The Sky is the limit: introducing the Aerial Spatial Impact Rating (ASIR)

Asserting dominance is what many managers talk about. They talk about controlling midfield, central areas, and “wanting” it more. According to their knowledge and view, this would also lead to winning games thanks to that asserted dominance. It seems a bit of a wild idea, and I’m not sure there is a correlation. However, this is a worthy exercise to conduct in terms of data, so that’s why I’m taking you on this research data.

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Which teams are leading the set piece leaderboards?

I don’t think I have to tell you that I love set pieces. The whole reason I started looking at data metrics and models more, was the fact that I felt that there are so few metrics concerning set pieces. I think that’s still the case, but we are getting that there, one day at the time.

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All’s Well That Ends Well… With a Long Throw-in

I was waiting for an excuse for me to look deeper into the long throw-ins for a while now, but I couldn’t find the right hook. Now, it’s all over the place, and I know my research is far from innovative, but I do still think there are so many interesting things we could talk about. Long throw-ins seem to be emerging in the Premier League, but is this a league specific trend or can we see some similar development in the other top leagues in Europe? Let me take you on a wonderful trip through long throw-ins.

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Using Hidden Markov Models on dribbles to see how much a dribble progresses attacks in the English Premier League 2025-2026.

I will be honest with you. This article is a passion project of mine and it combines two things I really like in football: data and dribbles. I get it, this is ridiculously vage and broad, but hear me out. There is something magical about a 1v1, a take-on, a dribble or a carry. A player with the ball on their feet, trusting vision, ability and pace to make something meaningful out of that situation. It’s a form of arts and its beauty is perceived differently by anyone looking at it.

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Stretched Play Value Model: evaluating which player stretch

One of the most difficult things in working with data is having the right data. There are different types of data, but sometimes we are not sure what we can do with all the data. Why am I saying this? This is because I have been adament to have tracking data for all the leagues. And, that’s simply not possible.

(meer…)