Word count: 3000 words
Objectives to cover:
Introduction to AI in Sports Analytics: Exploring how artificial intelligence is transforming performance analysis and decision-making in sports.
Data Collection in Sports: Identifying key data sources, types, and methods for gathering sports-related information.
Preprocessing and Feature Engineering: Cleaning, transforming, and selecting features to enhance predictive model accuracy.
Predictive Modeling Techniques: Applying machine learning and statistical methods to forecast sports performance and outcomes.
Deep Learning in Sports Prediction: Leveraging neural networks for advanced player, team, and match forecasting.
Real-Time and In-Game Analytics: Using AI for live predictions, tactical decisions, and performance optimization.
Evaluation and Validation of Models: Measuring predictive accuracy using metrics and ensuring model reliability.
Challenges and Ethical Considerations: Addressing fairness, bias, and limitations in sports data and AI predictions.
Conclusion and Future Opportunities: Highlighting research prospects, commercial uses, and AI’s evolving role in sports.
Reference: IEEE style