Adjusting for Scorekeeper Bias in NBA Box Scores

TL;DR

This paper develops models to quantify and correct for scorekeeper bias in NBA box score statistics, especially assists and blocks, using tracking data to improve accuracy and fairness in player evaluations.

Contribution

It introduces a novel approach to measure and adjust for scorekeeper bias in subjective NBA statistics using tracking data and contextual variables.

Findings

Scorekeeper bias significantly affects assist and block statistics.

Inclusion of spatio-temporal variables improves bias estimation.

Adjusted season assist totals reduce scorekeeper influence.

Abstract

Box score statistics in the National Basketball Association are used to measure and evaluate player performance. Some of these statistics are subjective in nature and since box score statistics are recorded by scorekeepers hired by the home team for each game, there exists potential for inconsistency and bias. These inconsistencies can have far reaching consequences, particularly with the rise in popularity of daily fantasy sports. Using box score data, we estimate models able to quantify both the bias and the generosity of each scorekeeper for two of the most subjective statistics: assists and blocks. We then use optical player tracking data for the 2014-2015 season to improve the assist model by including other contextual spatio-temporal variables such as time of possession, player locations, and distance traveled. From this model, we present results measuring the impact of the scorekeeper and of the other contextual variables on the probability of a pass being recorded as an assist. Results for adjusting season assist totals to remove scorekeeper influence are also presented.