Cricket Cover Drive Analyzer Development

Utkrisht Verma

Mathematician

Statistician

OpenCV

Python

Sports

Cricket Cover Drive Analyzer

A Python-based system for real-time biomechanical analysis of cricket cover drive shots using computer vision and pose estimation. The system processes video frames sequentially, extracts key biomechanical metrics, and provides comprehensive feedback on shot technique.

Features

Real-time Pose Estimation: Uses MediaPipe for accurate body landmark detection

Biomechanical Metrics: Calculates key angles and alignments for cricket technique

Live Video Overlays: Displays pose skeleton and real-time metrics on video

Comprehensive Evaluation: Provides detailed scoring and actionable feedback

Robust Error Handling: Gracefully handles occlusions and missing joint data

Multiple Output Formats: Generates annotated video, JSON evaluation, and detailed metrics

Biomechanical Metrics Analyzed

Front Elbow Angle (shoulder-elbow-wrist)

Optimal range: 80-120° (target: 90°)

Critical for swing control and power generation

Spine Lean (hip-shoulder line vs. vertical)

Optimal range: -15° to +15° (target: 0°)

Essential for balance and shot stability

Head-over-Knee Alignment (projected distance)

Optimal range: -20 to +20 pixels (target: 0)

Ensures proper head position over the ball

Front Foot Direction (ankle alignment)

Optimal range: 0-45° (target: 15°)

Critical for proper footwork and shot direction

System Requirements

Python 3.8 or higher

Windows 10/11, macOS, or Linux

Webcam or video file input

Sufficient RAM for video processing (4GB+ recommended)

Installation

Clone or download the project files:

git clone <repository-url>
cd cricket-cover-drive-analyzer

Create a virtual environment (recommended):

python -m venv venv

# On Windows:
venv\Scripts\activate

# On macOS/Linux:
source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Usage

Basic Usage

Analyze a cricket cover drive video:

python cricket_cover_drive_analyzer.py path/to/your/video.mp4

Advanced Usage

Specify a custom output directory:

python cricket_cover_drive_analyzer.py path/to/your/video.mp4 --output-dir my_analysis_results

Programmatic Usage

from cricket_cover_drive_analyzer import CricketCoverDriveAnalyzer

# Create analyzer instance
analyzer = CricketCoverDriveAnalyzer()

# Analyze video
evaluation = analyzer.analyze_video("path/to/video.mp4", "output_directory")

# Access results
print(f"Overall Score: {evaluation['overall_score']}/10")
print(f"Category Scores: {evaluation['category_scores']}")

Output Files

The system generates the following files in the output directory:

annotated_cover_drive.mp4: Video with pose skeleton and real-time metrics overlay

shot_evaluation.json: Comprehensive evaluation results including:

Overall score (1-10)

Category scores for each aspect

Detailed feedback for improvement

Average metrics across all frames

frame_metrics.json: Detailed frame-by-frame metrics for further analysis

Sample Evaluation Output

{
  "overall_score": 7,
  "category_scores": {
    "swing_control": 8,
    "balance": 7,
    "head_position": 6,
    "footwork": 8,
    "follow_through": 7
  },
  "feedback": {
    "swing_control": "Good elbow control, minor adjustments needed",
    "balance": "Good balance, slight adjustments needed",
    "head_position": "Good head position, minor adjustments needed",
    "footwork": "Excellent foot positioning and direction",
    "follow_through": "Good follow-through, maintain consistency"
  },
  "average_metrics": {
    "front_elbow_angle": 92.5,
    "spine_lean": 3.2,
    "head_knee_alignment": 8.1,
    "front_foot_direction": 18.3
  },
  "total_frames": 150
}

Video Requirements

For optimal analysis, ensure your video meets these criteria:

Resolution: 720p or higher recommended

Frame Rate: 30fps or higher for smooth analysis

Lighting: Good, even lighting to ensure pose detection accuracy

Camera Angle: Side view or 3/4 view of the batsman

Duration: 2-10 seconds covering the complete shot motion

Format: MP4, AVI, MOV, or other common video formats

Troubleshooting

Common Issues

"No pose detected" errors:

Ensure good lighting conditions

Check that the full body is visible in the frame

Try adjusting camera angle or distance

Poor metric accuracy:

Verify video quality and resolution

Ensure consistent lighting throughout the video

Check that the batsman is clearly visible

Memory issues with large videos:

Process shorter video segments

Reduce video resolution if necessary

Close other applications to free up RAM

Video codec issues:

Convert video to MP4 format using H.264 codec

Use tools like FFmpeg for video conversion

Performance Optimization

GPU Acceleration: Install CUDA-enabled OpenCV for faster processing

Batch Processing: Process multiple videos sequentially

Resolution Scaling: Reduce video resolution for faster processing

Technical Details

Pose Estimation

Uses MediaPipe Pose with 33 body landmarks

Model complexity: 2 (high accuracy)

Detection confidence threshold: 0.5

Tracking confidence threshold: 0.5

Metric Calculations

Angle Calculations: Uses vector mathematics for precise angle measurements

Coordinate Systems: Converts normalized MediaPipe coordinates to pixel coordinates

Error Handling: Gracefully handles missing landmarks and occlusions

Evaluation Algorithm

Scoring System: 1-10 scale based on deviation from optimal ranges

Category Weighting: Equal weighting across all categories

Feedback Generation: Context-aware feedback based on performance levels

Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

Development Setup

Fork the repository

Create a feature branch

Make your changes

Add tests if applicable

Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

MediaPipe team for the excellent pose estimation library

OpenCV community for computer vision tools

Cricket coaching community for biomechanical insights

Support

For support, questions, or feature requests, please open an issue on the project repository or contact the development team.

Note: This system is designed for educational and training purposes. For professional coaching applications, consider consulting with qualified cricket coaches and biomechanics experts.

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Posted Sep 13, 2025

Created a system for real-time biomechanical analysis of cricket cover drives using Python and MediaPipe.

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