I’ve first estimations.
I used the opponent ratings as provided by OGS (no recalculation of their rating history).
I only recalculated my rating history with OGS like Glicko2 (again with opponent ratings provided by OGS).
rms is calculated over (outcome - win_probability)
direct % as 1 - Σ(outcome XOR player_rating > opponent_rating) / number_of_games
(outcome = 1 if player win, and outcome = 0 if opponent_win
Prediction quality of my rating history: (rms: lower is better, direct %: bigger is better)
Player id: 449941
Games: 978
| algorithm | rms | direct % |
|---|---|---|
| OGS | 0.4645 | 64.93% |
| Glicko2 | 0.4637 | 65.95% |
There is no difference in predictability.
To get the right values for Glicko2, I would have to recalculate the histories for the whole player base. At the moment I’m not able to do that (rating history gets cropped to max 5000 games).
EDIT: Forgot to adjust ratings for handicap games in the calculation of the direct %. Now both are slightly higher.