A methodology for football players selection problem based on multi-measurements criteria analysis

Football is one of the most popular sports in the world. Professional football has become a significant contributor to global economics and business. The game attracts considerable funds, which motivate participants of the sporting process (players, coaches, club owners, administration, etc.) to strive for better athletic results. However, such a motivation simultaneously promotes internal and external rivalry. The increasing number of players, the teams' desire to attract better team members, and the improved athletes' performance boost the use of assessment and rating processes. The most popular and widely used player rating systems are based on performance statistics, which reflect situational factors of the game. Most specialists believe that such systems lack objectivity. Thus, this paper presents a new methodology to assess and rank football players based on multi-criteria decision making (MCDM). A hands-on study is conducted for the assessment. A sample of 24 players is grouped into four separate groups consisting of six players for each group. The age of U17 is examined by 12 tests distributed as follows: three anthropometrics, five fitness, and four skills tests. Players are ranked on the basis of a set of measurement metric outcomes using the technique for order performance by similarity to ideal solution (TOPSIS) method to select the appropriate player using a one-shot experiment. Then, this study utilizes the mean and standard deviation to ensure that the four groups of players undergo systematic ranking, respectively. Findings are as follows: (1) systematic: TOPSIS is an effective tool used to solve player selection problems, and (2) statistics: group number one is the best group among the four groups, identical to the results of the system.