[nuclearnerd]

Thanks William. That's a super useful paper. My takeaway is that while stats that incorporate defence might help a little, good old (LS based) OPR seems to be just about as good in most cases.

I have another area of study you could look into. I like to use OPR during tournaments for two purposes:

1) to derive a strength of schedule (basically the predicted winning margin). This is useful for deciding where best to spend limited resources on strategy planning, and alliance-mate repairs.

2) as a first order sort for pick list order

Both of these use cases suffer from even more drastic lack of data points. For the SOS, I use previous tournament data, and for the pick list, we've usually only completed 70% of the matches. For these use cases, it would be valuable to know:

for 1) Which predictor does best when the training set and the testing set are from different tournaments.

For 2) which predictor does best with 70% or less of the matches in the training set. (Maybe this is where MMSE solutions will shine)

Lastly, I'm intrigued by the possibility of incorporating scouting data (such as individual team point counts) into the MMSE calculation as a way to verify scouting records, but i have to think about that more.

If you can respond to any of these questions, I'd be obliged, otherwise thanks again for sharing your work!