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

An ANN Model For Individual Ratings in Group Competitions 17
players and 24 unique maps. The data included which players participated
in each map, how long they played on each team, which team won the map,
and how long the map lasted. The ANN model is trained as described in
section 3, using the update rule after every map. The model is evaluated
four times, once with no heuristics, once with only the certainty heuristic
from section 3.5, once with only the rating inflation prevention heuristic
from section 3.6, and once combining both heuristics. One way to measure
the effectiveness of these runs would be to look at how often the team with
the higher probability of winning does not actually win. However, this result
can be misleading because it assumes a team with a 55% chance of winning
should win 100% of the time—which is not desirable. The real question is
whether or not a team given a P% chance of winning actually wins P% of
the time. Therefore, the method of judging the model’s effectiveness chosen
uses a histogram to measure how often a team given a P% chance of winning
actually wins. For the results in section 5, the size of the histogram invertals
is chosen to be 1%. This measure will be called the prediction error because
it shows how far off the predicted outcome is from the actual result. A
prediction error of 5% means that when the model predicts team A has a
70% probability of winning, they may really have a probability of winning
between 65-75%. Or, in the histogram sense, it means that given all of the
occurences of the the model giving a team a 70% chance of winning, that
team may actually win in 65%-75% of those occurences.