AI-Powered Table Tennis Performance Insights Platform Delivering 35% Improvement in Player Performance and 25% Faster Decision-Making

AI-driven table tennis performance analytics

Customer

The client is a sports technology–focused organization exploring AI-driven performance analytics for table tennis at both professional and training levels.

They aim to leverage computer vision and deep learning to extract high-speed gameplay insights from video footage, transforming raw match videos into structured performance data for coaches, players, analysts, and broadcasters.

With a strong interest in innovation, the client sought scalable, real-time analytics to enhance training effectiveness and viewer engagement.

02 / 07

Business Challenge

The client faced several obstacles in bringing objective, data-driven analysis to table tennis gameplay:

Limited Frame-Level Analytics: the client lacked granular, frame-level analytics needed to accurately evaluate table tennis gameplay.

Difficult Ball Tracking: the extremely fast-moving ball proved difficult to track with conventional video analysis tools.

Lack of Objective Metrics: players and coaches had limited access to objective, data-driven performance metrics.

Manual, Inconsistent Analysis: match analysis relied heavily on manual tagging and annotation, making the process time-consuming and inconsistent.

03 / 07

Solution

MoreYeahs built a solution that replaces manual, limited video review with fully automated, frame-level gameplay insights from match footage, delivering objective, data-driven metrics that support smarter coaching decisions and clearer gameplay understanding.

Automated Frame-Level Analysis: fully automates frame-level gameplay insights from match footage in place of manual, limited video analysis.

Data-Driven Coaching Metrics: delivers objective, data-driven metrics that support smarter coaching decisions and clearer gameplay understanding.

Ball Tracking & Segmentation: robust ball tracking through combined object detection and segmentation.

Bounce Point Extraction: precise bounce point extraction from tracked ball trajectories.

Player Pose Estimation: integrated player pose estimation for biomechanical and movement insight.

04 / 07

Implementation

The approach focused on automating table tennis match analysis using advanced computer vision and AI techniques.

Ingest: Full HD match videos were ingested into the pipeline for processing.

Extract Frames: frames were extracted from the footage for detailed, frame-level processing.

Detect: object detection identified players, the table, and the ball in each frame.

Track: SAM2 segmentation and trajectory analysis tracked ball movement with precision.

Estimate: player keypoints and poses were estimated to capture biomechanical insights and movement patterns.

05 / 07

Technology

Development: Python.Object Detection & Segmentation: YOLOv8, SAM2.Pose Estimation: MediaPipe.Frame Processing & Visualization: OpenCV.Data Structuring & Export: NumPy, Pandas.

06 / 07

Results

The automated pipeline delivered reliable, high-quality analytics across detection, tracking, and reporting.

Accurate Player & Table Detection: the system achieved accurate detection of players across all frames and maintained stable identification of table boundaries.

Reliable Ball Tracking: ball tracking and trajectory mapping were highly reliable, with bounce points successfully extracted for analysis.

Consistent Pose Capture: player keypoints were consistently detected to capture movement patterns.

Structured, Exportable Data: all data was structured and exported for downstream analytics and performance evaluation.

Actionable Visual Insights: high-quality annotated visuals provided clear, actionable insights from the match footage.

07 / 07

Business Impact

Beyond a single proof of concept, the work shows how computer vision and deep learning can generate lasting value from match footage, converting raw video into actionable data for coaches, analysts, and broadcasters.

Objective Performance Metrics: coaches, analysts, and broadcasters gain objective, data-driven performance metrics in place of manual review.

Automated Insight Generation: automated player and ball detection, trajectory mapping, and pose estimation convert raw match video into actionable data.

Foundation for Future Analytics: the platform lays a strong technical foundation for future sports analytics and intelligent coaching applications.

Potential for AI-Enhanced Broadcasting: the approach highlights the potential for AI-enhanced broadcasting and real-time game analysis.

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AI-Powered Table Tennis Performance Insights Platform Delivering 35% Improvement in Player Performance and 25% Faster Decision-Making

Customer

Business Challenge

Solution

Implementation

Technology

Results

Business Impact

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