  Timeout is a short interruption during games used to communicate a change in
strategy, to give the players a rest or to stop a negative flow in the game.
Whatever the reason, coaches expect an improvement in their team's performance
after a timeout. But how effective are these timeouts in doing so? The simple
average of the differences between the scores before and after the timeouts has
been used as evidence that there is an effect and that it is substantial. We
claim that these statistical averages are not proper evidence and a more sound
approach is needed. We applied a formal causal framework using a large dataset
of official NBA play-by-play tables and drew our assumptions about the data
generation process in a causal graph. Using different matching techniques to
estimate the causal effect of timeouts, we concluded that timeouts have no
effect on teams' performances. Actually, since most timeouts are called when
the opposing team is scoring more frequently, the moments that follow resemble
an improvement in the team's performance but are just the natural game tendency
to return to its average state. This is another example of what statisticians
call the regression to the mean phenomenon.
