Category: Statistics

Statistics

Loyalty, recruitment, brain types and the ABC-model

Player agents often complain about the lack of loyalty of the players they have signed. They assume that loyalty is an inherent trait some players have and others don’t. Of course, it is painful to see one of your biggest talents sign with a different agency just before their big breakthrough. In most cases leaving the agency has little to do with loyalty and more to do with the player’s brain type and the ABC-model. In this article I will describe what an agent can do to breed loyalty into his players.

First of all, the whole idea of people having traits is a backward idea. In reality people acquire knowledge through associative learning and skills through instrumental learning. In terms of football: associative learning gives you game intelligence and instrumental learning gives you technique. How do we know whether a player has game intelligence or technique? We see that in how the player behaves. For we cannot look into the soul of the player.

The behavioral patterns of a player are, for the most part, acquired through instrumental learning. Through instrumental learning the brain creates probabilistic relationships between the behavior and what this behavior gets you. The brain of the star player has learned in extreme detail how to shoot the ball in order to get the result the player wants: a goal. Instrumental learning works according to the ABC-model. In this model A stands for Antecedent which is everything that happens before the behavior or is necessary to make the behavior possible. B stands for Behavior, the desired or undesired behavior you are targeting. C stands for Consequence which is everything that happens after the behavior. There is overwhelming evidence that Consequences have a much, much bigger influence on our future behavior than Antecedents. Nevertheless, in most cases we continue to try to influence people through Antecedents rather than through Consequences.

‘So when it comes to loyalty, there isn’t an inborn trait that some players have and others don’t. Instead, there is the history of all the Consequences that the agent has given in response to the behavior of the player. To understand this you first have to specify the desired behavior. To do this we have to take MARCO into account. Behavior is only behavior if it is:

  • Measurable. If you can’t measure it, it ain’t behavior.
  • Active. If a dead person can do it, it ain’t behavior.
  • Reliable. If you can’t measure it reliably, i.e. different people come up with completely different measurements, it ain’t behavior.
  • Controlled. If it is not under the control of the actor, it ain’t behavior.
  • Observed. If it is impossible to observe, it ain’t behavior.

As you can see: loyalty ain’t behavior. Loyalty can’t be measured, a dead person can be loyal, if you can’t measure it, you can’t measure it reliably, loyalty is not under the control of the actor and you can’t observe it directly. So we have to specify the behavior that makes us think that a player is loyal. Most agents would specify that behavior as the player not signing up with another agent. But here, the first mistake is made. Dead persons never sign contracts with other agents. So not signing a contract, ain’t behavior either. Instead the right specification is to honor the contract the player signed. Dead persons can’t honor contracts. You can measure how long the player is honoring the contract and you can measure this reliably. Honoring the contract is completely under the control of the player. And we can easily observe the player honoring the contract.

So the desired behavior is honoring the contract and the undesired behavior is signing with another agent. The ABC-model teaches us that players do more of what has been rewarded with positive consequences in the past; in the same way players do less of what has been punished or penalized in the past. A player breaking his contract and signing with another agency, doesn’t do so out of disloyalty, but because honoring the contract has not given him enough positive consequences. On top of that honoring the contract with the agency always has at least one negative consequence. For the fee that the player pays the agent, is experienced in the brain as a penalty. Players want money, so spending money is a negative consequence. It is the task of the agent to compensate for this negative consequence, by more positive consequences. At first the agent does this by promising the player more positive consequences. But these promises are Antecedents and have little impact on the future behavior of the player.

Only when the player really does get what he wants as a result of him honoring the contract, only then the player gets a positive consequence. So the ability to get the player signed with a big club for a high salary, is the most important job of the agent. Yet, this happens only every few years. That means that after the first signing the agent made possible, it will take a long time before the next big positive consequence will be there to reinforce the player’s brain to honor the contract. Furthermore, this future positive consequence is also uncertain. The player might get an injury that ends his career. Or it may turn out that he is less talented than thought before. Or just a case of bad luck. Research clearly shows that long term uncertain positive consequences have way less impact on the behavior of a player than short term certain consequences. Therefore the agent has to make sure that the player is rewarded short term with a high degree of certainty for honoring his contract. If an agent does this then the player will continue to honor his contract and everybody will think that he is a very loyal player. Whereas in fact it is the behavior of the agent rather than the player that makes the player appear to be loyal.

What kind of short term positive consequences are there for the agent to give to the player? In short the agent can choose between the following categories:

  1. Material rewards:
    1. Direct material rewards: food or drinks.
    2. Indirect material rewards: money or valuables.
  2. Social rewards:
    1. Attention. It is important that the agent regularly checks in with the player to ask how he is doing.
    2. Compliments. If the player achieves something on the pitch during a match, make sure he is complimented for it as soon as possible after the match.
    3. Status. An agent can create different classes of players within the agency so a player feels he is promoted within the agency as he develops. Just make sure that you set-up the program in such a way that there are only winners.
    4. Information. Many players love to have access to the statistics of how they did or video’s of their best actions.
    5. Opportunities to develop one self. Players not only want to become better at football, they also want to develop themselves mentally.
    6. Keep their social media up to date. Keeping their social media up to date has negative consequences for players as it takes time and energy. So often they love it if the agent takes care of it. Updating their social media accounts as soon as possible so the player sees his fans rewarded as soon as he comes off the pitch, is a positive consequence for most players. Also because this enhances their status.

As players most of the time get plenty of material rewards, the best choice for agents is to go for social rewards. The easiest way to discover what kind of rewards the player is looking for is by asking the player himself. This may seem obvious, yet it is the second mistake most people who use the ABC-model make. They fall into the pit called the Perception Error and assume they know what is a positive reward for the player. So ask your players, how they can be rewarded on top of everything they already get from the club. 

Brain types

The third mistake is disregarding brain types. In the same way that there are different body types, we also have different brain types. Your brain type determines your evolutionary behavioral patterns. These behavioral patterns determine:

  1. How you are motivated.
  2. How you deal with your emotions.
  3. How you learn.

Brain types determine in a large part how the Dopamine reward system in your brain works. Therefore, if you know someone’s brain type you can predict with a high probability how you can reward him with positive consequences. Here is the list of positive consequences for each brain type:

Type #1, the Perfectionist can be rewarded with control.

