Tennis Match Analytics Using Segmented Player Tracking
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Solution Overview
Problem
Current technological solutions for analyzing tennis matches lack depth and granularity, failing to provide players with valuable insights into their performance and opponents' tendencies, which limits their ability to improve their game.
Innovation Solution
A system that uses one or more processors to analyze video recordings and sensor data from tennis matches, determining player positions, shot difficulty metrics, and ball parameters to classify shots and provide detailed analytics, including graphical user interfaces that display tennis courts with zones and indicators, enabling players to gain actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed analysis of player positions, shot difficulty metrics, and ball parameters is implemented, then measurement precision and insight granularity are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The tennis court is divided into multiple zones with different shot difficulty metrics. Player positions are tracked in discrete zones rather than continuous space. Ball parameters are categorized into discrete classifications (e.g., shot type, spin direction). This segmentation enables detailed analysis while managing computational complexity through discretization.
Solution Approach 2:
The system transforms raw video and sensor data into multiple derived parameters including player position coordinates, shot difficulty metrics, ball speed, spin rate, and trajectory angles. These parameter transformations enable comprehensive performance analysis while structuring data for efficient processing and storage.
2Loss of information
If comprehensive match data including video recordings and sensor data is captured, then information completeness is improved, but data storage requirements and processing time increase
Solution Approach 1:
Player positions, ball parameters, and shot classifications are determined and stored during or immediately after match playback. Data structures are pre-configured with zone definitions and shot classification criteria. This preliminary processing enables rapid retrieval and analysis without requiring intensive computation during actual match review.
Solution Approach 2:
The system creates structured data representations (digital twins) of the physical match events, storing simplified models of player positions, ball trajectories, and shot characteristics rather than processing raw video frames continuously. This copying approach preserves essential match information while reducing processing requirements.
3Measurement precision
If shot classification and performance metrics are calculated for every shot, then analytical depth is improved, but computational power requirements increase
Solution Approach 1:
Different levels of analysis are applied to different shots based on their significance. Critical shots (winners, errors, breaking points) receive detailed classification and metric calculation, while routine shots receive simplified categorization. This local quality approach concentrates computational resources on analytically valuable events.
Solution Approach 2:
The system uses predefined thresholds and classification rules to determine when full analysis is necessary versus when simplified metrics suffice. Shot difficulty metrics and ball parameters are calculated only when they contribute meaningfully to performance insights, reducing unnecessary computational overhead.
Data Source
AI summary
Described herein are systems and methods for racket sports match analytics using data recorded through an image processing system or a data capture system. The data is then used to determine ball parameters and position data of a player. The data is then analyzed to assign tags to shots and provide an interface via which the shot can be displayed in a video player.