Author: admin

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), 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 46.900.000 euro and earned 26.000.000 euro for a loss of -20.900.000 euro. From 2021 on,players need to be born 2001 or later.

PlayedIDDate first in shadow teamValuation at dateTransferPositionFBM scoreBorn/playerContract/Sold
64612-4-2018126 million transfer to RennesRW8.66Jérémy Doku26M
804422-4-20190.5CM6.8420022022
87398-6-20190.2Transfered to U19 top tier teamCB6.7720022023
1261415-7-20200CB6.1620022023
1125817-9-20190.05CB6.052002unknown
1228918-5-20201CF5.9920022021
1112127-1-20200CM6.1320012021
1241422-6-20200.8RW5.620012021
1206325-1-20200.1CM7.7620002021
810727-1-20200.75CF7.2920002021
109571-8-20190.4AM7.1720002022
115137-3-20201.1RB7.0620002022
110465-3-20200.4DM6.8820002022
1165720-12-20190.1CB6.4720002023
74062-3-20200.7GK6.2420002021
823021-10-20193LB5.9720002024
1173230-10-20192.75AM7.2220012023
118414-12-20190LB7.4220012024
122305-6-20203.5CF8.220022023
126652-7-20200.4DM7.520012024
1272121-7-20200.1LW6.52000unknown
1274922-7-20200.4RW62001unknown
1279023-7-20202.5LW5.720012022
1279325-7-20200.3CF720012023
1279425-7-20200.5CF6.520022023
1279525-7-20200.15CB620002022
1279625-7-20200.8CB62001unknown
1234326-7-20202.3AM620012025
1280426-7-20200.1CB5.820032022
1281026-7-20200.05Transfered to U18 top tier teamRW5.720042023
1009328-5-20192.5CB7.420002024
128551-8-20200.2CF5.920022022
128562-8-20200CM5.620032021
128695-8-20200AM720002022
128745-8-20200.2LW6.320012023
128899-8-20200.9CB620002024
1290210-8-20200.1LW6.62003unknown
1290310-8-20200.15LW7.52003unknown
1291011-8-20200.25CM8.220012022
1292712-8-20200.2CF5.920042022
1292212-8-20200.15CF7.120012021
1292913-8-20200.5RW620012023
1034012-12-20180RW820002021
1035219-8-20200LB6.2520012024
1297519-8-20200CM5.520012021
1298322-8-20200.7CM6.1220022024
1302526-8-20200AM6.320052023
121033-3-20200.025Transfer to tier #1 clubCB7.3120002022
814517-8-20190.5RB6.8520012022
73835-9-20200.5Transfer to #1 in same tierLW7.8220002021
130755-9-20200.3DM7.1320002021
563929-5-20190.2AM5.9520002022
130968-9-20200.2CB5.6820002023
81303-5-20190.1DM7.8920002022
804619-4-20190.25CM7.3920002022
1209412-9-20200.05CM7.1120002022
1316215-9-20200.4CB7.5620002023
1315915-9-20200.05CB6.4320022025
132816-10-20200.05CB6.2220002022
1329914-10-20201.2DM7.520012024
1330018-10-20203CB5.720032023
1330122-10-20200.15CB5.8620002023
1334428-10-20203LW5.9620012023
133563-11-20200.1CM7.520022025
133573-11-20200.1CF720022025
134718-12-20201CF5.520022022
134729-12-20200CF6.620012023
1349110-12-20200.3CM620012022
1344210-12-20200.2CM820002023
986310-12-20200.3RB7.620002024
985010-12-20200.1RW6.820002022
1312613-12-20200.5LB620012023
1127024-12-20202CB6.320022021
135493-1-20211.8AM620022024
135536-1-20210.725CF620012023
135346-1-20210CM62004Unknown
135356-1-20210CM820052022
135366-1-20210AM82005Unknown
135376-1-20210CF720052023
135396-1-20210CF62004Unknown
135606-1-20210CF62004Unknown
135616-1-20210CF720042022
135626-1-20210CF720042022
135636-1-20210AM72005Unknown
1365211-1-20210GK6.220022022
1367714-2-20210RW7.520022022
1367914-2-20210LW6.92002Unknown
1368417-2-20210CF6.32002Unknown
1369419-2-20210CF6.420042021
1369519-2-20210AM6.720042022
1369619-2-20210RW72004Unknown
1399719-2-20210CF62004Unknown
1399819-2-20210CF5.720042021
1369919-2-20210LW5.820042022
1370019-2-20210AM6.92004Unknown
1370119-2-20210AM6.62004Unknown
1370220-2-20210CF6.820032021
1370324-2-20210RB7.92002Unknown
1370625-2-20210.5GK620012024
1370827-2-20211CF5.720032023
137224-3-20210CM620022025
1373415-3-20210.8RW720022023
1373516-3-20210.7CF620012024
1373616-3-20210CM7.520032021
1374822-3-20212DM6.520022022
1376924-3-20213CB5.920022021
1377125-3-20213CB7.120012024
1380630-3-20210.8CB72002Unknown
1381130-3-20210.1CB6.820012021
1383331-3-20210.5CM720032022
125256-4-20211CF620022025
138448-4-20210CM6.720022024
138428-4-20210LB5.820022024
1387016-5-20213.5CM6.320032024

