Abstract:Streaks of success have always fascinated people and a lot of research has been conducted to identify whether the “hot hand” effect is real. While sports have provided an appropriate platform for studying this phenomenon, the majority of existing literature examines scenarios in a vacuum with results that might or might not be applicable in the wild. In this study, we build on the existing literature and develop an appropriate framework to quantify the extent to which success can come in streaks—beyond the str… Show more
“…However, given that I essentially perform multiple statistical tests—one for each player—I expect some of them to deem statistically significant results even by chance. Therefore, I perform a meta-test to calculate the probability that all of the data points that came out as statistically significant are false positives 25 , 26 . In particular, under the—realistic in this case—assumption that the tests are not correlated I can use the Binomial distribution for a meta-test.…”
Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a potential violation. In this work I revisit and extend some of the existing work on implicit biases in refereeing. In particular, I focus on refereeing in the NBA and examine three different types of implicit bias; (i) home-vs-away bias, (ii) bias towards individual players or teams, and, (iii) racial bias. For this study, I use play-by-play data and data from the Last 2 min reports the league office releases for games that were within 5 points in the last 2 min since the 2015 season. The results indicate that the there is a bias towards the home team—particularly pronounced during the playoffs—but it has been reduced since the COVID-19 pandemic. Furthermore, there is robust statistical evidence that specific players benefit from referee decisions more than expected from pure chance. However, I find no evidence of negative bias towards individual players, or towards specific teams. Finally, my analysis on racial bias indicates the absence of any bias.
“…However, given that I essentially perform multiple statistical tests—one for each player—I expect some of them to deem statistically significant results even by chance. Therefore, I perform a meta-test to calculate the probability that all of the data points that came out as statistically significant are false positives 25 , 26 . In particular, under the—realistic in this case—assumption that the tests are not correlated I can use the Binomial distribution for a meta-test.…”
Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a potential violation. In this work I revisit and extend some of the existing work on implicit biases in refereeing. In particular, I focus on refereeing in the NBA and examine three different types of implicit bias; (i) home-vs-away bias, (ii) bias towards individual players or teams, and, (iii) racial bias. For this study, I use play-by-play data and data from the Last 2 min reports the league office releases for games that were within 5 points in the last 2 min since the 2015 season. The results indicate that the there is a bias towards the home team—particularly pronounced during the playoffs—but it has been reduced since the COVID-19 pandemic. Furthermore, there is robust statistical evidence that specific players benefit from referee decisions more than expected from pure chance. However, I find no evidence of negative bias towards individual players, or towards specific teams. Finally, my analysis on racial bias indicates the absence of any bias.
“…However, given that we essentially perform multiple statistical tests -one for each player -we expect some of them to deem statistically significant results even by chance. Therefore, we perform a meta-test to calculate the probability that all of the data points that came out as statistically significant are false positives 8,9 . In particular, under the -realistic in our case -assumption that our tests are not correlated we can use the Binomial distribution for a meta-test.…”
Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a potential violation. In this work we revisit and extend some of the existing work on implicit biases in refereeing. In particular, we focus on refereeing in the NBA and examine three different types of implicit bias; (i) home-vs-away bias, (ii) bias towards individual players or teams, and, (iii) racial bias. For our study, we use play-by-play data and data from the Last Two Minutes reports the league office releases for games that were within 5 points in the last 2 minutes since the 2015 season. Our results indicate that the there is a bias towards the home team - particularly pronounced during the playoffs - but it has been reduced since the COVID-19 pandemic. Furthermore, there is robust statistical evidence that specific players benefit from referee decisions more than expected from pure chance. However, we find no evidence of negative bias towards individual players, or towards specific teams. Finally, our analysis on racial bias indicates the absence of any bias.
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