Optimal Sports Math, Statistics, and Fantasy

Optimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required to objectively analyze sports teams, evaluate player performance, and predict game outcomes. These techniques can also be applied to fantasy sports competitions.

Readers will learn how to:

• Accurately rank sports teams
• Compute winning probability
• Calculate expected victory margin
• Determine the set of factors that are most predictive of team and player performance
  

Optimal Sports Math, Statistics, and Fantasy also illustrates modeling techniques that can be used to decode and demystify the mysterious computer ranking schemes that are often employed by post-season tournament selection committees in college and professional sports. These methods offer readers a verifiable and unbiased approach to evaluate and rank teams, and the proper statistical procedures to test and evaluate the accuracy of different models.

Optimal Sports Math, Statistics, and Fantasy delivers a proven best-in-class quantitative modeling framework with numerous applications throughout the sports world.

• Statistical approaches to predict winning team, probabilities, and victory margin
• Procedures to evaluate the accuracy of different models
• Detailed analysis of how mathematics and statistics are used in a variety of different sports
• Advanced mathematical applications that can be applied to fantasy sports, player evaluation, salary negotiation, team selection, and Hall of Fame determination

sports fans in the US and abroad, professional and college coaches, general managers, scouts, and students seeking a career in the sports industry

Chapter 1. How They Play the Game

• Abstract
• Bibliography

Chapter 2. Regression Models

• Abstract
• 2.1 Introduction
• 2.2 Mathematical Models
• 2.3 Linear Regression
• 2.4 Regression Metrics
• 2.5 Log-Regression Model
• 2.6 Nonlinear Regression Model
• 2.7 Conclusions
• References

Chapter 3. Probability Models

• Abstract
• 3.1 Introduction
• 3.2 Data Statistics
• 3.3 Forecasting Models
• 3.4 Probability Models
• 3.5 Logit Model Regression Models
• 3.6 Conclusions
• References

Chapter 4. Advanced Math and Statistics

• Abstract
• 4.1 Introduction
• 4.2 Probability and Statistics
• 4.3 Sampling Techniques
• 4.4 Random Sampling
• 4.5 Sampling With Replacement
• 4.6 Sampling Without Replacement
• 4.7 Bootstrapping Techniques
• 4.8 Jackknife Sampling Techniques
• 4.9 Monte Carlo Simulation
• 4.10 Conclusion
• Endnote
• References

Chapter 5. Sports Prediction Models

• Abstract
• 5.1 Introduction
• 5.2 Game Scores Model
• 5.3 Team Statistics Model
• 5.4 Logistic Probability Model
• 5.5 Team Ratings Model
• 5.6 Logit Spread Model
• 5.7 Logit Points Model
• 5.8 Estimating Parameters
• 5.9 Conclusion

Chapter 6. Football - NFL

• Abstract
• 6.1 Game Scores Model
• 6.2 Team Statistics Model
• 6.3 Logistic Probability Model
• 6.4 Team Ratings Model
• 6.5 Logit Spread Model
• 6.6 Logit Points Model
• 6.7 Example
• 6.8 Out-Sample Results
• 6.9 Conclusion

Chapter 7. Basketball - NBA

• Abstract
• 7.1 Game Scores Model
• 7.2 Team Statistics Model
• 7.3 Logistic Probability Model
• 7.4 Team Ratings Model
• 7.5 Logit Spread Model
• 7.6 Logit Points Model
• 7.7 Example
• 7.8 Out-Sample Results
• 7.9 Conclusion

Chapter 8. Hockey - NHL

• Abstract
• 8.1 Game Scores Model
• 8.2 Team Statistics Model
• 8.3 Logistic Probability Model
• 8.4 Team Ratings Model
• 8.5 Logit Spread Model
• 8.6 Logit Points Model
• 8.7 Example
• 8.8 Out-Sample Results
• 8.9 Conclusion

Chapter 9. Soccer - MLS

• Abstract
• 9.1 Game Scores Model
• 9.2 Team Statistics Model
• 9.3 Logistic Probability Model
• 9.4 Team Ratings Model
• 9.5 Logit Spread Model
• 9.6 Logit Points Model
• 9.7 Example
• 9.8 Out-Sample Results
• 9.9 Conclusion

Chapter 10. Baseball - MLB

• Abstract
• 10.1 Game Scores Model
• 10.2 Team Statistics Model
• 10.3 Logistic Probability Model
• 10.4 Team Ratings Model
• 10.5 Logit Spread Model
• 10.6 Logit Points Model
• 10.7 Example
• 10.8 Out-Sample Results
• 10.9 Conclusion

Chapter 11. Statistics in Baseball

• Abstract
• 11.1 Run Creation
• 11.2 Win Probability Added
• 11.3 Conclusion

Chapter 12. Fantasy Sports Models

• Abstract
• 12.1 Introduction
• 12.2 Data Sets
• 12.3 Fantasy Sports Model
• 12.4 Regression Results
• 12.5 Model Results
• 12.6 Conclusion

Chapter 13. Advanced Modeling Techniques

• Abstract
• 13.1 Introduction
• 13.2 Principal Component Analysis
• 13.3 Neural Network
• 13.4 Adaptive Regression Analysis
• 13.5 Conclusion