Type #2, the Helper can be rewarded with love and attention.

Type #3, the Successful Worker can be rewarded with material rewards and hopeless projects where he has a small chance of becoming the project’s hero.

Type #4, the Romantic can be rewarded with justice served.

Type #5, the Analyst can be rewarded with autonomy, personal freedom and being left alone.

Type #6, the Loyalist can be rewarded with safety.

Type #7, the Hedonist can be rewarded with new things to do and variation.

Type #8, the Boss can be rewarded with power.

Type #9, the Mediator can be rewarded with harmony.

As loyalty also is an evolutionary behavioral pattern, some brain types have special issues concerning loyalty as can be seen from this list:

Type #1, the Perfectionist has no special issues with loyalty. Yet, as Perfectionists feel that they must act in accord to the morals of the group, it helps if you make honoring your contract one of high principles endorsed by the whole group.

Type #2, the Helper has no special issues with loyalty. Yet their craving for love and attention is so high that if the agent fails to make compliments, give little presents and keep in touch with them, the agent risks being put in the so-called out group and that will lead to a parting of the ways.

Type #3, the Successful Worker has an issue with loyalty. Successful Workers are very loyal when they are relaxed. If they are stressed, they seek social stability. In both cases it is unlikely that they would break their contract. Unfortunately, when neither stressed, nor relaxed, they become reckless, antisocial and highly sensitive to material rewards and promises of material rewards. In that state, they can be easily poached by other agents.

Type #4, the Romantic has no issues with loyalty. In fact, if breaking the contract is seen as an injustice it is unlikely that the Romantic will break the contract. On the other hand, if the agent’s actions appear to be unjust toward the player, other players, clubs or people in general, they might very well break their contract even if it means a worse outcome for themselves.

Type #5, the Analyst has no issues with loyalty. If it is clear for the Analyst that he has lots of autonomy, personal freedom and is left alone, he will not risk losing this by signing with another agent.

Type #6, the Loyalist has issues with loyalty as the name implies. It will take quite some time and thorough research by the Loyalist before the Loyalist signs with an agent. Nevertheless, once they sign, they honor their contract. Not so much out of loyalty, but because they see too much risk in breaking the contract. Unfortunately, Loyalists are probably underrepresented in football as the game and the culture are not their thing.

Type #7, the Hedonist has no issues with loyalty. The one thing to watch out for is that if the Hedonist stresses he becomes quite sensitive to material rewards. Furthermore, there is the risk that he lacks a clear sense of morality. Meaning that if stressed, the Hedonist can easily be bribed, even illegally, to sign with another agent.

Type #8, the Boss has no issues with loyalty. In fact, the Boss likes to receive a clear manual from a higher power he respects. He then blindly follows the rules in the manual and will in fact enforce these rules with other players. So if there is a rule in the manual that states that you always will honor your contract he will do so and he will try to forcefully make other players within the agency comply with that rule as well.

Type #9, the Mediator has no issues with loyalty. In fact, the Mediator is likely to become dependent on the agent and would find it emotionally difficult to leave the agency. As most players have a type #9 brain, this is the common experience of agents. They mistake these dependency issues for loyalty and then complain that the other players lack these issues. 

So besides using the ABC-model to positively reinforce honoring the contract, it is also smart to take into consideration the brain type of each player.

Davy Klaassen to Ajax

Based on the Wyscout data for the 43 matches Klaassen played for Werder Bremen in season 19/20 & 20/21, he has:

  • 86% probability that he is able to contribute to the Werder Bremen overall,
  • 5% probability that he is able to contribute to the attack of Werder Bremen,
  • 97% probability that he is able to contribute to the defense of Werder Bremen,
  • 83% probability that he is able to contribute to the build up and transitioning of Werder Bremen.

Based on the Wyscout data for the 19/20 season Werder Bremen as a team had:

  • 31% probability that the team will win or draw a match,
  • 30% probability that the attack will score,
  • 43% probability that the defense will concede a goal (lower is better),
  • 44% probability that the build up and transitioning will create an opportunity.

Based on the Wyscout data for the 19/20 season the Bundesliga has a FBM League Strength score of 123 points. (91% correlation)

Based on the Wyscout data for the 19/20 season the Eredivisie has a FBM League Strength score of 114 points. (91% correlation)

Based on the Wyscout data for the 19/20 season Ajax has:

  • 87% probability that the team will win or draw a match,
  • 69% probability that the attack will score,
  • 28% probability that the defense will concede a goal (lower is better),
  • 53% probability that the build up and transitioning will create an opportunity.

Given that the League Strength of the Eredivisie is lower and that the club probabilities of Ajax are higher, it is a realistic idea to see Klaassen play for Ajax.

Based on the above data including minutes played, difference in league strength and difference in team strength, we calculate the following probabilities for Klaassen playing for Ajax.

  • 94% probability that he is able to contribute to Ajax overall,
  • 12% probability that he is able to contribute to the attack of Ajax,
  • 99% probability that he is able to contribute to the defense of Ajax,
  • 92% probability that he is able to contribute to the build up and transitioning of Ajax.

As you can see the performance of Klaassen will be very similar for Ajax as it was in the 19/20 season for Werder Bremen.

If we were to substitute Van de Beek for Klaassen Ajax would get the following probabilities:

  • 92% probability that the team will win a match (+6%),
  • 70% probability that the attack will score (+1%),
  • 26% probability that the defense will concede a goal (lower is better) (-2%),
  • 53% probability that the build up and transitioning will create an opportunity (+0%).

This would result in 5 additional points in the table.

To conclude: Ajax is slightly better off with Klaassen.

Shadow team born this century anonymized

To track how we are doing in finding talent at a relative early age (20 years or younger, from 2023 on players need to be born 2003 or later), we publish our shadow team anonymized and keep track of how these players are doing. As soon as any of these players transfer to another club or become a household name, we update this list and reveal the name. Or if they turn 25 in case the did not break through. Valuation at date is the price where would virtually buy the player. That way we can see how much profit would make virtually.

