Well I think the system can probably only do what it can with the data it’s presented. I don’t know the players as you do to be able to comment on whether they’re under or over ranked, or whether that’s down to incorporating 9x9 games in with 19x19.
All I know so far is that the effect, while it does impact the overall rating user for matchmaking doesn’t seem to be too important statistically. What I mean is last year this question was asked
I think the conclusion was that the overall rank, which incorporates different board sizes, worked just as well as a predictor of who is likely to win a game as using the ratings individually.
I can’t say I thoroughly read through everything or guarantee I understood everything, I’m just highlighting the relevant messages I recall.
Now of course we had a more recent ratings adjustment at the start of the year
So I also can’t guarantee the same findings still hold. Maybe one could do the same analysis again whenever the time presents itself.
I think if the player isn’t playing at a consistent strength then their rating might be more likely to fluctuate. Again there’s also two settings to view the ratings history graph which might be useful,
The per game view might be useful, if one wants to just see changes per game, and not have it skewed by long periods of no rating change for example. I’m imagining a rating graph that looks steady but it’s because no correspondence games have finished in that period.
I’m not so sure why a correspondence game should consider only the players long term (or long time?) strength. If long term, maybe that’s only because the games themselves take a long time? If long time, there’s also no guarantee that people actually spend any more time making moves in correspondence than in a live game. There’s certainly more time between moves, but some people just open a game and pick a move and move onto the next game
