Separating box-crashers from the box-blasters
Monday Night SCOUTED details the huge impact of a small subtraction.
At the 2025 Hudl StatsBomb Performance Insights Conference in November, Edin Terzić sat down with Raphael Honigstein to discuss his time away from management.
He spoke well and passionately about his projects. He spoke about the meticulous process of assembling a backroom staff ready for his next job. He spoke about creating fantasy league tables with data providers that pitted his side against elite teams that play the game based on his idea of how football should be played within a similar context as a tool to better evaluate his performance. He spoke about redefining football in his image, creating new metrics to develop player profiles that best informed that style of play. That concept is what I want to focus on this week.
- Get to know the best ball-carrying prospects in Europe
- Find out the key metrics for box-crashers like Jude Bellingham
- Meet the only under-23 forward that profiles like a veteran striker
SCOUTED Squad November 2025
ICYMI: The first Team of the Month was a selection of the most fascinating players from the 2025 MLS season.
That's 22 players born in 2003 or later you can add to your shortlists and start tracking.
Working with GAMECODE.AI, Terzić has ripped up definitions and created a personal bank of metrics he finds most useful. While there are obvious limitations to this process for media entities - it takes a long time for a new metric to become widely used and even then it’s not universally accepted or desired - this is a valuable way of thinking for analysts and scouts at any level. Just because a data provider provides data packaged in a certain way does not mean you should not explore, interrogate and even refine it in your image. My custom calculations are far from advanced and perhaps even frivolous in some cases, but they have helped me paint much clearer pictures of player profiles that we have tried to develop at SCOUTED.
This week, to show the value of that approach, I’m simply subtracting one metric from another. I want to find out what happens if you remove Carries into the Penalty Area from Touches in the Attacking Penalty Area. I want to discover players that penetrate the box with their movement, that receive in the box and perform actions in it. I want to remove the wingers that dominate the Touches in the Attacking Penalty Area metric because of their red carpet rolled out to them as they approach. I want to see what we will learn and what players we will discover find with one simple subtraction.
