The study found the effect of smaller jersey numbers on cognition

For decades, some NFL wide receivers have said they look better wearing lower numbers than in the traditional 80-89 range. Now there’s scientific research backing up this seemingly superficial feeling.

A study reviewed by researchers from the University of California found that cognition can be influenced by the associations made between numbers and size through the brain’s cognitive process. The study, which will be published this week in the journal PLOS One, exposed people to pictures of different numbers on soccer jerseys to gauge their perception of the person wearing the jersey. The smaller the number, the more likely a person will perceive a thinner player.

“We were surprised that there was a connection, and then even more surprised that the connection was so strong,” said Ladan Shams, a cognitive neuroscientist and professor of psychology, neuroscience and bioengineering at UCLA. “It’s not just about comparing the large numbers with the small numbers. When we looked at the relationship between the ratings of size, thinness, and numbers, [and] We did a very small range, like 17 to 19, and we see a very strong correlation.”

Shams was intrigued by the topic after being interviewed for a 2019 ESPN story about the recent migration of NFL wide receivers to numbers between 10 and 19, a process begun by former New York Jets receiver Keshaun Johnson in 1996. It relaxed its rules on eligible receiver numbers in 2004, and by 2019, about 80% of receivers wore numbers between 10 and 19. (After a series of subsequent rule changes, receivers in 2023 may wear numbers 0-49 and 80-89.)

Recipients interviewed by ESPN at the time gave different explanations for their preference for the lower numbers, but many described an image they thought a number between 10-19 would connect with.

Former NFL wide receiver Emmanuel Sanders said that numbers in the 1980s were for “big guys” who were “like 6-2, 6-3” and that it would “look weird” for the smaller receivers that put them on.

Johnson suggested that numbers affect the perception of players in other positions. He cited Hall of Fame quarterback Brett Favre, whose No. 4 “gave you the illusion that he could run and do a lot of things, even though he couldn’t.” In contrast, Johnson suggested that the perception of then-Jets quarterback Sam Darnold was diminished by wearing number 14.

ESPN reached out to Sun to understand if there is a psychological reason why the low numbers affect the perceptions of the players who wear them. Shams hypothesized that human brains can associate a small number with a lean body type, but said there is no accepted research to support this assertion.

During the COVID-19 pandemic, Shams turned her attention to testing this hypothesis. Her research group conducted two separate exercises, one virtually and one in person. Part of the experiment asked subjects to rate players with different numbers on a scale from “very thin” to “very hoarse”. According to the study, “Observers perceive low-numbered athletes as more agile compared to high-numbered athletes.”

In essence, the researchers found that the associations the brain makes over time with numbers — for example, a 20-pound bag of rice is thicker than a 5-pound bag of rice — can influence cognition in other areas.

The results have application far beyond the way footballers are perceived. According to Shams, they can explain how the brain creates implicit bias in the decision-making process.

“If the brain is very good at finding these patterns and regularities in numbers and size, the brain is also very good at finding other types of relationships, and those relationships are what are thought to give rise to implicit bias,” said Shams. “The brain is a learning machine that picks up all those statistical regularities without our knowledge. This creates biases and expectations in our brains without our knowledge. In other areas, this can affect decision-making.

“It basically shows the power of learning in the brain and how that leads to implicit bias and how we can harness the learning mechanisms themselves to undo those implicit biases.”