Machine Learning Model for Race Difficulty Prediction
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Solution Overview
Problem
Existing systems fail to accurately predict the difficulty level of race result predictions in public competitions, making it challenging for organizers to create attractive and balanced race programs, which affects ticket sales and participant engagement.
Innovation Solution
A program information providing apparatus that acquires and analyzes race organization information using a machine-learned model to predict the difficulty level of race results, allowing for the generation of prediction models that estimate the difficulty based on competitor attributes and race conditions, and outputs this information to support program organization decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine race difficulty level, then the process is simple and quick, but the prediction accuracy is insufficient
Solution Approach 1:
The patent replaces traditional manual or simple calculation-based difficulty level determination methods with a machine learning model. The model processes race organization information (competitor attributes, race conditions, historical data) to automatically predict difficulty levels, substituting complex analytical work with an automated intelligent system that provides both accuracy and efficiency.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between race organization information and difficulty level determination. This intermediary processes and analyzes multiple factors (competitor attributes, race conditions, historical results) to produce an accurate difficulty level prediction, bridging the gap between raw data and actionable insights.
2Productivity
If race program organization is made more attractive and balanced, then ticket sales and participant engagement improve, but the complexity of program organization increases
Solution Approach 1:
The machine learning model enables the race program organization system to self-assess and self-optimize. By automatically analyzing race organization information and predicting difficulty levels, the system can independently identify imbalances and suggest adjustments without requiring extensive manual analysis, thereby improving program quality while managing complexity.
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning model continuously analyzes race results and competitor performance data to refine difficulty level predictions. This feedback loop allows the system to learn from past races and improve future program organization, enabling data-driven decisions that enhance ticket sales and engagement while systematicallY managing complexity.
Data Source
AI summary
The program information providing apparatus includes an acquisition unit and a prediction unit. The acquisition unit acquires information regarding race organization in a target race of public competition. The prediction unit predicts the difficulty level of a result prediction of the target race using a model in which a relationship between information regarding the race organization and a difficulty level of a race result prediction is machine-learned and the information regarding the race organization of the target race.