SearcharxivSearch

arXiv · 2412.21181

Causal Hangover Effects

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andreas Santucci, Eric Lax. 2024-12-30. Causal Hangover Effects. https://arxiv.org/abs/2412.21181

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM