Manchester City’s recruitment is driven by a data operation built around a single question: “What does it take to win?” The club’s analysts break that down into three areas — identifying players, evaluating them, and assessing how City could improve them — all with the aim of raising the statistical probability of winning the Premier League. The approach reflects a wider trend across the top flight, where clubs increasingly use data for transfers and for monitoring performance, but at City one of the fundamental aims is to reduce the risk attached to any market transaction, whether a purchase or a sale.
City’s metrics offered a clear verdict last season, when the department projected they would be in the title conversation but identified Arsenal as favourites to be champions. That proved accurate. This time the club’s projections are being kept under wraps, though five wins from five Premier League games have prompted supporters and pundits to predict they will again be the Gunners’ closest challengers.
Since winning the Treble in 2023, City’s squad has undergone a vast overhaul using this approach — only four players from that historic campaign remain. Sporting director Hugo Viana is careful to stress the limits of the method. “Data plays a role in our decision-making, but it is just one part of the overall picture,” he said. “We know data cannot tell us everything about a player. It cannot fully show a person’s character, mentality or behaviour. That is why we study data insights but focus on the expertise of our scouts and other specialists.” Traditional scouts remain the primary gauge of whether City decide to move for a player.
The starting point for any signing is a database of 650,000 footballers from around the world. Work begins with a brief setting out the type of player wanted, usually starting with a position — though that process is not as simple as it sounds. City, for example, categorise full-backs into three types: those who are defensive, those who come inside, and those who play high and wide. Filters grading a player’s overall ability then whittle the 650,000 names down to a longlist of 20-50, which is cross-referenced with the scouting longlist; there is generally significant overlap between the players identified by data and those already on the scouts’ radar.
More refined metrics rank and prioritise that longlist before scouts complete further diligence to reduce it to a maximum of about six players. Deeper dives then follow, covering injury records, athletic performance and behavioural habits. The findings are compiled into individual acquisition packs containing the key metrics used to inform a decision. Among them are a radar chart rating specific on-field measures, a utilisation chart recording how often a player has been used over a three-year period, and a scouting grade indicating how highly traditional scouts rate him. Each player also receives a current impact score, predicting the immediate impression he would be expected to make on signing, and a potential impact score projecting his future contribution. Perhaps the most intriguing reading is the action value score, which positions a player in a league table that includes City players and rivals operating in a similar role. Once the acquisition packs are evaluated and City’s valuation — never a single number — is factored in, the list is cut to two or three players, with all of the shortlist worked on simultaneously in case circumstances change.
One player who has been on City’s radar is Juventus full-back Andrea Cambiaso, highlighted by the data for his ability to come inside and build play in midfield, with his comfort on both feet also rating highly. In accordance with the data and the scouting reports, the club decided not to proceed with their interest in the previous window, but their approach means he is likely to continue to be monitored.
Data also shaped City’s pursuit of Jeremy Doku in 2023. Then-manager Pep Guardiola wanted a direct winger in the mould of Leroy Sane, because at the time he had Jack Grealish and Riyad Mahrez, who preferred to keep the ball and cut inside. The brief included criteria such as making — running into — the box, pressure regains, take-on percentage and opposition-half pressure. Doku’s numbers ticked the boxes Guardiola was looking for, although his making the box score was among his lowest readings. Since his arrival he has improved that rating, an example City cite of how their data systems help develop existing players.
Being able to move according to the market is something the data team feel they have become adept at. The sudden emergence of Yan Diomande last season led to City completing checks on him before he joined Real Madrid from RB Leipzig. The club also paid close attention to Eduardo Camavinga’s metrics in case Real decided to sell a midfielder.
Elliot Anderson, the £116m signing, looked an obvious target for a top Premier League club: a 23-year-old who had developed into a key player for Nottingham Forest, was English and had cemented his position in the national team, and whom City fought off other leading clubs, including Manchester United, to sign. Even so, City ran the full process. At the start of last season, despite emerging uncertainty over Guardiola’s future, the club knew they would be in the market for at least one central midfielder in the summer. Bernardo Silva was leaving and there were doubts over the futures of Rodri and Nico Gonzalez. Replacing Silva and Rodri like-for-like was judged virtually impossible, so City looked for a player equally adept at building play from deep or high central midfield. Doubt later emerged over Tijjani Reijnders’ future, with Enzo Fernandez viewed as the favoured replacement for the Dutchman.
