BallinAI is building an intelligent basketball training platform that uses AI sports video analysis to help players and coaches improve performance. The platform focuses on extracting insights from game footage using computer vision and pose estimation. It targets structured evaluation of key basketball skills to enable data-driven training decisions.
Problem: manual processes limit AI sports video analysis adoption
Basketball training relies on manual video review, where coaches analyze footage to assess passing, scoring, rebounding, and defense. This process is slow, subjective, and hard to scale. BallinAI needed an AI sports video analysis system to automatically detect and evaluate skills from video clips without manual input.
Existing gaps included lack of basketball-specific pose detection, no structured system for skill classification, limited ability to measure performance at scale, and heavy reliance on human review, which slowed feedback and reduced consistency.
Solution: AI sports video analysis system for basketball skill detection
GoML designed a PoC AI sports video analysis system using its AI Data Analytics boilerplate, combining computer vision and pose estimation to automatically analyze basketball gameplay. The system processes short video clips and outputs structured performance metrics across four core skill areas.
Computer vision pipeline
The system uses a sequential pipeline:
- Stage 1: YOLOv11 detects players, ball, and rim in each frame
- Stage 2: Feature engineering extracts motion and spatial dynamics
- Stage 3: XGBoost model classifies basketball skills
- Stage 4: Probability calibration improves prediction reliability
This pipeline enables near real-time processing and scalable deployment.
Skill detection and classification
Unlike the earlier version with four categories, the system detects 7 basketball skills:
- Scoring (including layups and general scoring actions)
- Passing
- Defense perimeter containment
- Defense interior containment
- Rebounding inside radius
- Rebounding outside radius
- Scoring versatility
This allows granular analysis of gameplay events instead of simple binary outputs.
Video processing and constraints
- Supported formats: MP4 and MOV
- Maximum duration: 60 seconds
- Frame extraction with sliding window logic for temporal continuity
- Resolution and FPS normalization for consistent inference
Videos are uploaded directly to Amazon S3 and processed via AWS Lambda and SageMaker endpoints.
Data management
The system stores:
- Original video files in S3
- Annotated frames with detection outputs
- Metadata such as FPS, resolution, duration
- Structured inference results with timestamps and confidence
This enables reproducibility, auditing, and future model improvements.
User interface
A Gradio-based web interface allows users to:
- Upload videos or use S3 URIs
- Trigger analysis in one click
- View results with timestamps and summaries
- Download CSV reports
The interface provides real-time feedback and simplifies adoption for non-technical users.
Quality assurance
Validation combines model metrics and real-world testing:
- mAP @ 50: ~0.92 for object detection
- Precision / Recall: ~0.92 / 0.89
- Confidence-based filtering to reduce false positives
- Manual validation against labeled datasets
Accuracy depends on lighting, camera angle, and player visibility.
Impact
- 92% precision and 89% recall for skill detection
- Automated detection of 7 basketball skills with timestamps
- Analysis of 60-second clips within 15 to 45 seconds
- Reduced reliance on manual video review
- Structured performance reports for data-driven coaching
About
Before Gen AI and after Gen AI
“With AI sports video analysis, we turned raw basketball footage into structured performance insights that help players improve faster with data.”
Prashanna Rao, Head of Engineering, GoML
Key takeaways for sports analytics platforms
Common challenges
- Manual video analysis does not scale
- Lack of structured performance metrics
- Difficulty in consistent skill evaluation
Practical guidance
- Adopt AI sports video analysis for automated performance tracking
- Use pose estimation for sport-specific movement detection
- Focus on limited high-impact skills for MVP
- Build simple interfaces for faster validation and adoption
Ready to build AI sports video analysis solutions
Partner with GoML to build AI sports video analysis solutions with AI Matic.
