Machine learning-driven defensive strategy in baseball: Estimating optimal defensive positioning for outfielders

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.