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Data Mining and Knowledge Discovery

, Volume 31, Issue 6, pp 1622–1642 | Cite as

Adjusting for scorekeeper bias in NBA box scores

  • Matthew van Bommel
  • Luke Bornn
Article
Part of the following topical collections:
  1. Sports Analytics

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 2015–2016 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.

Keywords

Basketball Optical tracking Scorekeeper bias Fantasy sports Adjusted box score 

Notes

Acknowledgements

This work was partially supported by U.S. National Science Foundation grant 1461435, by DARPA under Grant No. FA8750-14-2-0117, by ARO under Grant No. W911NF-15-1-0172, by Amazon, by NSERC, and by the National Association of Basketball Coaches.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of StatisticsSimon Fraser UniversityBurnabyCanada

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