Are Pogba and Stones Really Worth The Money?

Executive Summary

  • Statistical models of the football transfer market show a very high level of systematic variation in transfer fees.
  • Transfer-fee inflation tends to be closely associated with revenue growth, particularly the growth of TV media revenues.
  • Transfer valuations of individual players depend on five main value-drivers: player quality, selling club, buying club, current contract expiry date, and market conditions.
  • Player quality can be captured using five quality indicators: age, career experience, current appearance rates, current and career scoring rates, and international caps
  • Comparative (or benchmark) valuations of players involve combining the quality indicators and other value-drivers of transfer fees using weights extracted statistically from actual transfer fees (via regression analysis).
  • Fundamental valuations of players involve estimating the incremental revenue value of player contributions on and off the field. In the invasion-territorial team sports this requires a player rating system to combine multi-dimensional performance data into a single composite measure of overall player performance.
  • My player valuation algorithm indicates that the differential in the transfer valuations of Pogba and Stones is justified by Pogba’s greater experience, his goals contribution, and the greater size and status of his previous club, Juventus.

 

This week saw the two Manchester clubs splash the cash, paying a combined total of £136.5m in transfer fees for just two players. Manchester City paid Everton £47.5m for John Stones while Manchester United paid Juventus £89m for Paul Pogba. Are Pogba and Stones really worth the money? It was just this type of question that got me into sports analytics 20 years ago. Working with my good friend and fellow applied economist and sports fanatic, Steve Dobson, we investigated the economics of the football players’ transfer market in England. In particular we wanted to know just how rational football clubs were in setting transfer fees. We put together a dataset covering 1,350 transfers between English clubs during the period from July 1990 through to August 1996 which included Alan Shearer’s world record transfer from Blackburn Rovers to Newcastle United for £15 million. We published a couple of journal articles on our findings and subsequently extended our research to include players transfers in non-league football.

 

In common with other studies of the English football transfer market in the mid-1990s, we found that the transfer market was very rational with our statistical model able to explain around 80% of the variation in transfer fees. Football clubs were using the available information on player quality in a very systematic way to set transfer fees. Also, because our data covered six seasons and four different divisions, we were able to look at trends in transfer fees over time and found some evidence that the rate of increase in transfer fees over time reflected revenue growth. The current transfer window reinforces the relationship between revenue growth and transfer fees. The largesse of the transfer fees paid for Pogba and Stones is just part of another surge in transfer fee inflation fueled by the massive jump in Premiership TV revenues.

 

When we first started to present our findings at economics conferences, the media took quite a bit of interest with several articles on the theme of “boffins apply science to the beautiful game”. We were repeatedly asked if the statistical analysis of transfer fees could be used to value players. This prompted me to start to develop the SOCCER TRANSFERS player valuation system and this really marks my switch from academic data analysis into sports analytics. My focus moved from building a statistical model to explain the variation in 1,350 transfer fees to developing a system to use player and market data to value individual players. Ultimately I constructed a valuation process, a way of bringing together different types of information about players and then converting that information into a financial value. Regression analysis identified the relevant information as well as estimating the conversion rates (known an implicit or hedonic prices) for converting the different types of player information into financial values.

 

My player valuation algorithm initially identified four main value-drivers: player quality, size and divisional status of the selling club, size and divisional status of the buying club, and transfer market inflation. In the mid-1990s there were no player performance data available beyond appearances, goals scored and disciplinary records. So player quality had to be measured using five principal quality inidactors – age, career experience, current appearance rates, current and career scoring rates, and international caps. And remember at that time there were no websites with comprehensive player data. Instead the data had to be painstakingly extracted by hand from the various editions of the Rothmans (now Sky Sports) Football Yearbook. Overseas players were particularly difficult to value because of the difficulties in obtaining data on leagues outside the UK.

 

Another problem I encountered was that the initial analysis was pre-Bosman. The Bosman ruling was first published by the European Court of Justice in September 1995 but initially only applied to cross-border transfers. It was not until 1998 that UK domestic transfers became subject to Bosman free agency with no transfer fees payable for out-of-contract players over the age of 23. Fortunately as I started to provide player and squad valuations for clubs, financial institutions and the courts, I was able to get access to confidential information on contract expiry dates which allowed me to construct an adjustment (formally a polynomial decay function) to capture the decline in transfer value as players entered the last two years of their contract.

 

The revised version of the player valuation algorithm, which I still use today, takes the general form:

SOCCER TRANSFERS Player Valuation System

Blog 5 Graphic

Effectively this approach provides a comparative (or benchmark) valuation of players in which the statistical analysis of actual transfer fees yields estimates of the implicit prices of the various indicators of player quality as well as valuing the impact of differences in buying and selling clubs, transfer market inflation and the remaining length of contract. This algorithm still works incredibly well today. In particular there is little improvement in accuracy by including the very detailed player performance data now produced by commercial companies such as Opta and ProZone. The indicators of player quality I used 20 years ago retain their predictive value. Over the years the player valuation algorithm has been used for a number or purposes including assisting teams in their transfer dealings, determining the required level of player insurance cover, providing an input into the corporate valuation of clubs, estimating the player asset values as security in debt transactions, and resolving legal and tax disputes. A variant of the algorithm was also developed to provide player salary benchmarks in the Scottish Premier League.

 

Although detailed player performance data provides little improvement in comparative player valuations, it does, however, open up the possibility of providing fundamental valuations of players based on an estimation of the incremental revenue gains generated by a player’s contributions on and off the field. Top players are very expensive assets. In any other business there would be an investment appraisal process involving the projection of the future stream of value expected to be generated by the acquired asset relative to the financial costs incurred. While professional sports teams will apply this type of due diligence to stadium and other tangible investments, most have deemed investment in playing talent to be too complex to be amenable to this type of approach. But the American sports economist, Gerald Scully, showed in a paper published in the American Economic Review in 1974 that it is possible to calculate financial values of players based on their playing contributions. Using data from Major League Baseball in 1968 and 1969, Scully developed a two-stage procedure in which he first estimated a regression model of the relationship between batting and pitching metrics (Scully used the slugging average and the strikeout-to-walk ratio) and team win%, and then estimated a second regression model for the relationship between team win% and team revenue. Using these two regression models, Scully could then calculate how much each player contributed to team performance and team revenue.

 

Of course, to apply Scully’s methodology to the invasion-territorial sports such as the various codes of football, hockey and basketball when player performance is multi-dimensional, you need to develop composite player rating systems to measure a player’s overall contribution. And you also need to build in an estimate of the player’s image value given the importance of media, merchandising and sponsorship revenues. The complexities of developing player rating systems in the invasion-territorial sports will be the subject of several future blogs.

 

But, to come back to the original question, are Pogba and Stones really worth the money? It is impossible to answer that question fully without knowledge of the total financial obligations involved in both deals including salary costs, transfer fees and agent fees. But it is possible to compare the transfer valuations of both players using my valuation algorithm. What I can say is that if Stones is valued at £47.5m under current market conditions, then the estimated valuation for Pogba on the same basis would be £86.4m. The difference between the two valuations reflects Pogba’s greater experience, his career scoring rate of around one goal every five games (Stones has only scored one league goal), and the greater status of Juventus compared to Everton. At the very top end of the market, even small differences in the value-drivers translate into exponentially large differences financially (which can be captured statistically by using a loglinear valuation algorithm). There is a clear rationality in the comparative transfer valuations of the two players. Only time will tell whether or not the huge transfer fees are justified by the ultimate bottom line in every player transaction whatever the sport, namely, performance on the field.

 

13th August 2016

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