@UniBonn Economics MRes | Recovering sports data user...
https://sites.google.com/view/kcolombe/
The clearest evidence appears with a continuous tone 𝐨𝐮𝐭-𝐠𝐫𝐨𝐮𝐩 𝐟𝐚𝐯𝐨𝐫𝐢𝐭𝐢𝐬𝐦 difference. The findings align with out-group favoritism rather than in-group discrimination.
The clearest evidence appears with a continuous tone 𝐨𝐮𝐭-𝐠𝐫𝐨𝐮𝐩 𝐟𝐚𝐯𝐨𝐫𝐢𝐭𝐢𝐬𝐦 difference. The findings align with out-group favoritism rather than in-group discrimination.
Baseline: As expected, 𝐧𝐨 𝐬𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 racial bias exists 𝐛𝐞𝐟𝐨𝐫𝐞 heightened awareness.
After attention: 𝐨𝐯𝐞𝐫𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧, where players receive 𝐟𝐞𝐰𝐞𝐫 fouls from crews of more “opposite-race/dissimilar-tone” officials.
Baseline: As expected, 𝐧𝐨 𝐬𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 racial bias exists 𝐛𝐞𝐟𝐨𝐫𝐞 heightened awareness.
After attention: 𝐨𝐯𝐞𝐫𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧, where players receive 𝐟𝐞𝐰𝐞𝐫 fouls from crews of more “opposite-race/dissimilar-tone” officials.
Using 11 seasons of data, quasi-random referee assignments, and Machine Learning to predict race and skin tone, we explore how increased public attention influences officiating.
Using 11 seasons of data, quasi-random referee assignments, and Machine Learning to predict race and skin tone, we explore how increased public attention influences officiating.
“𝐑𝐚𝐜𝐢𝐚𝐥 𝐁𝐢𝐚𝐬, 𝐂𝐨𝐥𝐨𝐫𝐢𝐬𝐦, 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧” (with
Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski)
🧵 below 👇
“𝐑𝐚𝐜𝐢𝐚𝐥 𝐁𝐢𝐚𝐬, 𝐂𝐨𝐥𝐨𝐫𝐢𝐬𝐦, 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧” (with
Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski)
🧵 below 👇