Tractable Algorithms for Changepoint Detection in Player Performance Metrics

TL;DR

This paper introduces a new, computationally feasible changepoint detection method tailored for sports performance data, effectively identifying significant in-season changes in baseball player metrics with broad applicability.

Contribution

The paper develops a likelihood-based changepoint detection algorithm with split-sample inference, incorporating a shift parameter for customizable change sensitivity, and demonstrates its effectiveness on baseball data.

Findings

Flags 91% of ground-truth change cases

Detects over 60% of changes occurring in-season

Applicable to various performance monitoring contexts

Abstract

We present a tractable framework for detecting changes in performance metrics and apply these methods to Major League Baseball (MLB) batting and pitching data from the 2023 and 2024 seasons. We propose a changepoint detection algorithm that combines a likelihood-based approach with split-sample inference to better control false positives, using either nonparametric tests or tests appropriate to the underlying data distribution. These tests incorporate a shift parameter, allowing users to specify the minimum magnitude of change to detect. We demonstrate the utility of this approach across simulation studies and several baseball applications: detecting changes in batter plate discipline metrics (e.g., chase and whiff rate), identifying velocity changes in pitcher fastballs, and validating velocity changepoints against a curated quasi-ground-truth dataset of pitchers who transitioned from relief to starting roles. Our method flags meaningful changes in 91% of these "ground-truth" cases and reveals that, for some metrics, more than 60% of detected changes occur in-season. While developed for baseball, the proposed framework is broadly applicable to any setting involving monitoring of individual performance over time.