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. PlayerID is the ID of the player in our FBM database.

Relative strength MLS

Here is the list of the relative strength of teams in the MLS based on their performance in the previous season according to Wyscout data. If it were a simple competition than this would also be our prediction for the league table. Previously predictions like these have had a 80% correlation with the actual league table a year later. So will see how it goes for the MLS:

RankFBM Wyscout score
1Atlanta73
2Los Angeles72
3Salt Lake71
4Toronto70
5Dallas67
6New York City65
7Columbus63
8Seattle62
9Portland61
10Chigago57
11Minnesota56
12San Jose56
13Colorado54
14New England54
15LA Galaxy52
16Montreal51
17Philadelphia50
18Kansas City48
19DC45
20Orlando42
21New York RB40
22Houston37
23Vancouver36
24Nashville35
25Cincinnati30
26Miami19

Showcase: Niklas Dorsch

Niklas Dorsch is a defensive midfielder of Heidenheim, playing in 2. Bundesliga. Dorsch has been on our radar for the last couple of years and with only one year left at Heidenheim, he is an interesting player to follow. He plays for Germany U21. We think he would do well in 1. Bundesliga. For that reason we show here that he would be a good addition to Eintracht Frankfurt.

Dorsch at Heidenheim

Here is Dorsch’ most recent FBM contribution chart:

Although Heidenheim lost and Dorsch did not play his best match, especially in the first half as can be seen from his contribution chart, he is still an exceptional player according to his FBM stats:

Yet, these are his stats for playing in the 2. Bundesliga. How would he do at Eintracht Frankfurt? We think that Dorsch is a good replacement for Hasebe at Eintracht Frankfurt. Hasebe’s most recent contribution chart shows he is not playing well at the moment:

Also his FBM stats are less than those of Dorsch:

Dorsch is slightly better than Hasebe at their highest performance, but Dorsch beats Hasebe on average performance, current performance and worst performance. Yet, Hasebe plays on a higher level. So we have to take that into account.

Dorsch playing for Eintracht Frankfurt

Taking into account minutes played, difference between both clubs and both competitions, we get the following results for Dorsch playing at Eintracht Frankfurt:

What you see in the first row, is the performance level of Eintracht Frankfurt in the 1. Bundesliga. In the second row we subtract Hasebe’s contribution to the performance of Eintracht Frankfurt. That is only a small difference because Hasebe is not contributing that much on average. In the third row we add Dorsch to the expected performance of Eintracht Frankfurt. Finally, we can see how Eintracht Frankfurt’s performance would increase or decrease in row 4. Overall performance of Eintracht Frankfurt would rise as would attack and transitioning. Defensive performance would suffer slightly.

