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Causal Hangover Effects

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  • Andreas Santucci
  • Eric Lax

Abstract

It's not unreasonable to think that in-game sporting performance can be affected partly by what takes place off the court. We can't observe what happens between games directly. Instead, we proxy for the possibility of athletes partying by looking at play following games in party cities. We are interested to see if teams exhibit a decline in performance the day following a game in a city with active nightlife; we call this a "hangover effect". Part of the question is determining a reasonable way to measure levels of nightlife, and correspondingly which cities are notorious for it; we colloquially refer to such cities as "party cities". To carry out this study, we exploit data on bookmaker spreads: the expected score differential between two teams after conditioning on observable performance in past games and expectations about the upcoming game. We expect a team to meet the spread half the time, since this is one of the easiest ways for bookmakers to guarantee a profit. We construct a model which attempts to estimate the causal effect of visiting a "party city" on subsequent day performance as measured by the odds of beating the spread. In particular, we only consider the hangover effect on games played back-to-back within 24 hours of each other. To the extent that odds of beating the spread against next day opponent is uncorrelated with playing in a party city the day before, which should be the case under an efficient betting market, we have identification in our variable of interest. We find that visiting a city with active nightlife the day prior to a game does have a statistically significant negative effect on a team's likelihood of meeting bookmakers' expectations for both NBA and MLB.

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  • Andreas Santucci & Eric Lax, 2024. "Causal Hangover Effects," Papers 2412.21181, arXiv.org.
  • Handle: RePEc:arx:papers:2412.21181
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    1. Michael L. Anderson, 2017. "The Benefits of College Athletic Success: An Application of the Propensity Score Design," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 119-134, March.
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