baseball. Structured quantitative analysis is capable of explaining many aspects of the game, for example, to examine how often a team should attempt to steal. Applications Sabermetrics can be used for multiple purposes, but the most common are evaluating past performance and predicting future performance to determine a player's contributions to his team. These may be useful when determining who should win end-of-the-season awards such as MVP and when determining the value of making a certain trade. Most baseball players tend to play a few years in the minor leagues before they are called up to the major league. The competitive differences coupled with ballpark effects make the exact comparison of a player's statistics a problem. Sabermetricians have been able to clear this problem by adjusting the player's minor league statistics, also known as the Minor-League Equivalency. Through these adjustments, teams are able to look at a player's performance in both AA and AAA to determine if he is fit to be called up to the majors. Applied statistics Sabermetrics methods are generally used for three purposes: To compare key performances among certain specific players under realistic data conditions. The evaluation of past performance of a player enables an analytic overview. The comparison of this data between players can help one understand key points such as their market values. In that way, the role and the salary that should be given to that player can be defined. To provide prediction of future performance of a given player or a team. When past data is available about the performance of a team or a specific player, Sabermetrics can be used to predict the average future performances for the next season. Thus, a prediction can be made with a certain probability about the number of wins and losses. To provide a useful function of the player's contributions to his team. When analyzing data, one is able to understand the contributions a player makes to the success/failure of his team. Given that correlation, one can objectively sign or release players with certain characteristics. Machine learning model A machine learning model can be built using data sets available at sources such as baseball-reference. This model will give probability estimates for the outcome of specific games or the performance of particular players. These estimates are increasingly accurate when applied to a large number of events over a long term. The game outcome (win/lose) is treated