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There's two ways you can go about this.
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There's two ways you can go about this.
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[b]Way One[/b]: You can assume that an individual's rating at any point in time - as caluclated by the Zero-K infrastructure - is reasonably correlated to their skill at the game and the effectiveness of their contributions to their team's victory, i.e. you can assume that that individual ratings are basically correct. You then suppose that the problem is the BALANCING method for team games, not the rating-determination method for individuals.
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[b]Way One[/b]: You can assume that an individual's rating at any point in time - as caluclated by the Zero-K infrastructure - is reasonably correlated to their skill at the game and the effectiveness of their contributions to their team's victory, i.e. you can assume that that individual ratings are basically correct. You then suppose that the problem is the BALANCING method for team games, not the rating-determination method for individuals.
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If that's your approach, then you can come up with your own algorithm which takes as its input the list of individuals who are playing a game and their respective Elo scores as of the time they play the game, and which produces an expected probability of one side winning. You can then do this for every game in the database. Then you bin the games and your resulting predictions into buckets of, say, 2% or 3% or 5% width. Then you see how well your predictions for each bucket match the actual results for that bucket.
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If that's your approach, then you can come up with your own algorithm which takes as its input the list of individuals who are playing a game and their respective Elo scores as of the time they play the game, and which produces an expected probability of one side winning. You can then do this for every game in the database. Then you bin the games and your resulting predictions into buckets of, say, 2% or 3% or 5% width. Then you see how well your predictions for each bucket match the actual results for that bucket.
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I.E. you end up with, say, 1,000 games where one side is predicted (by your algorithm) to win at 55% to 57%. If that side turns out to have won 560 of those thousand games, then your algorithm is pretty good. If that side turns out to have won 400 of those thousand games, then your algorithm is pretty bad.
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I.E. you end up with, say, 1,000 games where one side is predicted (by your algorithm) to win at 55% to 57%. If that side turns out to have won 560 of those thousand games, then your algorithm is pretty good. If that side turns out to have won 400 of those thousand games, then your algorithm is pretty bad.
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[b]Way
Two[/b])
You
can
assume
that
not
only
is
the
balancing
method
broken,
but
the
RATING
method
is
broken
as
well.
Now
your
task
is
more
complicated,
but
still
possible.
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[b]Way
Two[/b]:
You
can
assume
that
not
only
is
the
balancing
method
broken,
but
the
RATING
method
is
broken
as
well.
Now
your
task
is
more
complicated,
but
still
possible.
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You'll have to use the historical data to construct a new rating for each individual for each game they play, as of the time that they play that game, using your new rating method.
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You'll have to use the historical data to construct a new rating for each individual for each game they play, as of the time that they play that game, using your new rating method.
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THEN you can test your new balancing method as above, using the new ratings that your new rating method has produced.
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THEN you can test your new balancing method as above, using the new ratings that your new rating method has produced.
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You should probably also study the literature before you undertake this journey. If you are not already intimately familiar with the details behind FIDE's Elo calculations, Glicko, and TrueSkill, then you are not yet ready to be offering useful suggestions here.
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You should probably also study the literature before you undertake this journey. If you are not already intimately familiar with the details behind FIDE's Elo calculations, Glicko, and TrueSkill, then you are not yet ready to be offering useful suggestions here.
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