Key findings
The findings show bias from commentators who praised players with lighter skin tone as more intelligent, as being of higher quality, and harder working than players with darker skin tone.
Players with darker skin tone were significantly more likely to be reduced to their physical characteristics or athletic abilities -- namely pace and power -- than players with lighter skin tone players were.
In numbers:
When commentators talk about intelligence:
62.60% of praise was aimed at players with lighter skin tone
63.33% of criticism was aimed at players with darker skin tone
When commentators are talking about power they are 6.59 times more likely to be talking about a player with darker skin tone
When commentators are talking about speed they are 3.38 times more likely to be talking about a player with darker skin tone
When commentators talk about work ethic, 60.40% of praise is aimed at players with lighter skin tone
You may use material from this report as long as you refer to this original report. For special requests, or questions, email lead researcher Danny McLoughlin at danny@runrepeat.com.
Overview of methodology
Selection of Games: Selected a total of 80 games from 4 of Europe’s top leagues (Italian Serie A, Spanish La Liga, French Ligue 1, and English Premier League) from the 2019/20 season.
Transcription: Transcribed commentary from each game into a text document.
Coding (Categorization of Comments): Recorded every adjectival phrase and manually assigned whether the comment was positive (praise) or negative (criticism), a category, and the associated player.
Player Information and Identification of Race: Designated a player as “players with lighter skin tone” or “players with darker skin tone” based on skin tone attribute from Football Manager 2020 (an extensive database maintained by over 1,300 scouts).
Analysis and Results: Analyzed the ratio of praise and criticism for each category to determine differences in how players of different skin tones are talked about.
For a full, detailed methodology including references and definition of categories please see the full methodology section at the bottom of this document.
What does bias look like?
If there was no bias in commentary, the distribution of comments towards players of different skin colors would be similar.
Players with lighter skin tone should receive the same proportion of comments about, for example, their intelligence or their work-ethic as players with darker skin tone.
The fact this is not the case across a large sample size indicates there is bias in the way the media discusses players based on the color of their skin.
Control category
The control category of “in-game events” was included to support the theory that there would be an even distribution of comments in the absence of bias.
There was less scope for bias in this category given it is “considered probably factual unless additional adjectives characterizing a player were included.” Eastman & Billings (2001).
Comments such as “That's a good ball in for Rafael at the back post.” or “But not the best ball from Toni Kroos.” were recorded as a positive or negative “In-game event.”
When commentators made purely factual statements (in-game events), both groups received the same proportion of comments and were praised or criticized the same amount.
The even distribution in this control category (where commentators are stating facts) is further indication that bias is present in the other categories (which are based on the opinion of the commentator) where the distribution and split of praise and criticism looks different.