van Bommel, Matthew - Adjusting for Scorekeeper Bias in NBA Box Scores...

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This project has been submitted to the Library for purposes of graduation, but needs to be audited for technical details related to publication in order to be approved for inclusion in the Library collection.
Summer 2017
Degree type: 
Department of Statistics and Actuarial Science
Senior supervisor: 
Luke Bornn
Thesis title: 
Adjusting for Scorekeeper Bias in NBA Box Scores
Given Names: 
van Bommel
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.
Basketball; Optical tracking; Scorekeeper bias; Fantasy sports; Adjusted box score
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