as a confidence level for that prediction. 
     Referring now to the drawings,  FIG. 1  depicts a computing environment for providing statistical and crowd sourced predictions, according to embodiments disclosed herein. As illustrated, a network  100  may be coupled to a user computing device  102 , a remote computing device  104 , and an administrator computing device  106 . The network  100  may include any wide area and/or local area network, such as the internet, a mobile communications network, a satellite network, a public service telephone network (PSTN) and/or other network for facilitating communication between devices. If the network  100  includes a local area network, the local area network may be configured as a communication path via Wi-Fi, Bluetooth, RFID, and/or other wireless protocol. 
     Accordingly, the user computing device  102  may include a personal computer, laptop computer, tablet, mobile communications device, database, and/or other computing device that is accessible by a user. The user computing device  102  may additionally include a memory component  140 , which stores statistics logic  144   a  and crowd sourcing logic  144   b,  described in more detail below. 
     The remote computing device  104  is also coupled to the network  100  and may be configured as an online platform for accessing and/or contributing to predictions of various events, such as sporting events, stock market events, investment events, etc. As an example, sporting events may include football, baseball, basketball, soccer, swimming, horse racing, stock car racing, dog racing, golf, tennis, etc. Similarly, the administrator computing device  106  is coupled to the network  100  and may be utilized by an administrator to input statistical data related to the events that are being predicted by the remote computing device  104 . As an example, an expert may determine statistical information on the administrator computing device  106  that is then sent to the remote computing device  104 . Depending on the particular embodiment, the statistical data may be calculated by the human administrator or the administrator computing device  106 . In some embodiments, the statistical data may be received and/or calculated by the remote computing device  104 . 
     It should also be understood that while the user computing device  102 , the remote computing device  104 , and the administrator computing device  106  are each depicted as individual devices, these are merely examples. Any of these devices may include one or more personal computers, servers, laptops, tablets, mobile computing devices, data storage devices, mobile phones, etc. that are configured for providing the functionality described herein. It should additionally be understood that other computing devices may also be included in the embodiment of  FIG. 1 . 
       FIG. 2  depicts the remote computing device  104  for providing statistical and crowd sourced predictions, according to one or more embodiments shown and described herein. In the illustrated embodiment, the remote computing device  104  includes a processor  230 , input/output hardware  232 , network interface hardware  234 , a data storage component  236  (which stores statistical data  238   a  and crowd sourced data  238   b ), and the memory component  140 . The memory component  140  includes hardware and may be configured as volatile and/or nonvolatile memory and, as such, may include random access memory (including SRAM, DRAM, and/or other types of RAM), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of non-transitory computer-readable mediums. Depending on the particular embodiment, the non-transitory computer-readable medium may reside within the remote computing device  104  and/or external to the remote computing device  104 . 
     Additionally, the memory component  140  may be configured to store operating logic  242 , the data capturing logic  144   a,  and the interface logic  144   b,  each of which may be embodied as a computer program, firmware, and/or hardware, as an example. A local communications interface  246  is also included in  FIG. 2  and may be implemented as a bus or other interface to facilitate communication among the components of the remote computing device  104 . 
     The processor  230  may include any hardware processing component operable to receive and execute instructions (such as from the data storage component  236  and/or memory component  140 ). The input/output hardware  232  may include and/or be configured to interface with a monitor, keyboard, mouse, printer, camera, microphone, speaker, and/or other device for receiving, sending, and/or presenting data. The network interface hardware  234  may include and/or be configured for communicating with any wired or wireless networking hardware, a satellite, an antenna, a modem, LAN port, wireless fidelity (Wi-Fi) card, RFID receiver, Bluetooth receiver, WiMax card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices. 
     It should be understood that the data storage component  236  may reside local to and/or remote from the remote computing device  104  and may be configured to store one or more pieces of data for access by the remote computing device  104  and/or other components. In some embodiments, the data storage component  236  may be located remotely from the remote computing device  104  and thus accessible via the network  100 . In some embodiments however, the data storage component  236  may merely be a peripheral device, but external to the remote computing device  104 . 
