As we are progressing into a world where sports have become a vital part of our lives, it has also become a hot market for investors to gain greater income. We can also see that there has been a surge in sports viewership which leads to more tournaments. Capitalizing on them has become a difficult task for an investor. So a programmers team have come up with a strategy to build the best team whilst keeping in mind the investor’s budget which has a limit of 1 billion Euros relying on Machine learning algorithms to detect potential recruits for their clubs and potential assets for the investor to optimize their market gains.

How does that work ?

The team have a FIFA dataset in which there are few columns named — rating, release clause, and wages. They built 2 models using Supervised Learning on Rating variables and made it a classification problem by splitting this variable into 2 classes: greater than or equal to 70 and less than 70 ratings. They chose 70 as their threshold as most of the major clubs have only players with ratings greater than 70. In the second model, the rating class was obtained from previous best classifiers instead of « actual rating » and the release-clause was combined with annual wages to determine the cost to investors.

Dataset

The dataset used is FIFA 19 and FIFA 20 data which contain 18k+ unique player ratings with 100+ different attributes for each player:

• Player positions, with the role in the club and in the national team.
• Player attributes with statistics as Attacking, Skills, Defense, Mentality, GK Skills, etc.
• Player personal data like Nationality, Club, DateOfBirth, Wage, Salary, etc.

Example: Bangladesh National Team

Due to the miscoordination among the players recently, the national team of Bangladesh slipped to their worst ever FIFA ranking of 194th. The ultimate success of a team depends upon its player selection. Generally, the coach and the management team select the best 11 players for each match from a pool of players by evaluating various attributes of the players. Now, using these Machine Learning algorithms, a better player classification technique is proposed and the soccer team is formed automatically of the best player combination possible. The results achieved based on the performance attributes would help the coach and management team to build up a more successful soccer team.

Conclusion

The constructed 2 models that utilize Machine learning algorithms benefit investors while simultaneously select the rightfully classified players as good performers and then regress them in the budget of the investor. Ultimately, the narrowed down selection process of a player within a club is rather better than selecting at random.

Ressources:

• Buying A Soccer Team: A Machine Learning Approach
• Player Classification Technique Based on Performance for a Soccer Team Using Machine Learning Algorithms