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#14455: "Ways to improve Elo/EAS for 3+ player and luck based games"
#14455: "Ways to improve Elo/EAS for 3+ player and luck based games"
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建議:依我所見,下列調整將大幅改善此站
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建議:依我所見,下列調整將大幅改善此站
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# | Status | Votes | Game | Type | Title | Last update |
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細節描述
• 如果可以的話,請轉貼螢幕顯示的錯誤訊息。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 請說明你當時想做什麼,你做了什麼,然後發生了什麼事
• 你的瀏覽器是什麼?
Safari v13
• 請轉貼未翻譯的英文字句。 建議將此錯誤的螢幕截圖上傳到 Imgur.com 並轉貼連結。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 這些文字存在於 翻譯系統 中嗎?若為真,其是否已被翻譯超過 24 小時?
• 你的瀏覽器是什麼?
Safari v13
• 請簡明精確地解釋你的建議,以便讓人一目了然。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 你的瀏覽器是什麼?
Safari v13
• 當你不能動作時,螢幕上顯示什麼?(螢幕全黑?部份遊戲介面?錯誤訊息?)
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 你的瀏覽器是什麼?
Safari v13
• 遊戲規則的哪部分在 BGA 版本有所錯漏?
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 這項違反規則之處可否在遊戲重播中看到?若可以是在哪步?(重播時左上角資訊)
• 你的瀏覽器是什麼?
Safari v13
• 你當時想採取哪個遊戲行動?
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 你想觸發這個遊戲行動時做了什麼?
• 當你試著這麼做時發生了什麼(錯誤訊息、遊戲狀態條訊息...)?
• 你的瀏覽器是什麼?
Safari v13
• 問題發生在遊戲的哪一步?(當前遊戲指示是什麼)
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 當你試著進行遊戲動作時發生了什麼(錯誤訊息、遊戲狀態條訊息...)?
• 你的瀏覽器是什麼?
Safari v13
• 請描述顯示問題。 建議將此錯誤的螢幕截圖上傳到 Imgur.com 並轉貼連結。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 你的瀏覽器是什麼?
Safari v13
• 請轉貼未翻譯的英文字句。 建議將此錯誤的螢幕截圖上傳到 Imgur.com 並轉貼連結。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 這些文字存在於 翻譯系統 中嗎?若為真,其是否已被翻譯超過 24 小時?
• 你的瀏覽器是什麼?
Safari v13
• 請簡明精確地解釋你的建議,以便讓人一目了然。
Here is the current elo eas formula
For a Win:
RatingChange = K*P/(1 + 10 ^ ((YourRating - OpponentRating)/400)))
For a Loss:
RatingChange = -K*P/(1 + 10 ^ ((OpponentRating - YourRating)/400)))
K is a weight for each game (first 10 games is 60, next 10 is 40, after is 20 for Elo. All games are 40 for EAS)
P is a multiplier by number of players. P ~= 2/NumberPlayersInGame (more players decreases the amount gained or lost for each individual matchup, but increases the total amount of points at stake for a given game) (if you play with more players than the recommended number the P value actually becomes slightly lower than the above formula.
Proposed Changes
1 Adjust the 400 factor for number of players. Change it to 400*sqrt(numberOpponents).
Motivation: Equalize expected Elo/EAS for multiple player counts, could allow for a fair Arena competition where different player counts are allowed.
Under the current system players who only play 2 player matchups have a significant advantage over players who play with more than 2. This is because as the number of players goes up, the variance of a game goes up as well. Altering the 400 value based on number players compensates for the increased variance so that (on average) players in different player counts should be able to achieve similar ratings.
This would allow an arena mode competition to allow setups with multiple players. This would alleviate some of the wait time angst in arena mode.
2 Adjust the speed at which ratings change (K factor) depending on the length of the game. For EAS, make it a game length based variable between 20-50 (also a variable K value by games played wouldn’t be a bad thing)
Motivation: Too much variance in short generally luck based games, too much time required (too much time spent playing mismatched opponents for all players from weak to average to strong) in longer high skill games.
Right now EAS K factor is 40, Elo starts at 60 but drops to 20 after 20 games are played.
Some games take 2 minutes, others take 2 hours. Having the ratings move at the same rate per game means you can achieve a balanced rating in a few hours for some games (i.e. Backgammon, Can’t Stop, Reversi, 7 Wonders, Lost Cities), while for long games (Terra Mystica, Clans of Caledonia) it will take more than 100 hours. While longer games should take longer to reach a ‘true’ rating adjusting the K factor can be used to slightly mitigate the issue. You can’t make K factor too big otherwise the game to game
ratings swings would be too wild.
3 Normalize the ratings system so each game has roughly the same distribution of ratings by adjusting the 400 value based on the game luck factor.
Motivation: This would give a more realistic distribution of player skill from average through master in each game. Right now games with luck can’t have experts or masters (w/o matchup manipulation or a statistically improbable run of good luck anyway). Right now you could be a ‘perfect’ backgammon player but stall out at around a +100 rating because the luck factor in the game is large.
