A Bradley-Terry Artificial Neural Network Model for Individual ...

14 Joshua Menke, Tony Martinez
Since this can also be seen to effectively lower the learning rate, η is in
practice chosen to be twice as large for the individual updates as the fieldgroup
updates.
The downside to this method of modeling uncertainty is that is does
not allow for a later change in an individuals rating due to long periods of
inactivity or due to improving rating over time. This could be implemented
with a form of certainty decay that lowered an individual’s certainty value
γ i , over time. Also, notice a similar measure of uncertainty could be applied
to the field-group parameters.
3.6 Preventing Rating Inflation
One of the problems with averaging (or summing) individual ratings is that
there is no way to model a single individual’s expected contribution based
on their rating. An individual with a rating significantly higher or lower
than a group they participate in will have an equal update to the rest of
the group. This can result in inflating that individual’s rating if they constantly
win with weaker groups. Likewise, a weaker individual participating
in a highly-skilled but losing group may have their rating decreased farther
than is appropriate because that weaker individual was not expected
to help their group win as much as the higher rated individuals were. One
heuristic to offset this problem is to use an individual’s own rating instead
of that individual’s group rating when comparing that individual to the
other group. This will result in individuals with ratings higher than their