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Football · Level 8: Expert · Sub-level 8.84

Data-Driven Scouting

10 tests · 100 facts · updated 12 October 2026 · markdown

How clubs use data to find players: the recruitment funnel, similar-player searches, league strength, age curves, several seasons of data, radars, market values, data-led clubs, famous finds and the limits of the numbers.

Each test below is ten questions on Insporta. The list under a test is what the questions check: one fact per question, with the source it was written from. Read it before the test, or come back from a wrong answer.

Test 1The recruitment funnelTake a test

  1. Clubs start broad: data filters thousands of players down to a list worth watching.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  2. A handful of scouts can watch a few hundred players a season; data can look at every player in dozens of leagues.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  3. The long list comes out of the data screen and is narrowed by video and scouting into a short list.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  4. A 'ball-playing centre-back who can defend a high line' asks for different numbers than a 'centre-back who wins headers'.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. How a player reacts to mistakes, talks to team-mates and works without the ball is best seen in the stadium.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. A player who misses many matches gives the club less for the same fee and wages.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. A good season can be a peak; several seasons show whether it is the level he really plays at.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. Each step is more expensive in time than the one before, so it is applied to fewer players.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. Every stage removes players, so the funnel narrows from thousands to one.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. The long list is cut to a shortlist that scouts and analysts study on video and in person before a target is chosen.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 2Similar-player searches and role profilesTake a test

  1. It compares statistical profiles across a database and ranks players by how close they are to a reference player.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  2. A player built into the team's way of playing is best replaced by one who does the same things.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  3. A winger and a centre-back will never look alike in the numbers, so comparisons are made within a role.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  4. A 'deep-lying playmaker' profile, for example, weights progressive passes and passes under pressure over aerial duels.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  5. Similar numbers against weak opposition are not the same as similar ability, which is why league strength is adjusted.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. Being in the 90th percentile for his position says more than a raw count, which varies with league and style.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Adaptation, language, personality and ambition sit outside the event data a similarity score is built on.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. The ranking is a starting point; the video check decides whether the closest numbers mean the closest player.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. Each filter keeps only part of the players left by the one before it.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. The passing figures are close, 7.9 and 7.4, but the candidate wins three times as many aerial duels.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 3Adjusting for league strengthTake a test

  1. The same finishing produces more goals against poorer defenders and goalkeepers, so raw totals overstate him.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  2. They give every club a rating on one scale, which lets analysts compare leagues and adjust players' numbers.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  3. Where event data is full and consistent, a player's figures are reliable; thin coverage leaves more guesswork.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  4. The bigger the step up in opposition, the more a player's output tends to fall.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. If strikers moving from one league to another usually lose a third of their goals, the next one probably will too.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. Defenders who close faster and block better take away both the volume and the quality of his chances.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Those leagues develop and showcase talent at lower prices than the biggest leagues, and the data shows who stands out.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. UEFA's coefficients place Portugal well above Scotland and England at or near the top.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. The adjustment borrows the experience of earlier transfers between the same two leagues.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. Adjusting for the stronger opposition he would face lowers the goals he can be expected to score.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 4Age curvesTake a test

  1. Output usually rises through the early twenties, levels out in the mid to late twenties and then declines.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  2. A goalkeeper's reading of the game keeps improving into his thirties, while outfielders depend more on repeated running.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  3. A club buying before the peak gets the player's best seasons and can still sell him for a large fee.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  4. Past his peak, a player's output is more likely to fall than to repeat, so the last season overstates the next.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. It is built from many players' careers and shows the typical rise, peak and fall of output by age.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  6. Two 18-year-olds with the same numbers can end up years apart, depending on growth, coaching and playing time.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Buying young and cheap, developing them and selling at 24 to 26 is a business model built on the age curve.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. Roles built on pace peak earliest; defending on reading and positioning later; goalkeeping latest of all.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. The shape is typical, not fixed; recruitment models use it to judge what a player of a given age is likely to do next.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. Past the peak the index drops each season, so the later years of a long contract pay for less and less.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 5Seasons, minutes and availabilityTake a test

  1. Several seasons smooth out lucky and unlucky spells and show the level the player really sustains.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  2. A player who starts every match for a demanding coach has passed a test that raw numbers cannot show.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  3. A player who misses a third of each season gives two-thirds of what the club pays for.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  4. Players develop and decline, so last season's numbers are a better guide to next season than those from three years ago.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. A move from full-back to winger, or into a more attacking system, can transform the numbers without the player changing.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. Dominating his own age group is common among academy players; doing it against adults is much rarer.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Late cameos come when games are open and opponents are tired, which can inflate a per-90 rate.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. The more minutes and the more seasons, the less luck and context drive the numbers.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. Cleaning and weighting the data comes before any judgement against other players in the role.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. A drop from 2,800 minutes to 900 and back suggests an injury, a loan or a falling-out, each of which matters to a buyer.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 6Percentile radarsTake a test

