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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Mark Glickman PhD

4 Issues per year


SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358
Impact per Publication (IPP) 2015: 0.250

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ISSN
1559-0410
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Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness

Andrew Hoegh
  • Corresponding author
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
  • Email:
/ Marcos Carzolio
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
/ Ian Crandell
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
/ Xinran Hu
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
/ Lucas Roberts
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
/ Yuhyun Song
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
/ Scotland C. Leman
  • Virginia Tech – Department of Statistics, Blacksburg, VA, USA
Published Online: 2015-02-23 | DOI: https://doi.org/10.1515/jqas-2014-0054

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

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About the article

Corresponding author: Andrew Hoegh, Virginia Tech – Department of Statistics, Hutcheson Hall – RM 406A 250 Drillfield Drive, Blacksburg, VA 24061, USA, e-mail:


Published Online: 2015-02-23

Published in Print: 2015-03-01


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2014-0054. Export Citation

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