Context-Aware Sports Performance Evaluation System
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Solution Overview
Problem
Existing methods for evaluating player and team performance in sports, such as Markov Game models, are limited by their focus on discrete time windows and lack of context-awareness, failing to account for the full impact of actions on game outcomes and requiring extensive domain knowledge for player comparison and ranking.
Innovation Solution
A context-aware system using machine learning and AI to generate quantitative values for player and team performance, incorporating Markov Game Models with dynamic programming for unbounded look-ahead and location-based clustering to evaluate actions and events in sports games, allowing for the assessment of team and individual performance and player ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Markov Game models are used to evaluate player actions, then optimal strategies can be computed, but the models are limited to discrete time windows and lack context-awareness
Solution Approach 1:
The patent transforms the static, discrete-time Markov Game model into a dynamic continuous-time model. The system continuously updates player positions and action values in real-time as players move across the ice, rather than evaluating actions only at discrete time windows. This dynamic approach allows the model to adapt to changing game contexts and provide continuous evaluation of player actions throughout the game.
Solution Approach 2:
The patent adds spatial dimensionality to the traditional Markov Game model by incorporating player positions on the ice rink. The system uses the ice rink surface as a two-dimensional space where player locations are continuously tracked, adding this spatial dimension to the state space of the Markov model. This allows context-aware evaluation based on where actions occur on the ice, not just when they occur.
2Measurement precision
If regression techniques are used to improve +/−score, then goal contribution can be measured, but only goals are considered without other actions
Solution Approach 1:
The patent creates a universal evaluation framework that handles multiple types of player actions uniformly. Instead of having separate evaluation methods for goals, shots, passes, and other actions, the system uses a single continuous-time Markov model that can evaluate any player action. The model assigns action values to all types of player movements and interactions, making the evaluation system versatile and applicable to all actions on the ice.
Solution Approach 2:
The patent changes the fundamental parameters of the evaluation system from goal-centric to action-centric. Rather than measuring only goal contributions through regression coefficients, the system evaluates all player actions by computing the change in win probability caused by each action. This parameter change allows comprehensive measurement of player impact through various actions, not just goals.
3Adaptability or versatility
If THoR assigns values to all actions, then comprehensive evaluation is achieved, but fixed values are used without context and a 20 second window restricts look ahead value
Solution Approach 1:
The patent replaces the static 20-second fixed window with a dynamic continuous-time approach. The system continuously updates the evaluation of player actions as time progresses and player positions change. There is no fixed time window restricting the evaluation; instead, the model continuously computes the impact of actions on win probability, allowing for unbounded look-ahead and context-sensitive evaluation that adapts to the actual flow of the game.
Solution Approach 2:
The patent changes from fixed action values to dynamic action values that depend on game context. Instead of assigning predetermined values to actions, the system computes action values based on the current game state, player positions, and potential impacts on win probability. This contextual parameter change allows the evaluation to be sensitive to the specific situation in which each action occurs.
4Loss of time
If MDP-type models are used for player valuation, then temporal order of game states can be captured, but the models are limited to small number of states and actions in low dimensional space
Solution Approach 1:
The patent resolves the dimensionality limitation by utilizing the two-dimensional ice rink surface as the state space. Instead of being constrained to a small number of discrete states, the system represents player positions continuously across the ice rink plane. This spatial dimensionality provides an effectively infinite state space, allowing the model to capture temporal sequences of actions while maintaining high dimensional representation of game states through player locations.
Data Source
AI summary
A system and method are provided for creating and evaluating computational models for games (e.g., individual or team sports games, etc.), team performance, and individual player performance evaluation. The method comprises obtaining data associated with the team game, the information comprising at least one individual player activity, at least one team activity, at least one game event, and a location in space and time for each of the events and activities; generating quantitative values for the data associated with the team game; and evaluating either or both an individual player and a team using the quantitative values.