Wednesday, April 2, 2014

Recent Papers Reviewed

I have added several new papers to the Papers archive.  Short descriptions follow.

[Barrow 2013] D. Barrow, I. Drayer, P. Elliott, G. Gaut, and B. Osting, "Ranking rankings: an empirical comparison of the predictive power of sports ranking methods," 2013.

This paper compares a number of ranking systems on predictive power.  The main conclusions are that (1) ranking systems which use margin of victory are more predictive than those that use only win-loss data, and (2) least squares and random walkers are better than other methods for predicting NCAA football outcomes.
[Hvattum 2010] Lars Magnus Hvattum, , Halvard Arntzen, "Using ELO ratings for match result prediction in association football," International Journal of Forecasting 26 (2010) 460–470.
This paper looks at using ELO ratings to predict association football (soccer) matches.  ELO was better than all of the other rating systems, but failed to out-perform the market lines.
[Kain 2011] Kyle J. Kain and Trevon D. Logan, "Are Sports Betting Markets Prediction Markets?  Evidence from a New Test," January 2011.
This paper tests whether the point spread is a good predictor of margin of victory (it is) and whether the over/under is a good predictor of total points scored (it is not).
[Melo 2012] Pedro O. S. Vaz De Melo, Virgilio A. F. Almeida, Antonio A. F. Loureiro, and Christos Faloutsos, "Forecasting in the NBA and Other Team Sports: Network Effects in Action," ACM Transactions on Knowledge Discovery from Data, Vol. 6, No. 3, Article 13, October 2012.
This is a rather interesting paper that models NBA teams as networks exchanging players and coaches.  This allows the authors to look at hypotheses such as "trading players improves a team's performance," or "a player who has played for a number of teams is more valuable than one who hasn't."  They develop metrics such as "team volatility" and use these to predict future performance.
[Page 2007] Garritt L. Page, Gilbert W. Fellingham, C. Shane Reese, "Using Box-Scores to Determine a Position’s Contribution to Winning Basketball Games," Journal of Quantitative Analysis in Sports, Volume 3, Issue 4 2007 Article 1.
This paper looks at box scores for games from the 1996-97 NBA season to determine the importance of different basketball skills (e.g., defensive rebounding) were to each basketball position (e.g., point guard).  The surprising result was the importance of defensive rebounding by the guard positions and offensive rebounding by the point guard.
[Park 2005] Juyong Park and M. E. J. Newman, "A network-based ranking system for US college football," Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, 2005.
The authors develop a ranking system based upon the intuitive logic that "If A beat B and B beat C, then A indirectly beat C" and apply it to college football.
[Strumbelj 2012] Erik Štrumbelj, Petar Vračar, "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting 28 (2012) 532–542.
The authors build a possession-by-possession transition matrix for an NBA game based upon box score data and team statistics.  They then use this matrix to predict game outcomes.  The results were not statistically better than methods such as ELO, and worse than point spreads.

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