Key Capabilities
Commentary Analysis
Extract and analyze player performance from game commentary using natural language processing.
Video Verification
Verify player actions directly from video footage using computer vision models.
Play Chunking
Automatically segment games into individual plays for detailed analysis.
Action Recognition
Identify specific player actions including shooting, passing, dribbling, rebounding, and defending.
Commentary-Based Performance Analysis
BasketTube responds to natural language queries through a chat interface, extracting insights from game commentary. The system processes audio transcripts to identify player statistics, key moments, and performance trends.Example Query: “Analyze the player that scored the most in this game”AI Response: While the video does not provide a final leading scorer for the entire game, Stephen Curry is noted as having the most points among the individual player scores mentioned in the first half, reaching 12 points by (37:12).Here’s an analysis of his performance in the first half:Early Struggles and Defensive Coverage: The Lakers made it difficult for Curry early in the game (16:22). He took only three shots initially (15:17) as the Lakers forced him to get rid of the ball, often with two defenders when he came off a pick and roll (15:10-15:16).First Field Goal: Curry scored his first field goal later in the first half (11:06).Offensive Impact: By the end of the first half, he came “alive from three-point range” (42:22-42:25), hitting back-to-back three-pointers (37:09-37:12, 42:01-42:08), helping the Warriors extend their lead to 13 points by halftime (42:10).Overall Role: He was described as the oldest scoring champion in the NBA in 23 years, showcasing his elite scoring ability (19:09-19:12). LeBron James even called him the MVP, stating he had the best season all year (19:23-19:29).Underrated Aspects: Commentators highlighted his rebounding and finishing around the rim as underrated aspects of his game (18:54-19:06).
Video-Based Action Verification
Beyond commentary analysis, BasketTube verifies player actions directly from video footage using computer vision models. This provides objective, evidence-based performance metrics independent of commentary.Automatic Play Segmentation
BasketTube segments games into individual plays for granular analysis. The system identifies both offensive and defensive sequences: Offensive Plays Detected:- Pick and Roll: Screen-setting followed by roll to the basket
- Isolation (“Iso”): One-on-one attack with floor spacing
- Motion Offense: Continuous player movement and cutting
- Triangle Offense: Three-player formations creating passing angles
- Man-to-Man Defense: Individual player assignments
- Zone Defense: Area-based defensive coverage
- Full-Court Press: Full-court defensive pressure
Player Action Recognition
Within each play, the system tracks individual player actions and generates timestamped annotations:- Shooting: Shot attempts with location and outcome
- Passing: Ball distribution to teammates
- Dribbling: Ball handling and movement
- Rebounding: Offensive and defensive board control
- Defending: Defensive positioning and contests
- Assisting: Passes leading to made baskets
Structured Output
The system generates structured data for each action with timestamps, player identification, action classification, and spatial visualization:| Timestamp | Player | Action | Birds Eye View |
|---|---|---|---|
| 12:34 | Stephen Curry | 3-Point Shot | [View] |
| 15:20 | LeBron James | Assist | [View] |
Technical Implementation
Player Detection, Tracking & Identification
Detailed technical tutorial on implementing player detection, tracking, and jersey number identification using RF-DETR, ByteTrack, SmolVLM2, and SigLIP models. Includes complete source code, pre-trained models, and interactive Colab notebooks.
Technical Architecture
BasketTube combines Vision Language Models (VLMs) with computer vision pipelines to achieve:- Real-time Video Processing: Frame-by-frame analysis with sub-second latency
- Audio Extraction: Commentary transcription and semantic parsing
- Player Tracking: Multi-object detection and cross-frame identity maintenance
- Action Classification: Basketball-specific event recognition
- Spatial Visualization: Bird’s-eye view court mapping
- Natural Language Generation: Contextual performance summaries
Real-World Applications
BasketTube’s technology demonstrates practical applications across the sports industry:- Performance Analytics: Automated tracking of player statistics, shot charts, and efficiency metrics
- Coaching Intelligence: Frame-by-frame play breakdowns with action recognition for strategy development
- Enhanced Broadcasting: AI-generated insights and commentary augmentation for live games
- Talent Evaluation: Objective player assessment for scouting and recruitment
- Fan Experience: Interactive game analysis with natural language querying