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In the games in question, the shown win probabilities deviate strongly from 50%. Even though clan mates are put together, there should be enough team configurations with such high player numbers to balance the teams out. I think there are 2 possible reasons for the discrepancy:
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1 |
In the games in question, the shown win probabilities deviate strongly from 50%. Even though clan mates are put together, there should be enough team configurations with such high player numbers to balance the teams out. I think there are 2 possible reasons for the discrepancy:
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1. After a game, the whole time-dependency of player ratings changes. If a player looses a game, WHR thinks that the player was already worse before the game. The win probabilities shown on the website are according to player ratings during the game but with the knowledge of the game outcome. Indeed, the loosing teams of the games in question have a smaller win probability. Probably, the balancer estimated win probabilities closer to 50% before the game happened. This effect is especially strong if players with very uncertain ratings participate in the game.
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1. After a game, the whole time-dependency of player ratings changes. If a player looses a game, WHR thinks that the player was already worse before the game. The win probabilities shown on the website are according to player ratings during the game but with the knowledge of the game outcome. Indeed, the loosing teams of the games in question have a smaller win probability. Probably, the balancer estimated win probabilities closer to 50% before the game happened. This effect is especially strong if players with very uncertain ratings participate in the game.
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2. IIRC, teams are not balanced according to rating mean value as it should be, but according to "shown rating" which is an elo limitation fluff delayed version of mean value - x * standard deviation with x > 0. So players who don't play often are underestimated by the balancer.
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2. IIRC, teams are not balanced according to rating mean value as it should be, but according to "shown rating" which is an elo limitation fluff delayed version of mean value - x * standard deviation with x > 0. So players who don't play often are underestimated by the balancer.
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Furthermore, there is the effect that one team tends to have the medium players while the other team has the best and the worst players. For small teams, it is obvious to me that this is the ideal solution. For big teams though, it might be a problem of the algorithm: If there are many players, there are so many possible team configurations that it is enough to check through only some of them to find good balance. Maybe the algorithm checks those with the worst or best players on one team first and then stops because win chances are already nearly 50%.
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Furthermore, there is the effect that one team tends to have the medium players while the other team has the best and the worst players. For small teams, it is obvious to me that this is the ideal solution. For big teams though, it might be a problem of the algorithm: If there are many players, there are so many possible team configurations that it is enough to check through only some of them to find good balance. Maybe the algorithm checks those with the worst or best players on one team first and then stops because win chances are already nearly 50%.
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Considering unequal player numbers and skill deviations has been suggested before. AFAIK, the analysis so far showed that its effect on win probabilities is not that big, but more analysis could be done on that. For example, I have the theory that it actually does have an effect but it is compensated by distortions in the rating distribution. In any case, reducing deviation is nice to have additionally if player numbers are big enough to reach win probabilities close to 50% anyway.
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Does the balance in the lobpot work?