This is not exactly my area of expertise, but seems the author should read
Dawid, A. Philip. "The well-calibrated Bayesian." Journal of the American Statistical Association 77.379 (1982): 605-610.
which explains how you should interpret randomness in forecasting non-repeatable events. I don't know if 538 uses Bayesian modeling though. I think Andrew Gelman had written about this once, and as I just checked he just wrote another post on election forecast [1].
[1]: https://statmodeling.stat.columbia.edu/2020/07/31/thinking-a...