In a game like backgammon or can’t stop a strong player can expect a win rate of about 60% against average players. So this limits players ratings under the current system to about +100 from the base rating (a 100 rating point advantage indicates a 64% probability of winning). With the ratings volatility players can get on a lucky streak to get to +200 or rarely even +300, but will come back down to an average of around +100. Yet somehow players achieve +400-+500 ratings in these games...that can only be done by strategic opponent selection (or blatant cheating but we will ignore that for this argument). Basically you want to play only other high rated players and then get lucky by winning a few in a row to gain significant points. Arena mode should already mitigate this opponent selection tactic.
This idea isn’t as completely hashed out as the other 2 above, so I won’t offer concrete numbers, but there are a few ways to achieve this. The best is likely to increase the base 400 value for high luck games. A range from 400-900 based on luck factor (already games are ranked as 0-5 luck..could change 400 to 400 + 100*luck lvl). Combining this change with a change based on player number would however be more tricky, multiplying them together wouldn’t be great, it would likely be better to take the maximum of the two values. For this one to work luck ratings would have to be revisited b/c many of them aren’t very accurate (in checking about 8 games I saw at least two that were well off...kingdomino at a 4 and king’s guild at 3 both are definitely too high and lost cities at 2 is too low).• 你的瀏覽器是什麼?
Safari v13
案件沿革
2020年 2月13日 22:13 • sprockitz • 此建議尚未被開發者分析過:
Forum topic to discuss: boardgamearena.com/forum/viewtopic.php?f=9&t=14422
2020年 2月18日 15:13 • sprockitz • 此建議尚未被開發者分析過:
Just wanted to update this as it looks like P for EAS is different than for Elo. It appears to be a straight divider....so in a 4 player game each of the 3 head to heads have 40/3 or ~13.3 points at stake.
This actually exacerbates issues with how long it takes to reach an accurate EAS for long multiplayer games.
This actually exacerbates issues with how long it takes to reach an accurate EAS for long multiplayer games.
2020年 9月 4日 10:19 • compte obelète • 此建議尚未被開發者分析過:
And maybe also a bonus for starters if they was like, playing against a master or lower, depending how large the marge is (when there is your elo≤opponent's elo-200) and it will have bigger bonus if higher elo, ((like +2 whit the first one I said earlier), (+4 whit 300 difference), etc. (And it doesn't depend of the standard Elo calculation)).
2021年 8月16日 16:51 • Phoxtrot • 此建議尚未被開發者分析過:
As a first, the P factor should be made explicit and should be the same for everyone at the table.
Currently, it is higher for people who finish in the middle of the pack and the P factor can even be different for the player who finish 1st than for the player who finish last. (see boardgamearena.com/bug?id=46582 )
Currently, it is higher for people who finish in the middle of the pack and the P factor can even be different for the player who finish 1st than for the player who finish last. (see boardgamearena.com/bug?id=46582 )
2021年11月15日 15:49 • edo404 • 此建議尚未被開發者分析過:
ELO calculation is rediculous for more then 2 players.
when winning against 3 stronger players at once, elo-points are devided with 3. --> what for?
isn't that much more dificult that winning against one opponent?
see table 216760302
when winning against 3 stronger players at once, elo-points are devided with 3. --> what for?
isn't that much more dificult that winning against one opponent?
see table 216760302
2022年 1月31日 0:35 • excalabur • 此建議尚未被開發者分析過:
This is not a good fix for high variance games. The issue is that the system goes to 100% win probability for arbitrarily high rating difference, not that it does so at a rating difference of some number of points. All that changing "400" does is rescale the system.
A better answer is to use a different curve altogether, or at least scale the asymptote away from 100%, preferably based on game data (which should be easy to extract).
A better answer is to use a different curve altogether, or at least scale the asymptote away from 100%, preferably based on game data (which should be easy to extract).
2022年 2月12日 17:19 • FSKFSK • 此建議尚未被開發者分析過:
Changing the value of 400 won't work for luck-based games. It leaves the distribution the same, but changes the scale.
One solution is to decrease the K factor for luck-based games. Then ratings would be more stable.
A better solution is to change the probability distribution for luck-based games. ELO is designed for a game like chess, where a casual player would have zero chance against a grandmaster. A rating difference of 400 points means a 90% chance of winning. A rating difference of 800 means a 99% chance of winning. There are games here where the weaker player always has a 25% chance of winning.
For example, the probability distribution could be
chance of winning = (your rating) / (your rating + opponent's rating)
using a minimum value of 100 for people who just started playing a game.
One solution is to decrease the K factor for luck-based games. Then ratings would be more stable.
A better solution is to change the probability distribution for luck-based games. ELO is designed for a game like chess, where a casual player would have zero chance against a grandmaster. A rating difference of 400 points means a 90% chance of winning. A rating difference of 800 means a 99% chance of winning. There are games here where the weaker player always has a 25% chance of winning.
For example, the probability distribution could be
chance of winning = (your rating) / (your rating + opponent's rating)
using a minimum value of 100 for people who just started playing a game.
2023年 3月11日 1:02 • Dr Tumbleshlong • 此建議尚未被開發者分析過:
Please fix and balance the ELO-system. It is futile to be punished over 1st-time-players in a game, where luck plays major factor.
2023年 3月11日 15:51 • Phoxtrot • 此建議尚未被開發者分析過:
A newer topic to discuss (the old one is locked):
boardgamearena.com/forum/viewtopic.php?t=29584
I also provided there a few links to related papers for those interested in the maths.
boardgamearena.com/forum/viewtopic.php?t=29584
I also provided there a few links to related papers for those interested in the maths.
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