Transfer Values and Probabilities: the CIES Football Observatory Approach
Total Page：16
File Type：pdf, Size：1020Kb
CIES Football Observatory Monthly Report Issue 16 - June 2016 Transfer values and probabilities: the CIES Football Observatory approach Drs Raffaele Poli, Loïc Ravenel and Roger Besson 1. Introduction vested. This gap is explained by the inflation in transfer costs. Insofar as nothing indicates that the inflation has topped out, a supple- Transfer fees paid by football clubs to recruit ment of 10% was applied to each footballer new players have strongly increased over the whose transfer value appears in this report. past few years. With the growth in revenues This percentage corresponds to the underes- of the top-flight European teams along with timated amount observed on average so far. those of all English Premier League clubs, a new record for expenditure will most probably Our approach also takes into account the level be set during the next transfer window. of the team interested in acquiring the servic- es of a player. A wealthy club such as Man- The CIES Football Observatory is able to pre- chester City, for example, should pay more dict the footballers of the five major European than West Bromwich Albion for the same play- leagues who are the most likely to be trans- er. To simplify, the values presented refer to ferred for a sum of money during next sum- the corresponding fee for the team most like- mer. We are also capable of estimating the ly to recruit the player in question taking into transfer value of big-5 league players taking account his characteristics and performances. into account the amounts previously paid for footballers with similar characteristics. This Report first examines the criteria used to evaluate both players’ transfer values and Our estimations are based on statistical mod- probabilities. We then present the big-5 league els developed from a detailed analysis of deals footballers most likely to be the object of a concluded over the last six years. No subjective paid transfer during next summer. The follow- data is taken into account. Transfer rumours ing chapter lists the players with the highest have no place in our approach. Neither do our transfer value. In the conclusion, we reiterate estimates include clauses that fix the fee at the principle applications possible for the al- which certain players can be transferred. gorithms elaborated by the CIES Football Ob- Since the 2013 summer transfer window, the servatory research group. correlation between values estimated by our algorithm and the sums actually paid for the recruitment of big-5 league players has been close to 80%. The strength of this correla- tion shows that, on one hand, the footballers’ transfer market is rational and, on the other, that its rationality has been well understood by the econometric model developed by the CIES Football Observatory academic team. Moreover, the model estimating the proba- bilities of paying fee transfers has turned out to be very accurate. Of the twenty footballers that we identified as most likely to be trans- ferred for a fee in June 2015, twelve actual- ly left, five extended their contract and only three stayed in their home club without re- newing their contract. The estimated transfer values were, on aver- age, slightly lower than the sums actually in- 1 Monthly Report 16 - Transfer values and probabilities 2. Valuation criteria Figure 1: key indicators in estimating transfer values and probabilities To determine transfer values and probabilities on a scientific basis, our academic team first Age rs analysed in detail the trajectories of players e y Position Book having recently participated in the five ma- la value jor European leagues. Among these are over P 2,000 footballers that have been the object of paying fee transfers since July 2010. Contract Competition level Using statistical modelling techniques, we Values have been able to identify the criteria that af- fect the determination of transfer fees, as well Probabilities International Results as the factors influencing the probability of a status player being transferred for a sum of money. These variables refer to both players and their teams. s Experience Achivements m a e A first group of indicators concerns the char- Performance T acteristics of players such as age, position, length of contract remaining and the residu- al book value. The latter variable is calculated from the transfer fee amount paid by the em- ployer club, divided according to the percent- age of years of contract since the signature. A second group of indicators takes into ac- count the players’ performances, notably in terms of the amount of time played in the dif- ferent club competitions (domestic leagues, cups) or, eventually, in national teams. Recent performances are given more weight than pre- vious ones. The last family of indicators refers to the lev- el of the leagues where the footballers played their matches, as well as to the results ob- tained by the employer clubs. The level of the national team represented is also taken into account. 