| 1 |
I'm
sorry,
but
I
don't
quite
get
where
the
sqrt(
average
size
of
all
teams)
is
coming
from.
I
can
see
why
one
would
want
to
use
some
adjustment
to
adapt
to
elo-imbalances
in
games
with
high
player
numbers
more,
but
why
this
exact
term?
|
1 |
I'm
sorry,
but
I
still
don't
quite
get
where
the
sqrt(
average
size
of
all
teams)
is
coming
from.
I
can
see
why
one
would
want
to
use
some
adjustment
to
adapt
to
elo-imbalances
in
games
with
high
player
numbers
more,
but
why
this
exact
term?
|
| 2 |
\n
|
2 |
\n
|
| 3 |
Just to gain an understanding:
|
3 |
Just to gain an understanding:
|
| 4 |
For two teams of 4 players, that would yield an exponent of 2. Using exponentiation laws, this effectively means that the "simple" team elo (I mean the compounded strength without the sqrt() exponent, transformed back into elo via the normal formula) would be doubled for each team before arriving at the actual team strength, so it magnifies the elo differences between the teams.
|
4 |
For two teams of 4 players, that would yield an exponent of 2. Using exponentiation laws, this effectively means that the "simple" team elo (I mean the compounded strength without the sqrt() exponent, transformed back into elo via the normal formula) would be doubled for each team before arriving at the actual team strength, so it magnifies the elo differences between the teams.
|
| 5 |
\n
|
5 |
\n
|
| 6 |
In effect, your formula suggests that (for non-1v1) a higher elo/strength team is [i]underpredicted[/i] (and a lower one is overpredicted) by the "simple" team strength calculation. Did you base this on your data examinations?
|
6 |
In effect, your formula suggests that (for non-1v1) a higher elo/strength team is [i]underpredicted[/i] (and a lower one is overpredicted) by the "simple" team strength calculation. Did you base this on your data examinations?
|
math bork 2nd try