It's not unreasonable to think that game performance can be affected (in part) by what takes place off the court. We are interested to see if teams exhibit a decline in performance the day following a game in a party city. We exploit data on bookmaker spreads, the expected score differential between two teams after conditioning on observable performance. We expect a team to meet the spread half the time, since this is how bookmakers minimize risk on their end. We construct a model which attempts to estimate the causal effect of visiting a "party-city" the day before a game on the probability of meeting the spread.
We define a party city to be one of the following: Brooklyn, Los Angeles, or New York. We then look for other teams (not from one of these cities) who travel to a party city and then play a game in another city the day after; each of the thirty NBA teams encounters this situation on average twice per season.
What if we pick up the effect of a busy play-schedule, or travel from a far-away city? We consider a difference in difference model: compare the difference in performance when a team has visited a party city the day before to the difference in performance when a team has visited any other city the day before. Model sketch: $$ \texttt{meet spread} \sim \substack{\texttt{days since} \ \texttt{last game}} + \substack{\texttt{games played} \ \texttt{last week}} + \substack{\texttt{team} \ \texttt{ranking}} + \substack{\texttt{time}\ \texttt{difference}} + \substack{\texttt{distance} \ \texttt{traveled}} + \texttt{party} $$
We note that some of these predictors might have non-linear effects, e.g. distance traveled may only become important when it passes some threshold. Others, such as number of games played "recently", may benefit from some type of kernel.
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