A similar fun example is the distribution of Elo ratings on a chess site, e.g. here's the weekly distribution on Lichess for Bullet games (less than 3 minutes):
https://lichess.org/stat/rating/distribution/bullet
It's easy to understand why this happens:
- Player ratings will fluctuate by small amounts as they win and lose individual games.
- People are happy to stop playing when their rating is at e.g. 1503, but if it's 1497, they'd rather play just one more game than leave it that way.
- At any given time, most accounts are not playing, so the distribution shows a bias towards values just over a 100 Elo threshold.
The other neat thing is that you can see this effect reduce as you look at longer time controls:
Blitz (less than 10 min): https://lichess.org/stat/rating/distribution/blitz
Rapid (less than 30 min): https://lichess.org/stat/rating/distribution/rapid
Which makes sense because the time and effort of gambling just one more game to get the rating back over the line is higher at the longer time controls.
https://lichess.org/stat/rating/distribution/bullet
It's easy to understand why this happens:
- Player ratings will fluctuate by small amounts as they win and lose individual games.
- People are happy to stop playing when their rating is at e.g. 1503, but if it's 1497, they'd rather play just one more game than leave it that way.
- At any given time, most accounts are not playing, so the distribution shows a bias towards values just over a 100 Elo threshold.
The other neat thing is that you can see this effect reduce as you look at longer time controls:
Blitz (less than 10 min): https://lichess.org/stat/rating/distribution/blitz
Rapid (less than 30 min): https://lichess.org/stat/rating/distribution/rapid
Which makes sense because the time and effort of gambling just one more game to get the rating back over the line is higher at the longer time controls.