Eintracht Frankfurt’s FBM Team Score would increase to 115 points up from 102 points. There is an 80% correlation between FBM Team Score and future ranking in the league. If other teams would not improve Eintracht Frankfurt would rise to rank 10 in the league table if they played with Dorsch rather than Hasebe.

What is Dorsch worth?

Our model takes into account, position, highest transfer fee in the current season, record transfer fee, difference in competition, club, player age and length, international status and FBM stats. Due to the Corona crisis, it is much more uncertain how future transfer fees will develop. Our model is still based on the pre-Corona circumstances. 

When we calculate what we call the replacement fee for players. This is the amount of money the current employer of the player can expect to spend on a replacement who is as good as their current player. In short: clubs should not transfer players for less than the replacement fee, nor buy players for more than the replacement fee. As the replacement fee differs from club to club, there is room for negotiations. We also calculate what the player would be worth one year later if he is able to transfer to an even better club. All assuming his FBM stats remain the same.

Here are the replacement values for Dorsch:

Replacement value for Heidenheim£2,592,419
Replacement value for Eintracht Frankfurt£3,861,435
Replacement value for Schalke 04£6,672,353

TransferMarkt currently values Dorsch at £4.05m. We think that Dorsch is slightly overvalued on TransferMarkt. If Eintracht Frankfurt were able to buy Dorsch for less than £3,861,435, they would have a good deal. A deal that might make them almost £2m a year later, if they would transfer Dorsch to the next club and Dorsch would perform at the level we expect him to do. As a reminder, we predicted that Dalmau would be worth 1.75m euro to Heracles and they transferred him a year later for 1.7m euro.

For us the most important thing about FBM stats, is that we calculate the probabilities that a player is able to contribute to a specific team. Here are the probabilities that Dorsch is able to contribute to Eintracht Frankfurt:

Probability that Dorsch contributes to Frankfurt63
Probability that Dorsch contributes to the attack of Frankfurt72
Probability that Dorsch contributes to the defense of Frankfurt25
Probability that Dorsch contributes to the transitioning & build up of Frankfurt46

Case study: Watford

Many scouts wonder why their advice is being ignored by the higher ups. The reason is that whatever scouting report they have drawn up, their report fails to answer the most important question:

What is the probability that player X is able to contribute to the team?

The answer is a number between 0% and 100%. This answer is never given in any of the reports or presentation scouts give. That means that the decision makers have to calculate this answer based on the report the scout has provided. Of course, they never do this consciously. Yet, our brain makes these kinds of estimations unconsciously all the time. If a scout does NOT explicitly answer this question, the brain of the decision maker is going to make the probability estimation all by himself. In almost all cases, this estimation will be lower than the players the decision maker prefers himself. That is the reason why even the most successful scouts only have contributed to a handful of transfers. Most transfers happen for other reasons than provided by the scouting team.

It really doesn’t matter whether we are talking about data, video or live scouting. If the final report fails to answer the question about the probability that a player is able to contribute to the team, the decision maker is going to answer that question and probably in a less favorable way.

So let’s look at an example. If you are using Wyscout data as a data scout, how can you then answer this most important question: 

What is the probability that player X is able to contribute to the team?

First you need to build a model that transforms Wyscout data into probabilities. Bayesian networks are most suitable for this job, but there are other ways. We prefer to use Bayesian networks. Second step is to validate your model. For validation we have created a Bayesian network to transform Wyscout team data into team probabilities. We calculate the following four probabilities:

  1. What is the probability that a team is going to perform well?
  2. What is the probability that the attack of the team is going to perform well?
  3. What is the probability that the defense of the team is going to perform well?
  4. What is the probability that the passing game of the team is going to perform well?

