From hidden gems in Lagos to grassroots talents across Africa, AI is redefining how footballers are discovered. But can data ever replace the instinct of a seasoned scout?
In the dusty fields of Ojuelegba, a young striker might be playing the game of his life but without the right eyes watching, his dream ends there. Now, thanks to artificial intelligence, that could change. AI-powered scouting platforms are analyzing thousands of hours of match footage, player movements, and performance metrics to spot the next breakout star. But as the algorithms rise, a bigger question looms: can machines truly recognize football genius or just patterns? In this write up, we’ll take a deep dive on the new frontiers of scouting, how the technology works, data vs human intuition, the ethical and legal dimensions, and the African opportunity.
The New Frontiers of Scouting
Brentford Football Club happens to be one of the pioneers of data driven scouting in Europe. They are regularly described as the Moneyball club. Like Oakland Athletics, the baseball team featured in Martin Lewis’ book Moneyball which became a movie starring Brad Pitt, the West London Club has used data analysis to overcome the limitations of a stringent budget, doing things differently from other clubs. Their recent rise has been remarkable, but to simply say it is down to their use of spreadsheets and statistics would be an oversimplification. Data analysis has been an important tool for Brentford, one of the weapons they have used to fight football’s Goliaths, but just as important has been the intelligent and pragmatic way they have used it. As the club has moved up the football ladder, their transfer policy has been remarkably profitable. Brentford have shown a remarkable talent for finding undervalued players to sign at a cheap price, improving their skills on the training pitch and then selling them on for big fees. Since 2016, Brentford have spent about £75 million on transfer fees, but recouped over £190 million in sales. Neal Maupay was signed from French side Saint-Étienne for about £1.6 million in 2017 and sold to Brighton and Hove Albion two years later for a fee in the region of £20 million. Ollie Watkins, signed from Exeter City for about £6.5 million in the same year the club signed Maupay, was sold to Aston Villa for just over £30 million in 2020. The club has also made a very healthy profit on players such as Saïd Benrahma and Andre Gray. The players the club has sold for the biggest profits have been those they have found abroad, such as Maupay and Benrahma, both from French football, and those who were playing a lower league clubs in England, such as Watkins at Exeter, Andre Gray at Luton and Scott Hogan at Rochdale. One way they have used data to find such players is by creating models to compare the strength of teams from different leagues. This allows Brentford to find sides doing well in leagues which are less high profile and in which players are more cheaply available. They can then scout these players in more detail. As Ankersen has said, when describing the club’s transfer policy, ‘It’s not that data tells you who to pick, data can tell you where to look.’
In Africa, there are emerging tech startups which are focused on building and utilizing AI models to aid talent discovery. These ventures are focused on democratizing the scouting process, reduce geographical barriers, provide data-driven insightsfor player development and recruitment by various clubs. Examples include:
- Afriskaut: a digital football-scouting and talent-discovery platform focused on Africa. It utilizes artificial intelligence, video verification, and performance data to connect players with clubs, scouts, agents, and academies. The platform aims to modernise and democratise scouting by enhancing transparency, reducing fraud, and improving access to credible talent information across the continent.
- Deepscouting : an AI-driven football talent-analysis and scouting platform designed to enhance player evaluation through automated video analytics and performance data. Founded in France, the platform applies advanced computer-vision technology to match footage to generate reliable football intelligence at scale. It aims to democratise data access, improve scouting accuracy, and offer objective insight into talent across different levels of football.
- Mchezagi: swahili for “player” is a mobile-first sports-tech platform built for African athletes, scouts, teams and fans. It harnesses artificial intelligence and data analytics to help grassroots talent gain visibility, manage development and connect with opportunities. Founded in Kenya, it was designed specifically for the African sporting context of low bandwidth, mobile usage, and informal structures.
- aiScout: a mobile-first football scouting application based in the UK developed by ai.io. It allows amateur players worldwide to record and upload drills and matches for analysis, and enables clubs and scouts to view, rate, and sign players using artificial intelligence and video data.
- Wyscout: an Italian company specializing in football analytics. Founded in Genoa in 2004, it offers video analysis tools and databases to support scouting, match analysis, and player transfers.
How technology works.
