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2020/21 Richarlison

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Iwobi is the one I’m worried about. If we do decide to cut our losses I just can’t see anyone taking him. Nobody above us is going to want him, those around us will see what little impact he’s had at us and won’t pay either what we want as a fee or what he wants in wages. Anyone below us I just can’t see him agreeing to the move.

He’s been slightly better of late but not enough you could pin your hopes to building a team around him. He just doesn’t create enough. I think we might get our money back on Kean, PSG or anyone else if they decide to take him they know he doesn’t want to play for us. What are we going to do, have him sat on the bench? I’d be happy with what we paid for him back. Bernard I think we’ll be lucky to get 10 million. He won’t go anywhere in England so who abroad is going to give us anything substantial for him. They’d be mad to. Iwobi could be the new Sandro

If he doesn't go it's not the end of the world cause he can still be a useful squad member.

He'll be up for sale though, no doubt about it.
 
I don't think we should get rid of Richarlison but I feel like he has a bit of an inflated sense of worth which is, of course, fine. I genuinely can't see who would pay what we would want for him if he wants out. He was genuinely crap for about half the season so far. Got to have much more influence over the match consistently than the one who everyone can see after the first 10 minutes saying "well we know which Richarlison is turning up tonight".

Ideally, keep him around for next year and add to the team. If he desperately wants to move on, move him on. It's football, nobody is bigger than the club no matter how much some posters posture their opinions otherwise.
 

1 of only 3 players who play regularly, that has any goal threat and people want to get rid of him.

Who we playing instead, Bernard? Gordon? Iwobi? Them 3 would get you about 4 goals between them and that’s if they played every single game.

Richarlison is a very good player, we need to keep him and add other good players around him. Mainly in the midfield and on the other wing.
Richarlison is one of those one-man-army. Unfortunately, I think he won’t last too much on Everton before a big fish ends up signing him.
 
rico.webp
 
I mean how many games have we won without him? Got to be here next season, Carlo gotta convince him to give us one more season because he was agitating a bit last summer and hasn’t looked happy for most of the season. Next season is pivotal, after that James and Richy could be gone
 

£40-50mil for Grealish?
£90-120mil for Werner?
£30-40mil for Raphinha?

Well i never lol
Rashford, Saka and Neto seem wildly inflated.

I'm not going to spend the time searching, but I do wonder who is the worst player that site would suggest you could trade straight up for Grealish.

I wonder if they're getting their numbers from Woodward's staff.
 
Rashford, Saka and Neto seem wildly inflated.

I'm not going to spend the time searching, but I do wonder who is the worst player that site would suggest you could trade straight up for Grealish.

I wonder if they're getting their numbers from Woodward's staff.

Im sure you dont care, but there is a method in their madness.

Im not saying the figures are accurate by the way.

1. Introduction​

In 2019, for the first time ever, football clubs spent more than €10 billion on transfer fees. This figure has more than tripled over the past decade in parallel with the increase of club revenues. Contrary to what is often put forward by persons outside the football world, the amounts for transfer indemnities are not irrational. They are fixed by market actors based on criteria which are for the most part objective, and thus translatable into a statistical model.

Since 2010, the CIES Football Observatory has been at the forefront of pioneering work in developing a scientific method to estimate the transfer values of footballers based on sums previously paid for players with similar characteristics. This report goes through the variables included in the statistical model developed, reveals the predictive power of the latter and presents several applications of the approach put into place.

2. The variables​

The variables included in the statistical model of the CIES Football Observatory for estimating the transfer values for professional football players can be divided into three groups. The variables relative to clubs, those relative to players, as well as a contextual variable: the season during which the transfer took place. This latter variable allow us to take into account the evolution in values, all things being equal, in a strongly inflationary environment.

The variables relative to clubs refer to the level of teams where footballers were playing before their transfer on both a sporting and financial level. From the sporting point of view, the teams’ level is calculated on the basis of results obtained and the league of belonging. The level of the league they belong to is itself calculated on the basis of results achieved by clubs having represented the country in question in international competitions

From the economic point of view, the clubs’ level is calculated on the basis of recent investments undertaken in transfer fees. The equation also takes into account spending on the acquisition of players at the level of their league. This is notably helpful to anticipate the new economic force of freshly relegated or promoted clubs.