We paper trade the players as if we bought them for the valuation at data. The we sell the players when they reach the age of 25 or when the make a major transfer. That way you can see how well we do.

So far we have spent 171M euro and earned 367M euro for a profit of 196M euro. The valuation of these players has gone up to 1368M (+800%).

Of the 375 teenagers on the list 277 have increased in valuation (73.8%), 31 have decreased in valuation (8.3%) and 66 have no change in valuation mainly due to still being too young. (17.6%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/Sold
563929-5-20190.20.55AM5.9520002022
644322-4-201902.5LW620032023
644713-1-202000.5DM620022023
64612-4-2018126RW8.66Jérémy Doku26M
705916-2-2020125RB5.920002024
73835-9-20200.50.375LW7.8220002023
74062-3-20200.70.7GK6.2420002023
804422-4-20190.50.7CM6.8420022022
804619-4-20190.250.5CM7.3920002022
810727-1-20200.752CF7.2920002025
81303-5-20190.10.7DM7.8920002022
814417-8-201900.7LW6.520012022
814517-8-20190.53RB6.8520012022
823021-10-201938LB5.9720002024
87398-6-20190.21.5CB6.7720022023
87418-6-201904DM620022023
87528-6-20190.0750.5RW620012023
87588-6-20190.10.175RW720012022
88151-11-20180.325RW920012024
902222-4-201908LB620032025
902313-1-202000.8LB720022023
902427-9-20210.20.3DM620022024
90257-9-201901.3CM5.520032022
97778-6-20190.050.3RB820012022
985010-12-20200.12RW6.820002022
986310-12-20200.31.5RB7.620002024
1009328-5-20192.510CB7.420002024
1034012-12-201800.6RW820002023
1035219-8-2020010LB6.2520012024
109571-8-20190.40.7AM7.1720002022
110465-3-20200.40.55DM6.8820002022
1112017-8-201900.15CM820012022
1112127-1-202001.2CM6.1320012023
1125817-9-20190.051.5CB6.052002unknown
1127024-12-202020.6CB6.320022024
115137-3-20201.16RB7.0620002026
1165720-12-20190.11CB6.4720002023
1173230-10-20192.7530AM7.22Odilon Kossounou30M
118414-12-2019012LB7.4220012024
1206325-1-20200.10.45CM7.7620002024
1209412-9-20200.050.6CM7.1120002022
121033-3-20200.0255CB7.3120002022
122305-6-20203.54CF8.220022023
1228918-5-202011.5CF5.9920022024
1234326-7-20202.31AM620012025
1241422-6-20200.80.35RW5.620012024
125256-4-202113CF620022025
1261415-7-202001CB6.1620022023
126652-7-20200.435DM7.5Amadou Onana35M
1272121-7-20200.10.3LW6.520002023
1274922-7-20200.40.25RW62001unknown
1279023-7-20202.52.5LW5.720012025
1279325-7-20200.30.35CF720012023
1279425-7-20200.55CF6.520022023
1279525-7-20200.152CB620002022
1279625-7-20200.810CB620012022
1280426-7-20200.11.7CB5.820032022
1281026-7-20200.050.4RW5.720042023
128551-8-20200.20.25CF5.920022022
128562-8-202000.6CM5.620032022
128695-8-2020010AM720002022
128745-8-20200.270LW6.3Mykhaylo Mudryk70M
128899-8-20200.91CB620002024
1290210-8-20200.10.1LW6.620032022
1290310-8-20200.153LW7.520032025
1291011-8-20200.250.3CM8.220012022
1292212-8-20200.150.2CF7.120012023
1292712-8-20200.27CF5.920042022
1292913-8-20200.54RW620012023
1297519-8-202008CM5.5Sergio Arribas8M
1298322-8-20200.718CM6.1220022024
1302526-8-202000AM6.320052023
1304414-11-20210.30.8CM6.320022023
130755-9-20200.30.2DM7.1320002023
130968-9-20200.23CB5.6820002023
1312613-12-20200.56LB6Jayden Oosterwolde6M
1315915-9-20200.0522.7CB6.43Ilya Zabarnyi22.7M
1316215-9-20200.43CB7.5620002023
132816-10-20200.050.1CB6.2220002022
1329914-10-20201.2116DM7.5Moisés Caicedo116M
1330018-10-202032.5CB5.720032023
1330122-10-20200.21CB5.820002025
1334428-10-202031.8LW5.9620012023
133563-11-20200.115CM7.520022025
133573-11-20200.10.5CF720022025
1344210-12-20200.20.8CM820002023
134718-12-202013CF5.520022022
134729-12-202006CF6.620012023
1349110-12-20200.30CM620012022
135346-1-202100.1CM62004Unknown
135356-1-202100CM820052022
135366-1-202100.2AM82005Unknown
135376-1-202100CF720052023
135396-1-202100.15CF62004Unknown
135493-1-20211.80.5AM620022024
135536-1-20210.7250.3CF620012023
135606-1-202100CF62004Unknown
135616-1-202100.4CF720042022
135626-1-202100CF720042022
135636-1-202100.25AM72005Unknown
1365211-1-202100GK6.220022022
1367714-2-202100RW7.520022022
1367914-2-202100LW6.920022024
1368417-2-202100CF6.32002Unknown
1369419-2-202100.75CF6.420042024
1369519-2-202100.8AM6.720042022
1369619-2-202100.075RW72004Unknown
1369919-2-202100.1LW5.820042022
1370019-2-202100AM6.92004Unknown
1370119-2-202100.075AM6.62004Unknown
1370220-2-202100CF6.82003Without club
1370324-2-202100.6RB7.920022026
1370625-2-20210.510GK620012024
1370827-2-202115CF5.720032023
137224-3-2021016.9CM6Pape Matar Sarr16.9M
1373415-3-20210.85RW7Ansgar Knauff5M
1373516-3-20210.71.1CF620012024
1373616-3-202102CM7.520032022
1374822-3-202123DM6.520022022
1376924-3-202135.5CB5.9Radu Drăgușin5.5M
1377125-3-202130CB7.120012024
1380630-3-20210.80.75CB72002Unknown
1381130-3-20210.10.175CB6.820012025
1383331-3-20210.540AM720032022
138428-4-202101.3LB5.820022024
138448-4-202101.2CM6.720022024
1387016-5-20213.513CM6.3Arsen Zakharyan13M
1387117-5-20210.0750.3CB8.520012022
1387217-5-202128RW720032022
1399719-2-202100CF62004Unknown
1399819-2-202100CF5.720042023
1416824-9-20210.050.55CM5.920022022
1425913-7-202100CM72006Unknown
144543-8-20210.11RW720022025
144583-8-20210.10.7LW720022025
1450018-8-20211.53AM620022024
1450118-8-2021213CB7.520012024
1458314-9-202100.3LB6.620032022
1460116-9-202100.25AM620012022
1462124-9-202101.5CB620042023
1463427-9-202100.45CB5.520032022
1463527-9-20210.30.9LB620032024
1468612-10-202100.25CM5.620022022
1469413-10-202100.6CB5.82001Unknown
1471019-10-202100.75CB9.120042023
1471219-10-202100.15CB8.22003Unknown
1471419-10-20210.10.3CM6.520032022
1471619-10-202100.2LW6.320032022
1471819-10-202100CF5.920052023
1471919-10-202100.15GK6.32003Unknown
1472019-10-20210.10CB620022023
1472119-10-202100.15CM7.22003Unknown
147426-11-2021110LW82002Unknown
147466-11-202100.35RB7.52002Unknown
1476411-11-20211.212.85CB6Andrew Omobamidele12.85M
1477011-11-20210.614GK7Gavin Bazunu14M
1477312-11-2021312CM720022024
1478814-11-20210.050.35AM6.820012023
1479014-11-20210.40.25CB6.720012023
1479114-11-20210.20.65RW5.820022024
1479424-11-202100.3LW62005Unknown
1479524-11-202100.7RB620032023
1479624-11-20210.20.3CB5.520012022
1479724-11-202100.3CB62005Unknown
1479825-11-20210.750.4CB620022025
1479913-12-202100.2RB720052023
1480114-12-202100.3CB720042022
1480224-12-202110.7CB620012026
1480324-12-20210.10.5CB720022023
1480424-12-20210.51.2CB6.520012024
148054-1-202200.6DM820042023
148064-1-2022240CB920032023
148074-1-202200.5CB62003Unknown
148085-1-2022112CB72002Unknown
148095-1-20220.31.8CB620032023
