Three policies from the Football Behavior Management course that you can implement right away

Football Behavior Management is Organizational Behavior Management (OBM) for football clubs. Here are three smart policies that help strengthen your club immediately:

I) Start measuring your scouts, training staff and decision makers

Why only use statistics for your players, when statistics works as well – if not better – for scouts, training staff and decision makers.

The first step of FBM or OBM is to specify desired behavior. The number #1 desired behavior for your scouts is to find players that are highly likely to be able to contribute to the team. The same goes for the training staff for as far as they are involved in the recruitment process. The desired behavior for decision makers is to hire players that are highly likely to be able to contribute to the team. Often this means that decision makers have a secondary desired behavior and that is to listen to their scouts and stick to the recruiting rules as they have been decided upon beforehand.

To measure your scouts, training staff and decision makers, you ask them to subjectively grade potential players on a scale of 1% to 99% of how likely they are to be able to contribute to the team before they are actually recruited. 

You can use all of these predictions to actually calculate the risk of hiring this new player as well as the chance for a million euro or more transfer fee. That way you can actually see which player has the best risk/reward ratio. Yet, you can also use these risk analyses to make sure that all the combined small risks don’t make for one big risk. Because for smaller clubs the problem of ruin is very big in football. And even for big clubs the problem of ruin involves too much stress for the people involved. The problem of ruin is that if a small club hires the right players 95% of the time, they will be relegated once every twenty years. So clubs need a very high success rate to stay out of trouble. Formal risk management helps a lot.

At the end of the next season your team and you decide which new players have been successfully contributing to the team. Most of the time this is obvious. If there is a discussion one can look at predicted stats, minutes or his new replacement value. A successful player scores 100% and an unsuccessful player 0%. Then you can use Brier’s Rule to determine how well your team predicted these successes. Now you have the first data on who are good predictors in the club and who are less so.

This information is now fed back into the risk management by giving the good predictors more weight so that for the next season the risk analysis is improved even if all the same people are still working at the club. Keep doing this and the risks go down, the rewards go up and the problem of ruin becomes smaller and smaller.

II) Create a Viable Systems Model of your club

The Viable System Model (VSM) is a cybernetic model that models any organization. Any organization that exists for more than five years follows the general structure of the VSM model. Yet, most of the time these organizational structures are organically grown rather than thought out and structured by design. That means that at best they are inefficient and at worst that they are detrimental to the health of the organization.

The VSM for most clubs is quite easy to model as they are generally organized along the same lines. Most importantly, the VSM model structures who can command who. By using the VSM model you can make it absolutely clear what the relationship and balance between the manager of the first team and the technical or sporting director is. The VSM doesn’t prescribe what to do. The VSM only shows what the best implementation is for your choices. 

Finally, cybernetics teaches us that any regulator of a system is only as good as the model he has of that system. Good regulators have good models and bad regulators have bad models. This is why clubs spend so much time looking for a good manager or a good technical director. They are actually searching for a manager and a technical director with a good model. Do these good managers and good technical directors have an explicit model? Seldom of course. The model is inside their brain. That is what makes good managers and good technical directors so valuable.

By introducing the VSM in your club, you can make these unconscious models explicit so that not only the rest of the club can learn from them, but that you can actually optimize them and use them long after the manager or the technical director has left the club. In other words: creating the VSM model of your club actually enriches the club.

III) Hire one player less

On average, clubs hire six new players each season. Of those six players, two players tend to be unsuccessful, again on average. By hiring one player less and spending his salary and transfer fee, if any, on the scouting and recruiting department, chances are that they suddenly have a much bigger budget than before. As it seems that for most clubs the scouting and recruiting department has too small a budget. At the same time the scouting and recruiting department has the potential to make the club the most money.

This situation of too small a budget for scouting and recruiting seems irrational, but FBM and OBM explain when it is still a rational decision by the decision makers to spend as little as possible on scouting and recruiting. They do this, often unconsciously of course, because they already know that they are not going to listen to their scouts and recruiters. That is why actually listening to your scouts and staff is such an important desired behavior for decision makers. That is why measuring scouts, staff AND decision makers how well they predict is so important. That is why it is important to have a Viable System Model of the club so decision makers understand better what makes the club viable.

Use risk management and risk analysis to determine which player is most likely to fail at the club and refuse to hire him nor any other player. Instead be satisfied with the players you did recruit and spend the money of that one player on recruitment and scouting so that the next time you hire even better players with less risks and bigger chances for big rewards while at the same time keep hiring one player less each and every season. This policy will increase the probability of steering the club towards greater heights while at the same time reducing the probability of ruin.

These are three examples of what is being taught at the Football Behavior Management course we deliver for the VU-university of Amsterdam or in house for clubs. For more info, feel free to connect with us for more information and an introduction or presentation. To connect, please fill in the form below:

We respect your email privacy

How to read a FBM chart

Maybe you have come across a FBM chart like the following on Twitter and you are curious how to read these charts:

What you see is the answer to the following three questions:

  1. What is the probability that this player is able to contribute to the finishing of his current team?
  2. What is the probability that this player is able to contribute to the defending of his current team?
  3. What is the probability that this player is able to contribute to the passing game of his current team?

