Optimal Sports Math, Statistics, and Fantasy

1st Edition - April 6, 2017
Authors: Robert Kissell, James Poserina
Language: English
Hardback ISBN:
9 7 8 - 0 - 1 2 - 8 0 5 1 6 3 - 4
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 0 5 2 9 3 - 8

Optimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required… Read more

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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.

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

Robert Kissell

Robert Kissell, Ph.D., is President of Kissell Research Group, a global financial and economic consulting firm specializing in quantitative modeling, statistical analysis, and algorithmic trading. He is also a professor at Molloy College in the School of Business and an adjunct professor at the Gabelli School of Business at Fordham University. He has held several senior leadership positions with prominent bulge bracket investment banks including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and at JP Morgan where he was Executive Director and Head of Quantitative Trading Strategies. He was previously at Citigroup/Smith Barney where he was Vice President of Quantitative Research, and at Instinet where he was Director of Trading Research. He began his career as an Economic Consultant at R.J. Rudden Associates specializing in energy, pricing, risk, and optimization. Dr. Kissell has written several books and published dozens of journal articles on Algorithmic Trading, Risk, and Finance. He is a coauthor of the CFA Level III reading titled “Trade Strategy and Execution,” CFA Institute 2019.”

Affiliations and expertise

President, Kissell Research Group; Professor, Molloy College; Adjunct Professor, Fordham University

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

Jim Poserina is a web application developer for the School of Arts and Sciences at Rutgers University in New Brunswick, New Jersey. He has been a member of the Society for American Baseball Research for over 15 years.