Module #1 Introduction to Sports Data Analytics Overview of the importance and applications of data analytics in sports, including examples of how teams and leagues use data to gain a competitive edge.
Module #2 Data Sources in Sports Exploration of various data sources in sports, including play-by-play data, tracking data, and external data sources like social media and ticket sales.
Module #3 Descriptive Analytics in Sports Introduction to descriptive analytics, including methods for summarizing and visualizing sports data, such as box scores and shot charts.
Module #4 Inferential Analytics in Sports Introduction to inferential analytics, including hypothesis testing and confidence intervals, with applications to sports data.
Module #5 Predictive Analytics in Sports Introduction to predictive analytics, including regression analysis and machine learning, with applications to sports forecasting and game prediction.
Module #6 Sports Data Visualization Best practices for visualizing sports data, including the use of heat maps, shot charts, and other visualizations to communicate insights.
Module #7 Player Performance Metrics Introduction to player performance metrics, including advanced statistics like plus/minus, true shooting percentage, and WAR.
Module #8 Team Performance Metrics Introduction to team performance metrics, including advanced statistics like pace, offensive efficiency, and defensive rating.
Module #9 Coaching Analytics Introduction to coaching analytics, including decision analysis and strategic decision-making in sports.
Module #10 Game Theory in Sports Introduction to game theory, including concepts like Nash equilibrium and mixed strategies, with applications to sports strategy.
Module #11 Sports Betting and Odds Analysis Introduction to sports betting and odds analysis, including expected value and probability.
Module #12 Player Valuation and Salary Cap Management Introduction to player valuation and salary cap management, including concepts like marginal value and ROI.
Module #13 Radar Charts and Player Comparison Introduction to radar charts and player comparison, including methods for visualizing and comparing player performance.
Module #14 Sports Data Mining Introduction to sports data mining, including methods for discovering patterns and relationships in large datasets.
Module #15 Machine Learning in Sports Introduction to machine learning in sports, including methods for predictive modeling and pattern recognition.
Module #16 Text Analytics in Sports Introduction to text analytics in sports, including methods for sentiment analysis and topic modeling.
Module #17 Social Media Analytics in Sports Introduction to social media analytics in sports, including methods for tracking engagement and sentiment analysis.
Module #18 Sponsorship and Revenue Analytics Introduction to sponsorship and revenue analytics, including methods for evaluating the effectiveness of sponsorship deals.
Module #19 Fan Engagement and Attendance Analytics Introduction to fan engagement and attendance analytics, including methods for tracking and predicting fan behavior.
Module #20 Injury Analytics and Prevention Introduction to injury analytics and prevention, including methods for predicting and preventing injuries in sports.
Module #21 Sports Technology and Innovation Overview of emerging trends and technologies in sports, including wearables, virtual reality, and artificial intelligence.
Module #22 Ethics in Sports Data Analytics Discussion of ethical considerations in sports data analytics, including issues of privacy, bias, and fairness.
Module #23 Case Studies in Sports Data Analytics Real-world examples of sports data analytics in practice, including applications to team decision-making and strategic planning.
Module #24 Career Paths in Sports Data Analytics Overview of career paths and opportunities in sports data analytics, including roles in teams, leagues, and sports technology companies.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Sports Data Analytics and Metrics career
