Recent trends in Major League Baseball (MLB), including the “fly-ball revolution” and restrictions on defensive shifts, have increased the defensive burden on outfielders. This study aimed to optimize outfield defensive positioning using machine learning techniques, specifically k-means clustering and logistic regression. Analyzing 51,290 batted ball landing locations from the 2023 MLB season, we examined how the distance between an outfielder’s position and a batted ball’s landing spot affects the hit probability. Outfielder positions were reconstructed using trigonometric conversions of depth and angle, and three distance metrics – Euclidean, x-coordinate, and y-coordinate – were measured. The results indicate that shorter distances between an outfielder’s position and a batted ball’s landing spot significantly increase defensive success rates. Notably, k-means clustering produced optimal defensive positions closer to the ball in Euclidean and y-coordinate distances than actual outfielder positions. These findings provide practical insights that can assist coaches and analysts in optimizing defensive alignments, improving player positioning strategies, and enabling real-time defensive adjustments based on pitcher-batter matchups to enhance overall team defensive efficiency.
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