     Included in the memory component  140  are the operating logic  242 , the statistics logic  144   a,  and the crowd sourcing logic  144   b.  The operating logic  242  may include an operating system and/or other software for managing components of the remote computing device  104 . Similarly, the statistics logic  144   a  may be configured to cause the remote computing device  104  to utilize information regarding past events (such as player performance, team performance, etc.) to create a statistical model and/or predict outcomes for future performances for teams and/or players. The crowd sourcing logic  144   b  may cause the remote computing device  104  to collect prediction data from users of the remote computing device  104 , as well as user biases, and other information. The crowd sourcing logic  144   b  may additionally cause the remote computing device  104  to provide an overall predication by utilizing both the crowd sourcing data and the statistical data. 
     It should be understood that the components illustrated in  FIG. 2  are merely exemplary and are not intended to limit the scope of this disclosure. While the components in  FIG. 2  are illustrated as residing within the remote computing device  104 , this is merely an example. In some embodiments, one or more of the components may reside external to the remote computing device  104 . 
       FIG. 3  depicts a user interface  330  for providing default player ratings, according to one or more embodiments shown and described herein. As illustrated, the user interface  330  includes a listing of a plurality of different players. The players may be ranked and/or rated, based on historical performance data. The rating a player receives may be determined based on a predetermined number of past events (such as the previous  16  games), which are subjected to a weighting algorithm that awards points according to predetermined performance statistics. As an example, player statistics may include pass percentage, fumble percentage, sack percentage, average gain, scoring average, save percentage, etc. Each of these statistics may be weighted and assigned a valued that is used to rate the player. If a certain statistic is determined to be less valuable at predicting future performance, that statistic will receive a lower weighting when determining the player&#39;s ranking. As discussed briefly above, this may be performed by a human expert, by a human expert via the administrator computing device  106 , and/or via the remote computing device  104  utilizing the statistical logic  144   a.    
     Also provided in  FIG. 3  are a crowd option  332 , a week option  334 , a quarterback position option  336 , a running back position option  338 , a wide receiver position option  340 , a tight end position option  342 , a defense position option  344 , and a kicker position option  346 . In response to selection of the crowd option  334 , the sub-options  348  may be provided. The sub-options  348  include an add my view sub-option  348   a,  a crowd sub-option  348   b,  a team specific sub-option  348   c,  an wagering sub-option  348   d,  and a fantasy sub-option  348   e.  In response to selection of the my view sub-option  348   a,  options may be provided that allow the user to rank the players for his/her account. In response to selection of the crowd sub-option  348   b,  the user may be provided with information related to the current crowd sourced rankings. As an example, the remote computing device  104  may compile the ranking of players and/or teams into a compilation of rankings, from one or more of the users that submitted rankings. Thus, by selecting the crowd sub-option  348   b,  the user may be provided with the compilation of the users&#39; rankings. 
     It should be understood that in some embodiments, the crowd sourced ranking data may be provided as a simple average ranking for all or a subset of users. However in some embodiments, the remote computing device  104  may determine the most relevant aspects of the user rankings that provide the most accurate prediction of future performance and weight those aspects higher than other aspects. This may include not using one or more statistics in the ratings; not using some user&#39;s rankings; weighting some users higher than others; and/or performing other action to arrive at the most accurate crowd sourced data. 
     In response to selection of the team-specific sub-option  348   c,  only ranking data from fans of a predetermined team (or group) may be provided. As an example, if the user is a Dallas fan, the user may trust Dallas fans over other fans as having “inside information” regarding a team or player. Similarly, some teams&#39; fans may simply be less biased and/or more accurate in their rankings (or vice versa). As such, ranking data from particular groups of users may be compiled and provided to the user. 
     In response to selection of the wagering sub-option  348   d,  enhanced wagering strategies may be provided to the user. These strategies may be derived from statistical expert data and/or crowd sourced data. In response to selection of the fantasy sub-option  348   e , statistical and/or crowd sourced data that may assist the user in making fantasy football decisions may be provided. 
     Also included in the user interface  330  is a ranking of a plurality of players. The players may be ranked according to an administrator expert that utilizes statistical information to rank the players. In some embodiments however, the players may be ranked and/or rated by the remote computing device  104  and/or via other mechanism. Regardless, for each player depicted in the user interface  330 , a statistics portion  350  and a rating are provided. The rating may be a fantasy rating, a rating determined from the ranking, and/or other type of rating. As also depicted, players at other positions may be provided via selection of the running back option  338 , the wide receiver option  340 , the tight end option  342 , the defense option  344 , and the kicker option  346 . For different event types, different options may be provided for these rankings. 
