A splendid graph, showing high-frequency data on water consumption in Edmonton during the men’s Olympic hockey final (on February 28), and comparing it with the rather smoother pattern seen the day before.
Graph: EPCORI suspect that those spikes reflect toilets flushing in response to earlier beer consumption. (Hat tip:?Todd Sinai)
and 
. You can also watch it on 
I agree with your interpretation, but I wonder why the area above the green line is so much smaller than the area below the green line. It looks like the peaks were higher, but that total water consumption for the day was much lower. Perhaps there were fewer total bathroom visits? But that would imply that lots of people visit the bathroom multiple times per hour. Surely some do, but that many?
I think it was the Canadians wetting their pants over the possibility of losing to America. No one was drinking–THIS WAS FAR TOO IMPORTANT TO WATCH DRUNK!
The thing I notice is that the water consumption does not level back off until, it appears, well into the next period. That is, there seems to be a rush for the exits…then a slow migration back to watching TV. That’s kind of interesting.
Perhaps total water consumption for the day was lower because fewer people took showers?
The graph only covers from noon until 6pm. The total day consumption is probably made up before and after the graphed time period.
” but I wonder why the area above the green line is so much smaller than the area below the green line. ”
Check the vertical axis: the bottom of the graph is at 300, not zero, so the change in total water consumption is pretty small.
But there is a definite change. I’d say that it’s not so much bathroom visits, but everything else. People are going to postpone doing laundry, dishes, etc. during game time too.
Aaron S – “The thing I notice is that the water consumption does not level back off until, it appears, well into the next period. That is, there seems to be a rush for the exits…then a slow migration back to watching TV. That’s kind of interesting.”
I’d assume it’s the same phenomenon as at live sporting events – too many people, too few toilets. The available facilities go into 100% usage pretty quickly at the end of a period, so the queue builds up quickly – then dissipates, more slowly, over the course of the break and into the beginning of the next period.
Dave_W (#6)…
Good point, but if they facilities had a line, wouldn’t we seen a “plateau” of usage–i.e., intead of a peak and fall, we’d see more or less continual usage for a good while (at least until the line played out), wouldn’t we?
But you do bring to mind something else…. It might be that many people, knowing there will be a rush for the bathrooms, simply keep their seats for a few minutes, waiting for the lines to clear out, THEN they go themselves.
From the graph, it appears that there is a rush…then an almost immediate, but gradual, falling off (maybe some people going back to their seats, deciding to delay matters a few minutes). Maybe it’s the “Queue” effect?
In any case, there might be something important to be learned here.
Ah, the good ol’ ice cream-shark correlation fallacy, i.e. whenever ice cream sales rise, so do shark attacks, ergo ice cream consumption make’s you taster to sharks (or some other equally silly causation, http://pineda-krch.com/2008/09/03/causal-basis-of-the-ice-cream-shark-correlation-fallacy/),
It’s baffling how people are irresistibly drawn to blindly attribute causation based on correlations. Logic, journalism, and stats at it’s worst.