How Data-Led Recruitment Is Shaping Scottish Football
Recruitment across the Scottish Premiership is increasingly being driven by data and platform-led scouting. Rangers, Hearts and St Mirren are all cited as examples of how clubs are adapting.
The landscape of recruitment in the SPL has undergone a significant shift in recent seasons, driven by post-Brexit regulations and a data-led approach by clubs looking for high potential talent at a low transfer cost.
So far this season foreign players now account for approximately 66% of total playing minutes in the league.
Foreign nationalities by minutes played
| Rank | Country | Number of Players |
|---|---|---|
| 1 | England | 64 players |
| 2 | Republic of Ireland | 18 players |
| 3 | Wales | 16 players |
| 4 | Australia | 15 players |
| 5 | France | 8 players |
Why clubs are leaning into the numbers
The shift toward data-led recruitment is no longer a luxury but a necessity when competing with wealthier leagues. With most clubs increasingly moving away from traditional scouting to integrated "Intelligence Platforms" that filter tens of thousands of players globally into actionable shortlists. I thought it might be useful to share some insight on the platforms I've seen in use.
Examples across the league, including Rangers
1. Jamestown Analytics (Hearts). Hearts secured an exclusive partnership with Jamestown Analytics, a firm linked to Tony Bloom and the data-driven success of Brighton and Hove Albion.
This is based on predictive modelling. The core of the system is its ability to predict how a player from an obscure league would rate specifically in the Scottish Premiership by comparing physical attributes to those who play in the league.
The process begins with the coaches coordinating with the analysts to create a player profile, detailing the specific physical and attributes the coach requires. Jamestown then generates a list of "realistic and achievable" targets from its massive, continually updating global database, and details the anticipated impact the players will have on the squad.
2. Driblab (St Mirren). St Mirren has partnered with Driblab to modernise their scouting through advanced performance metrics using XY Data and Simulation.
A key element to this platform is the use of AI match simulations. This technology allows the coaches and analysts to take a target and "simulate" how they would have perform alongside current teammates in an AI match simulator.
Driblab creates simulations that allow the club to visualise how a potential signing would impact team dynamics and how they would adapt to tactical demands, theoretically reducing the risk of a player not fitting the coaches system.
3. "Multi-Tool" Platforms (Rangers). Larger clubs like Rangers typically use a suite of overlapping tools to cross-reference data.
TransferLab (Analytics FC). We use this to filter potential targets across predominantly Europe and South America. It provides a "shadow squad" feature to identify immediate replacements for all players based on their role within the squad. In doing so it evaluates the impact of the outgoing players and the incoming players across multiple elements of the squad, from tactical experience, physical attributes and also team dynamics.
Kitman Labs: We recently unified our medical, performance, and talent data into a single "Intelligence Platform" using Kitman Labs to monitor player development and injury risks alongside recruitment, which was a bold step by the club. However, in my opinion, we don't have the required skillset yet to fully optimise its use.
Wyscout and StatsBomb: Nearly every SPL club uses these for video analysis and deep event data (expected goals, progressive carries, aerial duels), which provide the raw data for their internal analytics team to interpret and feed back to the coaching teams.
The rewards and the risks
Data-led recruitment has transformed football from a game of "gut feelings" to a high-stakes numbers game. While it offers a potential competitive edge, it is not a guaranteed silver bullet.
The rewards are clear. Clubs can identify undervalued gems in obscure leagues before they become household names, allowing for "buy low, sell high" business models. Data removes human bias, ignoring a player's reputation or a single "lucky" performance, focusing instead on consistent output over hundreds of matches. Advanced tooling can predict how a player's specific style will mesh with an existing squad, ensuring a new signing fits the coaches system immediately.
But the risks matter just as much. Data cannot measure a player's mental resilience, leadership, or how they will handle "the Glasgow fishbowl" or a cold Tuesday night in Dingwall. And if a club uses poor-quality data or lacks the expertise to interpret it, they can end up with a "spreadsheet player", someone who looks elite on paper but lacks the physical or technical reality required for the pitch.
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