Machine Learning for One-on-One Pass Rush Matchup Evaluation
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Solution Overview
Problem
Existing metrics for evaluating pass rush production in American football are deficient, failing to accurately represent one-on-one matchups and accounting for the quality of opportunities and competition, leading to ambiguous and incomplete data.
Innovation Solution
A system using machine learning to analyze tracking data, identify true one-on-one pass rush matchups, and update rankings based on predicted success, incorporating an Elo model to refine player ratings and model performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metrics are used to evaluate pass rush production, then evaluation simplicity is maintained, but measurement precision deteriorates due to inability to distinguish quality of opportunities and competition
Solution Approach 1:
The patent segments pass rush evaluation into distinct components: identifying specific defensive player-offensive player matchups, determining whether they are true one-on-one situations, and evaluating the quality of competition. This segmentation allows for precise measurement of pass rush production by breaking down the complex evaluation into measurable elements including player identification, matchup type classification, and outcome tracking.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process raw tracking data and transform it into refined pass rush metrics. These models serve as mediators between the complex raw data (player positions, movements, matchups) and the simplified evaluation output, automatically distinguishing true one-on-one matchups from other situations and calculating quality-adjusted metrics.
2Measurement precision
If comprehensive tracking data analysis is performed to distinguish true one-on-one matchups, then measurement precision improves, but loss of time increases due to complex data processing
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing tracking data during games, structuring it in ways that facilitate rapid later analysis. The system continuously accumulates and organizes player position, movement, and matchup data as it occurs, preparing it for efficient processing when evaluation is needed, rather than attempting to analyze all data from scratch after the fact.
Solution Approach 2:
The patent replaces manual or rule-based mechanical analysis of matchups with machine learning models that automatically identify true one-on-one situations. These models process tracking data through learned patterns and relationships, substituting complex mechanical data filtering with intelligent algorithms that can rapidly distinguish matchup types and evaluate quality without explicit programming for every scenario.
Data Source
AI summary
A method for using machine learning to predict a success of a matchup in a sporting event, the method including accessing tracking data from a data store; identifying, from the tracking data, one or more matchups wherein each matchup includes an identification of a first player, an identification of a second player, and a success of a corresponding outcome; filtering the identified matchups to create a subset of matchups; providing the subset of matchups to a trained machine learning model; receiving, from the machine learning model, a prediction of success of the matchup; comparing the prediction of success with a measured outcome; and adjusting a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.