The variables relative to players are more numerous. They include the length of contract remaining with the owner club (the transfer indemnity being de facto a compensation for breach of contract), the age, the international status, career progression, as well as the performances in the different competitions played both for clubs and national teams (minutes, goals, assists, dribbles, passes, etc.).

Each of the variables contributes in a significant manner (<5% margin of error) in determining the values of football players. The strong correlation measured between the sums estimated and those actually paid (see next chapter) indicates that the variables used reflect particularly well the way in which the market actors negotiate fees. In specific cases, particular circumstances can nevertheless have a singular importance.

Figure 1: variables included in the model

Figure 1: variables included in the model

Among the aspects that are not taken into account in the model but could explain the discrepancies observed between sums estimated and actually paid, notably are a club’s urgent need for liquidity, a disagreement between a coach or fellow team members, the recruitment of another footballer who plays in the same position, disciplinary or physical problems, outstanding performances in youth competitions, etc.

3. The results​

The statistical model to estimate the transfer values for players from teams of the five major European leagues was built from a sample of 1,790 paid transfers carried out between July 2012 and January 2020. The correlation between the sums paid and estimated is greater than 80%. This shows that the variables taken into account explain more than four-fifths of the gaps in the level of fees paid for transfers included in the model.

Figure 2a: correlation between sums estimated and paid
Model without buyer club’s financial force
Figure 2a: correlation between sums estimated and paid, without buyer club’s financial force

The addition of the variable for the financial strength of the recruiting club reinforces the model and allows it to attain a correlation of 85%. This indicates that transfer prices also partially vary according to the economic force of the buyer club. The more financially powerful the club, the more it has to pay when recruiting a new player.

Figure 2b: correlation between sums estimated and paid
Model with buyer club’s financial force
Figure 2b: correlation between sums estimated and paid, with buyer club’s financial force

The model developed by the research team of the CIES Football Observatory not only has a strong explanatory power, it is also robust. The cross-validation tests carried out by splitting the sample randomly in two, 80% to train the model and 20% of remaining transfers to which apply the coefficients obtained for each variable, shows the correlations remain high and the estimated values are close.

Figure 3: example of cross-validation
Model without buyer club’s finance force
Figure 3: example of cross-validation (model without buyer club’s finance force)

4. Conclusion​

The pioneering approach developed by the CIES Football Observatory in the field of the scientific evaluation of transfer values has a wide range of uses. Market actors have already availed of it for:

(1) Transfer negotiations

In a highly speculative context where fake information is often leaked by clubs, agents and the various media involved, it is crucial to base oneself on an objective value before starting any negotiation. The projection of future values can also be beneficial, notably when it comes to the negotiation of sell-on percentages and add-on fees.

(2) Contractual negotiations

Thanks to the algorithm developed, it is possible to envisage likely scenarios on the future transfer values of players. This approach is particularly useful in defining the level of salary offered to a player without involving excessive risk, in determining the optimum length of a new contract or in fixing the amount of eventual buy-out clauses.

(3) Transfer litigation

Our algorithm is highly suited to situations of litigation over transfer amounts. For example, in fixing an indemnity fee in case of a unilateral breach of contract on a player’s part, when former clubs have a right to a percentage fee for players sold on or exchanged, as well as when footballers or other parties are entitled to a share of transfer indemnities.

(4) Credit negotiations

The objective and independent estimate of transfer values also proves useful when negotiating credits. Indeed, the transfer value of the squad constitutes a reliable indicator of the ability of a club to honour its engagements. This is not necessarily the case when credit worthiness is based on players’ book value. The transfer valuation of the squad is also useful from a financial communication perspective.

(5) Taking out insurance

With the increase in transfer costs, it is becoming more and more worthwhile to take out insurance policies covering the possibility of the loss of value of a player, notably through injury. Thanks to our algorithm, we can monitor precisely the current and future values of players under contract according to different scenarios to mitigate risks.

(6) Club sale or purchase

For most of the teams worldwide, players are the major asset. An objective and neutral estimate of the overall transfer value of squad members is a key criterion to be considered when it comes to negotiate the buying or selling of a club. Our algorithm is ideally suited for this need.

Aside from any applications by market actors, our approach and independence allows us to bring more transparency and objectivity to transfer operations. Indeed, up until the present, to the extent of our knowledge, no other organisation is capable of judging the validity of transactions on such a solid scientific basis.
 

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