Same league, different world
To show how their model tries to avoid that trap, Hinton walked through a live example: recruiting a Premier League winger.
Three players – Andreas Schjelderup at Benfica, Saïd El Mala at Köln and Kevin Schade at Brentford – all looked strong on raw output.
“All the players look strong on key metrics… they pass the basic filters,” Hinton said. But that is where most clubs’ data process stops.
“The analytics team gets you a short list and then the process fully hands over to other pieces like subjective scouting, the eye test.”
The problem is those raw numbers aren’t comparable.
“You can’t just compare their outputs, their percentiles – they’re in different leagues, different competition levels, different systems,” Hinton said. “Playing on the dominant team in Portugal is going to have a different opportunity than playing for the 14th place side in Germany or a mid-table team in the Premier League.”
Teamworks’ context-adjustment models attempt to strip that environment out. Hinton walked through the workings using Schjelderup’s 2025/26 season at Benfica – the Norwegian winger scored seven goals and set up six more in 28 Liga Portugal appearances.
“This model is asking how much of that is him and how much of that is the environment,” Hinton said.
From there, three adjustments are applied in turn. First, league quality: Portugal’s top flight is an easier competition than the model’s baseline, so that brings the number down. Second, role: a winger has more attacking opportunity than a player in a deeper position, which brings it down again.
Third, team style: Benfica’s possession-heavy approach in the final third means more of Schjelderup’s output is down to the system rather than the player, adjusting the number down a third time.
“Now you can normalise players to the exact same global baseline and compare their adjusted outputs.”