| 1 |
Maybe this can be tracked back to the non-transitivity of the win chance calculation: If players A and B play against each other for many times, their win chance against each other will determine their rating difference. The same goes for players B vs C. From the resulting rating difference between A and C, the system calculates the win chance between A and C which is not necessarily correct. To see the more general effect, we can replace A by the category of low rated players, B by medium rated players and C by highly rated players. The effect may be increased by the matchmaker enforcing a maximum rating difference and by the matchmaker using a ladder rating that is calculated from the actual rating in strange ways.
|
1 |
Maybe this can be tracked back to the non-transitivity of the win chance calculation: If players A and B play against each other for many times, their win chance against each other will determine their rating difference. The same goes for players B vs C. From the resulting rating difference between A and C, the system calculates the win chance between A and C which is not necessarily correct. To see the more general effect, we can replace A by the category of low rated players, B by medium rated players and C by highly rated players. The effect may be increased by the matchmaker enforcing a maximum rating difference and by the matchmaker using a ladder rating that is calculated from the actual rating in strange ways.
|
| 3 |
Simple
ways
to
reduce
the
problem
are
to
increase
the
rating
difference
that
the
matchmaker
allows
and
(
to
reduce
the
deviation
between
ladder
rating
and
actual
rating
or
to
let
the
matchmaker
use
actual
rating)
.
In
a
complicated
way,
the
problem
can
be
solved
completely
by
using
a
composition
of
multiple
logistic
function
neurons
in
a
neural
network
trained
by
game
data
instead
of
the
current
single
logistic
function
to
calculate
win
chances.
|
3 |
Simple
ways
to
reduce
the
problem
slightly
are
to
increase
the
rating
difference
that
the
matchmaker
allows
and
(
to
reduce
the
deviation
between
ladder
rating
and
actual
rating
or
to
let
the
matchmaker
use
actual
rating)
.
In
a
complicated
way,
the
problem
can
be
solved
completely
by
using
a
neural
network
of
multiple
logistic
function
neurons
trained
by
game
data
instead
of
the
current
single
logistic
function
to
calculate
win
chances.
|
Ladder thought