Quantifying and Predicting Pitch Sequence Effectiveness Using Visual Deception Metrics

Abstract

Pitcher performance is influenced not only by individual pitch characteristics, but also by the relationship and sequencing between consecutive pitches. In this study, we construct a sequence-level framework using MLB Statcast data from 2021-2025 to quantify the value of pitch deception with two geometric features: Decision Angular Separation (DAS) and Average Angular Separation (AAS). These metrics measure visual similarity of back-to-back pitches from the batter's perspective at a hitter's swing "decision point" during the flight of the ball from the pitcher's hand to the plate. We introduce Swinging Strike Lift (SwStr Lift), a residualized outcome isolating sequencing value beyond player identity, pitch type, location, and count. Across more than 740,936 two-pitch sequences, we find that tighter tunnels (low DAS/AAS) produce significant behavioral changes, for well-tunneled FB-to-BR (fastball-to-breaking-ball) sequences in the Shadow/Chase regions. This elevated swing propensity is consistent with swing induction as a primary behavioral pathway underlying the observed gains in swinging strikes. Our predictive modeling confirms the importance of tunneling, as adding DAS/AAS to gradient-boosted models yields substantial relative improvements in out-of-sample R-squared (e.g., +38% for FB-to-BR and +163% for FB-to-OS [fastball-to-off-speed]). These results demonstrate that tunneling generates a measurable and behaviorally coherent sequencing advantage, offering a tractable, perceptually grounded tool for evaluating pitcher deception which informs pitch design and game strategy. Quintile analyses confirm the effect is not confined to extreme tunnels; ICE plots show model-predicted lift declining sharply as DAS increases. Keywords: pitch tunneling; pitch sequencing; baseball analytics; predictive modeling; XGBoost; perceptual geometry; decision point; Statcast