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Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness

Author

Listed:
  • Hoegh Andrew

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Carzolio Marcos

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Crandell Ian

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Hu Xinran

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Roberts Lucas

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Song Yuhyun

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

  • Leman Scotland C.

    (Virginia Tech – Department of Statistics, Blacksburg, VA, USA)

Abstract

Recently, the surge of predictive analytics competitions has improved sports predictions by fostering data-driven inference and steering clear of human bias. This article details methods developed for Kaggle’s March Machine Learning Mania competition for the 2014 NCAA tournament. A submission to the competition consists of outcome probabilities for each potential matchup. Most predictive models are based entirely on measures of overall team strength, resulting in the unintended “transitive property.” These models are therefore unable to capture specific matchup tendencies. We introduce our novel nearest-neighbor matchup effects framework, which presents a flexible way to account for team characteristics above and beyond team strength that may influence game outcomes. In particular we develop a general framework that couples a model predicting a point spread with a clustering procedure that borrows strength from games similar to a current matchup. This results in a model capable of issuing predictions controlling for team strength and that capture specific matchup characteristics.

Suggested Citation

  • Hoegh Andrew & Carzolio Marcos & Crandell Ian & Hu Xinran & Roberts Lucas & Song Yuhyun & Leman Scotland C., 2015. "Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 29-37, March.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:29-37:n:2
    DOI: 10.1515/jqas-2014-0054
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    References listed on IDEAS

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    1. West Brady T, 2006. "A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-16, July.
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