An Integrated Framework for Team Formation and Winner Prediction in the
  FIRST Robotics Competition: Model, Algorithm, and Analysis
Federico Galbiati Ranier X. Gran Brendan D. Jacques Sullivan J. Mulhern Chun-Kit Ngan
This research work aims to develop an analytical approach for optimizing team
formation and predicting team performance in a competitive environment based on
data on the competitors' skills prior to the team formation. There are several
approaches in scientific literature to optimize and predict a team's
performance. However, most studies employ fine-grained skill statistics of the
individual members or constraints such as teams with a set group of members.
Currently, no research tackles the highly constrained domain of the FIRST
Robotics Competition. This research effort aims to fill this gap by providing
an analytical method for optimizing and predicting team performance in a
competitive environment while allowing these constraints and only using metrics
on previous team performance, not on each individual member's performance. We
apply our method to the drafting process of the FIRST Robotics competition, a
domain in which the skills change year-over-year, team members change
throughout the season, each match only has a superficial set of statistics, and
alliance formation is key to competitive success. First, we develop a method
that could extrapolate individual members' performance based on overall team
performance. An alliance optimization algorithm is developed to optimize team
formation and a deep neural network model is trained to predict the winning
team, both using highly post-processed real-world data. Our method is able to
successfully extract individual members' metrics from overall team statistics,
form competitive teams, and predict the winning team with 84.08% accuracy.