Tractable Algorithms for Changepoint Detection in Player Performance Metrics
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908745964978176 |
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| author | Glazer, Amanda |
| author_facet | Glazer, Amanda |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_25961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tractable Algorithms for Changepoint Detection in Player Performance Metrics Glazer, Amanda Applications 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. |
| title | Tractable Algorithms for Changepoint Detection in Player Performance Metrics |
| topic | Applications |
| url | https://arxiv.org/abs/2510.25961 |