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

Hoegh Andrew (), Carzolio Marcos, Crandell Ian, Hu Xinran, Roberts Lucas, Song Yuhyun and Leman Scotland C.
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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

Journal of Quantitative Analysis in Sports, 2015, vol. 11, issue 1, 29-37

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.

Keywords: K nearest neighbors; matchup effects; relative strength; transitivity (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/jqas-2014-0054

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