Kyle Meyer writes:
Last August you wrote about [1] a PNAS paper that looked at “jet lag” and a bunch of metrics across twenty MLB seasons. I’ve played around with incorporating their measure of jet lag into a model of run differentials [2], working from your posts about estimating team abilities in soccer [3-5]. I don’t think the model I came up with is particularly useful. Assuming that I didn’t make any stupid mistakes, the model doesn’t do a good job of estimating home field advantage, which makes me question all of its estimates, including the ones for the jet lag parameters.
But, anyway, perhaps others will be interested in the data set that I generated [6]. As far as I can tell, the authors of the original study didn’t release the lag values or the code they used to generate them. Based on my attempts to reproduce their summary tables [7], I think my set of lag values is pretty similar.
[1]: http://gelmanstatdev.wpengine.com/2017/08/04/hadnt-jet-lag-junior-certainly-wouldve-banged-756-hrs-career/
[2]: https://kyleam.github.io/mlb-rundiff/
[3]: http://gelmanstatdev.wpengine.com/2014/07/13/stan-analyzes-world-cup-data/
[4]: http://gelmanstatdev.wpengine.com/2014/07/15/stan-world-cup-update/
[5]: http://gelmanstatdev.wpengine.com/2017/05/17/using-stan-week-week-updating-estimated-soccer-team-abilites/
[6]: https://kyleam.github.io/mlb-rundiff/log-with-lags-cleaned.csv.gz
[7]: https://kyleam.github.io/mlb-rundiff/lag-calculation-checks.html
For those interested in this stuff, I looked at the same issue from a slightly different angle in a series of blog posts 5 years ago:
http://www.bravesjournal.us/2013/01/21/how-big-is-home-field-advantage-part-two-by-jonathanf/#s
There’s even more dramatic distance and time-zone variation in the Super Rugby league (New Zealand, Australia, South Africa, Argentina, Japan), but Wil Undy found he couldn’t improve on a simple same-country/different-country term. https://areaundythecurve.wordpress.com/2018/03/01/super-rugby-prediction-methodology/