Abstract:
"The football player transfer market poses a challenge for low-budgeted clubs due to its
unpredictability, leading to a rapid rise in value for players with average key
performance metrics. This project aims to implement a data-driven solution to optimize
team composition while adhering to budget constraints, addressing matters such as the
lack of data-driven tactics, overlooking budget constraints, and focusing on short-term
benefits. During the designing phase of the project, a thorough requirements analysis
was carried out. By adopting an iterative and incremental approach, the project aims to
continuously refine the solution based on new insights, promoting progress
transparency, flexibility, and a deep understanding of project requirements.
The PRINCE2 project management methodology provides a consistent framework for
development, promoting planning, implementation, monitoring, and control. To ensure
a thorough evaluation of the finished work, tasks are divided into logical phases with
specified timeframes. In addition, the Rational Unified Process (RUP) software
development approach was applied aligning with the project's iterative nature, dividing
solution development into phases such as elaboration, construction, and transition.
These methodologies facilitate the systematic revision of project objectives and ensure
that a solution is effectively developed.
The effectiveness of the solution was demonstrated through the comparative analysis
of three machine learning models: K-Means Clustering, Support Vector Machines
(SVM), and Random Forest, across different team selection modes. The results
highlighted the superior performance of SVM and Random Forest in key scenarios,
particularly with SVM achieving an accuracy of 0.8461 in Possession Mode,
significantly aiding strategic team building within budget constraints. This approach
not only bridges the identified gap in data-driven football strategies but also sets a
foundation for extending similar methodologies to other sports domains, leveraging
machine learning to optimize decision-making processes. "
