Using Computer Vision and Machine Learning to Automatically Classify NFL Game Film and Develop a Player Tracking System

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Author:
Omar Ajmeri, Ali Shah

Abstract: NFL coaches spend countless hours tagging and mining game film, searching for tendencies and patterns to exploit in upcoming matchups. Film tagging alone is a tedious and error-prone task – coaches need to label formations, personnel, and routes. Depending on the play, there can be up to 6 designed routes to account for, generating a wealth of data for each play. While self-scouting is fairly simple given a coach’s understanding of his own playbook, competitively scouting every team in the league on a week to week basis is an extremely time-consuming task. Player tracking data can be used as an effective tool to efficiently label formations and plays, however NFL teams currently can only access their own team’s data. As a result, we developed an algorithm that can capture player tracking data for all teams. Through a series of computer vision techniques looking at pixel density and weighted spatial reasoning, we have automated the classification of NFL All-22 game film from start (offensive formation labeling) to finish (video player tracking coordinates throughout the life of a play). This not only includes formations, but also player routes and player speeds. This effective player tracking system has implications for game planning, scouting, and better evaluation of individual players and coaches. The ability to analyze player location data on a mass scale in a short period of time will fundamentally change how football coaches scout and analyze players and opposing coaches throughout the league.

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