Optimal Sports Math, Statistics, and Fantasy
Description
Key Features
- 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
Readership
Table of Contents
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
Product details
- No. of pages: 352
- Language: English
- Copyright: © Academic Press 2017
- Published: April 6, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128052938
- Hardcover ISBN: 9780128051634
About the Authors
Robert Kissell
Affiliations and Expertise
James Poserina
Jim Poserina is a web application developer for the School of Arts and Sciences at Rutgers, the State University of New Jersey. He has been a web and database developer for over 15 years, having previously worked and consulted for companies including AT&T, Samsung Electronics, Barnes & Noble, IRA Financial Group, and First Investors. He is also a partner in Doctrino Systems, where in addition to his web and database development he is a systems administrator.
Mr. Poserina has been a member of the Society for American Baseball Research since 2000 and has been published in the Baseball Research Journal. He covered Major League Baseball, NFL and NCAA football, and NCAA basketball for the STATS LLC reporter network. In addition to the more traditional baseball play-by-play information, the live baseball reports included more granular data such as broken bats, catcher blocks, first baseman scoops, and over a dozen distinct codes for balls and strikes.
Mr. Poserina took second place at the 2016 HIQORA High IQ World Championships in San Diego, California, finishing ahead of over 2,000 participants from more than 60 countries. He is a member of American Mensa, where he has served as a judge at the annual Mind Games competition that awards the coveted Mensa Select seal to the best new tabletop games.
Mr. Poserina has a B.A. in history and political science from Rutgers University. While studying there he called Scarlet Knight football, basketball, and baseball games for campus radio station WRLC.
Affiliations and Expertise
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