These probabilities are predictive and hold for the next upcoming game. It is important to note that almost all statistics in football are backward looking and descriptive. As useful as that can be, descriptive statistics is way less useful than predictive statistics like these FBM stats. After the game these FBM probabilities are updated using Bayes Theorem. For reliable players these probabilities are consistent over time. For more unreliable players they fluctuate more. So you can use FBM statistics to determine how reliable a player is.

Finishing consists of scoring goals, giving assists and shooting on target. The probability of being able to contribute to the finishing of the current team decreases due to shots off target.

Defending consists of all actions of the player and results where the player has a contribution to said result while the opposing team is in possession of the ball. The most positive result is of course gaining possession of the ball.  The probability of being able to contribute to the defending of the current team decreases due to the opposing team getting significantly closer to the goal, fouls being made or goals scored against the team.

Passing game consists of all actions of the player and results where the player has a contribution to said result while the team is in possession of the ball. This includes actions without the ball like drawing out defenders or occupying space, progressive passing, packing and the pre-assist. The probability of being able to contribute to the defending of the current team decreases due to losing possession of the ball.

The distribution of probability

The graphs you see are the Poisson distribution of the underlying FBM stats for finishing, defending and passing game. Whenever you see a football statistic given as a single number be very suspicious. Reality is too complex to be captured in numbers, even if there are a whole bunch of them. In fact, the more different statistics are used, the less valuable the information becomes, because the more data you have the more you can prove. Yet, the more you can prove, the more what you prove is confirmation bias as you are going to prove what you already think you know. That is the reason why statistics should now and then shock you. Because if statistics doesn’t shock, the chance is that you use statistics to confirm your biases.

So rather than present players in single numbers, we present players as a Poisson distribution. The distribution gives you the area where the player’s probabilities will lie after the next match. Of course, given that each match only slightly moves these probabilities, if they move at all, in practice these probabilities hold for the whole season or to whenever a major change occurs, like for instance an injury, a new manager or a new team.

A new team is important, because FBM probabilities are always for a player playing for a specific team in a specific league. As soon as the player moves to a different team or even a different league, these probabilities change. We have a Bayesian transfer model that calculates how these probabilities change whenever you move a player from one team to another. Most of what we do is help clubs understand how likely it is that a potential player they like to hire is going to do well in their team.

How to read these distributions

There is a very simple rule to reading these charts:

The more to the right, the better. Ignore the peaks.

Somehow our attention is being drawn to the peaks, but the peaks are a mathematical artifact of the Poisson distribution. You could say that the Poisson distribution tries to distribute 100 points around the average of the statistics. The less space the Poisson distribution has to achieve this, the higher the peak. But the less space means that the probabilities used are very low. Hence the rule to ignore the peaks and just look for what is most to the right.

Graphs may overlap, so for instance in our example of Oscar Fraulo, he both maxed out on finishing and passing game probabilities and so they overlap turning the graph into some greenish blueish color to indicate that both the green and the blue chart overlap.

If we compare two players in a chart the overlapping area is quite important. Because then the overlapping area is actually the chance that the lesser player will do as good as the better player or even better! So if the graphs of two players overlap a lot, the lesser player has a decent chance of outperforming the better player in the future. Nevertheless, the better player still has the biggest chance of outperforming the lesser player.

When you want to see how one of your favorite players looks in FBM stats, please let us know on Twitter through a Tweet or a DM. Or fill in the form below to request a free sample report:

We respect your email privacy

What is FBM replacement value?

With FBM replacement value we calculate what a club is to be expected to pay to replace one of their players. This calculation is based on the following stats:

  1. The FBM players stats. There are four FBM players stats: 
    1. The probability to be able to contribute to the team overall.
    2. The probability to be able to contribute to the finishing of the team.
    3. The probability to be able to contribute to the passing game of the team.
    4. The probability to be able to contribute to the defense of the team.
  2. The historical transfer fees actually paid for the position of the player in the current league.
  3. The rank of the team in the table.
  4. The player’s age.
  5. The player’s length.
  6. The player’s international status.

Replacement value calculates the amount of money a club probably needs to spend to get a replacement player coming to the club. That means that replacement value is more about the new player coming to the club then the player leaving the club. So replacement value is not the most likely transfer fee, but can be used to determine a fair transfer fee by the selling or buying club, or both. That is the reason why replacement fees at some clubs are so much lower than expected transfer fees. These are smart clubs that hire new players for low transfer fees and let them go for high transfer fees. So for example Jurrien Timber of Ajax is expected to leave the club for a transfer fee of thirty million euro. Yet, his replacement value is “only” ten million euro. Yet, this is the amount Ajax needs to spend to get a player back who will produce the same stats as Jurrien Timber.

The way the calculation works is that we start with the perfect player. The FBM player stats are created by using Bayesian statistics. The highest possible probability to be able to contribute is 100%. That means that the perfect player has 100% probability to be able to contribute to the team overall, 100% probability to be able to contribute the finishing of the team, 100% probability to be able to contribute to the passing game of the team, 100% probability to be able to contribute to the defense of the team. Furthermore, the perfect player also has the perfect age and perfect length based on the length and age that have historically gotten the highest transfer fees. The perfect player also plays for the #1 ranked team in the league and he plays for the national team.

