A generation ago, a player's value was judged by goals, assists and the opinions of scouts who had watched him live. Today, recruitment departments employ analysts who model a player's contribution in numbers: how many goals his team should have scored from the chances he created, how often he wins the ball back high up the pitch, and how his performance compares with players of the same age in the same position. These models increasingly decide what clubs are willing to pay. Expected goals and expected assists are now standard tools in recruitment, but the best analysts also track defensive actions, pressing intensity and how a player performs against stronger opposition.

The most influential metric is expected goals, which measures the quality of chances a player creates or takes, rather than the goals themselves. A striker who scores few goals but consistently gets into great positions may be undervalued by the market, while a player scoring from low-quality chances may be overvalued. Similar models exist for defenders, measuring how often opponents progress past them, and for midfielders, measuring how effectively they move the ball forward. Age is one of the strongest predictors of transfer value, because a 21-year-old with good numbers has a decade of potential improvement and resale value, while a 31-year-old with the same numbers has neither.

Age curves are equally important. Statistical models show that most outfield players peak in their mid-to-late twenties, so a 22-year-old with strong numbers is worth more than a 29-year-old with identical numbers, because the younger player has more peak years ahead and greater resale value. Position matters too: goalkeepers and centre-backs tend to peak later, which changes how clubs value them. Data can also mislead when a player benefits from a system built around him, and clubs must check whether his numbers will survive in a different tactical setup with different teammates.

No serious club buys on data alone. Analysts produce shortlists; scouts then watch the players live to judge things numbers struggle to capture: attitude, leadership, decision-making under pressure, and how a player behaves when his team is losing. The best recruitment departments treat data as a filter that stops them missing players, and scouting as the check that stops them buying the wrong ones. Smaller clubs have embraced analytics enthusiastically, because data helps them identify players that bigger clubs have missed and compete for talent despite smaller scouting budgets.

Data also helps clubs sell. A club that can show a player's underlying numbers are strong, even if his goal tally is modest, can demand a higher fee. Conversely, a club buying a player whose numbers are declining can negotiate the price down. In this way, analytics have made the market more efficient, but also more competitive, because every club now has access to similar data. The most advanced clubs now combine tracking data from matches with physical data from training, building a complete picture of a player's fitness, workload and injury risk before making an offer.

Data has blind spots. It struggles with players in weak leagues, where the opposition is poor; with young players who have played few minutes; and with qualities like mentality that do not appear in a spreadsheet. A player can have excellent numbers in a team built around him and struggle in a different system, which is why tactical fit remains a judgement call, not a calculation. Some metrics, such as progressive passes and carries, have become fashionable, but wise clubs focus on the numbers that predict future performance rather than those that simply describe past games.

For supporters, analytics explain why clubs sometimes sign players fans have never heard of, and why a popular player can be sold despite decent performances. The numbers are not replacing judgement; they are sharpening it. The clubs that use data best are those that know both its power and its limits, and that never let a model overrule what their scouts see with their own eyes. The future of player valuation will likely involve even more data, including psychological profiling and social factors, but human judgement will remain essential for the decisions that matter most.