MLB Player Development Analytics

A Statcast-based analytics pipeline for evaluating player development through changes in contact quality.

The project transforms MLB Statcast data into player-level performance metrics, rolling contact-quality measures, development signals, and visual player dashboards.

Objective

The goal is to move beyond traditional box-score statistics and identify meaningful changes in the quality of a player's contact.

The current analysis focuses on:

• Average Exit Velocity
• Hard-Hit Rate
• Barrel Rate
• 30-day rolling metrics
• Baseline vs. recent performance
• Player development signals
• Individual player dashboards

Methodology

The pipeline follows four primary stages:

1. Ingest MLB data

   • Game schedules
   • Rosters
   • Player information
   • Statcast pitch-level data

2. Transform and validate

   • Standardize raw MLB data
   • Filter to relevant team games
   • Select analysis fields
   • Validate game-level data

3. Calculate player metrics

   • Aggregate batted-ball outcomes
   • Calculate contact-quality metrics
   • Build 30-day rolling windows

4. Evaluate development

   • Establish a baseline
   • Compare against recent performance
   • Calculate changes in contact quality
   • Classify the player's performance trend
   • Generate a player-level dashboard

Development Signal

Player development is evaluated using changes in:

Metric	Description
Exit Velocity	Average velocity of balls in play
Hard-Hit Rate	Percentage of batted balls hit at 95+ mph
Barrel Rate	Percentage of batted balls meeting barrel criteria
Rolling Metrics	Recent contact quality calculated over a 30-day window

The development analysis compares an earlier qualifying period against the player's most recent qualifying period.

A player is classified as Improving, Declining, or Mixed based on changes across the three contact-quality metrics.

Example

The current project includes an example analysis of Elly De La Cruz using 2026 Cincinnati Reds Statcast data.

The pipeline produces:

• Player-level metrics
• 30-day rolling contact-quality metrics
• Baseline vs. recent comparisons
• A performance trend
• A player development dashboard

Example Dashboard

Running the Pipeline

Create and activate the virtual environment:

python -m venv .venv
source .venv/bin/activate