Uniform Manifold Approximation and Projection Analysis of Soccer Players

Area of Science:

• Sports Analytics
• Data Science in Sports
• Computational Statistics

Background:

• Team lineup formation in professional soccer is critical for success.
• Current player style assessment by experts is often subjective and prone to misclassification.
• Objective player style analysis can aid in team preparation, injury replacement, and recruitment.

Purpose of the Study:

• To develop an automated method for recognizing soccer players' styles based on diverse skills.
• To provide coaches and technical directors with objective tools for player comparison and team strategy.
• To leverage data science techniques for enhanced decision-making in professional soccer.

Main Methods:

• Utilized dimensionality reduction techniques to process complex player attribute data.
• Applied clustering algorithms to group players based on skill vectors and similarities.
• Employed computer visualization tools to represent player data and highlight inter-player relationships.

Main Results:

• Successfully compared soccer players based on numerical skill vectors and calculated distances.
• Generated meaningful representations of player datasets, revealing similarities and differences.
• Demonstrated the effectiveness of dimensionality reduction, clustering, and visualization for complex sports data.

Conclusions:

• The integration of dimensionality reduction, clustering, and visualization offers a powerful modeling approach for sports analytics.
• This data-driven methodology provides objective insights into player styles, surpassing traditional subjective evaluations.
• The findings underscore the value of computational tools in optimizing team management and player recruitment in professional soccer.