{
  "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",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35714",
  "author_total": 2,
  "authors": [
    {
      "name": "Joseph DiGiovanni"
    },
    {
      "name": "Justin Kurland"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d5a7pd9x14w0s.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35714",
  "fulltext_url": null,
  "id": "d5a7pd9x14w0s",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35714",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2002|Sports Analytics and Performance"
    ]
  ],
  "openalex": "W7223932295",
  "pdf_url": "https://arxiv.org/pdf/2610.35714",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d5a7pd9x14w0s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:49Z",
      "native_id": "2610.35714",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35714"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Quantifying and Predicting Pitch Sequence Effectiveness Using Visual Deception Metrics",
  "title_text": "Quantifying and Predicting Pitch Sequence Effectiveness Using Visual Deception Metrics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:49Z",
  "url": "https://research.draconitic.com/paper/d5a7pd9x14w0s",
  "venue": "arXiv",
  "versions": []
}