  1. Each spoke is a metric, and its length is his percentile rank against players in the comparison group.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  2. Knutson's radars, first published on his blog and then at StatsBomb, became a standard way to show a player's profile.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  3. Each slice is a metric, and how far it is filled shows the player's percentile rank.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  4. The 90th percentile among League Two midfielders is a very different claim from the 90th among Premier League ones.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. Change the peer group or swap a metric and the same player's shape changes, which is why the small print matters.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. A percentile ranks him against peers; it says nothing directly about his success rate or total.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Placing strong metrics next to each other makes a bigger filled shape than spreading them out, though the numbers are the same.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. The comparison group and the metrics must be fixed first, because every percentile depends on them.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. The narrower the group, the more like-for-like each percentile becomes.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. He is in the 92nd percentile for passing and the 88th for tackles, but only the 20th for aerial duels.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 7Market-value modelsTake a test

  1. The values are crowd-sourced estimates discussed by the site's users and set by its editors, not official prices.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  2. The site began in 2000 and grew into the most widely quoted source of player market values.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  3. The CIES Football Observatory estimates values with a statistical model built on past transfer fees.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · recall

  4. Published estimates move slowly and use less data than a club has, so clubs model fees themselves.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. The selling club holds the player's registration for longer, so it can ask for more.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. Buyers know they can wait and sign him for nothing, so the selling club's bargaining power shrinks.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. A club in financial trouble sells cheaply; one under no pressure can hold out far above any published value.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. The more years of registration the seller can offer, the higher the fee it can demand.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. The model learns what drove past fees and applies that to the player the club wants.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. With the same form, the drop from 40 to 15 million follows the years left on his deal, from three to one.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Test 8Data-led clubs beyond Brentford and LiverpoolTake a test

  1. Bloom, a Brighton supporter, made his fortune in betting and brought a data-led approach to the club.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  2. Starlizard's modelling of football matches sits behind the analytical culture at Bloom's clubs.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  3. Toulouse beat Nantes 5-1 in the final, a first major trophy under owners known for a model-driven approach.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  4. Beane, who ran the Oakland Athletics on statistical principles, took an advisory role at the Dutch club in 2015.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  5. The deal, around £100 million plus add-ons, was the biggest fee a British club had paid at the time.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  6. Buying undervalued players and selling them at full price is the model; the fee pays for the next round of finds.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. Mac Allister came through at Argentinos Juniors and later won the World Cup with Argentina while a Brighton player.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  8. Mac Allister signed in January 2019, Caicedo in February 2021 and Mitoma in August 2021.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  9. Bloom became chairman in 2009, Benham bought Midtjylland in 2014 and RedBird bought Toulouse in 2020.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  10. Mitoma signed from Kawasaki Frontale in 2021 and spent his first season on loan in Belgium.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

Test 9Famous data-assisted signingsTake a test

  1. At the University of Tsukuba he studied dribbling, filming himself with a head-mounted camera.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  2. Without enough international caps for a permit, he spent 2021-22 on loan in Belgium and returned the next summer.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  3. Independiente del Valle's academy has produced several players sold to Europe.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  4. Mahrez cost Leicester a small fee from Le Havre in Ligue 2 and became the PFA Player of the Year in 2016.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  5. Walsh's scouting brought in both players cheaply before the 2015-16 title, and he then left for Everton.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  6. When few clubs have spotted a player, the seller cannot start a bidding war, and the first buyer pays less.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. The numbers that attracted the club came from a different context, and adapting to the new one is part of the risk.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. He arrived in 2021, spent part of that year on loan at Beerschot, broke into Brighton's team and left in 2023.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  9. Vardy arrived from Fleetwood in 2012, Mahrez from Le Havre in 2014 and Kanté from Caen in 2015.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

  10. Mitoma spent 2021-22 at Union Saint-Gilloise, the Belgian club also owned by Tony Bloom.

    Source: Club statements and match reports (BBC Sport, The Athletic) · recall

Test 10What data cannot tell a scoutTake a test

  1. Effort without the ball, reactions to mistakes and conduct in training leave no trace in event data.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  2. A player's mentality, motivation and how he will fit the group decide whether his numbers carry over.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  3. A player who struggles off the pitch in a new place may not reproduce the numbers that got him signed.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  4. A wing-back in a back three or a free roamer may put up numbers that the buyer's own system cannot recreate.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  5. A scout can explain why the numbers look as they do: a role, an injury, a team-mate, or an opponent's tactics.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  6. Analysts or scouts who like a player can pick the metrics that support him and ignore those that do not.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  7. A skinny 17-year-old may be well behind physically today and far ahead of his peers in five years.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  8. Event data logs passes directly, tracking data can show positioning, and leadership barely shows up anywhere.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  9. The checks move from what the data implies to the person, and the medical comes last, once a deal is close.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

  10. How a player handles pressure is not logged as an event; the other options are standard event-data counts.

    Source: Football analytics literature (Opta, StatsBomb, FBref definitions) · applied

Sources

IFAB Laws of the Game / FIFA & UEFA technical reports / Opta, StatsBomb and FBref definitions / club and analyst publications / peer-reviewed research

  • Football analytics literature (Opta, StatsBomb, FBref definitions)83
  • Club statements and match reports (BBC Sport, The Athletic)17