The success of a football team depends on various individual skills and performances of the selected players as well as how cohesively they perform. This work proposes a two-stage process for selecting optimal playing eleven of a football team from its pool of available players. In the first stage, for the reference team, a LASSO-induced modified trinomial logistic regression model is derived to analyze the probabilities of the three possible outcomes. The model takes into account strengths of the players in the team as well as those of the opponent, home advantage, and also the effects of individual players and player combinations beyond the recorded performances of these players. Careful use of the LASSO technique acts as an appropriate enabler of the player selection exercise while keeping the number of variables at a reasonable level. Then, in the second stage, a GRASP-type meta-heuristic is implemented for the team selection which maximizes the probability of win for the team. The work is illustrated with English Premier League data from 2008/09 to 2015/16. The application demonstrates that the model in the first stage furnishes valuable insights about the deciding factors for different teams whereas the optimization steps can be effectively used to determine the best possible starting lineup under various circumstances. Based on the adopted model and methodology, we propose a measure of efficiency in team selection by the team management and analyze the performance of EPL teams on this front.

翻译：一支足球队的成功不仅取决于所选球员的各项个人技能和表现，还取决于他们作为整体的协同发挥。本文提出一个两阶段流程，用于从球队可用球员池中选出最优的十一人首发阵容。在第一阶段，针对参考球队，推导出一个基于LASSO的修正三分类Logistic回归模型，用于分析比赛三种可能结果（胜、平、负）的概率。该模型综合考虑球队球员的实力、对手实力、主场优势，以及球员和球员组合超出已记录表现之外的个体及协同效应。谨慎使用LASSO技术有助于在保持变量数量合理的前提下，有效支持球员选择工作。随后在第二阶段，采用一种GRASP型元启发式算法进行球队选择，以最大化球队获胜概率。本文使用2008/09至2015/16赛季的英格兰足球超级联赛（EPL）数据进行验证。应用结果表明，第一阶段的模型能够为不同球队提供关于决定因素的重要见解，而优化步骤可有效用于在各种情况下确定最佳首发阵容。基于所采用的模型和方法，我们提出了一种衡量球队管理层选人效率的指标，并据此分析了EPL各球队在此方面的表现。