Seeking Excellence: Improving Objectivity in Player Analysis in Professional Basketball

This thesis details the creation and testing of an original statistical metric for analyzing individual basketball players in the National Basketball Association (NBA) by both their commonly measured statistics and their so-called “intangibles.” By using existing methods as both guides and a caution against potential shortcomings, an inclusive statistic with multiple layers of data can be built to best reflect an individual player’s overall value to his team. This metric will be adjusted to account for the differences across multiple eras of NBA play and the levels of talent with which a player played in order to avoid penalizing a player for the unique aspects of his career

Projecting the Future Individual Contributions of NHL Players

Professional sports are a multibillion-dollar industry with millions of people invested in the outcomes of games and seasons. Owners, management, and fans sit on the edges of their seats wondering what will happen next. Lots of work has been done forecasting success at the team level across a variety of sports, but player level predictions are less common. Predictive work related to the NHL is even rarer. This thesis explores the ability to predict NHL player performance in a given season using publicly available information via statistical learning methods. Data featured in the analysis includes play-by-play and shift information, box score statistics, a variety of composite and catch-all statistics, injury information, and player biographical information. Data was compiled and analyzed to find meaningful relationships between past and future performance. The results of the analysis found the most predictive values in . season’s raw numbers can be supplemented with more information to improve predictive power

Projecting the Future Individual Contributions of NHL Players

Professional sports are a multibillion-dollar industry with millions of people invested in the outcomes of games and seasons. Owners, management, and fans sit on the edges of their seats wondering what will happen next. Lots of work has been done forecasting success at the team level across a variety of sports, but player level predictions are less common. Predictive work related to the NHL is even rarer. This thesis explores the ability to predict NHL player performance in a given season using publicly available information via statistical learning methods. Data featured in the analysis includes play-by-play and shift information, box score statistics, a variety of composite and catch-all statistics, injury information, and player biographical information. Data was compiled and analyzed to find meaningful relationships between past and future performance. The results of the analysis found the most predictive values in . season’s raw numbers can be supplemented with more information to improve predictive power

Adjusting for Scorekeeper Bias in NBA Box Scores

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

Adjusting for Scorekeeper Bias in NBA Box Scores

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