148105-1-20221.30.8CB720032022
148115-1-20220.350.5CB620032025
148125-1-20220.47DM620022024
148135-1-202200.8DM620042022
148146-1-20221.20.45GK620032023
148156-1-20220.80.5GK720022023
148166-1-202207LB820032023
148176-1-202200.2RB720032022
149193-2-20220.53CB6.520022023
149288-2-202200AM6.120042023
149298-2-202200CF6.12006Unknown
149449-2-202200.4RB6.720032024
149539-2-20220.30.3CF6.420042023
149479-2-202200.15RW5.920032022
150122-3-202200.2GK6.920032022
150152-3-202200.125CB62003Unknown
150282-3-202200.6CB5.820032023
150577-3-202200AM620062022
1507825-3-202200.5CF5.520042025
1507925-3-20220.40.5RW620032023
150867-4-202200CM620052024
1508710-4-202200.5CM5.520062022
1508810-4-202200LB620052022
1452015-4-20220.510RW5.520032025
1509018-4-202200CF82006Unknown
1509128-4-2022020DM720062024
151197-5-202202CM620042024
151208-5-20220.51.2CB5.520032023
151219-5-20220.72.5AM62003Unknown
1512210-5-2022115CB6El Chadaille Bitshiabu15M
1512310-5-202221CM6.520052024
1512411-5-20220.20.7CB720022026
1512512-5-202200.6CM5.520042023
1512614-5-20220.21CB720042024
1512715-5-202201LW620052024
1512815-5-202222.5CF720032025
1512915-5-202200.5AM720052023
1258316-5-20220.90.8RW620022023
1513121-5-2022035 million transferCF6Endrick35M
1513222-5-202200.25CB620042024
1513323-5-2022220AM7Arda Güler20M
1513430-5-20220.10.8CF72004Unknown
1513531-5-20221.22CB720022024
151361-6-20220.753.5AM620052025
151371-6-20222.89CB6Ahmetcan Kaplan9M
151382-6-20220.30.55CB5.520022024
123932-6-20220.2750.8CB62004Unknown
1513911-6-20221.21.5LB620022025
1514012-6-2022020AM7Matheus França20M
1514113-6-2022015DM720042023
1514216-6-20220.350.5CM720032023
1514317-6-20220.5750.5CB72003Unknown
1514417-6-202201CF5.520032026
1514517-6-20220.250.2DM5.520032025
1514617-6-20220.51.5RW5.52003Unknown
1514718-6-20220.40.75AM720022024
1514928-6-20220.351CF720022024
87575-7-20220.20.2LW820032026
151505-7-202200LB820052023
151515-7-202200.6DM62005Unknown
1515211-7-202200CF7.52004Unknown
1515312-7-20220.712CF7David Datro Fofana12M
1515413-7-202201CF620052023
1515513-7-20220.45CF620052025
1515715-7-20220.350.45CB5.52002Unknown
1515817-7-20220.0750.1RB62003Unknown
1515919-7-20221.22CB620032024
1516019-7-20220.61CM820032025
1518023-7-202200.8CB62005Unknown
1518123-7-20220.450.6RB720032023
1522531-7-2022225LW720042023
152262-8-202224LW720032023
152273-8-202216CM6Sivert Mannsverk6M
152289-8-20220.50.4CF720022022
1522911-8-2022112CM920032024
1523012-8-202200.15CF5.52002Unknown
1523114-8-202201CF920062025
1523214-8-20220.750.75CM720052025
1523316-8-20220.30.75AM620032025
1523416-8-202200.075LW7.520032024
1523518-8-202221.8RW620032025
1523618-8-20220.40.5LB820032024
1523722-8-2022210CB620032025
1523825-8-20220.717.87LB7Milos Kerkez17.87M
1523926-8-202210.5LW720042024
1524027-8-202200CF720052025
1524127-8-202200.5RW720042024
1524227-8-20220.345CB720032027
1524328-7-20220.23RB72002Unknown
1524429-8-202200CM920062025
1524529-8-202215.5CB72002Unknown
152462-9-20221.52AM72004Unknown
152472-9-202223.5CM62003Unknown
152488-9-202200LW720042024
1524911-9-20220.415AM720052024
1525012-9-202200.4DM62004Unknown
1525113-9-20220.050.3CM620042023
1525213-9-20220.250.6AM820042024
1525313-9-20220.10.05CM820022023
1525413-9-20220.050.1LW720022022
1525513-9-20220.1250.15CM720022023
1525613-9-20220.0250.025AM620062024
1525713-9-20220.10.15AM620032024
1525813-9-20220.10.15CB620032024
1525913-9-20220.10CB620032022
1526013-9-20220.050.05CF620022023
1526113-9-20220.40.6CF620032023
1526213-9-20220.150.15CB620032023
1526313-9-20220.050.05RB620022023
1526413-9-202200.15LB720052023
1526514-9-20220.2750.7RW62002Unknown
1526614-9-20220.10.3LW720052025
1526714-9-202200CB620032025
1526816-9-20220.0750.1AM820032026
1526920-9-202200CB820062023
1527021-9-20220.250.75LB720042023
1527121-9-202200.5AM720042023
1527222-9-20220.0750.1CB72003Unknown
1527324-9-20221.21.5LB720022025
1527424-9-202200CB720062024
1527524-9-202200CF620062024
1527624-9-202201.5CM820042024
1527727-9-202200.15RW620042024
1527828-9-202200AM820062023
1527929-9-202200.5CF720042023
152804-10-20220.51.5AM820042023
152814-10-202200CM720042023
152824-10-202213CF620052024
152834-10-202200RW820052024
152844-10-202200.35AM620032023
152854-10-202200.5CF620042023
152864-10-20220.32.5LW5.52003Unknown
152876-10-202200CF620062025
152886-10-20221.55AM720042025
152898-10-20220.11RW720042026
1529010-10-20221.515CM6Hákon Arnar Haraldsson15M
1529111-10-202203.5CF720052023
1529211-10-20220.88AM820062025
1529311-10-202200AM720052023
1529412-10-20220.120CF620022026
1529513-10-2022212CM820032023
1529616-10-20220.810AM6Farès Chaïbi10M
1529717-10-202200.3RB62004Unknown
1529831-10-202203RW82005Unknown
152992-11-20220.11.5DM720042025
1530016-11-20220.30.4CF720042025
1530116-11-202200DM620062025
1530224-11-202200.1AM720032024
1530328-11-20220.30.9LW720042023
1530429-11-202211.5CB620032025
153051-12-20220.2250.4AM620042024
153061-12-202202CM720062023
153074-12-20220.251CF720032023
153084-12-202200.4AM620062025
153094-12-202200CF62006Unknown
153105-12-202200LW720062025
153115-12-202200LW82006Unknown
153125-12-202200.05AM720052024
153135-12-202206CB820072024
153147-12-20220.21CM820022025
153157-12-20221.55AM82005Unknown
1531614-12-20220.86.5RB6Tiago Santos6.5M
1531727-12-20221.32DM820022027
153181-1-20230.55CM620032025
153191-1-20230.450.4AM620042023
153202-1-2023112RB5.520052025
1513410-1-20230.80.8RW72004Unknown
1532112-1-20230.47.5CB6Ousmane Diomande7.5M
1532216-1-20230.1250.9LW72004Unknown
1532316-1-202300CF62005Unknown
1451619-1-20230.350.6LB620042025
1532422-1-2023130CF720042026
1532527-1-20230.41CF620052025
153262-2-202311CF62004Unknown
153271-3-20230.151.5CF620032027
153282-3-202311CF620052023
153292-3-20231.32RW620032023
153302-3-20230.20.3CM720052024
153315-3-20230.30.3CB620042023
153325-3-20230.10.1CB620032023
1533312-5-202313CM6.520042024
1533430-5-20230.1250.125CM720052025
1533531-5-202300.3LW72004Unknown
1533631-5-20230.30.6CF720062025
153371-6-202322.8RW620052024
153384-6-20230.32RW620042024
153395-6-20230.30.3LB820042024
153405-6-20230.80.8DM920042023
153418-6-20230.250.25CB620032025
1534211-6-202300.7RB720032023
1534321-6-20230.150.15CF62004Unknown
1534425-6-20230.10.1LB62003Unknown
1534526-6-20230.20.4CF62003Unknown
153466-7-202333CB720032024
1534712-7-20230.50.5CB620032024
1534816-7-20231.21.2CM720032026
1534920-7-20230.251LB720032025
1535025-7-20230.70.7LB620032024
1535128-7-202300.3LW820052025

