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Archetypoid analysis for sports analytics

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Abstract

We intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete’s careers.

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Notes

  1. http://bayes2.ucd.ie:3838/gvinue/AppBasketball.

  2. library (shiny); runUrl(‘http://www.uv.es/vivigui/softw/AppPlayers.zip’)

  3. All data was downloaded from: http://www.basketball-reference.com/play-index/pgl_finder.cgi?lid=front_pi.

  4. Obtained from www.linguasport.com/futbol/nacional/liga/Liga_15.htm.

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Acknowledgements

The authors would like to thank the Editors and three reviewers for their very constructive suggestions, which have led to improvements in the manuscript.

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Authors and Affiliations

  1. Department of Statistics and O.R., University of Valencia, 46100, Burjassot, Spain

    G. Vinué

  2. Dept. Matemàtiques and Institut de Matemàtiques i Aplicacions de Castelló. Campus del Riu Sec, Universitat Jaume I, 12071, Castelló, Spain

    I. Epifanio

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  1. G. Vinué
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  2. I. Epifanio
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Correspondence to G. Vinué.

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Responsible editors: A. Zimmermann and U. Brefeld.

This work has been partially supported by Grant DPI2013-47279-C2-1-R. The databases and R code (including the web application) to reproduce the results can be freely accessed at www.uv.es/vivigui/software.

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Vinué, G., Epifanio, I. Archetypoid analysis for sports analytics. Data Min Knowl Disc 31, 1643–1677 (2017). https://doi.org/10.1007/s10618-017-0514-1

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