Anderson made the initial cut from the 650,000-man database to the longlist but was not high in that group, and his metric readings did not immediately scream out the no-brainer he ultimately became. He did not score many goals for Forest or consistently provide assists, and because Forest are generally not a possession team like City, his general metrics in the algorithm did not grab attention. Analysts therefore had to consider that, playing closer to his own goal than he would at City, he was not getting the opportunities to impress in the final third, and they modelled his potential role in a City side that plays closer to the opposition’s goal, dominates possession and has more elite players. His radar chart graded defensive regains, duel success, line-breaking passes, aerial-duel success and defensive errors; on the latter it was acknowledged that, because Forest were a transitional side, some errors were a result of the area of the pitch in which he was asked to operate. “Just because a player doesn’t have the opportunity to perform a particular action on a regular basis because of the team he is playing doesn’t mean he can’t do it,” said a source. Anderson’s readings improved significantly as the season developed, compelling the club’s scouts to watch him on a regular basis, and he stood up very favourably against City’s existing midfielders and a list of rival players identified as possible signings in that area. Even though his price increased beyond what City believed was his true valuation, the diligence of the data team, scouts and other experts meant they felt the risk was low. “Elliot Anderson is a great example of how the process works,” Viana said. “The data was positive for Elliot and gave us real confidence in his profile, but it was the insight from our scouts and wider performance team that helped us understand the player and his attributes and this proved our belief that he was the right fit for the club.”
Julian Alvarez offers another illustration. The initial recommendation to sign him from River Plate for £14m in 2022 came from scouts, and because he was not a player originally identified by his metrics, the data team took a closer look. With the Argentine league not recognised as one of the strongest globally, City’s general data settings mean players reach a natural ceiling in their readings compared with those playing in Europe’s top five divisions, so the club dug deeper into his stats and his impact on both the ball and River’s scorelines. The numbers showed he was by far the best player in the Argentine top flight, and City believed they had enough evidence that he would develop into a starter for a top-half club in one of Europe’s top five leagues within two-and-a-half years. Their model valued him between £26m and £40m, so at £14m City believed they were signing an undervalued player. His projected value was £70m after three years; he was eventually sold to Atletico Madrid in 2024 for £81m.
The signing of Abdukodir Khusanov from Lens also shows City’s reliance on data. The 22-year-old did not initially make the 50-man longlist, but on closer inspection City found that while his output on the ball needed to improve, his physical data — for example his covering runs behind — was through the roof. His on-the-ball metrics have, City say, improved significantly since he signed in January 2025.
The departure of Guardiola and the arrival of Enzo Maresca is not expected to alter the club’s approach significantly. The key metrics Maresca looks for in a player are understood to be very similar to Guardiola’s, with the notable difference that the Italian prefers his full-backs to be more direct. Rooted in City’s coach recruitment process is a certain team philosophy and way of playing: any new manager is expected to produce a side that is possession dominant, can press high, regains possession high up the pitch, produces quality chances and entertains. That philosophy was designed before Guardiola’s appointment, and the Spaniard produced the purest form of City’s footballing ideology, so the change of manager has not drastically altered how the data department approaches its work. Maresca will continue to work closely with them as he looks to build his own dynasty.
Before the Treble-winning season, internal data indicators told City they would soon need to overhaul a squad that had reached its peak. Selling players such as Kevin de Bruyne, Kyle Walker, Riyad Mahrez and Ilkay Gundogan was not easy given their illustrious service and the fact they were still performing at a very high level, but data allows the club to spot declining trends well in advance — often two or three years — before a player is offloaded, and to identify the right time to sell, perhaps even before the buying club has spotted a drop in performance. From there the club decide whether to start embedding a player from the academy or enter the market to replace them. Ayyoub Bouaddi’s arrival from Lille this summer is a prime example: at 18, City realise he is not necessarily going to have a profound impact this season, but the club believe the Moroccan will be able to make a bigger contribution within two years.
After the Treble season, the recruitment remit at City was to lower the average age of the squad and build a set of players capable of sustained success — complicated at a club where the team are always expected to compete for trophies. City’s data systems had previously identified the former Brighton trio of Moises Caicedo, Marc Cucurella and Carlos Baleba, who have been lauded as data successes, but there was a sense at City that the development of such signings would take too long to make an impact on a team with a remit of winning titles. City have since lowered the average age of the squad to the extent that they now have the third youngest set of players in the Premier League, behind Newcastle and Chelsea, while adding players such as goalkeeper Gianluigi Donnarumma, defender Marc Guehi, and Fernandez and Anderson, who can grow with the new generation but already have vast experience of top-level football. City’s data indicates their current squad will not peak for another two to three years, but also projects they are likely to be among the title challengers this season.