Introduction to BaseballCV

BaseballCV represents a significant advancement in baseball analytics, offering a comprehensive suite of computer vision tools specifically designed for baseball analysis. At its core, BaseballCV combines cutting-edge object detection models with specialized baseball domain knowledge to create a powerful toolkit for understanding and analyzing the game through video.

The official package information can be found on BaseballCV’s PyPI page.

Core Components and Capabilities

The framework integrates several key technologies that work together seamlessly. At its foundation, BaseballCV utilizes different types of CV models like: YOLO (You Only Look Once), DETR, and enhanced Vision-Language Models (VLMs) like Florence 2 and PaliGemma 2, which add contextual understanding and advanced analytical capabilities.

Player Detection and Tracking

BaseballCV excels at simultaneously tracking multiple players on the field, starting with pitcher, hitter and catcher locations at the moment. The system’s pitcher-hitter-catcher (PHC) detection model provides precise tracking of these key players throughout each play. This capability enables:

  • Analysis of pitcher mechanics and delivery consistency
  • Study of batter stance and swing characteristics
  • Evaluation of catcher positioning and receiving techniques
  • Understanding of spatial relationships between players

The model maintains tracking through various camera angles and can handle complex scenarios such as multiple players moving through the frame simultaneously.

Ball Trajectory Analysis

Another BaseballCV’s feature is its ball trajectory tracking system. This system can track baseballs through complex flight paths with remarkable accuracy, providing detailed data for possible analysis, like:

  • Pitch characteristics possibly including velocity, movement
  • Release point consistency and variations
  • Ball flight path
  • Impact location on batted balls

The system uses sophisticated computer vision algorithms to maintain tracking even in challenging conditions such as varying lighting, different camera angles, and partial occlusions. This capability allows analysts to extract meaningful data from standard broadcast footage, making advanced analytics accessible to a wider range of organizations.

Catcher Intent Location Analysis

A particularly innovative feature is BaseballCV’s catcher glove tracking system. This specialized model tracks the catcher’s glove position with high precision, enabling analysis of:

  • Target location and presentation
  • Pitch framing techniques
  • Receiving patterns and tendencies
  • Battery coordination and pitch calling patterns

The system can detect subtle movements and positioning adjustments, providing insights into catcher technique and strategy that were previously difficult to quantify.

Real-World Applications

These capabilities come together to support various practical applications in baseball:

Performance Analysis: Teams can analyze player mechanics in detail, identifying areas for improvement and tracking development over time. The system’s ability to process video efficiently makes it practical to maintain comprehensive player development databases.

Scouting and Recruitment: Organizations can standardize their evaluation processes using objective measurements of player attributes and tendencies. The system’s consistent analysis helps scouts compare players across different levels and conditions.

Broadcast Enhancement: Media organizations can use BaseballCV to generate real-time analytics and visualizations, enhancing their broadcast coverage with detailed insights into game events and player performance.

Through its combination of advanced computer vision technology and deep baseball knowledge, BaseballCV provides organizations and baseball analysts or fans in general with powerful tools for understanding and improving every aspect of the game.