Current value is public valuation of the player by TransferMarkt in million euro. FBM score is our propietary score to rank players. Only players who score 5.5 or higher make it to the list. Players in the right age group who get at any time a FBM score of 5.5 or higher are automatically added to the list. PlayerID is the ID of the player in our FBM database.

Showcase Sheffield: Sander Berge or John Lundstram

What we do with FBM contribution statistics is calculate what the probability is that a player is able to contribute to a specific team. For a player can strengthen team A, but weaken team B. We do this for four scenarios: 

  1. The best case.
  2. The most likely case.
  3. The worst case.
  4. The current form case, based on the current form of the players involved.

To calculate the probability that a player is able to contribute to a new team we look at:

  1. Difference between competitions.
  2. Difference between teams.
  3. Minutes played.
  4. FBM contribution stats.

We always look at how team A would do if they replace player X with player Y. In this showcase we look at how Sheffield United would do when they replace John Lundstram with Sander Berge.

The FBM contribution stats for John Lundstram are:

John Lundstram

The FBM contribution stats for Sander Berge are:

Sander Berge

At first sight one can see that Berge scores higher in almost every category. Only the probability that Berge is able to contribute to the attack in the most likely scenario (average) is significantly lower than that of Lundstram. Yet, it is important to take into account that the probabilities for Berge are with Genk playing in the Belgium competition and Lundstram’s probabilities are with Sheffield playing in the Premier League. So we have to adapt these values.