     Also included are an account option  354  and a sports betting option  356 . In response to selection of the account option  354 , the user may log into an account with the remote computing device  104  and/or may otherwise access the user account, as described in more detail below. In response to selection of the sports betting option  356 , information related to wagering on sporting events may be provided. 
       FIG. 4  depicts a user interface  430  for providing user ranking options for players, according to one or more embodiments shown and described herein. The user interface  430  may be provided in response to a user selection of the add my view option  348   a  from  FIG. 3 . As illustrated, the user interface  430  includes a my fantasy teams option  432 , an add team option  434 , and a social media option  436 . In response to selection of the my fantasy football teams option  432 , the user may be provided with options related to the players that are currently on the user&#39;s fantasy team. In response to selection of the add team option  424 , options may be provided for the user to select the players on are on the user&#39;s fantasy team manually. In response to selection of the social media option  436 , the user&#39;s fantasy team may be automatically loaded from a social media outlet with which the user has an account. Specifically, while selection of the add team option  434  allows the user to manually add his/her fantasy team (and/or other teams in his/her fantasy league), selection of the social media option  436  may automatically upload the user&#39;s fantasy team and/or league. By signing in with social media, updates to the league may be automatically uploaded as well. 
     Also provided in the user interface  420  are a ranking section  438  and a simulation option  440 . The ranking section  438  is similar to the user interface  330  from  FIG. 3 , with the exception that the user may rank players of different positions. Specifically, the user may establish who the best quarterback is; who the second best is, etc. Based on these rankings, the remote computing device  104  may provide a rating for that player. In addition to ranking the starting players for each of plurality of positions, the user may also rank second string (substitute) players for those positions. As an example, the highest ranked starting player of a position may be provided with a rating equal to the highest ranked substitute player at that position. Other levels (third string, fourth string, etc.) of substitutes may also be ranked and rated. 
     Once the user has ranked one or more of the players according to his/her preference, the user may select the simulation option  440  to simulate the results of the rankings. Depending on the particular embodiment, selection of the simulation option  440  may cause the remote computing device  104  to perform a play-by-play simulation of a plurality of games with the players that have been ranked. The remote computing device  102  may make one simulation, or dozens, hundreds or thousands of simulations, depending on the embodiment. Additionally, other information may be utilized to simulate the games. As an example, the remote computing device  104  may utilize strategies of each of the teams, such as play calling, strengths, weaknesses, etc. As an example, if Team A passes more than an average team and Team B′s pass defense is worse than average, the simulations may take this into consideration when predicting the outcome of the games between Team A and Team B. 
     It should be understood that while some embodiment may be configured to simulate a game before the game has started, other embodiments are not so limited. As an example, some embodiments may be configured to provide and update predictions, as the game is progressing. Specifically, the remote computing device  104  may make predictions prior to a game. However the game itself may deviate from that prediction. As a result, the predictions and probabilities for outcome may change as the game progresses. As an example, if the remote computing device  104  determines that a first team will score  48  points in the first half, but after the first quarter, the first team has only scored  3  points, the remote computing device  102  may alter the prediction for the halftime score, the final score, and/or other predicted data. Additionally, remote computing device  104  may determine accuracy data of the original prediction, as well as alter the prediction algorithm, based on the reasons for the originally incorrect prediction. As such, embodiments described herein simulate a game play-by-play to provide predictions, not just on the outcome of the final score, but predictions based on which play may be run next, the predicted outcome of a particular play or possession, probabilities of success of a play or possession, and/or other data. 
       FIG. 5  depicts a user interface  530  for providing a prediction of a specific player, according to one or more embodiments shown and described herein. As illustrated, the user interface  530  includes a fantasy section  532  and a player section  534 . The fantasy section  532  may provide the projected and actual ratings of the user&#39;s fantasy team and players. In response to selection of the actual fantasy option  532   a,  the user interface  530  may provide the current player and team ratings for the fantasy league with which the user has a team. In response to selection of the projected fantasy option  532   b,  the user interface  530  may provide a prediction of future performance for players and teams, based on historical statistical data, as well as rankings and ratings provided by users (crowd sourced data). 