2 Monthly Report 16 - Transfer values and probabilities 3. Probabilities of fee paying Figure 2: Players with an estimated value greater than €25 million most likely to be transferred for transfers money This chapter presents the rankings of football- 1. Gonzalo Higuaín* 60.9 ** ers identified by our model as being the most Napoli (ITA) - fw - 28 - 2018 likely to be transferred for a fee during the 2. Alexandre Lacazette 41.5 Lyon (FRA) - fw - 25 - 2019 2016 summer transfer window. Players on loan 3. Michy Batshuayi 25.2 have not been included in the analysis. Marseille (FRA) - fw - 22 - 2020 Numerous footballers from relegated clubs 4. Antoine Griezmann 120.2 Atlético Madrid (ESP) - fw - 25 - 2020 figure among those for whom a paid depar- 5. Romelu Lukaku 58.1 ture is the most probable. Indeed, relegation Everton (ENG) - fw - 23 - 2019 obliges teams to compensate decreasing rev- 6. Carlos Bacca 35.4 enues by selling players. This also gives an Milan (ITA) - fw - 29 - 2019 incentive to the players themselves to leave. 7. Bernardo Silva 31.5 Consequently, relegated clubs generally offer Monaco (FRA) - am - 21 - 2020 interesting recruitment possibilities. 8. André Gomes 41.2 Valencia (ESP) - dm - 22 - 2020 There are many top-flight forwards among the 9. Leroy Sané 34.0 players whose transfer value is over €25 mil- Schalke (GER) - am - 20 - 2019 lion and who are most likely to be transferred. 10. Mauro Icardi 49.9 Gonzalo Higuaín heads the rankings. The Inter (ITA) - fw - 23 - 2019 28-year-old Argentinean has only two years of 11. Shkodran Mustafi 29.2 his contract left to run. According to our anal- Valencia (ESP) - cb - 24 - 2019 ysis, it is very probable that he will be signed 12. Ross Barkley 39.7 by a wealthier club than Naples. Everton (ENG) - am - 22 - 2018 13. Koke Resurrección 50.3 Three other players whose transfer value is Atlético Madrid (ESP) - am - 24 - 2019 over €50 million are likely to leave: Antoine 14. Hakan Çalhanoğlu 27.2 Griezmann and Koke Resurrección from Atléti- Leverkusen (GER) - am - 22 - 2019 co Madrid, as well as Everton’s Romelu Luka- 15. Paco Alcácer 31.0 Valencia (ESP) - fw - 22 - 2020 ku. Mauro Icardi (Inter) and Alexandre Lacaz- 16. Henrik Mkhitaryan 33.6 atte (Lyon) are also strong contenders for the Dortmund (GER) - am - 27 - 2017 most expensive summer transfers. 17. Mohammed Salah 38.7 Roma (ITA) - fw - 24 - 2019 18. Ilkay Gündoğan 26.4 Dortmund (GER) - dm - 25 - 2017 19. Sadio Mané 35.5 Southampton (ENG) - fw - 24 - 2018 20. Jamie Vardy 34.7 Leicester (ENG) - fw - 29 - 2019 * Name - Value (million €) Club (League) - Position - Age - Contract end ** [gk] : goalkeeper, [cb] : centre back, [fb] : full back, [dm] : defensive midfielder, [am] : attacking midfielder, [fw] : forward 3 Monthly Report 16 - Transfer values and probabilities Newcastle’s surprise relegation will probably Numerous talents are among the 20 players result in the departure of quality players. Ac- with a transfer value between €7.5 and €15 cording to our analysis, four footballers from million who are the most likely to leave their the club whose value is between €15 and €25 current club. The youngest of them is Stutt- million are likely to be transferred: Georgin- gart’s Timo Werner. Only Nicola Sansone (Sas- io Wijnaldum, Aleksander Mitrović, Chancel suolo) is more likely to be transferred for a fee Mbemba and Jonjo Shelvey. Mario Götze (Bay- than the German striker. Three 21-year-old ern Munich) is also likely to find a new club as players are also in the top 20: Leon Goretz- his contract has only one year left to run. ka (Schalke 04), Karim Rekik (Olympique Mar- seille) and Bruno Fernandes (Udinese). Figure 3: players with an estimated value between €15 and €25 million most likely to be Figure 4: players with an estimated value transferred for money between €7.5 and €15 million most likely to be transferred for money 1. Georginio Wijnaldum 24.8 Newcastle (ENG) - am - 25 - 2020 1. Nicola Sansone 13.7 2. Mario Götze 24.4 Sassuolo (ITA) - fw - 24 - 2017 Bayern (GER) - am - 24 - 2017 2. Timo Werner 8.5 3. Aleksandar Mitrović 24.2 Stuttgart (GER) - fw - 20 - 2018 Newcastle (ENG) - fw - 21 - 2020 3. Nathan Redmond 9.9 4. Filip Kostić 15.4 Norwich (ENG) - am - 22 - 2017 Stuttgart (GER) - am - 23 - 2019 4. Robert Brady 7.7 5. Maximilian Meyer 22.4 Norwich (ENG) - am - 24 - 2018 Schalke (GER) - am - 20 - 2018 5. Loïc Rémy 8.2 6. André Schürrle 20.8 Chelsea (ENG) - fw - 29 - 2018 Wolfsburg (GER) - am - 25 - 2019 6. Jean Seri 9.8 7. Chancel Mbemba 15.8 Nice (FRA) - dm - 24 - 2019 Newcastle (ENG) - cb - 21 - 2020 7.