Replacing Romelu: Using xGOLD to identify a new 9 for Inter – data analysis

It’s all occurring in the wild world of football transfers now. Lionel Messi has left Barcelona. Lionel Messi has left Barcelona. Sorry, I just had to write that out twice to make sure it was real. Jack Grealish has finally made his £100m move  a British transfer record  to Pep Guardiola’s Man City. Let’s not forget that Romelu Lukaku is seemingly on his way back to the club he supported as a kid, Chelsea. After months of suggesting through Twitter Q&As that he was categorically “staying” at Inter Milan despite the tumultuous grounds he stands on there with Conte leaving, Lukaku is on his way out.

Fair enough too  a player of his calibre deserves to be fighting for league titles every season, and Inter might not offer that in the coming season despite being the reigning Scudetto holders. Joining a team whose primary weakness is scoring goals, seems like a good idea for both parties. That leaves new coach Simone Inzaghi with the task of replacing the indispensable Belgian. 24 goals and 11 assists in league play leaves a gaping hole in their attack. In this analysis, we’ll be scouring the transfer market to find someone who could replicate his unique set of skills. No, thats not a Taken reference.

In this data analysis, we will use statistics to find a ready-made (using that term quite loosely) replacement for Lukaku, or potentially someone who could grow to become a player of his elite standard. Our dataset will consist of purely players playing in Europe’s top five leagues, but I will take into consideration players outside of our dataset who are performing to a high enough level. They will also be aged between 18-30 years old, with at least 1500 minutes of league play last season.

All data used in this piece is correct as of 06/08/2021. 

What does Lukaku’s profile look like?

In order to find a player to fit the mould of Lukaku, we first need to denote what that mould looks like. We are looking for a player who can provide a similar set of benefits within their style of play, we are not looking for players who even share a similar height or weight, but a similar playing style. It’s impossible to find an exact replica, but someone who can drive at defenders with the ball at their feet, find teammates with neatly timed passes, and of course, can score at a high rate.

Lukaku’s player profile above shows us where he excels, and where he has his drawbacks, in a visual format. Although, in the case of many statistics, low volume and (as a result) a low percentile does not always = bad. For example, Lukaku puts up a relatively low number of passes per 90 (20.95), but this just means he positions himself high up the pitch, and a good majority of these touches are within the penalty area, which is a massive plus considering Lukaku only needs a couple of touches to score inside the area. Lautaro Martínez, Lukaku’s striking partner, completed an even lower 14.8 passes per 90. This tells us that Inter’s forward’s are not the types to drop deep and get involved in build-up play.

His low number of passes does not limit his ability to score goals, for example. An 0.68 xG per 90 puts him in the 93rd percentile when compared with other strikers in Europe’s top five leagues. He can produce this high scoring output (0.53 non-penalty goals per 90) via his 5.69 touches inside the box per 90 which is also in the 93rd percentile. Intriguingly, Lukaku does not take a proportionately high number of shots, only 2.76 shots per 90, which indicates his shot locations must be strong and each shot would represent a high chance of scoring.

Lukaku is also hugel