A mixture-of-modelers approach to forecasting NCAA tournament outcomes

Lo-Hua Yuan 1 , Anthony Liu 1 , Alec Yeh 1 , Aaron Kaufman 2 , Andrew Reece 3 , Peter Bull 4 , Alex Franks 1 , Sherrie Wang 1 , Dmitri Illushin 1  and Luke Bornn 1
  • 1 Harvard University – Statistics, Cambridge, Massachusetts, USA
  • 2 Harvard University – Government, Cambridge, Massachusetts, USA
  • 3 Harvard University – Psychology, Cambridge, Massachusetts, USA
  • 4 Harvard University – Institute for Applied Computational Science, Cambridge, Massachusetts, USA
Lo-Hua Yuan, Anthony Liu, Alec Yeh, Aaron Kaufman, Andrew Reece, Peter Bull, Alex Franks, Sherrie Wang, Dmitri Illushin and Luke Bornn


Predicting the outcome of a single sporting event is difficult; predicting all of the outcomes for an entire tournament is a monumental challenge. Despite the difficulties, millions of people compete each year to forecast the outcome of the NCAA men’s basketball tournament, which spans 63 games over 3 weeks. Statistical prediction of game outcomes involves a multitude of possible covariates and information sources, large performance variations from game to game, and a scarcity of detailed historical data. In this paper, we present the results of a team of modelers working together to forecast the 2014 NCAA men’s basketball tournament. We present not only the methods and data used, but also several novel ideas for post-processing statistical forecasts and decontaminating data sources. In particular, we highlight the difficulties in using publicly available data and suggest techniques for improving their relevance.

  • Boulier, Bryan L. and Herman O. Stekler. 1999. “Are Sports Seedings Good Predictors?: An Evaluation.” International Journal of Forecasting 15(1):83–91.

    • Crossref
  • Brown, Mark and Joel Sokol. 2010. “An Improved LRMC Method for NCAA Basketball Prediction.” Journal of Quantitative Analysis in Sports 6(3):1–23.

    • Crossref
  • Bryan, Kevin, Michael Steinke, and Nick Wilkins. 2006. Upset Special: Are March Madness Upsets Predictable? Available at SSRN 899702.

    • Crossref
  • Carlin, Bradley P. 1996. “Improved NCAA Basketball Tournament Modeling via Point Spread and Team Strength Information.” The American Statistician 50(1):39–43.

  • Cesa-Bianchi, Nicolo and Gabor Lugosi. 2001. “Worst-Case Bounds for the Logarithmic Loss of Predictors.” Machine Learning 43(3):247–264.

    • Crossref
  • Cochocki, A. and Rolf Unbehauen. 1993. Neural Networks for Optimization and Signal Processing. 1st ed. New York, NY, USA: John Wiley & Sons, Inc., ISBN 0471930105.

  • Cover, Thomas M. and Joy A Thomas. 2012. Elements of Information Theory. John Wiley & Sons, Inc., Hoboken, New Jersey.

  • Demir-Kavuk, Ozgur, Mayumi Kamada, Tatsuya Akutsu, and Ernst-Walter Knapp. 2011. “Prediction using Step-wise L1, L2 Regularization and Feature Selection for Small Data Sets with Large Number of Features.” BMC Bioinformatics 12:412.

  • ESPN. 2014. NCAA Division I Men’s Basketball Statistics – 2013–14, 2014. (http://kenpom.com/index.php?s=RankAdjOE). Accessed on February 22, 2014 and March 28, 2014.

  • Friedman, J. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 2:1189–1232.

    • Crossref
  • Fritsch, Stefan, Frauke Guenther, and Maintainer Frauke Guenther. 2012. “Package ‘Neuralnet’.” Training of Neural Network (1.32).

  • Hamilton, Howard H. 2011. “An Extension of the Pythagorean Expectation for Association Football.” Journal of Quantitative Analysis in Sports 7(2). DOI: 10.2202/1559-0410.1335.

    • Crossref
  • Harville, David A. 2003. “The Selection or Seeding of College Basketball or Football Teams for Postseason Competition.” Journal of the American Statistical Association 98(461):17–27.

    • Crossref
  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer.

  • Huang, Tzu-Kuo, Ruby C. Weng, and Chih-Jen Lin. 2006. “Generalized Bradley-Terry Models and Multi-Class Probability Estimates.” Journal of Machine Learning Research 7(1):85–115.

