标题： 一个综合框架用于FIRST机器人竞赛中的团队组建和胜者预测：模型、算法与分析 显示英文标题

标题： An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis

摘要： 这项研究工作旨在开发一种分析方法，基于团队形成前竞争对手的技能数据，在竞争环境中优化团队组建并预测团队表现。 科学文献中已有几种方法用于优化和预测团队表现。 然而，大多数研究都采用个体成员的细粒度技能统计数据或诸如固定成员组的约束条件。 目前，没有研究涉及FIRST机器人竞赛的高度受限领域。 本研究旨在通过提供一种分析方法来填补这一空白，该方法在允许这些约束条件下，仅使用以往团队表现的指标，而不是每个成员的表现指标，来优化和预测竞争环境中的团队表现。 我们将该方法应用于FIRST机器人竞赛的选拔过程，这是一个技能每年都会变化、团队成员在整个赛季中会变动、每场比赛仅有表面的一组统计数据，并且联盟组建是竞争成功的关键的领域。 首先，我们开发了一种可以根据整体团队表现推断个体成员表现的方法。 开发了一个联盟优化算法来优化团队组建，并训练了一个深度神经网络模型来预测获胜团队，两者均使用高度后处理的真实世界数据。 我们的方法能够成功地从整体团队统计数据中提取个体成员的指标，组建有竞争力的团队，并以84.08%的准确率预测获胜团队。 显示英文摘要

摘要： 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.