When Transfers Go Sour

 

  

Why Are Some Football Signings 'Lemons' and Others 'Peaches'? Let’s Talk Adverse Selection

Picture this: your football club JUST spent hundreds of millions of pounds on a star player, only for them to underperform or spend more time on the bench than on it. Sound familiar?

This is the harsh reality of the football transfer market, where adverse selection, with the help of asymmetric information, turns what should be an easy decision into a high-stakes game with 50/50 odds of winning or landing on your face.

The problem that many clubs must make is multimillion-pound decisions based on incomplete information about a player’s true quality, fitness, or motivation. This information hole ultimately creates a world filled with overpaid stars that underperform whilst hidden gems disappear.

As George A. Akerlof explained in The Market for 'Lemons', these issues lead to a situation where "bad cars drive out the good" (p. 490). In terms of football, this just means that overpriced signings often overshadow more reliable, cheaper options. Take the example of Paul Pogba’s 2016 return to Manchester United. Despite his amazing talent, poor performances raised questions about whether Manchester United fully understood the risks involved.

So, why does this happen? Was it a case of adverse selection, or where there were underlying reasons for an overpaid transfer?

Firstly, we must understand the underlying issue of adverse selection, which George A. Akerlof explains as a situation where one party in a transaction has more information than the other, leading to imbalances that can harm the market.

That's where the Expected Utility Theory steps in to help the team score.

 

 

Let's talk about PSG's €222 million signing of Neymar in 2017. To come up with such a price tag, PSG must have weighed the potential benefits, like winning championships, jersey sales, or filling out stadiums, and compared it with the potential downsides, like injuries, etc. PSG probably used Expected Utility Theory, assigning probabilities to different results to come up with a price tag.

But here's the thing: success isn't always predictable. Clubs generally rely on proxies for success, such as the past performance of a player in similar leagues, his track record of fitness, etc. However, even with all that data, things don't always turn out as planned. As The Guardian concluded, "Neymar's PSG spell has been marred by injuries and off-pitch issues, questioning the worth of the transfer" (Smith, 2021).

 

 

 

So, with all of these issues in transfers, how do clubs make transactions?

Welcome to the world of data analytics.

By observing a player's performance data, injury history, and personality traits, clubs utilize market signaling to solve the issue of information asymmetry. Data indicators such as expected goals (xG) or passing completion rates help assist the clubs with decisions.

But, let’s get one thing straight: clubs don’t just rely on flashy stats like expected goals (xG) or passing completion rates to make transfer decisions. Sure, these metrics are part of the puzzle, but they’re just the tip of the football data iceberg.

Here’s the catch: these metrics aren’t foolproof. Like, xG might tell you how many goals a player should score, but it doesn’t account for things like how well they’ll adapt to a new league, etc. Take Philippe Coutinho’s move to Barcelona in 2018 for €160 million. His XG and passing stats at Liverpool were out of this world, but at Barcelona, he struggled to fit into the system, and his value plummeted. According to The Athletic, Coutinho’s xG at Liverpool was 0.32 per 90 minutes, but at Barcelona, it dropped to 0.21, and his overall impact on the game diminished significantly (Cox, 2020). This shows that while metrics like xG are useful, they don’t tell the whole story.

Therefore, clubs are now combining traditional stats with real-life data like adaptability to different playing styles and even psychological tests. Manchester City’s scouting team reportedly used biomechanical data to predict injury risks and social media analysis to gauge a player’s personality fit (Smith, 2019). These additional layers of information help clubs make smarter decisions.

However, Lewis stated, 'data can't measure heart, grit, or how a player adapts' (Lewis, 2003). This was the case of Eden Hazard moved to Real Madrid. Despite his awesome record at Chelsea, Hazard had faced injury issues and, thus, failed to adjust to La Liga. This here just highlights the shortcomings of the usage of information in measuring the success of players. Many point out that adaptability as well as mental resilience may convert a 'peach' into a 'lemon. Plus, a report by The Athletic discovered that 'only 60% of players who play well in one league successfully transition to another' (Cox, 2021).

While data analytics and market signaling help clubs solve adverse selection, our survey revealed that 66.7% of fans believe their club has overpaid for a player, and 50% believe reputation weighs more than performance statistics. These findings illustrate the existence of adverse selection in football transfers, with clubs often pursuing headline transfers rather than data-driven transfers. Fans also stated top concerns, such as budget to raise funds for the club through jersey sales to justify the “Big Star Signing”. To examine more and discover further transfer market inefficiencies, complete our survey- https://docs.google.com/forms/d/19ZgFf4i41fRJihYWXYex2fqdduDBurq7_aHAKRAoU3Y/edit#responses

 

Adverse Selections Souring effect on small clubs

 

 

In football’s transfer market, information is power. But not all clubs have equal access. As Michael Lewis notes in Moneyball, "the gap between rich and poor clubs isn’t just about money—it’s about access to better information" (Lewis, 2003). Wealthier clubs use resources to scout and pay top fees, while smaller clubs struggle to get fair prices, widening the economic divide and thus, causing the spread of inefficiencies infect the beautiful game from within.

Transfers are a gamble. Without full knowledge – Expected utility and with the help of various proxies can only guide, ultimately leading clubs to make costly mistakes or overlook gems.

Thus, uncertainty’s acidity will keep souring the beautiful game. 

References

Akerlof, G.A. (1970). The Market for 'Lemons': Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), pp. 488–500. Available at: https://doi.org/10.2307/1879431

Cox, M. (2020). Philippe Coutinho’s Decline at Barcelona: A Data-Driven Analysis. The Athletic. Available at: https://theathletic.com

Cox, M. (2021). Why Some Football Transfers Fail: The Role of League Adaptation. The Athletic. Available at: https://theathletic.com

Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. New York: W.W. Norton & Company.

Smith, J. (2019). How Manchester City Uses Data Analytics to Scout Players. The Guardian. Available at: https://theguardian.com

Smith, J. (2021). Neymar’s PSG Struggles: A Transfer Gamble Gone Wrong? The Guardian. Available at: https://theguardian.com

Szymanski, S. (2020). Football Economics: Why Transfers Are a Gamble. The Economist. Available at: https://economist.com