Pedro O. S. Vaz de Melo
Christos Faloutsos
Abstract
The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.
- Abbot, H. 2007a. Bad use of statistics is killing Anderson Varejao. True Hoop.Google Scholar
- Abbot, H. 2007b. Meet adjusted plus/minus. True Hoop.Google Scholar
- APBRMETRICS. www.apbrmetrics.com.Google Scholar
- Barzilai, A. and Ilardi, S. 2008. Adjusted plus-minus: 2007-2008 midseason results. 82games.Google Scholar
- Ben-Naim, E., Vazquez, F., and Redner, S. 2007. Parity and predictability of competitions. J. Quant. Anal. Sports 2, 4, 1.Google Scholar
- Bradley, R. 2009. Labor pains nothing new to the NBA. APBR.org.Google Scholar
- Cowan, C. 2006. The line on NBA betting. Business Week.Google Scholar
- Da Costa, J. P. and Soares, C. 2004. A weighted rank measure of correlation. Australian New Zealand J. Stat. 47, 4, 515--529.Google ScholarCross Ref
- Databasesports.com. 2010. Database basketball. www.databasebasketball.com.Google Scholar
- Dilger, A. 2002. Never change a winning team: An analysis of hazard rates in the NBA. SSRN eLibrary.Google Scholar
- Easterbrook, G. 2006. The five-month NFL forecast. ESPN.com. http://sports.espn.go.com/nba/playoffs/2009/news/story?id=4135263.Google Scholar
- Fast, A. and Jensen, D. 2006. The NFL coaching network: Analysis of the social network among professional football coaches. In Proceedings of the AAAI Fall Symposium on Capturing and Using Patterns for Evidence Detection.Google Scholar
- Fessler, J. A. and Hero, A. O. 1994. Space-alternating generalized expectation-maximization algorithm. IEEE Trans. Signal Process. 42, 10, 2664--2677. Google ScholarDigital Library
- Girvan, M. and Newman, M. E. 2002. Community structure in social and biological networks. Proc. the Nat. Acad. Sci. 99, 12, 7821--7826.Google ScholarCross Ref
- Hambach, W. and Schottle, H. 2006. The German sports-betting market: Uncertainty and chaos for private providers like BWIN. Gaming Law Rev. 10, 6.Google ScholarCross Ref
- Ilardi, S. 2007. Adjusted plus-minus: An idea whose time has come. 82games.com.Google Scholar
- Kelly, D. and Teevan, J. 2003. Implicit feedback for inferring user preference: A bibliography. SIGIR Forum 37, 2, 18--28. Google ScholarDigital Library
- Kendall, M. G. and Gibbons, J. D. 1990. Rank Correlation Methods 5th Ed. Oxford University Press, Oxford, UK.Google Scholar
- Lahman, S. 2008. The Lahman baseball database. baseball1.com.Google Scholar
- Lewis, M. 2009. The no-stats all-star. NYTimes.com.Google Scholar
- Lightman, A. 2010. Open prediction: How sports fans can help save the world. h+ Mag.Google Scholar
- Looney, D. S. 1976. The start of a chain reaction? Sports Illustrated.Google Scholar
- Luckner, S., Schrder, J., and Slamka, C. 2008. On the forecast accuracy of sports prediction markets. In Negotiation, Auctions, and Market Engineering, LNBIP, vol. 2, Springer, Berlin, 227--234.Google Scholar
- NBA.com. 2008. www.nba.com.Google Scholar
- Neville, J., Simsek, O., Jensen, D., Komoroske, J., Palmer, K., and Goldberg, H. 2005. Using relational knowledge discovery to prevent securities fraud. InProceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD'05). ACM, New York, 449--458. Google ScholarDigital Library
- Newman, M. 2010. The structure and function of complex networks. ACM Trans. Embed. Comput. Syst. 9, 4, Art. 39.Google Scholar
- Nichols, D. M. 1998. Implicit rating and filtering. In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering. 31--36.Google Scholar
- Onody, R. N. and De Castro, P. A. 2004. Complex network study of Brazilian soccer players. Physical Rev. E 70, 037103.Google ScholarCross Ref
- Page, G., Fellingham, G., and Reese, C. 2007. Using box-scores to determine a position's contribution to winning basketball games. J. Quant. Anal. Sports 3, 4, 1.Google Scholar
- Pandit, S., Chau, D. H., Wang, S., and Faloutsos, C. 2007. Netprobe: A fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th International Conference on the World Wide Web (WWW'07). ACM, New York, 201--210. Google ScholarDigital Library
- Park, J. and Newman, M. E. J. 2005. A network-based ranking system for us college football. J. Stat. Mech. Theory Exper. 10, P10014.Google ScholarCross Ref
- Paulsen, J. 2006. Efficiency per minute. The Scores Report.Google Scholar
- Reheuser, R. 2010. Bucking the trend. NBA Encyclopedia.Google Scholar
- Rosenbaum, D. T. 2004. Measuring how NBA players help their teams win. 82games.Google Scholar
- Shetty, J. and Adibi, J. 2005. Discovering important nodes through graph entropy the case of Enron email database. In Proceedings of the 3rd International Workshop on Link Discovery (LinkKDD'05). ACM, New York, 74--81. Google ScholarDigital Library
- Spann, M. and Skiera, B. 2009. Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters. J. Forecast. 28, 1, 55--72.Google ScholarCross Ref
- Stekler, H., Sendor, D., and Verlander, R. 2010. Issues in sports forecasting. Int. J. Forecast. 26, 3, 606--621.Google ScholarCross Ref
- Vaz De Melo, P. O., Almeida, V. A., and Loureiro, A. A. 2008. Can complex network metrics predict the behavior of NBA teams? In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08). ACM, New York, 695--703. Google ScholarDigital Library
- Weinberg, A. 2003. The case for legal sports gambling. Forbes.com.Google Scholar
Index Terms
- Forecasting in the NBA and other team sports: Network effects in action
Recommendations
Luck is Hard to Beat: The Difficulty of Sports Prediction
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPredicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This ...
Spatio-Temporal Analysis of Team Sports
Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements ...
Can complex network metrics predict the behavior of NBA teams?
KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data miningThe United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. ...
Comments