Calculating Sander Berge’s expected contribution to Sheffield

Because probabilities don’t point to a single event happening for sure, but for multiple possible events happening with a certain probability, we always look at the four different scenarios mentioned earlier. What we do in each scenario is subtract the actual contribution of Lundstram from the FBM stats for Sheffield and then add the expected contribution of Sander Berge to Sheffield. Sander Berge’s expected contribution is based on his actual contribution to Genk deflated or inflated to compensate for the differences between the competitions, teams and minutes played.

The most likely scenario

In the most likely scenario Sheffield will have a very similar overall performance. The attack of Sheffield will be a bit less strong with Berge rather than Lundstram. Defense will be the same. Transitioning & build up will be much better with Berge. The reliability of the team will remain the same. A 3 points higher FBM team score translates into one additional point in the competition for Sheffield.

The best scenario

In the best scenario Sheffield will not improve much. Sheffield will trade a slightly better performance overall and in attack to an even less functioning transitioning & build up. As you can see the best scenario is worse than the most likely scenario … worse for Sheffield. But with the best scenario, we look at the best performance of both players and compare those.

The worst scenario

As you can see in the worst case, Sheffield is better off in almost every category except defense. The reason is that Sander Berge, even after compensation for a different team and a different competition, still has a higher floor in his performance than John Lundstram.

The current form scenario

The current form scenario shows that impact that Berge can make on the play of Sheffield if he is able to keep his current form at Sheffield. Our FBM contribution stats predict that he probably performs a bit less than this as per the most likely scenario. But there is a decent chance that Berge will perform like this at Sheffield, netting Sheffield 2 extra points at the end of the season when they start with Berge instead of Lundstram.

Conclusion

In all scenarios Sheffield is better off with Berge than with Lundstram except in the best case scenario. When both players play at their best, it makes little difference to Sheffield who is in the starting XI. Yet, Berge is currently performing close to his top performance, while Lundstram is currently performing below his median performance. This means that in the short term, Sheffield’s performance is most likely to improve when they start with Sander Berge.

Probability that Sander Berge contributes to Sheffield84%
Probability that Sander Berge contributes to the attack of Sheffield75%
Probability that Sander Berge contributes to the defense of Sheffield84%
Probability that Sander Berge contributes to the transitioning & build up of Sheffield22%


What happened to the players of Sudamericano U15 2017?

In 2017 we analyzed all youth players of the Sudamericano U15. Now, two years later, it is interesting to see what happened to them and how that relates to their FBM contribution statistics. We look at all youth players who played at least 3 matches. Normally, we want at least ten matches for the most probable estimation of the worth of a player. Fortunately, one of the strongest features of Bayesian statistics is that even with a few data points Bayesian statistics is still able to draw valid conclusions. That means that Bayesian statistics is ideal for estimating which youth players have the best chance of making it.

In 2017 we analyzed 193 players born in 2002 or 2003. They played between 3 and 7 matches. All youth players get the following probabilities assigned:

Player
ScoreOverallAttackDefenseTransition & buildupReliabilityNumber of games analyzed
C. de Oliveira Costa – KakàCF6.2999.1796.8375.4996.0891.775

Score is a summation of the other five probabilities. These five probabilities are:

  1. Overall is the probability that a player is able to contribute to the team in general.
  2. Attack is the probability that a player is able to contribute to the attack.
  3. Defense is the probability that a player is able to contribute to the defense.
  4. Transition & build up is the probability that a player is able to contribute to transitioning & building up.
  5. Reliability is the probability that the overall, attack, defense and transitioning & buildup probabilities remain the same in the next match. Yet, it also is an indication of how reliable the player is.

Score is a number running from 0 to 10 with players approaching 10 will be the best players in the toughest competitions. So the score of 6.29 for Kakà in the Sudamericano U15 is quite good. We use a score of 2.5 to distinguish between players who are more likely to make it in pro football. Players not able to score 2.5 points are less likely to make it.

In the Sudamericano U15 of 2017 there were 93 youth players who scored 2.5 points or more. There were 100 youth players who scored less than 2.5 points. Two years later we looked at whether they played at all in 2019 and if so how many minutes they played and how valuable the team is that they play for. Here are the results (we have also included the data for the top 30 youth players):

CriteriumTop 30 youth players according to FBM player scoreYouth players who scored 2.5 or higherYouth players who scored less than 2.5
Players still playing86.67%77.42%63%
Average value of the team the player plays for7.7 million3.6 million euro3 million euro
Average minutes played in 2019601512356

As you can see youth players who score well in FBM contribution statistics have a higher chance of still playing two years later. They play for more highly valued teams and they play more minutes.

The above results were achieved by only looking at the FBM player score. Basically, this means letting the computer decide who is the better player. When we actively evaluate these youth players ourselves, we look more closely at the FBM contribution statistics. We remove the attackers who scored 2.5 points or more, but who also have an attack probability of less than 50%. And we remove the defenders who scored 2.5 points or more, but also have an defense probability of less than 50%. 

When we evaluate players as described we only keep 78 of the 93 youth players who scored 2.5 points or more. Their results are as follows:

CriteriumTop 30 youth players according to FBM player scoreYouth players who scored 2.5 or higherYouth players who scored less than 2.5
Players still playing90%83.33%63%
Average value of the team the player plays for7.7 million4.3 million euro3 million euro
Average minutes played in 2019608542356

When you compare this second table with the first table, you can see that by not steering blindly on FBM player score, but actually looking at the underlying probabilities, allows you to select the most promising youth players even more accurately. What this means for clubs is that they can have their youth players analyzed and use FBM contribution statistics to determine which players are best to continue working with.

Match preparation PSV vs FC Basel 23-7-2019

The FBM tool makes it easy to predict how upcoming matches will unfold. If we use the most recent starting XI we get the following. As soon as the actual starting XI we’ll update our FBM tool to see what the prediction is based on the actual lineup.

The most important numbers are:

  1. PSV will dominate 43% of the match and Basel 8%.
  2. PSV will have 12% of the chances and Basel 88%.
  3. The most likely outcome is 1-3, but this happens only 9% of the time.

