ML/Computer Vision-Powered Data Annotation for Sports

Our client is a sports analytics firm that uses AI to disrupt how performance analysis and player recruitment are done today. They wanted to create a solution to analyze player movement and key events in a game straight off the broadcast camera feeds without incurring additional hardware costs. The project’s main objective was to track players extensively, plot the field coordinates of crucial on-field actions, and present coaches and scouts with data intelligence to aid informed decision-making. Our platform provided Predictive Analytics that improved team performance assessments and informed player recruitment strategies by identifying the exact player movement and field coordinates where any particular action was performed.

To meet the project deliverables, we assembled a project team comprising experts from all relevant fields. The platform is powered by Computer Vision and Machine Learning technologies. Our solution provided real-time tracking of all 22 players on the field, intercepting their field movements and attitudes concerning their usual on-field positions or particular patterns of play formations.

We used event tracking to mark precise coordinates on the field where specific instances occurred, enabling in-depth performance analysis. We developed custom Data Annotation tools that accurately label game footage, providing high-quality training data for our ML models. We use AWS for cloud services to enable scalable, secure, and real-time data processing.

• Programming Languages: Python, C++
• Machine Learning Frameworks: TensorFlow, PyTorch
• Computer Vision Libraries: OpenCV
• Data Annotation Tools: Custom-built annotation platforms
• Cloud Services: AWS (Amazon Web Services)
• Database Systems: PostgreSQL
• MLOps Tools: Docker, Kubernetes, CI/CD Pipelines
• Data Analytics Tools: Pandas, NumPy, SciPy

Comprehensive Player Tracking: 

• Tracks all 22 players on the field, updating every second in real-time
• Analyzes formation, positioning, and movement patterns
• Real-time monitoring to provide detailed analysis

Event Analysis:

• Marks the exact coordinates where crucial actions and events have occurred on the field
• Calculates success rates for particular player events
• Creates datasets comprised of precise action coordinates

Predictive Analytics:

• Provides insight into team performance and game strategy
• Enables informed player recruitment decisions with metrics
• Enhances data-driven strategy based on real-time data

Integration with AI and Machine Learning:

• Activity recognition by using advanced algorithms
• Trains models using huge labeled datasets
• Allows for continuous improvements by re-training the models

Custom Data Annotation Tools:

• Efficient annotation of game footage for quality data
• High-quality training datasets
• Scale to handle large volumes of annotation work

Cloud-Based Deployment:

• Hosted on AWS for secure and scalable computing
• Integrates seamlessly with the client’s systems
• Reliable in data storage and access

MLOps Practices:

• Uses automated CI/CD pipes for fast deployments
• Uses Docker for containerization
• Uses Kubernetes for orchestration

• Improved team strategies allow coaches to make tactical decisions based on concrete data.
• Improved recruitment processes with metrics that enable detailed evaluation of the players.
• The platform is highly cost-efficient because it leverages existing broadcast feeds as much as possible, thereby minimizing hardware requirements.
• Competitive advantage offered by sports analytics AI implementation as this technology is still rare in the industry.