The Burden of Being Better
Being the favorite is an enviable position, but it comes with the burden of high expectations. Bradford Tuckfield explains why favorites sometimes withdraw from competition when they’re behind.
When we watch competitions like sports, we tend to pity the underdog. The story of the little guy against the powers that be – David against Goliath, or the unknown upstart against the world champion – is compelling and exciting. The underdog seems to have all the disadvantages, and maybe that’s why it’s so satisfying to see an underdog win against a favorite. Could it be, though, that there are also significant disadvantages to being the favorite? Should we really be pitying the favorite instead?
I’ve done some research with Berkeley Dietvorst, Katy Milkman, and Maurice Schweitzer at Wharton about a disadvantage that comes with being a favorite. This disadvantage comes from the asymmetric expectations people have for favorites and underdogs. Specifically, favorites are expected to win, so winning meets expectations and losing falls short of expectations, while underdogs are expected to lose, so losing meets expectations and winning exceeds expectations. In the face of loss, these different expectations can make a big difference. Favorites who are losing are desperate to avoid a complete loss, and may consider pursuing any strategy so that they don’t look like a loser. In particular, they might forfeit a competition and make up an excuse about why they didn’t win. For them, the expectations that are placed on them can cause them to quit and forego a chance to win in order to be sure that they don’t fall short of expectations by unequivocally losing.
To begin to investigate the decision processes of individuals who enter competitions as favorites or underdogs, we conducted interviews with competitive athletes. We also conducted a survey of professional tennis players, including some of the top 100 in the world. These athletes were able to help us understand the situations that favorites and underdogs face. One athlete told us about the role of expectations in reactions to losses:
“People are expecting you to win, and they hear that you lose, and it shocks them. If they didn’t watch the match, they don’t know what happened… you could say it’s embarrassing.”
Another athlete explained how quitting can provide an excuse for loss:
“[Quitting] is kind of a way out because you can say ‘oh I lost because.’ It’s not ‘I lost because the girl was better than me,’ it’s ‘I lost because I’m hurt,’ or ‘I lost because I couldn’t play anymore.’”
One athlete succinctly summarized the thought process of someone who quits in order to avoid falling short of expectations:
“If [players are] losing to someone that they don’t think they should be losing to, they’d rather make it seem like they’re injured and they can’t keep playing or they’re sick and they can’t keep playing, almost as an excuse as to why they were even down in the match, rather than just losing completely and making it look like the other person is better than them.”
Competitive athletes find it plausible that favorites may quit more because of the great expectations placed on them. However, it’s reasonable to ask whether this really happens in serious competitions. We tested our ideas with analysis of archival data. We examined favorites and underdogs in professional tennis, a competitive setting where experts compete for high stakes under piercing public scrutiny. We have a database of records of more than 300,000 tennis matches, including who played, what the players’ rankings were, the score of each set, the match outcome, the ages of players, prize money, match round, court surface, and the match date. We can use a comparison of players’ rankings to determine who is the favorite and who is the underdog in each match, and we can see whether status as a favorite or an underdog is related to quitting rates in this huge set of matches.
In order to make inferences about the effect of being a favorite, we use an econometric method called a regression discontinuity (RD) design. An RD design tests for “jumps” in data. We constructed a continuous measure of underdog/favorite status using the logarithm of the ratio of players’ ranks, and our RD design tests for a jump in quitting rates at the threshold between being an underdog and being a favorite. The advantage of this design is that by examining this threshold, it enables us to compare predictions about players who are essentially identical except for their status as a favorite or an underdog. In other words, it enables us to make causal inferences about the effects of underdog/favorite status.
Our findings were striking – we found that simply being a favorite in a match causes players to have a more than 10% greater likelihood of quitting, even after controlling for a host of other variables and performing a variety of robustness checks. It appears that favorites do indeed quit more than underdogs.
Our research indicates that favorites quit more than underdogs. We argue that this happens because favorites who are facing a loss would rather quit and make the reason for loss ambiguous than risk looking bad by losing completely and removing all doubt about the reason. Surprisingly, even experts who are competing for stakes of millions of dollars are willing to do this. The expectations that are placed on favorites are a burden that weighs quite heavily on them. Maybe next time you’re watching a competition, you should have sympathy for the favorite and root for them instead of the more traditionally compelling underdogs.
Bradford Tuckfield is a PhD student at the Wharton School. He graduated from Brigham Young University in 2011 with a degree in math. You can reach him at email@example.com.