Player Performance Prediction Using Offset and Complementary Skill Ratings
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Solution Overview
Problem
Current fantasy sports applications fail to accurately predict sports player performance by excluding subjective attributes and external factors, leading to inaccurate future performance depictions.
Innovation Solution
A predictive analysis system that incorporates statistical data with expert subjective input, including skill ratings, importance ratings, and predicted strength ratings of offsetting and complementary skills, to calculate future performance predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical data alone is used to rate players, then the rating system is simple and objective, but the accuracy of future performance prediction deteriorates
Solution Approach 1:
The patent combines objective statistical data with subjective expert ratings into a unified player evaluation system. The skill rating is calculated by integrating both data sources, merging quantitative statistics with qualitative expert assessment to achieve more accurate predictions while maintaining system manageability.
Solution Approach 2:
The system transforms the evaluation parameters by introducing skill ratings that incorporate multiple factors including statistical data, expert subjective input, and relationships with offsetting and complementary skills. This parameter transformation enables more nuanced and accurate performance predictions compared to traditional statistical-only approaches.
2Reliability
If only past statistics are considered, then data collection is straightforward, but the reliability of performance prediction deteriorates due to exclusion of external factors
Solution Approach 1:
The patent segments the player evaluation into distinct components: core skills, offsetting skills, and complementary skills. This segmentation allows the system to systematically incorporate multiple factors including external conditions and expert assessments without creating an unmanageably complex evaluation framework.
Solution Approach 2:
The skill rating serves as an intermediary that bridges objective statistical data and subjective expert assessments. By using this intermediate metric that incorporates both data types along with skill relationships, the system achieves reliable predictions while managing evaluation complexity through a structured approach.
Data Source
AI summary
A method includes receiving statistical data associated with a past performance of a player at a position at a past sporting event. The player is associated with a plurality of skills that correspond to the position of the player. Each skill has a corresponding offsetting skill and complementary skill. A skill rating is calculated for each skill based on the received statistical data and an expert's subjective input. The subjective input includes (1) an importance rating of that skill to the position of the player, (2) a predicted strength rating of the corresponding offsetting skill at a future sporting event, or (3) a predicted strength rating of the corresponding complementary skill at the future sporting event. An output associated with the calculated skill ratings is sent to an output module and is used to predict a future performance of the player at the position at the future sporting event.