Football Data
Analyst

I still remember when I was a kid, the first time my father brought home a Jabulani ball, the official match ball from the 2010 World Cup. It wasn't just a ball to me. It was the start of something. I grew up loving football, playing as a winger through high school, and dreaming about playing professionally one day.

But life had other plans. I went to university, and the dream of playing professionally faded. The love for football never did. I just found a different way to stay close to the game.

I studied Business Information Technology, specializing in Business Intelligence. That gave me a foundation in working with data, building systems, and finding useful information in large datasets.

Over time, I started looking at football through the same lens. Instead of only asking whether a team played well, I became interested in why. What led to a chance? Why did one team control the game? Where were they finding space? What could the data show that I might miss just by watching?

That curiosity led me into football analysis. Today, I work with match events, player movements, tracking data, and video to study performance and understand how teams play. I also build data pipelines and computer vision workflows to turn match data and video into structured information that can be analysed.

I enjoy the whole process, from finding and cleaning the data to using statistics, machine learning, simulation, computer vision, data visualization, Power BI, and Tableau to explore the results and figure out what they actually tell us about the game.

I'm still learning, and that's part of what I enjoy about it. There is always another question to explore, another match to study, or another way to test an idea about the game.

For me, it comes down to something simple: I love football, I enjoy working with data, and I want to find better ways to understand the game.