Weitere Kapitel dieses Buchs durch Wischen aufrufen
Predictive modeling has long been the goal of many individuals and organizations. This science has many techniques, with simulation and machine learning at its heart. Simulations such as basketball’s BBall can model an entire season and can deduce optimal substitution patterns and scoring potential of players. Should unforeseen events occur such as an unexpected trade or long-term injury, additional simulations can be performed to assess new forms of action. Aside from the potential of simulations, machine learning techniques can uncover hidden data trends. Greyhound racing is one such area that has been explored with many different machine learners. While the choice of algorithms used in each study may differ, they all had one common similarity, they beat the choices human track experts made and were able to use the data to create arbitrage opportunities.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Albert, J. 2008. Streaky Hitting in Baseball. Journal of Quantitative Analysis in Sports 4(1).
Arnovitz, K. 2009. Stephen Curry, Blake Griffin, and Hasheem Thabeet: Inside the Numbers. Retrieved Aug 31, 2009, from http://myespn.go.com/blogs/truehoop/0-41-131/Stephen-Curry--Blake-Griffin--and-Hasheem-Thabeet--Inside-the-Numbers.html.
Burns, E. & R. Enns, et al. 2006. The Effect of Simulated Censored Data on Estimates of Heritability of Longevity in the Thoroughbred Racing Industry. Genetic Molecular Research 5(1): 7–15.
Chen, H. & P. Rinde, et al. 1994. Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment in Greyhound Racing. IEEE Expert 9(6): 21–27. CrossRef
Colston, C. 2009. In Playoffs, Crunching Picks, Crunching Numbers. USA Today. 8C.
Johansson, U. & C. Sonstrod 2003. Neural Networks Mine for Gold at the Greyhound Track. International Joint Conference on Neural Networks, Portland, OR.
Kelley, D. & J. Mureika, et al. 2006. Predicting Baseball Home Run Records Using Exponential Frequency Distributions. Retrieved Jan 15, 2008, from http://arxiv.org/abs/physics/0608228v1.
Koning, R. 2000. Balance in Competition in Dutch Soccer. The Statistician 49: 419–431. CrossRef
Lee, C. 1997. An Empirical Study of Boxing Match Prediction Using a Logistic Regression Analysis. Section Statistics Sports, American Statistical Association, Joint Statistical Meeting, Anaheim, CA.
Rue, H. & O. Salvensen 2000. Prediction and Retrospective Analysis of Soccer Matches in a League. The Statistician 49: 399–418. CrossRef
Schumaker, R. P. 2007. Using SVM Regression to Predict Greyhound Races. Information Systems Dept. Research Seminar, New Rochelle, NY.
Schumaker, R. P. & H. Chen 2008. Evaluating a News-Aware Quantitative Trader: The Effects of Momentum and Contrarian Stock Selection Strategies. Journal of the American Society for Information Science 59(1): 1–9. CrossRef
Seder, J. & C. Vickery 2005. The Relationship of Subsequent Racing Performance to Foreleg Flight Patterns During Race Speed Workouts of Unraced 2-Yr-Old Thoroughbred Racehorses at Auctions. Journal of Equine Veterinary Science 25(12): 505–522. CrossRef
Smith, L. & B. Lipscomb, et al. 2007. Data Mining in Sports: Predicting Cy Young Award Winners. Journal of Computing Sciences in Colleges 22(4): 115–121.
Solieman, O. 2006. Data Mining in Sports: A Research Overview. Dept. of Management Information Systems. The University of Arizona. Tucson.
Stern, H. 1991. On Probability of Winning a Football Game. Journal of American Statistics Association 45: 179–183.
Thomas, A. 2006. The Impact of Puck Possession and Location on Ice Hockey Strategy. Journal of Quantitative Analysis in Sports 2(1).
Tversky, A. & T. Gilovich 2004. The Cold Facts About the “Hot Hand” in Basketball. In Preference, Belief, and Similarity: Selected Writings, A. Tversky & E. Shafir. MIT Press, Cambridge, MA.
Willoughby, K. 1997. Determinants of Success in the CFL: A Logistic Regression Analysis. National Annual Meeting to the Decision Sciences Institute, Atlanta, GA.
Yang, T. Y. & T. Swartz 2004. A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball. Journal of Data Science 2(1): 61–73.
- Predictive Modeling for Sports and Gaming
Robert P. Schumaker
Osama K. Solieman
- Springer US
- Chapter 6
Neuer Inhalt/© ITandMEDIA