Moneyball, but for Selling: Using xG/Goals Ratio to Profit

One of the most overused clichés in all of the Internet is “Moneyball, but for…”1. Moneyball, correctly applied, is the idea of using undervalued stats to figure out which players to sign at a bargain price. By exploiting an information asymmetry, small clubs were able to find value in players that bigger teams who focused on traditional stats were missing. However, soccer has a version of this that American sports don’t have: the ability to sell players for a profit rather than simply signing or trading for them. This, along with sophisticated analytics work, can help teams identify inefficiencies in the transfer market as a way to profit or make money to reinvest in multiple players.

Soccernomics identified strikers as potentially the most inefficient purchase in soccer. Teams consistently overpay because they focus on goal output above all other statistics. This seems to fit reality, as we’ve seen countless examples of top EPL teams investing in attacking players even when they have more options in attack than they could ever use (Chelsea last season and Manchester City this season are two great examples of this, especially given the lack of a second world class playmaker in David Silva’s absence). So what is the potential asymmetry here, and how can smaller clubs exploit it?

If we assume teams over value goals, then it makes sense to sell strikers who score a lot of goals, right? Well maybe, but maybe not. Some strikers are just really good and are really valuable because of that. But more importantly, one can assume that if a team gets a premium for their striker they’ll have to pay a similar premium to replace that striker and add in the uncertainty penalty (will the new striker achieve the same level with us?). So there’s no real market inefficiency to exploit there. However, if a team knows about Expected Goals (how many goals a striker should be expected to score given the type and number of shots), then they can potentially exaggerate this premium to find players who had a “hot” season, scoring above expectation. This player will likely be sold for even more, and the buying team will have to pay for last season’s over-production rather than the potential regression to the mean next season. This generates a significant profit to the selling team while minimizing the loss of production.

Ravi (@Scribblr_42) mentioned Darryl Murphy as a great example of a team not selling an overachieving striker, and paying for it with a lack of goals the subsequent season, but as a Milan fan my mind went immediately to Stephan el Shaarawy. A few years ago, in Milan’s last successful season, a young el Shaarawy carried the team on his shoulders. In 2012-2013, a 21 year old el Shaarawy scored 16 goals for Milan, basically single-handedly keeping them in the Champions League. Rumors were that Manchester City made some ridiculous overbid for him, but Berlusconi resisted, making him the center of Milan’s new youth movement.

Since then he has scored only 3 goals at the club level , and has since been loaned out to Monaco, hoping they will purchase him at a significant discount to what Manchester City was offering. Without seeing his xG scores, it  was pretty clear he was overachieving that season. If Berlusconi sells then, maybe he doesn’t have to make some of the desperate sales he does later, saving some of the club’s better players. El Shaarawy’s Transfermarkt values back this up: he was overvalued for a season or two and has dropped below his initial purchase price.



Teams need to think about whether their players are overachieving, and analytics has the perfect measure of this: Expected Goals/Actual Goals tells you whether a player is scoring more than expectation, and if that number is too high then a smart team may be able to exploit inefficiencies in other teams’ thinking who only focus on goals. Selling el Shaarawy and letting Man City deal with his regression to the mean would have been ideal for Milan, and more sophisticated analytics might have let them figure that out and do smarter transfer business than they did around that time. This is even more critical for smaller clubs who don’t have the institutional backing that Milan has, and a small investment in analytics could reap huge dividends for a club.

  1. “Uber, but for…” Has to be a close second

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