Performance-environment mutual flow model using big data on baseball pitchers

Abstract

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IntroductionThe study investigated the baseball pitching performance in terms of release speed, spin rate, and 3D coordinate data of the release point depending on the ball and strike counts.MethodsWe used open data provided on the official website of Major League Baseball (MLB), which included data related to 580 pitchers who pitched in the MLB between 2015 and 2019.ResultsThe results show that a higher ball count corresponds to a slower release speed and decreased spin rate, and a higher strike count corresponds to a faster release speed and increased spin rate. For a higher ball count, the pitcher's release point tended to be lower and more forward, while for a higher strike count, the pitcher's release point tended to be to the left from the right pitcher's point of view. This result was more pronounced in 4-seam pitches, which consisted the largest number of pitchers. The same tendency was confirmed in other pitches such as sinker, slider, cut ball, and curve.DiscussionOur findings suggest that the ball count is associated with the pitcher's release speed, spin rate, and 3D coordinate data. From a different perspective, as the pitcher's pitching performance is associated with the ball and strike count, the ball and strike count is associated with pitching performance. With regard to the aforementioned factor, we propose a “performance-environment flow model,” indicating that a player's performance changes according to the game situation, and the game situation consequently changes the player's next performance.

Keywords

• baseball
• pitcher
• Major League Baseball (MLB)
• hot hand
• game flow