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

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