Scouting with data has drastically transformed professional football. Gone are the days when scouts relied solely on watching matches and gut feeling. Today, advanced tools make it possible to take more informed decisions and reduce uncertainty. But using data doesn’t guarantee success — it can also lead to mistakes.
The transformation of scouting with data in football
The rise of data scouting has allowed clubs to fine-tune their recruitment strategies and squad planning with much more precision. Teams no longer depend solely on live scouting — they can now analyse patterns of play, assess long-term performance and spot talent in less explored markets. What’s more, global databases make it easier to compare players from different leagues and identify the right profiles to fit into a team’s tactical setup.
From notepads to predictive models
Data scouting has completely changed how clubs assess talent. Thanks to advanced technologies, clubs can now analyse tactical and physical aspects in much greater detail, helping them optimise resources and minimise risks when signing players. The combination of Big Data and Artificial Intelligence has made scouting faster and more effective, reducing subjectivity and generating reports based on solid facts. In the past, data scouting meant jotting things down manually while watching matches. Now, tools like Power BI, machine learning and statistical models can predict how a player will perform based on hundreds of games.
Data scouting has helped clubs sharpen their recruitment strategies and squad planning
Advantages of scouting with data
- More accurate player evaluation – Metrics such as duels won, key passes, speed, and performance under pressure are analysed.
- Smaller margin for error – Combining expert insight with objective data leads to better decisions.
- More detailed comparisons – You can contrast similar player profiles across different leagues and conditions.
- Time saving – You can filter options before travelling or watching in person.
Hits and misses in data scouting
The success story of Vedat Muriqi and Mallorca
The signing of Vedat Muriqi by RCD Mallorca in 2022 is a clear example of how data-backed scouting can play a key role in decision-making.
The problem
During his time at S.S. Lazio, Muriqi struggled. Between 2020 and 2022, he played 49 official matches and scored only 2 goals — a disappointing return that raised doubts about his ability to perform at top level.
The data analysis
Despite a modest spell in Italy, Mallorca’s analysts dug deeper into his career and found compelling stats. Back at Fenerbahçe S.K. in Turkey, he had been a prolific striker — 17 goals in 36 matches during the 2019–20 season. On top of that, his record for Kosovo’s national team was impressive, with 18 goals in 37 appearances by the time he signed for Mallorca.
These numbers showed that when Muriqi felt valued and played a key role, his performances improved significantly. His physical style and aerial ability also matched Mallorca’s tactical needs.
The decision
Based on this analysis, RCD Mallorca brought Muriqi in on loan in January 2022. He made an instant impact — 5 goals and 3 assists in 16 league matches, playing a crucial role in keeping the team in LaLiga. As a result, the club activated the buy option in July 2022, signing him for five seasons and making him the most expensive signing in the club’s history, at €9.3 million.
Results
Since his arrival, Muriqi has become a key player in Mallorca’s attacking setup. In the 2022–23 season, he led the team with 15 league goals, finishing as top scorer. His partnership with players like Dani Rodríguez was particularly effective — together they were involved in 65% of the team’s goals since his debut.
This case shows how deep data analysis can uncover talents whose potential may not be obvious from surface stats — helping clubs make smart, strategic signings.
A common mistake – ignoring the tactical system
One of the most frequent errors in data-driven scouting is overlooking how a player fits into the tactical setup of their new team. Focusing only on individual stats without context can lead to failed transfers.
Real-life case – Lázaro Vinicius at Almería (2024)
In 2024, Almería spent €7 million to sign Lázaro Vinicius, a 20-year-old Brazilian forward with huge potential. But his time in Andalucía was underwhelming. Despite standout moments — including three goals against Mallorca and one at the Bernabéu versus Real Madrid — Lázaro struggled to adapt to Almería’s playing style. He lacked rhythm and connection with teammates, making it clear that he didn’t fit into the coach’s tactical approach. The club even considered loaning him to Palmeiras, but the Brazilian side chose not to activate the purchase option, and he returned to Almería.
The issue
Lázaro had shone previously in a system that highlighted his best qualities. But Almería’s tactical scheme was different and required him to adjust his game. The lack of analysis on how his strengths would fit the new system led to below-par performances.
Conclusion
This highlights how data scouting must not stop at assessing individual performance. It also needs to consider how a player’s skills blend into the team’s overall playing style. Contextual analysis is key to avoiding failed signings and ensuring new arrivals truly strengthen the squad.
The right mix of data and observation is what will define success or failure in the scouting of the future
AI and sensors – is this the future?
Football technology is moving fast, and Artificial Intelligence (AI) along with smart sensors are changing how performance is analysed and improved.
Automated scouting with AI
AI is revolutionising scouting by spotting emerging talent using advanced algorithms. Companies like Eyeball have built systems that analyse videos and automatically track the performance of nearly 180,000 young footballers worldwide. These systems can identify players who match specific profiles — like a “box-to-box midfielder” or “modern striker” — based on traits of current top stars. This approach not only speeds up talent detection but also democratises scouting, giving players in lesser-known regions a fair chance.
Movement and biomechanics analysis
Motion sensors and data capture devices now allow detailed biomechanical analysis. Tools like IMU sensors track acceleration, speed and movement patterns, offering valuable insights to improve technique and prevent injury. For example, stride analysis might reveal muscle imbalances or inefficient movements that, once corrected, boost performance and reduce injury risk.
Injury prediction
AI also plays a key role in preventing injuries. By analysing data such as workload, fatigue patterns and medical history, predictive models can flag players at higher risk of specific injuries. For instance, a sharp increase in training load without proper recovery could trigger muscle or joint problems. Knowing this in advance lets coaching staff tailor training and recovery plans, reducing injuries and extending careers.
In short, combining AI and sensor technology doesn’t just help spot and develop talent — it also deepens understanding of physical performance and helps prevent injuries. It’s taking football to new levels of precision and safety.
Data scouting is transforming football — but it’s not bulletproof. The real key is blending technology with human insight to make the best decisions. The future clearly points towards deeper integration of Artificial Intelligence, but the judgement and experience of scouts will always be vital.
The right balance between data and observation will be the deciding factor between success and failure in future scouting.
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