  • Jacobson, Sheldon H. and Douglas M. King. 2009. “Seeding in the NCAA Men’s Basketball Tournament: When is a Higher Seed Better?” Journal of Gambling Business and Economics 3(2):63.

  • Kaplan, Edward H. and Stanley J. Garstka. 2001. “March Madness and the Office Pool.” Management Science 47(3):369–382.

    • Crossref
  • Koenker, Roger and Gilbert W. Bassett, Jr. 2010. “March Madness, Quantile Regression Bracketology, and the Hayek Hypothesis.” Journal of Business & Economic Statistics 28(1):26–35.

    • Crossref
  • Liaw, Andy and Matthew Wiener. 2002. “Classification and Regression by Randomforest.” R News 2(3):18–22.

  • Massey, Kenneth. 2014. College Basketball Ranking Composite. (http://www.masseyratings.com/cb/compare.htm). Accessed on February 22, 2014 and March 28, 2014.

  • Matuszewski, Erik. 2011. “March Madness Gambling Brings Out Warnings From NCAA to Tournament Players.” Bloomberg News, March 2011. (http://www.bloomberg.com/news/2011-03-17/march-madness-gambling-brings-out-warnings-from-ncaa-to-tournament-players.html).

  • McCrea, Sean M. and Edward R. Hirt. 2009. “March Madness: Probability Matching in Prediction of the NCAA Basketball Tournament”. Journal of Applied Social Psychology, 39(12):2809–2839.

    • Crossref
  • MomentumMedia. 2006. NCAA Eliminates Two-in-four Rule. (http://www.momentummedia.com/articles/cm/cm1406/bbtwoinfour.htm). Accessed on February 22, 2014 and March 28, 2014.

  • Moore, Sonny. 2014. Sonny Moore’s Computer Power Ratings. (http://sonnymoorepowerratings.com/m-basket.htm). Accessed on February 22, 2014 and March 28, 2014.

  • Platt, John C. 1999. Probabilities for SV Machines. MIT Press. (http://research.microsoft.com/apps/pubs/default.aspx?id=69187). Accessed on February 22, 2014 and March 28, 2014.

  • Pomeroy, Ken. 2014. Pomeroy College Basketball Ratings, 2014. (http://kenpom.com/index.php?s=RankAdjOE). Accessed on February 22, 2014 and March 28, 2014.

  • Ridgeway, Greg. 2007. “Generalized Boosted Models: A Guide to the GBM Package.” Update 1(1):2007.

  • Riedmiller, Martin and Heinrich Braun. 1993. “A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm.” Pp. 586–591 in IEEE International Conference on Neural Networks.

  • Sagarin, Jeff. 2014. Jeff Sagarin’s College Basketball Ratings, 2014. (http://sagarin.com/sports/cbsend.htm). Accessed on February 22, 2014 and March 28, 2014.

  • Schwertman, Neil C., Thomas A. McCready, and Lesley Howard. 1991. “Probability Models for the NCAA Regional Basketball Tournaments.” The American Statistician 45(1):35–38.

  • Smith, Tyler and Neil C. Schwertman. 1999. “Can the NCAA Basketball Tournament Seeding be Used to Predict Margin of Victory?” The American Statistician 53(2):94–98.

    • Crossref
  • Sokol, Joel. 2014. LRMC Basketball Rankings, 2014. (http://www2.isye.gatech.edu/jsokol/lrmc/). Accessed on February 22, 2014 and March 28, 2014.

  • Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 288:267–288.

    • Crossref
  • Timthy P. Chartier, E. Kreutzer, A. Langville and K. Pedings. 2011. “Sports Ranking with Nonuniform Weighting.” Journal of Quantitative Analysis in Sports 7(3):1–16.

    • Crossref
  • Toutkoushian, E. 2011. Predicting March Madness: A Statistical evaluation of the Men’s NCAA Basketball Tournament.

Purchase article
Get instant unlimited access to the article.
Log in
Already have access? Please log in.

Log in with your institution

Journal + Issues

JQAS, an official journal of the American Statistical Association, publishes research on the quantitative aspects of professional and collegiate sports. Articles deal with subjects as measurements of player performance, tournament structure, and the frequency and occurrence of records. Additionally, the journal serves as an outlet for professionals in the sports world to raise issues and ask questions that relate to quantitative sports analysis.