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

An ANN Model For Individual Ratings in Group Competitions 21
comes useful. Besides being able to rate individual players and predict the
outcome of matches in a muliplayer game like Enemy Territory, the predictions
can also be used during a given map to balance the teams. If a team
is more than a chosen threshold P% likely to win, a player can be moved
from that team to the other team in order to “even the odds.” The Bradly-
Territory ANN model as adapted for Enemy Territory is now being run on
hundreds of Enemy Territory servers involving hundreds of thousands of
players world-wide. A large number of those servers have enabled an option
that keeps the teams balanced, and therefore, in theory, keeps the gameplay
enjoyable for the players.
One extension of this system would be to track player ratings across multiple
servers world-wide with a master server and then recommend servers to
players based on the difficulty level of each server. This would allow players
to find matches for a given online game in which the gameplay felt “even”
to them. This type of “difficulty matching” is already used in several online
games today, but only for either one-on-one type matches, or teams where
the players never change. The given Bradly-Terry ANN model could extend
this to allow for dynamic teams on public servers that maintain an even
balance of play.
This can also be applied to improving groups chosen for sports or military
purposes. Individuals can be rated within groups as long as they are
shuffled through several groups and then, over time, groups can be either
chosen by using the highest rated individuals, or balanced by chosing a