     The user interface  530  may also provide other information, such as the ability to view available players for trades, other user&#39;s teams, current point totals, predicted point totals, etc. Also included are player options  532   c.  In response to selection of one of the player options  532   c,  the user interface  530  may provide the projected player section  534 . The projected player section  534  includes a projected option  536   a  and an actual option  536   b.  The projected player section  534  also includes a game prediction section  538  that provides a prediction on the final score of the upcoming game in which the selected player is playing. This predicted final score may be determined by taking player rankings of each player on the two teams and utilizing those rankings to determine various team and sub-team ratings. With this information, the remote computing device  104  may simulate a game between the two teams several times (in some embodiments hundreds or thousands of times). These simulations may then be processed to determine a predicted final score. 
     Also included in the projected player section  534  are a statistics option  540 , a schedule option  542 , and a news option  544 . In response to selection of the statistics option  540 , the statistics  546  for that player and/or team may be provided. Since the player section in  FIG. 5  is depicted as the projected player section  534 , the statistics  546  that are provided may be predicted statistics, based on the user rankings, crowd sourced rankings, statistical ratings and/or other criteria. 
     Also included in the user interface  530  is a view simulated graph option  548 . As discussed in more detail below, in response to selection of the view simulation graph option  548 , a graphical representation of the simulated player and/or team performances may be plotted and utilized for further predictions. 
       FIG. 6  depicts a user interface  630  for providing a player performance variance, according to one or more embodiments shown and described herein. In response to selection of the view simulation graph option  548  from  FIG. 5 , the user interface  630  may be provided. As illustrated, the user interface  630  is similar to the user interface  530  from  FIG. 5 , except that the user interface  630  includes a simulation area  632 , which provides a graphical representation of at least a portion of the simulations that are run for the selected player. In the depicted example, the selected player played 16 games that are being considered (each with a different set of simulations). In those games, the player achieved a player ranking above a predetermined threshold twice. The player&#39;s highest rating was 48.1 and the lowest rating was 11.9. The player only had one game with a rating below a predetermined threshold. 
     In some embodiments, the simulation area  632  may provide the user with a consistency rating for a particular player or team. Specifically, some players may have very highly rated games and very low rated games. Such a player would thus have a wide performance curve. This information may be helpful to a user who needs a player for a fantasy team with a moderate ranking, but who may be capable of playing at a high level. Similarly, some users would prefer to acquire a consistent player, who does not play at as high a level, but will have very few bad games. 
     It should be understood that, while not explicitly depicted in  FIG. 6 , the simulation area may additionally provide a consistency rating and/or a peak rating to provide the user with a single indicator of the potential and/or consistency for a particular player or team. Other information, such as statistics from the outlying simulations, may also be provided, such that more sophisticated users may delve deeper into the projections. 
       FIG. 7  depicts a user interface  730  for providing actual performance information of a player, according to one or more embodiments shown and described herein. In response to selection of the actual option  536   b  from  FIG. 5 , the user interface  730  may be provided with the actual current statistics for the selected player. As illustrated, the user interface  730  includes a game result section  732 , which provides the actual score of a previously played game. Similar to the user interface  630  from  FIG. 6 , a statistics section  734  is also provided, which provides the actual statistics from the previously played game. 
     Also included in the user interface  730  from  FIG. 7  is an edit rankings option  736 . In response to selection of the edit rankings option  736 , the user may be provided with the user interface  430  from  FIG. 4  for altering the rankings of the players. As an example, a player may have a good game and the user may wish to upgrade that player&#39;s ranking. Similarly, the user may simply learn more about a player and decide to alter the ranking. This new ranking will be re-simulated for all players and teams to provide updated crowd sourced information. 
       FIG. 8  depicts a user interface  830  for providing a user scorecard for player and team prediction, according to one or more embodiments shown and described herein. In response to selection of the account option  354  from  FIG. 3 , the account section  832  may be provided. The user section  832  includes an edit settings option  834 , as well as information regarding the user and the user&#39;s ranking accuracy. In response to selection of the edit settings option  834 , the user may select their favorite team, set passwords, addresses, user names, etc. Additionally, the user section  832  provides a user grade, a user ranking, and other information related to the prediction accuracy by the user. As discussed above, the user may rank players based on position and, based on the results of the following games, that ranking may be compared with the actual performance of those players. An accuracy percentage may then be determined and provided to the user. The user section  832  may also provide which players were ranked by the user most accurately as well as which games were predicted by the user most accurately. With this information, the remote computing device  104  and/or administrator may determine which users are best at predicting outcomes of games. Those users may be incentivized to continue providing predictions, such as through payment, greater access to the website, and/or via other incentives. 
