| 1 |
Very nice. I assume by GeneralTeamstrength you mean GeneralEloTeamstrength and that you used K=32 as standard value? I think the latter is the reason for the low values for GeneralTeamstrength while my proposals for GeneralEloTeamstrength are quite good. It's all about optimizing K. We know that we need K~64 with K mod. Therefore I have proposed a higher K (80) for without K mod without D mod and it was indeed better than its K=64 version. Probably it can become even better with another K>64. (If everything was used K<64 would be better.)
|
1 |
Very nice. I assume by GeneralTeamstrength you mean GeneralEloTeamstrength and that you used K=32 as standard value? I think the latter is the reason for the low values for GeneralTeamstrength while my proposals for GeneralEloTeamstrength are quite good. It's all about optimizing K. We know that we need K~64 with K mod. Therefore I have proposed a higher K (80) for without K mod without D mod and it was indeed better than its K=64 version. Probably it can become even better with another K>64. (If everything was used K<64 would be better.)
|
| 3 |
As
expected,
the
values
for
GeneralEloTeamstrength
are
similar
to
elo
for
teams.
But
for
FFA
I
expected
a
bigger
improvement.
ZK
elo
doesn't
even
do
correct
FFA
calculation
(
probability
sum
>
1)
.
[spoiler]How
do
you
do
FFA
scoring
btw?
Do
you
weigh
every
game
prediction
equally
(
what
I
would
do)
or
every
team
prediction
equally
(
wich
means
a
2v2v2
would
be
weighted
1.
5
times
as
much
as
a
2v2)
.
It's
not
a
big
difference,
though.
Furthermore
predicting
a
1/(
number
of
teams)
probability
for
every
team
would
yield
a
score
>
0
for
more
than
2
teams.
It
seems
to
be
rated
as
a
valuable
insight
that
the
outcome
for
a
team
depends
on
the
outcome
of
other
teams.
Both,
FFA
weighting
>1
and
score
>0
for
guessing,
could
be
fixed
by
only
giving
the
winner
team
a
rating
of
1+log_(
number
of
teams)
(
p)
and
ignoring
loser
teams
which
also
saves
computation
time.
(
The
rating
of
winner
and
loser
team
for
2
teams
is
the
same
anyway.
)
On
the
other
hand
this
approach
would
ignore
that
a
more
equal
distribution
of
predicted
win
probabilities
for
losers
should
be
rated
better
than
if
some
losers
are
predicted
higher
win
probabilities.
And
it
couldn't
be
applied
on
the
current
system
because
its
sums
of
probabilities
are
>1
for
FFA.
So
maybe
it's
the
best
to
keep
the
current
scoring
rule
but
with
equal
weighting
for
games.
[/spoiler]
|
3 |
As
expected,
the
values
for
GeneralEloTeamstrength
are
similar
to
elo
for
teams.
But
for
FFA
I
expected
a
bigger
improvement.
Maybe
this
is
also
because
you
did
GeneralEloTeamstrength
for
FFA
only
with
K=32?
ZK
elo
doesn't
even
do
correct
FFA
calculation
(
probability
sum
>
1)
.
[spoiler]How
do
you
do
FFA
scoring
btw?
Do
you
weigh
every
game
prediction
equally
(
what
I
would
do)
or
every
team
prediction
equally
(
wich
means
a
2v2v2
would
be
weighted
1.
5
times
as
much
as
a
2v2)
.
It's
not
a
big
difference,
though.
Furthermore
predicting
a
1/(
number
of
teams)
probability
for
every
team
would
yield
a
score
>
0
for
more
than
2
teams.
It
seems
to
be
rated
as
a
valuable
insight
that
the
outcome
for
a
team
depends
on
the
outcome
of
other
teams.
Both,
FFA
weighting
>1
and
score
>0
for
guessing,
could
be
fixed
by
only
giving
the
winner
team
a
rating
of
1+log_(
number
of
teams)
(
p)
and
ignoring
loser
teams
which
also
saves
computation
time.
(
The
rating
of
winner
and
loser
team
for
2
teams
is
the
same
anyway.
)
On
the
other
hand
this
approach
would
ignore
that
a
more
equal
distribution
of
predicted
win
probabilities
for
losers
should
be
rated
better
than
if
some
losers
are
predicted
higher
win
probabilities.
And
it
couldn't
be
applied
on
the
current
system
because
its
sums
of
probabilities
are
>1
for
FFA.
So
maybe
it's
the
best
to
keep
the
current
scoring
rule
but
with
equal
weighting
for
games.
[/spoiler]
|
Evaluating rating systems