This case study is illustrative and anonymized. It describes the kind of outcome Scoutlytics-style talent signals are designed to produce. It is not a claim about a named client.
The problem
A mid-tier European club ran recruitment on instinct and a long manual shortlist. Scouts watched hundreds of hours of video to find a handful of profiles, and the list was slow to narrow.
The approach
Talent signals ranked candidates against the club's own criteria, pairing event data with the specific minutes worth watching. Instead of watching whole matches, scouts watched the clips the data flagged.
The illustrative outcome
In this scenario the shortlist narrows faster because the data removes obvious mismatches early. Scouts spend their time on the candidates most likely to fit, and the discussion shifts from gut feeling to comparable, explainable metrics.
The point
The model does not replace the scout. It points the scout at the right minutes. That is the Lemeister stance across every product. The human stays in charge, and the data makes the human faster.
See Scoutlytics for the product this case study illustrates.
