Meta-analytics: tools for understanding the statistical properties of sports metrics

Franks Alexander M. (), D’Amour Alexander, Cervone Daniel and Bornn Luke
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Franks Alexander M.: University of Washington - Department of Statistics, Seattle, WA, USA
D’Amour Alexander: University of Berkeley - Department of Statistics, Berkeley, CA, USA
Cervone Daniel: New York University, New York, NY, USA
Bornn Luke: Simon Fraser University - Department of Statistics, Burnaby, British Columbia, Canada

Journal of Quantitative Analysis in Sports, 2016, vol. 12, issue 4, 151-165

Abstract: In sports, there is a constant effort to improve metrics that assess player ability, but there has been almost no effort to quantify and compare existing metrics. Any individual making a management, coaching, or gambling decision is quickly overwhelmed with hundreds of statistics. We address this problem by proposing a set of “meta-metrics”, which can be used to identify the metrics that provide the most unique and reliable information for decision-makers. Specifically, we develop methods to evaluate metrics based on three criteria: (1) stability: does the metric measure the same thing over time (2) discrimination: does the metric differentiate between players and (3) independence: does the metric provide new information? Our methods are easy to implement and widely applicable so they should be of interest to the broader sports community. We demonstrate our methods in analyses of both NBA and NHL metrics. Our results indicate the most reliable metrics and highlight how they should be used by sports analysts. The meta-metrics also provide useful insights about how to best construct new metrics that provide independent and reliable information about athletes.

Keywords: analysis of variance; basketball; hockey; reliability (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1515/jqas-2016-0098

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