Live Tennis Prediction Models for Point-Level What-If Analysis

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Solution Overview

Problem

Tennis analytics lack fine-grained scoring and contextual features, such as player strength, style, and court-type information, limiting the ability to provide granular performance predictions and 'what-if' analyses.

Innovation Solution

A system that utilizes prediction models to generate live match predictions based on pre-match and in-match data, including player strength, style, and court-type information, providing granular predictions and 'what-if' analyses for next-point, game, and match outcomes, and generating new metrics like 'clutch,' 'plus/minus,' and 'momentum'.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional tennis scoring systems are used, then the scoring structure is simple and easy to understand, but the granularity and detail of performance analysis is insufficient

Engineering Contradiction:

Improvescoring granularityVSAvoidanalysis system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the match analysis into multiple hierarchical levels (match, set, game, point) and further divides performance metrics into distinct categories (serve statistics, return statistics, winners, unforced errors, momentum). This segmentation enables granular analysis while maintaining organizational structure through standardized scoring frameworks.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent adds multiple analytical dimensions beyond traditional scoring by incorporating player strength ratings, style characteristics, contextual factors (surface type, weather), and dynamic metrics (momentum, clutch performance). This dimensional expansion transforms simple scoring into multi-faceted performance analysis.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Reliability

If comprehensive player statistics and contextual data are collected, then prediction accuracy is improved, but data processing complexity and system requirements increase

Engineering Contradiction:

Improveprediction accuracyVSAvoiddata processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent performs preliminary data processing by pre-calculating and storing player strength ratings, style characteristics, and historical performance metrics before the match occurs. Contextual data such as surface type and weather conditions are also pre-processed and stored, reducing real-time processing requirements while maintaining prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces prediction models as intermediary components that process and synthesize multiple data sources (player statistics, contextual factors, match state) into predictions. These models act as mediators between raw data and analytical outputs, managing complexity through algorithmic processing.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If real-time match data is processed continuously, then live prediction accuracy is improved, but computational resources and processing time are consumed

Engineering Contradiction:

Improvelive prediction accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements periodic updates of predictions based on key match events (serve, return, game conclusion, set conclusion) rather than continuous processing. This event-driven approach maintains prediction accuracy at critical moments while reducing overall computational burden compared to continuous real-time processing.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The patent processes and analyzes only the most relevant match data elements at each stage (point-level data for live predictions, set-level data for broader context, match-level data for overall outcomes). This selective processing approach achieves sufficient prediction accuracy without the computational expense of analyzing every possible data point continuously.

Inventive Principle:

Principle #16Partial or excessive action

4Adaptability or versatility

If multiple prediction models and analytical metrics are implemented, then analysis capability is enhanced, but system complexity and implementation difficulty increase

Engineering Contradiction:

Improveanalysis capabilityVSAvoidsystem implementation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent develops a unified prediction model framework that can handle multiple analysis tasks (match outcome prediction, set prediction, game prediction, point-level predictions) and generate various metrics (win probability, momentum, clutch performance, what-if scenarios) using consistent data processing approaches. This multi-functional design enhances analysis capability while reducing implementation complexity through standardized methodologies.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250352879A1Live prediction of player performances in tennis

Publication Date: 2025.11.20 STATS LLC

AI summary

A computing system receives pre-match data for an upcoming match between a first player and a second player. The computing system generates, using one or more prediction models, one or more pre-match predictions based on the pre-match data. The computing system receives in-match data for the match currently in progress. The computing system generates, using the one or more prediction models, one or more live match predictions based on the in-match data.