This is the raw data. So the second step is to interpret these numbers. The first thing to notice is that while PSV has the most domination, Basel has the most chances. That means that the chance of a draw is increased. 

The second thing to notice is that although PSV has the most domination, for 49% of the time no team has any domination. This often reflects chaotic periods where no team is able to dominate or control the ball for long. If the no domination percentage is higher than the domination percentage of either team, then more often than not, the team with the highest percentage in chances will disappoint.

For that reason our FBM tool predicts the following (the text is computer generated):

Most likely winner: PSV 1
PSV 1 wins 66% of the time based on 35 matches.
FC Basel 1 wins 34% of the time based on 35 matches.
Most likely outcome: 1 – 3 (happens 9% of the time) 
This result is based on pattern Brown: Both teams are only able to dominate small parts of the game and there is the risk that the underdog becomes overconfident in which case the favorite has a bigger than average chance of winning. (Brown 5)

Most valuable players

With our FBM tool one can look of course at all players of both teams, but for this match preparation we will only look at the most valuable players of PSV and Basel. The idea is that if you are able to neutralize these players, you increase your chance to win a lot.

For PSV the most valuable player is Donyell Malen as can be seen from his most recent FBM contribution chart:

As can be seen Malen is contributing a lot to PSV’s attack (yellow), defense (red) and overall play (blue). Transitioning (green) improved, but against an easier opponent than Basel, so we don’t expect Malen to contribute in that regard that much in this match.

For Basel the most valuable player is Jahlil Okafor as can be seen from his most recent FBM contribution chart:

Okafor has a very similar FBM contribution chart as Malen. The differences are a slightly lesser defensive contribution (red), but a considerable higher transitioning contribution (green).

Weakest defender

While the most valuable player is the number one target to neutralize, the weakest defender is the best area of defense to target for the attack.

For PSV the weakest defender is Luckassen. So attacking through the center is most likely to result in a goal for Basel.

For Basel the weakest defender is Widmer. So for PSV attacking Basel’s right flank would give the best chance to score.

We’ll update this article as soon as the actual starting XI are known.

Update with actual lineup:

PSV comes up with a very surprising starting XI and formation. A 3-5-2 formation according to the UEFA (displayed in our tool as a 5-3-2 formation as that is what happens most of the time when the wings are really wing backs, but in this case the wings are really wingers):

This new formation is good and bad news for PSV both at the same time. The good news is that both domination and chances have increased. Also due that Basel is not playing in their strongest formation according to our data as Basel is not starting with Okafor.

The bad news is that with this formation Basel is probably going to risk less and defend more. It has become unlikely that Basel becomes overconfident. That this means for PSV is that PSV now actually lesss chance to win and the most likely outcome is a draw. There is also less risk for PSV to lose the match. So in that sense, even though the new formation is quite innovative, it is still playing on safe. The same goes for Basel, but then by using a conservative approach to the match.

So the new numbers look like this:

  1. PSV will dominate 77% of the match and Basel 11%.
  2. PSV will have 20% of the chances and Basel 80%.
  3. The most likely outcome is 1-2, but this happens only 9% of the time.

And the computer generated description of the match reads as follows:

Most likely outcome: draw
PSV 1 wins 42% of the time based on 155 matches.
FC Basel 1 wins 28% of the time based on 155 matches.
Most likely outcome: 1 – 2 (happens 9% of the time) 
This result is based on pattern White: The favorite dominates most of the game, but the underdog has most of the chances (mostly through countering), so more than average it becomes a draw. (White 4)

Post match update

Although PSV won with a 3-2 result, the match unfolded pretty much as we predicted. Orginially we thought that PSV would win 66% of these kind of games. With the actual lineup we brought this percentage down to 42%. A big difference with the 67% win chance that the sports betting industry thought it would be. Given that Basel was leading 1-2 5 minutes before the end of the match, we think that 67% – although ultimately correct – was estimating PSV too strongly.

Our final estimation of a draw also turned out to be wrong, but very reasonable. A 1-2 result would be too much for Basel, but PSV was also very lucky in the dying moments of the match. So a draw was the most reasonable estimate before the match.

The weakest defenders were also correctly predicted with both defenders (Luckassen and Widmer) failing to prevent the opening goals. Looking at Widmer’s FBM contribution chart one can see that his performance in the match was the same as what we expected before the match:

Widmer in PSV vs FC Basel 23-7-2019

Basel did have a lot less chances than we predicted, although we correctly predicted the number of goals Basel scored. PSV had a bit more chancees than expected, but scored a lot more than we predicted. So all players will be updated with their performance in this match and the match that the teams play the coming week. Then we will create a new prediction for the return.

Why Răzvan Marin is a decent replacement for Frenkie de Jong

One of the questions that many people have when it comes to the upcoming 19/20 Eredivisie season is whether Răzvan Marin is a good replacement for Frenkie de Jong. In this article I want to show you how to answer that question using FBM statistics. Our approach consists of three steps:

  1. Subtract De Jong from Ajax.
  2. Add Marin to Ajax.
  3. Compare Ajax with De Jong to Ajax with Marin.

With FBM statistics you can literally subtract players from a team as we have an FBM team score which is the same kind of data as an FBM players score. Ajax with De Jong has the following FBM team score:

ClubOverallAttackDefenseTransitionSurpriseFBM team score
Ajax7338594815203

These numbers are the average FBM players scores of the starting XI of Ajax in the 18/19 season. Besides the absolute numbers, which indicate the strength of the team for all football clubs, one also has to look at the ratio of these numbers as that gives more insight in how well balanced the team is. For Ajax with Frenkie de Jong the balance of the team looks as follows:

(The less defense the better, the more transition and attack the better.)

The score for Frenkie de Jong looks almost the same:

PlayerOverallAttackDefenseTransitionSurprise
Frenkie de Jong94197916

Yet, we have to divide these numbers by 11 and normalize for the percentage of the minutes that De Jong actually played. Then we can subtract those numbers of Ajax’ FBM team score to see how Ajax looks without De Jong. These numbers are:

ClubOverallAttackDefenseTransitionSurpriseFBM team score
Ajax with de Jong7338594815203
Ajax minus de Jong (with 10 players)6638524115182

You can immediately see that De Jong had very little impact on Ajax’ attack, but as only one player in a team of eleven players (9.09%), he had a major impact on defending (11.86%) and transitioning (14.58%).