Here are the results for the Premier League and Watford:

Validation comes from the 89% correlation (R2=80%) between the probability to perform well and the rank of the team. This is in line with this correlation in other competitions. So to be clear: 

  1. The probability of Watford to perform well is 38%
  2. The probability of Watford to attack well is 37%
  3. The probability of Watford to defend well is 54%
  4. The probability of Watford to pass well is 47%

The next step is to look at the individual players of Watford. Normally we would look at all the players (except the keeper), but for this exercise we only look at the most recent starting XI:

Again, these stats answer the following four questions:

  1. What is the probability that a player is able to contribute to the team?
  2. What is the probability that a player is able to the attack of the team?
  3. What is the probability that a player is able to the defense of the team?
  4. What is the probability that a player is able to contribute to the passing game of the team?

As long as a player has at least one of these four probabilities quite high, he is an asset to the team. Of course, if it is only one category, he is a specialist rather than a generalist, unless that category is the overall category.

Taking into account minutes played we can then calculate the contribution each player has made to the team probabilities of Watford:

The contribution of these ten players is:

Here one can see that although Sarr has quite weak data in Wyscout, his contribution to the attack of Watford is on par to what is expected of him.

One can also immediately see that Pereya is the weakest link. So let’s look at a replacement for Pereya. As this is an example only, I am going to use a replacement who obviously would be better suited than Pereya. The player I am going to use is Liverpool’s Mané.

Here we use our transfer model. This gives the following results:

Let me explain this. First we start with the probabilities of Watford and Pereya we have already seen. Taking into account minutes played, we subtract Pereya from the probabilities of Watford. What this means for Watford is that the probability to perform well remains unchanged, but the probabilities to attack, defend and pass well drop a bit. 

Then we look at the probabilities of Mané playing at Liverpool. As you can see, for all but defense, these probabilities are much higher than Pereya’s probabilities. But in part, Mané is playing well at Liverpool because he is playing together with other great players. That won’t be the case if he transfers to Watford. So we have to take into account that his performance will drop a bit. But how much? Fortunately, we have a Bayesian model to calculate precisely that by taking into account the relative strength of both teams and minutes played. To make it explicit:

  1. The probability that Mané is able to contribute to Watford is 87%.
  2. The probability that Mané is able to contribute to the attack of Watford is 98%.
  3. The probability that Mané is able to contribute to the defense Watford is 5%.
  4. The probability that Mané is able to contribute to the passing game of Watford is 52%.

What this would mean for Watford is that their probabilities also go up when we add Mané with his Watford probabilities to Watford as is shown in the final row. With Mané playing for Watford the new probabilities for Watford are:

  1. The probability of Watford to perform well is 45%
  2. The probability of Watford to attack well is 43%
  3. The probability of Watford to defend well is 46%
  4. The probability of Watford to pass well is 55%

GIven the correlation between overall team performance probability and rank, we can also see that Watford would rise to somewhere between rank 10 and rank 15 in the competition once Mané is playing for Watford. 
Rational decision makers use these kinds of models to calculate for every player they are seriously considering hiring what the probability is that the player is able to contribute to the team and what this means for the team. Once you have ranked all players according to their probability to be able to contribute to the team, you try to hire the best player available. That is how we were able to transfer Dalmau to Heracles for instance.

This is the kind of work that we are going to teach at the Football Behavior Management summer school at the VU-university in Amsterdam in juli 2020. Due to the current circumstances this will be an online course.

Wyscout data to Bayesian team ranking

Without live matches I found time to work on my third iteration of my Bayesian model to turn Wyscout data into Football Behavior Management (FBM) data. To be clear: we only accept correlation above 80% and R2 above 60%. So far all 7 competitions checked have a correlation of at least 80% and sometimes it goes up till an astonishing 95%!

