Baseball Research

Pitching Under Pressure: Do Pitchers Change their Strategy?

Do pitchers change their decisions when stakes are higher? This project sets out to answer this question by using pitch-by-pitch MLB data to see if pitchers follow trends when they are under pressure

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Key Takeaways:

  • High leverage pitches are rare, but decisive

    • Only 17.58% of pitches are thrown under pressure

  • Most pitchers stick to their game plan

    • Overall pitch distribution changes very little when pressure changes (top figure)

  • Outliers stand out

    • Notable significant positive effect: Luis Castillo (15.9% more likely to throw a fastball under pressure)

    • Notable significant negative effect: Felix Hernandez (9.89% less likely to throw a fastball under pressure)

University of Texas at Dallas Baseball: 2025 Report and 2026 Season Outlook

This project is a full pitch-by-pitch performance analysis of UT Dallas Baseball’s 2025 season, built from automated scraping of every available game PDF. With no public data available other than PDFs, I constructed my own complete dataset — parsing play-by-play text into structured pitch, count, and runner-state records. The final product is a report used by the coaching staff to evaluate hitters, assign bullpen roles, and prepare for the 2026 season.

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    • Pull URL links to each game in the schedule

    • Download box score PDFs

    • Extract play-by-play strings

    • Normalize raw text formatting

    • Identify batters and pitchers

    • Construct counts & pitch sequences based on PDF strings

    • Resolve special cases and unique baseball situations (sac bunts, infield fly, errors, stolen bases)

    • Construct hitting & pitching metrics only possible with pitch-by-pitch data

    • Pitching with RISP/two strikes and pressure-pitch indicators

    • Count-based OBP and two-strike survival

    • Pitch result classification

    • Aggregated hitter/pitcher profiles

    • Role-based evaluations for lineup & bullpen usage

    • Run prevention and contact/K-rate scatterplot

    • Per-count performance heatmaps

    • Team-level and player-level charts

    • Full season team summary

    • Returning player evaluations for 2026 season

    • Exportable, coach-ready report

Hitting Analysis

UTD’s 2025 offense was defined by elite plate discipline and relentless contact. The lineup posted a .457 OBP with the fewest strikeouts in the conference, generating constant baserunners and extending innings. Hitters showed strong two-strike competitiveness and consistent situational execution, particularly in advantage counts like 2–0 and 3–1. These approach profiles identify where the lineup can be optimized and which hitters are best suited for table-setting, middle-order production, and RISP-heavy roles.

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To generate this report, I built a reproducible R pipeline that collects PDFs directly from UTD’s website, extracts unstructured play descriptions, merges split lines, reconstructs counts, and assigns events at the pitch level. On top of this foundation, I engineered baseball-specific performance metrics, including two-strike survival, RISP conversion, pressure-pitch share, count-based OBP, and command profiles for every pitcher. The entire workflow is automated, allowing rapid updates throughout the season or across multiple years.

Pitching Analysis

The pitching staff demonstrated reliable strike-throwing and strong composure late in games. First-pitch strike rates and overall command created predictable leverage patterns, revealing where individual pitchers excelled — and where dangerous walk-prone counts emerged. Blue-chip arms such as Klyng and Vandament handled a disproportionately high share of pressure pitches, while Garza’s aggressive early-count approach suggests potential for expanded late-inning responsibility. These insights support bullpen role assignment and strategic in-game deployment.