OptiMatch is my personal project to explore football through data. Using machine learning, I’ve analyzed player stats to uncover patterns and roles that define how they contribute on the pitch.

The main idea is to group players into archetypes—unique roles based on their playing style and performance. This helps break down the complexity of football into something easier to understand, showing how players fit into teams and what makes them stand out.

OptiMatch isn’t about giving all the answers but starting a new way of thinking about football using data.

Archetypes are the different roles players take on during a match, such as a creative playmaker, a strong defender, or a goal-focused striker. Each archetype highlights the style and skills that a player brings to the game. Using clustering, a machine learning technique, I let the data uncover natural groupings of players based on their stats. This approach avoids assumptions and instead allows the numbers to reveal patterns on their own.

Archetypes simplify the game by showing what a player is good at, how they fit into a team’s strategy, and what sets them apart from others in similar positions. By focusing on these roles, we can better appreciate the unique contributions of each player and the dynamics they create within a team.

Clustering works by grouping players with similar stats into natural categories, creating a more data-driven way to understand their roles. Unlike traditional methods that classify players into rigid positions, clustering captures the variety and overlap in how football is played. For example, it can reveal hidden roles or styles of play that might not fit neatly into conventional labels. It’s valuable because it simplifies the game’s complexity while staying grounded in real performance numbers.

However, clustering isn’t perfect. It depends on good data, and while it can uncover patterns, it doesn’t fully capture the messiness and fluidity of real football. The results need to be interpreted in the context of football knowledge to make sense. Despite these limitations, clustering helps us ask interesting questions: What roles do players naturally fit into? How do players in the same role compare? Are there hidden gems excelling in unexpected ways?

The Archetype Efficiency Score (AES) builds on clustering by measuring how well a player performs in their assigned role. It compares their stats to others in the same group and gives them a score from 0 to 100. This helps highlight players who excel in their role and provides a clearer picture of their strengths and contributions.

Clustering gives us a fresh perspective on football by showing how players actually play instead of forcing them into predefined positions. It can highlight hidden talent, revealing players who shine in unexpected ways. It also helps us understand modern tactics by capturing the evolving roles in football, like fullbacks contributing more in attack or strikers dropping deeper to link up play. By grouping players based on their playing styles, clustering makes it easier to see who fits where and why, helping teams make smarter decisions.

Although clustering isn’t perfect, it’s a powerful tool for seeing the bigger picture. Combined with football knowledge, it offers a way to better understand players, teams, and strategies while still appreciating the creativity and unpredictability that make the game special.

OptiMatch is a way for me to explore and share how data can change how we think about football. It’s not a finished platform, but it shows what’s possible when machine learning and data science are combined with a love for the game. In the future, ideas like this could grow into tools for better squad building and team planning, finding and comparing players based on specific needs, and uncovering new roles that reflect the evolution of modern football.

By experimenting with projects like OptiMatch, we can explore football in exciting new ways while keeping the heart of the game alive.