Summary of Pitchernet: Powering the Moneyball Evolution in Baseball Video Analytics, by Jerrin Bright et al.
PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics
by Jerrin Bright, Bavesh Balaji, Yuhao Chen, David A Clausi, John S Zelek
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel automated system called PitcherNet is introduced to analyze pitcher kinematics directly from live broadcast video, extracting valuable pitch statistics including velocity, release point, and more. This end-to-end system leverages player tracking, 3D human modeling, and kinematic-driven analytics to achieve robust analysis results with high accuracy (96.82%). The system demonstrates superior analytics compared to baseline methods, enabling the optimization of pitching strategies, prevention of injuries, and a deeper understanding of pitcher mechanics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PitcherNet is a new way to analyze baseball pitchers using live video from broadcasts. It helps coaches make better decisions by analyzing things like how fast pitches are thrown, where they start, and how long they take to reach the plate. The system uses three main parts: recognizing players in the game, creating 3D models of their movements, and tracking the movement of the ball. This allows it to calculate important statistics about each pitch with high accuracy (96.82%). This technology can help make baseball better by allowing coaches to improve pitching strategies, prevent injuries, and learn more about how pitchers throw. |
Keywords
» Artificial intelligence » Optimization » Tracking
