Hi all, I recently implemented the front-end for score-based AI review, and it got me thinking about what else we could do with KataGo’s data.
I was reading this 2020 rank thread and wondered How accurately can we guess somebody’s rank only by looking at their “loss per move (LpM)”?
Imagine if we could have a LpM-based rank where:
- Ratings would not drift due to natural deflation
- Ratings would be comparable through time (imagine comparing Lee Sedol against Shusaku)
- Ratings comparable across different servers and “official” organizations
- Two players could play each other over and over again and still see rating improvements
Immediate caveats I can think of:
- Fighting styles will probably have a higher LpM
- Players that are ahead may try to simplify the game even at the cost of losing point differential. The reverse is also true.
Nonetheless, I think it could be a really fun data analysis project if we had access to a bunch of KataGo reviews and the corresponding players’ rank.
Ultimately, if we really wanted to create a rating system that could assign you a rank based on how you played and not by your wins and losses. We could probably take the KataGo trained networks, and fine-tune it to predict ranks based on players’ moves. This system would have the same benefits as stated above, but should be immune to the caveats too.