The Wyscout team data we use are:

  • Average goals scored
  • Average goals conceded
  • Shots off Target
  • Shots on Target
  • Passes inaccurate
  • Passes accurate
  • Recoveries (low, medium, high)
  • Losses (low, medium, high)
  • Challenges failed
  • Challenges won

Als please note that we use team data for these correlations that can NOT be traced back to individual players. Unlike the correlations we get with FBM probabilities that are based on stats of individual players and that can be traced back to these players.

Premier League

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Liverpool19984707454
ManCity28193728353
ManUnited36677636651
Chelsea46676547451
Leicester56275606652
Tottenham65964546249
Wolves75961545850
Arsenal85664527947
Sheffield95452535450
Burnley105441444850
Southampton115244445149
Everton124945425747
Newcastle134433365147
Crystal144336365447
Brighton154149406248
West Ham163944435448
Aston Villa173533365246
Bournemouth183433355346
Watford193431335147
Norwich202125235944
Correlation with rank 93% (R2=88%) and points 90% (R2=81%)

Bundesliga

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Bayern16193738054
Dortmund25789697852
Leipzig35484706852
Leverkusen45380607350
Gladbach55269606149
Wolfsburg63951485450
Freiburg73750455749
Schalke83745415847
Hoffenheim93651408047
Koln103444435448
Hertha113443435546
Augsburg123033414646
Berlin133033384848
Frankfurt142846435450
Mainz152730345145
Dusseldorf162431315547
Bremen172131305744
Paderborn181831345444
Correlation rank = 91% (R2=82%) and points = 96% (R2=93%)

Bundesliga 2

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Bielefeld15380656751
HSV24675606849
Stuttgart34575547350
Heidenheim44456515649
Darmstadt53952495549
Aue63846495147
Kiel73855486048
Greuther83751505447
Hannover93545387650
Regensburg103434444447
St. Pauli113348465547
Bochum123246445647
Osnabruck133042435347
Sandhausen143042415250
Nurnberg153040385448
Karlsruher162833394648
Wiesbaden172833394748
Dresden182430404447
Correlation rank = 88% (R2=78%) and points = 93% (R2=87%)

Bundesliga 3

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Duisburg15164546150
Mannheim25065566150
Bayern II34851436445
Braunschweig44850525049
Unterhaching54757545450
Wurzburger Kickers64751515249
Ingolstadt74654535151
1860 Munchen84652515349
Hansa Rostock94555517351
Uerdingen104445445548
Meppen114351525248
Kaiserslautern124149485349
Viktoria Koln133852446346
Magdeburg143756515452
Chemnitzer153747485248
Zwickau163636484247
Hallescher173349445749
Munster183338435046
Sonnenhof192524284847
Jena201825295046
Correlation rank 79% (R2=62%) and points 85% (R2=73%)

Eredivisie

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Ajax15687697253
AZ25688707651
Feyenoord35075606850
PSV44972576650
Willem II54450505448
Urecht64172606449
Vitesse74160535949
Heracles83656535648
Groningen93554477452
Heerenveen103354466148
Sparta113343465147
Emmen123247396247
VVV132826305046
Twente142747406246
Zwolle152643386046
Sittard162636385446
ADO171926315046
RKC181532296045
Correlation with rank 91% (R2=83%) and correlation with points 92% (R2=85%)

Jupiler Pro League

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Club17074616353
Gent25574606651
Charleroi35463645051
Antwerp45356555350
Standard54959535849
Mechelen64448485448
Genk74452446147
Anderlecht84374596849
Zulte93647447546
Mouscron103349455748
Kortrijk113345455448
STVV123343376245
Eupen133033335447
Cercle142331315446
Oostende152224324645
Waasland162019235144
89% correlation with rank (R2=80%), 89% correlation with points (R2=79%)

Dutch Eerste Divisie

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Cambuur16681716252
Graafschap26274645953
Volendam35567556449
Jong Ajax45473596847
NAC55064555951
Go Ahead64851535247
Excelsior74749525247
NEC84561565849
Almere94450497151
Telstar104448455846
Den Bosch113860526247
Jong Utrecht123845505046
Eindhoven133440415446
Jong AZ142848406544
MVV152730384646
Top Oss162533325646
Roda JC172234395046
Jong PSV182245376544
Dordrecht192031325445
Helmond Sport201720254845
91% correlation with rank (R2=83%) and 88% with points (R2=78%)

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.

