Forecasting in the NBA and Other Team Sports: Network Effects in ActionThis paper looks at predicting the overall season performance of NBA teams (won-loss record) based upon features having to do with the team's year-to-year composition, such as "team volatility", "team inexperience," and so on. (The authors call these features "network effects" because they model the NBA as a network of nodes representing players & coaches, with network links representing business relationships like "played for" or "played with".) The model does surprisingly well at predicting season performance when compared against a variety of other models.
PEDRO O. S. VAZ DE MELO, VIRGILIO A. F. ALMEIDA, and ANTONIO A. F. LOUREIRO,
Universidade Federal de Minas Gerais
CHRISTOS FALOUTSOS, Carnegie Mellon University
From the viewpoint of predicting NCAA basketball games, this work has limited applicability. First of all, these authors are predicting the outcome of the entire season, not individual games. Second, the nature of the NBA -- with the most important players having 10+ year careers and often changing teams -- makes the year-to-year movement of players more relevant than in the NCAA game. On the other hand, any predictive value this information has seems likely to be orthogonal to the information from past game performances, which would be valuable.
A network-based ranking system for US college footballThis paper ranks college football teams by calculating a score based upon "total win score" and "total loss score". The total win score is the sum the team's total wins plus the total win score of all the opponents it beat (discounted by a constant factor). Total loss score is calculated in a similar way, and the final score is total win score minus total loss score.
Juyong Park and M. E. J. Newman
Department of Physics and Center for the Study of Complex Systems,
University of Michigan, Ann Arbor, MI 48109
This approach is similar to systems like infinitely deep RPI, or Govan ratings, although the former uses win percentage rather than wins and losses, and the latter uses points scored/allowed. This approach seems to do fairly well at ranking (the authors didn't use it for prediction) and may be worth trying for college basketball.
Are Sports Betting Markets Prediction Markets?This paper looks at the predictive value of point spreads and over/under lines from bookmakers on NFL, NBA, NCAA college football, and NCAA college basketball games from 2004-2010. Without delving into the details, the bottom line from the paper is:
Evidence from a New Test
Kyle J. Kain, and Trevon D. Logan
Our joint tests revealed that while the betting line is an accurate predictor of the margin of victory, the over/under is a poor predictor of the sum of scores in a contest.I suspect this is because over/under is much more difficult to predict. But this suggests that if you're out to beat the bookmakers, you might want to focus your efforts on predicting over/under rather than margin of victory.
Using ELO ratings for match result prediction in association footballThis paper applies the ELO rating to association football and compares it to various other predictors. Vanilla ELO uses just the match outcome, but the authors modified the algorithm to use the score differential as well. Performance was on par with other statistical predictors, but did not beat the oddsmakers.
Lars Magnus Hvattum, Halvard Arntzen