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Abstract:
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In the NBA, there is a constant effort to improve metrics which assess player ability, but there has been almost no effort to quantify and comparing existing metrics. Any individual making a management, coaching, or gambling decision is already overwhelmed with hundreds of statistics. This clutter threatens statistical advances in player evaluation. We address this problem by proposing a set of "meta-analytics" which can be used to identify the metrics that provide the most unique, reliable, and useful information for decision-makers. Specifically, we develop methods to evaluate metrics based on four criteria: 1) reliability: does the metric measure the same thing over time? 2) discrimination: does the metric differentiate between players? 3) independence: does the metric provide new information? and 4) interpretation: does the metric have a simple, clear meaning? Our methods are easy to implement and applicable outside of the NBA, so they should be of interest to the broader sports statistics community.
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