Intelligent Decision Making Technique for Handling Game Tactics in Soccer Sports

Understanding the game tactics applied by the opponent team is crucial in shaping the team tactics in soccer sports. Traditionally, various football clubs, organizations, coaches, players, and sports enthusiasts have relied on observations during the match. However, these kinds of subjective measures cannot give detailed insight into the tactics of the opponent side. In this paper, on the contrary, a completely objective technique is proposed, which can provide purely quantitative results. The proposed intelligent decision making technique for game tactics (IDMTGT) presents a novel soccer formation construct n1-n2-n3-nj, which signifies contextual and positional features on the soccer pitch to identify different positions and try to find the relation between diverse player positions. K-means clustering has been applied to partition the player positions with average player position id values 5.42, 19.69, and 14.41 for representing the defensive, attacking, and mid-fielders respectively. Everyone is not privileged to hire a professional soccer coach, manager, or analyst due to financial barriers or geographic factors. Hence, there is a need for such a technique, which can provide decision-making support to coaches, sports enthusiasts, or players for handling game tactics in soccer sports. The proposed technique (IDMTGT) will provide effective decision-making support in terms of automated suggestions for team formation by creating the ball passing pattern network of the opponent team for handling game tactics in soccer sports. The proposed IDMTGT served the purpose by eliminating coach biasness for suggesting the formations. To statistically validate the effectiveness of IDMTGT and to find out the difference between games without change of formation and after the formation changes suggested by the proposed IDMTGT technique based algorithm, Cohen’s d effect sizes have been computed. The proposed technique (with own team mean effect size 0.64 and opponent team mean effect size 0.37), evidently indicates the effectiveness and successful validation of the IDMTGT. By analyzing the passing sequences of the other team, significant insights can be obtained, in the form of their attacking or defensive approach, preferred areas of the pitch, and style of play. The study has exploited a total no. of 31 matches, 26314 passes and 875 shots made in UEFA Women's Euro Cup 2022, both group and knock-out stages.