Such a player has never yet existed. Even Messi, who scores 100% probability to contribute to the team overall, in finishing and in passing game for most of his career, had a low probability of being able to contribute to the defense of the team.

Nevertheless, even though the perfect player has never existed, we take it that if he did exist, a club should have paid the all time highest transfer fee for that particular position in that particular league for him. In other words, the all time transfer fee actually being paid is to be taken to be paid for the perfect player even if in reality it was not. We don’t want to extrapolate based on the highest fee ever paid, because that fee might be a market top. 

The next step is to check how long ago this top transfer fee was paid and what has happened to the transfer fees paid for that particular position in that particular league. We do this because the market might have topped and we have to take into account that transfer fees in the future go down. This gives us the top transfer fee for the perfect player in the current season.

Finally we calculate how different the actual player is from the perfect player and this gives us the ratio between the actual player and the perfect player. The replacement value is calculated using this ratio and the top transfer fee for the perfect player for that particular position in that particular league.

How replacement value works for clubs and agents

Replacement value is not the most likely transfer fee. The transfer fee is whatever clubs and agents can get away with. Replacement value helps clubs and agents in their negotiations though. Because if club A pays club B a transfer fee that is above the replacement value of the player for club A, then club A is being weakened as the overpay and now have less money available for other transfers. The same goes the other way: if club B sells a player for less than the replacement value of the player for club B, club B is being weakened because they got too little money for the player and have less money left to spend on a replacement or for other players.

Fortunately, this means that it is possible that a transfer is a win/win for both clubs. This is due to the fact that replacement values are specific for each and every club. Player valuations like those on TransferMarkt suggest that the player has an intrinsic worth. This is a statistical mistake. The player only has a value to a specific club. If no club wants a certain player, no matter how high that player is valued, his future transfer fee is zero. So player valuation always needs to be made in the light of the club he is currently playing for and the club he is going to play for, including possible differences in leagues and the rank in the league of both clubs. That means that it is possible, even quite common, that the transfer fee is higher than the replacement value of the player for the club selling the player and at the same time lower than the replacement value of the player for the club buying the player. Both clubs are strengthened by the deal and it is a win/win situation.

Of course, in more cases one club profits at the expense of another club. In that case in very real terms (i.e. money) one club is getting stronger and the other club is getting weaker. So it is very important for clubs to keep checking whether their deals are favorable or not, in the light of the replacement value of the player.

To give an example of how this worked out in practice, we advised one of the consultants working for Willem II in regard to the transfer of Fran Sol to Dinamo Kiev. According to TransferMarkt the value of Fran Sol was 6 million euro at the time. Willem II was trying to get this amount from Dinamo Kiev, but Dinamo Kiev was unwilling to pay this amount. In our calculations Fran Sol had a replacement value of only 2 million for Willem II and 4 million for Dinamo Kiev. So any amount between 2 million and 4 million would be a win/win for both clubs. The final deal was for 3.5 million euro.

Given that the player’s age is a factor in the calculation of the replacement value, one can easily calculate the replacement value of a player in the future. This way clubs and agents can see whether a potential transfer has the chance of being profitable if the player leaves his new club after one or two seasons. That is how we were able to predict that Dalmau, who came to Heracles as a free agent, would have a replacement value of 1.75 million euro one season later. Indeed, after one season playing for Heracles he was transferred to FC Utrecht for 700.000 euro plus the transfer of Dessers to Heracles. Dessers at the time was valued at 1 million euro, bringing the value of the complete deal to 1.7 million euro, very close to the replacement value of 1.75 million euro we predicted for Heracles. Dessers left Heracles also after playing there for just one season for a 4 million euro transfer fee.

Why there, sometimes, is a big gap between FBM replacement value and transfer fees valuations

Take the interesting case of Noussair Mazraoui

His replacement value is 6.5 million euro playing for Ajax in the Eredivisie. Yet, his valuation at TransferMarkt is 20 million euro. Noussair Mazraoui is leaving as a free agent, so we will never know what the transfer fee would have been, but it doesn’t seem that Ajax has gotten a very attractive offer for Noussair Mazraoui. 

Nevertheless, there is a big gap between 6.5 million and 20 million. This gap is a great example of why replacement value is not the same as transfer fees. The FBM replacement value is a measure for Ajax to limit their spending on a replacement for Noussair Mazraoui to 6.5 million euro. We might be mistaken, but we think it is highly unlikely that Ajax would spend more than 6.5 million euro. Cases like these validate the replacement value model.

Some superstar players have extremely high valuation on sites like TransferMarkt. Mainly because they play in one of the best leagues for some of the best clubs, often getting very far in the Champions League. These high valuation are more a token of appreciation than a likely transfer fee as most of these superstar players will stay with their current club until (almost) the end of their career. Without the prospect of a transfer, the real value of those players is much closer to zero than the extremely high number quoted everywhere. So once more clubs find more use in the replacement value of those players as at some point they have to replace their retiring superstars with other players.