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Millions of people play fantasy baseball using leagues that are typically draft- or auction-based. Choosing a team based on your favorite players or on last year’s performance is likely to field a weaker team. Baseball is one of the most “documented” of all sports, statistics-wise. With the wealth of collected information, you can derive a better estimate of each player’s true talent level and their likely performance in the coming year. This allows for better drafting and also helps avoid overpaying for players coming off of one-of-a-kind (“career”) seasons.
When drafting players for fantasy baseball, you must make decisions based on the player’s performance over their career to date. Basing evaluation on personal interpretation of the player’s performance is likely to overvalue a player’s most recent performance. In other words, it’s common to overvalue a player coming off a career year or undervalue a player coming off a bad year. The goal is to generate a better estimate of the player’s value in the next year based on what he has done in prior years. If you build a machine learning model to predict a player’s performance in the next year based on their previous performance, it will help you identify when these over, or under, performances are flukes versus when they are actual indicators of that player’s future performance.
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Sports organizations use AI for player performance analysis, injury prediction, and fan engagement. AI tools also assist in game strategy formulation, ensuring competitive advantage and optimal outcomes.