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Amanda Glazer

Publications and source records attributed to Amanda Glazer.

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Commanding the Foul Shot: A New Ensemble of Free Throw Metrics

With the NBA's adoption of in-game limb tracking in 2023, Sony's Hawk-Eye system now captures high-resolution, 3D poses of players and the ball 60 times per second. Linking these data to key events opens a new era in NBA analytics. Here, we leverage a large dataset of 21,964 shot attempts from 72 NBA players to introduce a novel ensemble of metrics for evaluating free-throw shooting. Inspired by baseball analytics, we introduce command, which quantifies the quality of a free throw by measuring a shooter's accuracy and precision near the basket's bullseye. This metric recognizes that some makes (or misses) are better than others and captures a player's ability to execute quality attempts consistently. We demonstrate that command captures underlying skill more effectively than traditional make-or-miss statistics; early-season command predicts late-season success more reliably than traditional shooting percentage. To identify what drives command, we define launch-based metrics assessing consistency in release velocity, angle, and 3D position. Players with greater touch, i.e., more consistent launch dynamics, exhibit stronger command as they can reliably control their shot trajectory. Finally, we develop a physics model to identify the range of launch conditions that result in a make and to determine which launch conditions are most robust to small perturbations. This framework reveals ''safe'' launch regions and explains why certain players excel at free throws, providing actionable insights for player development.

stat.AP

Tractable Algorithms for Changepoint Detection in Player Performance Metrics

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.

stat.AP