AI scouting technology uses advanced machine learning and data analysis to evaluate players more efficiently and objectively than traditional scouting methods. Data allows scouts to compare players across different leagues, age groups, and positions using objective metrics. Instead of relying on human intuition, AI tools analyze vast amounts of data from multiple sources to identify talent, predict potential, and find the right tactical fit for a team. Key performance indicators include, expected goals, key passes, assist probability, tackles, pressing actions, defensive actions, distance covered, acceleration, high intensity run and so on. Also, GPS and wearable technology are used by academies and first team to gather physical condition performance data.
Data vs Human Intuition
Data-driven decisions have a plethora of benefits that can significantly enhance organizational performance. One of the primary benefits is to make objective choices based on quantifiable substance rather than subjective opinions. This reduces biases that cloud judgement and leads to more consistent outcomes. It also allows continuous monitoring and evaluation of decisions over a period of time. Organizations can track key performance indicators to assess the effectiveness of their strategies and make necessary adjustments in real time. On the other hand, Intuition-based decision-making offers several advantages that complement the analytical rigor of data-driven approaches. One of the most significant benefits is speed; intuitive decisions can often be made rapidly without the need for extensive analysis. This agility is particularly valuable in fast-moving industries where conditions change rapidly, and waiting for comprehensive data analysis could result in missed opportunities.
Additionally, intuition allows leaders to tap into their emotional intelligence and empathy, which can be crucial when making decisions that affect people such as hiring or team dynamics. On the flip side, intuition-based decisions are not without their pitfalls. They can be heavily influenced by cognitive biases or personal experiences that may not be relevant to the current situation. Although there are no widely documented cases of scouts from major clubs explicitly overlooking prospects such as Victor Osimhen or Terem Moffi in their formative years, the career paths of these players emerging through less conventional scouting channels underscore the shortcomings of traditional scouting approaches. Artificial intelligence is increasingly regarded as a tool capable of identifying such talent earlier and with greater objectivity, thereby mitigating the geographical and resource constraints associated with human scouting.
Ethical and Legal Dimensions
The ownership of data from sports academies and grassroots tournaments is not always explicitly defined by a single law and often depends on the specific agreements, terms of service, and relevant data protection regulations (such as the GDPR) in the applicable jurisdiction. Generally, the data is owned and controlled by the organization that collects it, provided they have a legal basis for processing. Key entities that may own or control the data include the Academy/Tournament or Organization, Third party technology/analytics provider, Participants/Athletes, Governing bodies or leagues. The academy or tournament organizer typically owns the collected data for their use (e.g., player development, scouting), but they are legally obligated to process participants’ personal data according to data protection laws and often require explicit consent from the players or their guardians. AI systems carry a significant risk of bias that could lead them to systematically overlook or disadvantage certain types of players. This bias primarily stems from skewed training data, algorithmic design choices, and human prejudices embedded during development, which can result in discriminatory outcomes for underrepresented or minority groups.
The African Opportunity
Nigeria, Ghana, and Senegal have established themselves as leading exporters of athletic talent particularly in football and athletics. However, talent discovery remains fragmented, informal, and geographically limited. Artificial Intelligence (AI) presents a transformative opportunity to modernise scouting, enhance athlete development, and build Africa into a global hub for sports-data innovation. AI tools including computer vision, machine learning, biometric tracking and predictive performance analytics can identify and nurture high-potential athletes from grassroots to elite level. By formalising the talent pipeline and strengthening data-driven decision-making, African countries can transition from raw-talent exporters to structured talent-economy leaders.
In an era where data increasingly shapes talent discovery and performance evaluation, the future of football scouting will likely belong to those who can blend human intuition with technological precision. Traditional scouts with notebooks and keen eyes will always have a role in identifying heart, hunger, and raw instinct, but advanced analytics, machine learning, and shared data ecosystems are rapidly transforming how potential is recognised and nurtured. The emerging generation of football stars may be unearthed not through chance sightings on dusty pitches, but through intelligent systems capable of detecting patterns invisible to the human eye. Ultimately, the evolution of scouting is not about replacing people with machines, but about expanding the bandwidth of opportunity ensuring that every gifted player, regardless of geography or circumstance, has a fair chance to be seen, measured, and championed on the global stage. The next Osimhen might not be found by a man with a notebook but by an algorithm with an open dataset.
