RolandSC2 Posted August 21, 2018 Report Share Posted August 21, 2018 (edited) I noticed some bloke lose 64 points on his first game against a similarly rated player. The victor went from 1204 to 1219, and the loser went from 1200 to 1136. Seems like losing that many points could be discouraging at that point in the player's experience (his/her first game). Edited August 21, 2018 by RolandSC2 Quote Link to comment Share on other sites More sharing options...
Tiber7 Posted August 21, 2018 Report Share Posted August 21, 2018 (edited) I Edited March 3, 2022 by Tiber7 Quote Link to comment Share on other sites More sharing options...
Guest Posted August 22, 2018 Report Share Posted August 22, 2018 # Lower ratings "move faster" and change more # dramatically than higher ones. Anything rating above # this value moves at the same rate as this value. elo_k_factor_constant_rating = 2200.0 # This preset number of games is the number of games # where a player is considered "stable". # Rating volatility is constant after this number. volatility_constant = 20.0 Copy-pasted snippet from 0.A.D's ELO implementation. I suppose thats just how ELO works. Quote Link to comment Share on other sites More sharing options...
coworotel Posted August 22, 2018 Report Share Posted August 22, 2018 https://en.wikipedia.org/wiki/Elo_rating_system Quote Link to comment Share on other sites More sharing options...
Tiber7 Posted August 22, 2018 Report Share Posted August 22, 2018 (edited) T Edited March 3, 2022 by Tiber7 Quote Link to comment Share on other sites More sharing options...
sarcoma Posted August 23, 2018 Report Share Posted August 23, 2018 I think scythe's implementation departs too far from true ELO https://blog.mackie.io/the-elo-algorithm Rn = Ro + K * (S - E) n=400 x = Ra - Rb s = n/ln(10) exponent = -(x/s) E = 1/(1+e^exponent) K = (2*n)/20 vs player_volatility = (min(games_played, volatility_constant) / volatility_constant + 0.25) / 1.25 rating_k_factor = 50.0 * (min(rating, elo_k_factor_constant_rating) / elo_k_factor_constant_rating + 1.0) / 2.0 volatility = rating_k_factor * player_volatility difference = opponent_rating - rating return round(max(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation)) And maybe other systems would be more appropriate, like Glicko or the ones that work for teams too. Quote Link to comment Share on other sites More sharing options...
Guest Posted August 23, 2018 Report Share Posted August 23, 2018 For an RTS game, I am not sure whether true ELO could be even used. 58 minutes ago, sarcoma said: And maybe other systems would be more appropriate, like Glicko or the ones that work for teams too. Curren implementation can handle team game ratings. Its just not used. Understandable considering how broken even 1v1 ratings are. Regarding Glicko, @mapkoc has a thread detailing his implementation of it. IMO, current ELO is pretty much good enough. Whats really needed is to make sure that all 1v1 rated games change players ratings. Quote Link to comment Share on other sites More sharing options...
sarcoma Posted August 23, 2018 Report Share Posted August 23, 2018 This free online/proprietary system promises wonders and is based in glicko just like trueskill 1 Quote Link to comment Share on other sites More sharing options...
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