     Additionally, the accuracy data may be utilized by the remote computing device  104  to determine which pieces of information were most helpful in accurately predicting an outcome of a game. As an example, if the remote computing device  104  determines that the highest rated users focus primarily on quarterback proficiency, the statistical model used to predict results may be altered to weigh quarterback performance higher. Additionally, some embodiments are configured to provide this information to other users to know which statistics provide the greatest probability for predictive success. 
       FIG. 9  depicts a user interface  930  for simulating a game based on user rankings, according to one or more embodiments shown and described herein. In response to selection of the fantasy option  348   e  from  FIG. 3 , the user interface  930  may be provided. As illustrated, the user interface  930  includes a user option  932 , a crowd option  934 , and an expert option  936 . Specifically, after selection of the user option  932 , the statistics section  938  may be provided. The statistics in the statistics section  938  may be determined based on the user&#39;s rankings of the players, and/or other information, as described in more detail below. As an example, if the user ranks the Baltimore offense as the highest ranked and San Francisco&#39;s defense as the lowest ranked, such rankings would help determine the predicted points that Baltimore will likely score. As discussed above, the remote computing device  104  may run a plurality of simulations, based on these rankings. An aggregate of the simulations may be utilized to determine the predicted result. 
     In some embodiments, the aggregate may simply be an average of all simulations. Some embodiments may aggregate the simulations by removing outlier simulations and averaging the remaining simulations. Some embodiments may be configured to utilize results of past games and/or predictions to determine the most accurate mechanism for aggregating the simulations. As an example, if the most accurate simulations of Team A occurred when Player B performed highly, a weighting of those games may be made in the aggregation. 
     Also included in the example of  FIG. 9  are an edit rankings option  940  and a simulation option  942 . As discussed above, the user may select the edit rankings option  940  for changing player rankings and/or other rankings. In response to editing the user rankings and/or selecting the simulation option  942 , the simulations may be re-run to account for the changes. 
     As an example, some embodiments may be configured to allow the user to manually edit the predicted statistics depicted in the statistics section  938 . Specifically, the statistics provided in the statistics section  938  are determined based on the simulations using the player rankings provided by the user. If the user feels that the score will be different, some embodiments are configured to provide an option for the user to manually change the score. If the user feels that the yards or other statistic will be different, the user may alter the desired statistic and select the simulation option  942  to recalculate the final score (and/or other statistics). 
       FIG. 10  depicts a user interface  1030  for simulating a game based on crowd sourcing, according to one or more embodiments shown and described herein. In response to selection of the crowd option  934  from  FIG. 9 , the user interface  1030  is provided. As illustrated, the user interface  1030  provides projected results that have been predicted via the crowd sourced data. As discussed above, the remote computing device  104  may compile rankings from a plurality of users and use this information to create a more accurate prediction model. Also included in the user interface  1030  are an edit rankings option  1034  and a simulation option  1036 . As discussed above, in response to selection of the edit rankings option  1034 , the user may be provided with options to edit his/her player rankings and/or other selections. Similarly, selection of the simulation option  1036  re-simulates the user&#39;s selections for including into the crowd sourced data. 
     It should be understood that while the crowd sourced data may include predictions and data from all users of the system, this is merely an example. Depending on the user&#39;s selections and the particular embodiment, the crowd sourced data may be taken from a subset of all users, such as fans of a particular team, users that have grouped themselves together, users from a predetermined location, users with a prediction score above a predetermined threshold, etc. 
       FIG. 11  depicts a user interface  1130  for simulating a game based on statistical analysis, according to one or more embodiments shown and described herein. In response to selection of the expert option  936  from  FIG. 9 , the user interface  1130  may be provided, which includes game predictions, based on expert and statistical data. Specifically, embodiments disclosed herein may be configured to analyze statistical data from past performances of players and teams. Based on the historical statistical data, the remote computing device  104  may determine which statistics to weigh more than other statistics, as well as a mechanism for altering the prediction algorithm, based on successful predictions by the remote computing device  102 , the crowd sourced predictions, or elsewhere. Similar to the user interfaces  930  and  1030  from  FIGS. 9 and 10 , respectively, the user interface  1130  includes an edit rankings option  1134  and a simulation option  1136 . 