Next we take Marin’s numbers at Standard Liege:

PlayerOverallAttackDefenseTransitionSurprise
Marin682045113

Those numbers look a lot less than Frenkie de Jong’s numbers. But we also have to compensate for the difference in leagues (Eredivisie vs Jupiler Pro League) and quality of the team and the team members (Ajax vs Standard Liege). When we use our Bayesian model to compensate for these matters, Marin’s most probable numbers for his play at Ajax are:

PlayerOverallAttackDefenseTransitionSurprise
Marin84396836

These numbers still look less than the numbers of Frenkie de Jong. But let’s see what happens when we add these numbers to Ajax. Again, in our model we normalize for played minutes and differences between the team. We then get:

ClubOverallAttackDefenseTransitionSurpriseFBM team score
Ajax with de Jong7338594815203
Ajax with Marin7341574115197

If we only look at the FBM team score, you can see that the scouts of Ajax did a great job as with Marin, Ajax is only 3% weaker (197 vs 203). If we look at the more detailed numbers, we can see that this is mainly due to the fact that Marin more strongly supports Ajax’ attack (41 vs 38). At the same time, transitioning will be less effective with Marin instead of De Jong (41 vs 48). To most observers that would be obvious. Nevertheless, we are always happy when our model comes up with obviousness. It is an interesting trade-off. Yet, given how hard it is to find players that support transitioning, it is also very understandable that Ajax has made this trade-off.

The most interesting part though is the balance of the team:

The slight increase in defense reflects that Ajax has become a little bit weaker with Marin instead of De Jong. Nevertheless, this is compensated by having transitioning and attacking more in balance. That’s why we think that Marin is a decent replacement for Frenkie de Jong.

How probable is it that Marin is able to contribute?

As we always stress that football data is meaningless, unless you can answer the question: “What is the probability that a player is able to contribute?”, let me make this more explicit in the case of Marin. As Marin’s numbers above are his probabilities. 

PlayerOverallAttackDefenseTransitionSurprise
Marin84396836

So let’s answer the following questions:

  1. What is the probability that Marin is able to contribute to Ajax overall? Answer: 84%
  2. What is the probability that Marin is able to contribute to Ajax’s attacking? Answer: 39%
  3. What is the probability that Marin is able to contribute to Ajax’ defending? Answer: 68%
  4. What is the probability that Marin is able to contribute to Ajax transitioning? Answer: 3%

These numbers have a plus or minus 6% points room to deviate (the surprisal rate).

The less you need to defend to better your team is

Teams prefer to attack (blue) rather than defend (red). But if your team is up against a stronger team, the chance is that they’ll force you to defend. Transitioning is a lot harder than kick and rush. For that reason the weakest teams have very little effectiveness in transitioning.

Here are the results of all the teams in the Dutch Eredivisie. As you can see Ajax is the most balanced team. It also shows that even though the teams ended very close in the ranking, Ajax is obvious a stronger team.

These numbers are based on the FBM team score, which in turn is based on the attacking, defending and transitioning efficiencies of the individual players in the starting XI. There is a 67% correlation (reversed) between the defensive score and the points a team scored in the league.

Given that these numbers are about effiencies, these percentages are not about the time spent by each team on either attacking, defending or transitioning. Instead these numbers are about what is working for the team.

These numbers show that strong team first focus on transitioning. If that isn’t working out for them, then they go for kick & rush or long balls. If that fails, then all that is left to do is defending.

With FBM statistics we can find players for you club that strengthen the transition or the attack as we can calculate exactly how much a player contributes to the transitioning, attacking and defending of the team.

Ajax
PSV
Feyenoord
AZ
Vitesse
FC Utrecht
Heracles
FC Groningen
ADO Den Haag
Willem II
Heerenveen
VVV
PEC Zwolle
FC Emmen
Fortuna Sittard
Excelsior
De Graafschap
NAC Breda

Dynamical player statistics

Football is a very dynamic game. A lot is happening at the same time. So a static description of a football match or of football players is lacking a crucial element, namely the dynamics of the game.

Here are two video’s that shows how two great players, Frenkie de Jong and Hakim Ziyech, differ in the dynamics of their statistics. We will never know for sure why Frenkie de Jong got a 75 million transfer whereas Hakim Ziyech was unable to find the club of his dreams in the summer of 2018, but we take it that it has a lot to do with the difference in the dynamics of the statistics of both players.

A short interlude of how to read the graphs in both video’s. FBM measures the efficiency of football players. We make a graph of each match analyzed, because it is very important to understand how statistics differ within a single game. That is the only way to discover whether players are able to continu to play well at the end of the match.

The blue line is the overall efficiency. The blue line gives the probability that a player strengthens the team. The red line is the defensive efficiency, the green line the transitioning efficiency and the orange line is the attack efficiency. These lines give the probabilities of the player strengthen the defense, transition and attack. Few player are able to keep their attack efficiency at a high level and even fewer players are able to keep their transition efficiecy at a high level.

Here is the video for Frenkie de Jong. The matches are shown for the season 18/19 up to today.

As you can see Frenkie de Jong is very stable. In the last few weeks his effiency is suddenly dropping. Most commentators link that to his megatransfer. But you also see how quickly Frenkie recovers. The high transfer fee is justified by him consistently having a high transition efficiency.

Here is the video for Hakim Ziyech. The matches are shown for the last 12 months and include his less than successful world championship playing for Morocco.

Here we see that Ziyech struggled to get high effiency. That doesn’t mean that he didn’t do any good on the pitch in that period, but it means that he made even more errors so that his efficiency was quite low at times. It is the one reason why we think he did not get transferred in the summer of 2018.

We can also see that nowadays his efficiency is much higher. Nevertheless, the dynamics still show that he is quite fickle. Given that his efficiencies are so much higher this season than last season, we think he will transfer to a great club for a big, but not huge amount. That club has to realize though that Ziyech has a bit of fickleness.