| 1 |
Very good! Interesting results. I didn't think that K has such a big influence in the long run. With how many games did you do it? Nearly all?
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1 |
Very good! Interesting results. I didn't think that K has such a big influence in the long run. With how many games did you do it? Nearly all?
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| 2 |
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| 3 |
A weighting system like zk's effectively increases K for certain battles and decreases it for others and will therefore probably yield better results than the best constant K.
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3 |
A weighting system like zk's effectively increases K for certain battles and decreases it for others and will therefore probably yield better results than the best constant K.
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| 4 |
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| 5 |
Also, zk elo multiplies K by sqrt(number of all players in game / 2) (like you also did in your code). This is only for 2 teams. Generally it would have to be sqrt(average number of players per team). I didn't use this factor. Can you test if teamstrength becomes better with it or zk elo without it? If the ideal K of zk elo with sqrt factor is ~64 then the ideal K for teamstrength without sqrt is probably higher because the sqrt factor increases the effektive K. So it would be best to always compare systems with their ideal K instead of the same nominal K but another effective K.
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5 |
Also, zk elo multiplies K by sqrt(number of all players in game / 2) (like you also did in your code). This is only for 2 teams. Generally it would have to be sqrt(average number of players per team). I didn't use this factor. Can you test if teamstrength becomes better with it or zk elo without it? If the ideal K of zk elo with sqrt factor is ~64 then the ideal K for teamstrength without sqrt is probably higher because the sqrt factor increases the effektive K. So it would be best to always compare systems with their ideal K instead of the same nominal K but another effective K.
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| 6 |
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| 7 |
The sqrt factor says that real results of games with many players are more meaningfull. I rather think that the importance of every game is the same, but that predictions of games with many players should be more distinct. Therfore you can also try to use the factor D := sqrt(average number of players per team) to modify predicted probabilities p to (p^D)/(p^D+(1-p)^D) instead.
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7 |
The sqrt factor says that real results of games with many players are more meaningfull. I rather think that the importance of every game is the same, but that predictions of games with many players should be more distinct. Therfore you can also try to use the factor D := sqrt(average number of players per team) to modify predicted probabilities p to (p^D)/(p^D+(1-p)^D) instead.
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| 8 |
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8 |
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| 9 |
So on the whole we would have 2^3=8 systems if we don't want to code a complicated weighting system:
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9 |
So on the whole we would have 2^3=8 systems if we don't want to code a complicated weighting system:
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| 10 |
1. zk elo or teamstrength
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10 |
1. zk elo or teamstrength
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| 11 |
2. with/without sqrt factor to modify K
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11 |
2. with/without sqrt factor to modify K
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| 12 |
3. with/without D factor to modify p
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12 |
3. with/without D factor to modify p
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| 13 |
(
all
with
their
ideal
K)
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13 |
(
all
with
their
ideal
K,
but
even
with
constant
K
you
can
still
compare
systems
that
both
either
have
sqrt
factor
to
modify
K
or
not)
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| 14 |
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14 |
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| 15 |
I don't know if you want to test them all.. :) [spoiler]Even if original zk elo turns out to be the best a semi teamstrength system can be used to be the same for teams, but consider FFA. But we don't know..[/spoiler]
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15 |
I don't know if you want to test them all.. :) [spoiler]Even if original zk elo turns out to be the best a semi teamstrength system can be used to be the same for teams, but consider FFA. But we don't know..[/spoiler]
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Evaluating rating systems