       FIG. 12  depicts a user interface  1230  for simulating a game based on user predicted strategies, according to one or more embodiments shown and described herein. In response to selection of the edit rankings options  934 ,  1034 , and/or  1134  from  FIGS. 9 ,  10 , and  11 , the user interface  1230  may be provided. The user interface  1230  may depict a matchup between a plurality of teams and includes a listing of the starting players on each team. In response to selection of one of the edit options  1236 ,  1238 , the user may alter the rankings of one or more of the players. In response to selection of the play calling option  1232  and/or  1234 , the user may select the type of offense, defense, or other strategy that a team is predicted to play. Upon setting the desired player rankings, strategy, and selecting the simulation option  1240 , the remote computing device  104  will re-simulate the data and return to the user interface  930  from  FIG. 9  to provide the updated prediction. 
     Some embodiments may also include a player performance option, for the user to indicate whether a player will have a hot streak, a cold streak, or perform as in the past. As an example, if the user feels that a certain player will have a great game, he may indicate this hot streak in the player performance option. Similarly, a user may learn that a player has a minor injury, but will still play. As such, the player may indicate that the player will have a cold streak for this game or for a predetermined number of games. Based on the user indications via the player performance option, the player&#39;s temporary ranking may change, as well as the predicted outcome of the game, the use of substitute players for that player, etc. 
     It should also be understood that embodiments described herein may be configured to determine the types of plays that a team will run. As an example, if the teams are football teams, the remote computing device  104  may access historical data (such as a predetermined number of past games) on the teams to determine the percentage of running plays for first down at a first field location, second down, for a second field location, etc. This play calling analysis may be utilized to further predict the outcome of the game. As an example, if a team is primarily a running team and is playing the best run defense in the league, this will affect the outcome of the game. Additionally, in response to the user selection of “pass aggressive” on the play calling option  1232  the prediction of that team&#39;s strategy will be altered, thus likely affecting the outcome of the game. 
     Depending on the embodiment, the play calling option  1232  may take any of a plurality of different forms. As an example, some embodiments may provide the user with the simple interface depicted in  FIG. 12 , with “run aggressive,” pass aggressive,” and “balance” options for offense and similar options for defense. However, some embodiments may be configured for the user to identify exactly in which situations a team will call which type of plays. As an example, these embodiments may provide a user interface with options such as “first down, own  20  pass” and provide a field for the user to identify the percentage of plays that will be pass plays. Other options may be “first down, own  20  run,” “second down, own  20  pass,” etc. This level of freedom provides an advanced user the ability to specify the exact plays or types of plays that he/she predicts will be run for many or all game situations. In at least one of these embodiments, these fields may be automatically populated, based on the predictions made by the remote computing device  104  or crowd. 
       FIG. 13  depicts a user interface  1330  for providing wagering predictions for a game, according to one or more embodiments shown and described herein. In response to selection of the sports betting option  356  from  FIG. 3 , the user interface  1330  may be provided. The user interface  1330  includes a wagering section  1332 , a sports book section  1334 , a confidence section  1336 , a confidence details section  1338 , and a statistics section  1340 . Specifically, the wagering section  1332  provides an indication of on which team the user should place a wager, based on the spread. Specifically, many sports books determine the expected outcome of a game and determine the spread, based on that predicted outcome. As illustrated in the user interface  1330 , the spread of the depicted example is provided in the sports book section  1334 . In the example of  FIG. 13 , the sports book indicated that predicted that San Francisco would beat Baltimore by  3 . 5  points. Accordingly, embodiments disclosed herein predict the outcome of the game, based on statistical data and crowd sourced data. Based on this prediction, the remote computing device  104  may compare this prediction to the spread to determine on which team the user should wager. 
     Additionally, the confidence section  1336  includes a percentage of predicted accuracy of the betting strategy that is provided in the wagering section  1332 . This is determined based on the simulations and the number of simulations that agreed with the prediction versus the number of predictions that disagreed with the prediction. Specifically, based on the players&#39; consistency rating and thus the teams&#39; consistency rating, simulations may be such that different teams win a game, based on the simulation. As a result, the remote computing device  104  may predict an outcome of a game, based on the simulation, but that choice may have more uncertainty, depending on the consistency factor and/or other data related to the teams. 
