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AtBatLabs

About AtBat Labs

A statistical modeling platform for MLB at-bat outcome predictions using traditional and advanced sabermetrics.

Our Mission

AtBat Labs combines traditional and advanced baseball statistics with modern web technology to create educational predictions for MLB at-bat outcomes. Our goal is to make baseball analytics accessible and help fans understand the complex statistical factors that influence game outcomes.

Currently Implemented

What our prediction system actually uses right now

Core Player Statistics

  • • Batting: AVG, OBP, SLG, K%/BB%
  • • Pitching: ERA, WHIP, K/9, BB/9
  • • Bounded scaling to weigh player skill

Advanced Matchup Analysis

  • • **Pitch Repertoire:** Batter performance vs. pitcher's top pitches
  • • **Zone Performance:** Batter hot/cold zones vs. pitcher's target zones
  • • **Handedness:** Platoon advantage (L/R matchups)
  • • **Historical Data:** Head-to-head performance (when available)

Statistical Model Architecture

Our system uses a multi-layered statistical model with four main components:

1. League Baseline

Uses 2023 MLB league averages as the foundation for all predictions.

2. Player Adjustments

Adjusts probabilities based on individual player performance vs. league average.

3. Matchup Factors

Applies adjustments for handedness, pitch-type, and zone-based interactions.

4. Calibration & Normalization

Applies realistic bounds to outcomes and ensures all probabilities sum to 100%.

Not Yet Implemented

Advanced features we're planning to add in the future

Missing Contextual Data
  • • Ballpark factors (e.g., Coors Field vs. Oracle Park)
  • • Weather conditions (temperature, wind, humidity)
  • • Umpire tendencies (strike zone size)
  • • Player fatigue or injury status
Planned Integrations
  • • Real-time Statcast integration for pitch-level data
  • • Count-specific adjustments (0-0 vs. 3-1 counts)
  • • Situational statistics (Runners in Scoring Position)
  • • Machine learning for pattern recognition

Data Sources & Methodology

Current Data Sources

  • • 2023 MLB season statistics (batting and pitching)
  • • Pitch-type performance data (vs. Fastball, Breaking, Offspeed)
  • • Pitch location data aggregated into zones
  • • Basic player biographical information

Calculation Method

Predictions are generated using a multi-layered statistical model. It starts with league-average baselines, adjusts for individual player skills, and then applies nuanced matchup factors for handedness, pitch repertoire, and zone performance. The system does not use machine learning or neural networks at this time.

Model Accuracy & Validation

Planned Validation Steps

  • • Back-test predictions against the full 2023 season outcomes.
  • • Compare model performance to Vegas odds and other public models.
  • • Implement cross-validation techniques to ensure robustness.
  • • Publish transparent accuracy metrics (e.g., Brier score, log loss).

Technology Stack

Frontend

  • • Next.js 14 with App Router
  • • React 18 with TypeScript
  • • Tailwind CSS for styling
  • • Recharts for data visualization

Data & Analytics

  • • Static JSON data (2023 MLB stats)
  • • Client-side statistical calculations in TypeScript
  • • No external APIs currently integrated for live predictions
  • • No database or real-time data feeds

Development Roadmap

Phase 1

Model Validation & Accuracy

Test current model against historical data and establish baseline accuracy metrics.

Phase 2

Contextual Factors

Integrate ballpark, weather, and umpire data into the model.

Phase 3

Real-time Integration & ML

Connect to live MLB data feeds, add count/situational stats, and explore machine learning models.