@thisisanfield does it? Couldn’t it also mean that his injury is more severe than first thought? Hopefully it’s not & he’ll be fit! #InFormI had been looking out for an example like this after reading that Bayesian probability means that two people can update their beliefs differently on the same information.
— AnfieldIndex (@AnfieldIndex) September 7, 2013
When This Is Anfield is correct is when we can observe the length of an injury. If a player is expected to be out for two weeks but then does not play in two weeks time, it is far more likely that the manager was being cautious than that he is now out for two months.
I have set up a fiddle http://pythonfiddle.com/modelling-injuries-bayesically for the case where Anfield Index is correct - the case where we have not observed the expected length of the injury. I have taken the following injury lengths in weeks [1,2,3,4,6,8,13,26,52] and given them probabilities of 40%^(array index + 1) - except for 52 which gets whatever is left over.
So what do we see?