     Similarly, the confidence details section  1338  provides additional insight and wagering strategies, based on the simulations. As an example, the remote computing device  104  may provide betting strategies, such as suggesting a wager on a final score, a money line wager, and an over-under wager, etc. The statistics section  1340  provides the predicted score and statistics, based on the simulations. 
     It should be understood that some embodiments may be configured for the user to actually place wagers on the game, based on the prediction and the spread data of  FIG. 13 . While some embodiments may provide these wagering options within the user interface  1330 , some embodiments may provide a link to an external website for wagering. Regardless, these embodiments may be configured to track the user&#39;s wagers to determine which predictions yield the best wagers and/or provide other information related to the wager. 
       FIG. 14  depicts a user interface  1430  for providing wagering results for a past game, according to one or more embodiments shown and described herein. After the game has been played, the user interface  1430  may be provided to indicate the accuracy of the predictions made prior to the game. As illustrated, the user interface  1430  provides a results section  1432 , a results details section  1434 , and a statistics section  1436 . The results section  1432  provides the final score of the game, the current records of the teams, and the spread at the time of the wager. In the results details section  1434 , the outcome column is populated, indicating which of the listed wagers were accurate. The statistics section  1436  provides the actual statistics of the game. 
       FIG. 15  depicts a flowchart for simulating a game based on statistical data and crowd sourcing data, according to one or more embodiments shown and described herein. As illustrated in block  1570 , default player ratings may be determined based on statistical data. As discussed above, the default player ratings may be provided by an administrator, determined by the remote computing device  104  based on statistics from previous games, and/or may be a compilation of the crowd sourced rankings. In block  1572 , user player rankings may be received from a plurality of users. In block  1754 , the user player rankings may be converted into user ratings. Based on where a particular user ranks player, the remote computing device  104  determines the assigned rating of that player. Additionally, certain corrections may be made by the remote computing device  102  to the ratings, based on which team that the user is a fan, the user&#39;s location, the user&#39;s previous ranking history, the user&#39;s wagering history, and/or other data. In block  1576 , team data for a plurality of teams may be determined, where each of the plurality of teams includes a player that has been rated. As an example, based on the user rankings of players, and thus the player ratings, a team may be rated for offense, defense, special teams, overall performance, and/or for other purposes. In block  1578 , a game between at least two of the plurality of teams may be simulated, based on the default player ratings, the user ratings, and the team data. In block  1580  an outcome of the game may be determined based on the simulation. In block  1582 , the outcome may be provided to the users for display. 
       FIG. 16  depicts a flowchart for simulating a portion of a game, based on performance in that game, according to one or more embodiments shown and described herein. As illustrated in block  1670 , default player ratings may be determined based on statistical data. In block  1672 , user player rankings may be received from a plurality of users. In block  1674 , the user player rankings may be converted into user ratings. In block  1676 , a rating for a first subset of a first team and a second subset of a second team may be determined. The subsets may be for an offense, a defense, a kicking team, a special team, a starting team, a substitute team, and/or other subsets, depending on the teams, the sports, and/or other data. In block  1678 , a first play strategy for the first subset and a second play strategy for the second subset may be determined. In block  1680 , a game between the first subset and the second subset may be simulated based on the first play strategy, the second play strategy, the default player ratings, and the user ratings. In block  1682 , an outcome of the game may be determined from the simulation. In block  1684 , the outcome may be provided to the users for display. 
       FIG. 17  depicts a flowchart for determining a wagering strategy for a game, according to one or more embodiments shown and described herein. As illustrated in block  1770 , a first rating for a first team and a second rating for a second team may be determined. In block  1772 , play between the first team and the second team may be simulated. In block  1774 , an outcome from the simulation may be determined. In block  1776 , a predicted wagering outcome of a game between the first team and the second team may be determined. In block  1778 , the predicted betting outcome may be compared with the simulation to determine a wagering strategy for the game. In block  1780 , a confidence level of the wagering strategy may be determined. In block  1782 , the wagering strategy and the confidence level may be provided to a user for display. 
     As discussed above, embodiments described herein provide both crowd sourcing and statistical predictions to determine a predicted outcome to a game, match, or other event. To this end, embodiments provide the ability to simulate the event play-by-play to predict every occurrence in the event, as well as provide wagering strategies for various outcomes of the event. This provides a greater prediction capabilities, as well as better wagering accuracy. 
     While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.