Predicting the winter champion in the Eredivisie

Here is a challenge: predict the number of points teams will have in the first half of the season the moment the transfermarkt closes. Here is the catch: you are only allowed to use statistics of individual players. No team statistics like wins, goals scored, goals conceded or historical team records are allowed. The reason why no historical team data is allowed is that if you are able to predict sufficiently accurately how many points each team scores, you have established a clear predictive relationship between the statistics of individual players and the number of points the team score in the league.

That is also the reason why we only look at the prediction half way through the season. Otherwise your statistic is more likely to correlate with the richness of the club, rather than the quality of the players. For rich clubs who disappoint in the first half of the season, can buy themselves better players and improve their situation. 

Football Behavior Management (FBM) predicted on September 1st 2019 for the Dutch Eredivisie using only statistics of individual players. Even though the Eredivisie had quite a different season than usual, here are the correlations between our prediction and the actual points scored:

  • Correlation = 80%
  • R² = 64%

This establishes a strong and clear relationship between how well players do in the FBM system and how many points the clubs get that employ them. If you want more points, hire players who do well in the FBM system. That doesn’t mean that if a player does bad in the FBM system, that he is automatically a bad player. The FBM system is set up with a strong bias to underestimate players, rather than overestimate them. That means that a player who does badly according to us, could very well play better next season. But more importantly, it does mean that hiring that player increases the risk of hiring the wrong players. Whereas hiring a player who does well in the FBM system lowers this risk while at the same time increase the chance of winning more points!

Prediction & evaluation

Here is our original prediction and what actually happened:

RankClubPredictionActualityDifferenceNotes
1Ajax43441We predicted the performance of Ajax quite well.
2AZ294112We predicted AZ strength, but underestimated how strong AZ was.
3PSV3534-1PSV weakness is remarkable this season and we are very happy that we predicted PSV weakness so well. 
4Willem II25338We predicted Willem II strength, but underestimated how strong Willem II was
5Feyenoord28313Feyenoord weakness is remarkable this season and we are very happy that we predicted Feyenoord weakness so well
6Vitesse26304We predicted the performance of Vitesse quite well.
7Utrecht29290We predicted the performance of Vitesse quite well.
8Heerenveen23285We predicted the performance of Heerenveen quite well.
9Heracles162610Just like last year we underestimated Heracles.
10Groningen18257We underestimated Groningen.
11Sparta20233We predicted the performance of Sparta quite well.
12Twente2419-5We overestimated the performance of FC Twente.
13Fortuna17192We predicted the performance of Fortuna Sittard quite well.
14Emmen2018-2We predicted the performance of FC Emmen quite well.
15Zwolle1816-2We predicted the performance of PEC Zwolle quite well.
16VVV2215-7We overestimated the performance of VVV.
17ADO2013-7We overestimated the performance of ADO.
18RKC1511-4We predicted the performance of RKC quite well.

Showcase: Noa Lang

Noa Lang first gotten on our radar January 22nd 2018, almost two years ago. This is his FBM contribution chart at that time of that match:

Jong Ajax vs Cambuur January 22nd 2018 (3-2)

Compared to his most recent FBM contribution chart, the only difference is that Noa Lang today has a higher transitioning & build up then two years ago:

Ajax 1 vs FC Utrecht 1 2019-11-10 12:15:00 (4-0)

Given Noa Lang’s high attacking contribution it comes as no surprise that Noa Lang scored a hattrick in the match FC Twente vs Ajax of December 1st 2019.