Optimal Sports Math, Statistics, and Fantasy

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