MLB Pitching Intelligent Analysis System: A Data Science Practice in Baseball Integrating Biomechanics and Machine Learning

This project simulates the MLB R&D process to build an end-to-end pitching performance analysis and scouting intelligence generation system. The core goal is not only to predict pitching velocity but also to understand the pitcher's biomechanical prototype and mechanical efficiency patterns, providing data support for scouting decisions. The system integrates biomechanics, Statcast data, cluster analysis, and machine learning technologies, reflecting the typical application value of data science in professional sports.

In professional baseball, pitching velocity is a comprehensive result of biomechanical efficiency, release technique, and pitch design. MLB teams continuously explore extracting actionable intelligence from massive tracking data. The core objectives of this project are: to build an end-to-end pitching intelligent analysis system, predict pitching velocity, understand the pitcher's biomechanical prototype and mechanical efficiency patterns, and assist scouting decisions.

Analysis Subjects: Selected multiple top contemporary MLB pitchers such as Gerrit Cole and Spencer Strider, covering different styles and velocity levels.

Biomechanical Features: Extracted four key feature categories from Statcast data—release efficiency (kinetic energy conversion ability), pitch characteristics (lateral/vertical displacement), rotation-related metrics (spin rate, spin axis angle), and velocity differences (velocity gaps between pitch types and relative to league average velocity).

Supervised Learning: Used XGBoost regression model to predict pitching velocity with biomechanical metrics as input. The advantages of XGBoost include handling non-linear relationships, built-in feature importance evaluation, strong robustness, and good interpretability; combined with SHAP for feature importance analysis.

Unsupervised Learning: Two-step strategy—UMAP dimensionality reduction (preserves local and global structures, faster than t-SNE), HDBSCAN clustering (automatically identifies natural groups without presetting the number, marks abnormal pitchers), to classify pitcher biomechanical prototypes (e.g., velocity type, pitch type, etc.).

Four core outputs: 1. Velocity prediction model: Personalized velocity prediction equation to evaluate rookie prospects or monitor active players' status; 2. Mechanical efficiency scoring system: Comprehensive efficiency score to quantify the degree of kinetic chain optimization; 3. Pitcher prototype clustering: Classify biomechanical categories to assist comparison among similar types; 4. Automated scouting report: PDF format, integrating analysis results and visual charts for direct decision-making.

Adopted Python ecosystem tools: Data processing (pandas, numpy), machine learning (XGBoost), model interpretation (SHAP), dimensionality reduction and clustering (UMAP, HDBSCAN), data acquisition (pybaseball library to obtain Statcast data).

Potential expansions: 1. Injury risk modeling: Integrate data such as Tommy John surgery to predict injury risk; 2. Pitching tunnel analysis: Quantify the deceptive effect of release point trajectory similarity between different pitch types; 3. Pitching sequence prediction: Predict pitch selection strategies based on context and opponent characteristics; 4. Interactive dashboard: Build an interface with Streamlit to facilitate self-service exploration for non-technical personnel.

Value embodiment: 1. Data-to-intelligence transformation: Convert raw Statcast data into actionable scouting intelligence; 2. Multi-dimensional evaluation framework: Combine quantitative prediction (velocity) and qualitative classification (clustering); 3. Interpretability priority: Use XGBoost and SHAP to ensure results are understandable and trustworthy. For sports data science learners, it is a reference case with complete structure, mainstream technology, and clear business logic.