Category: voetbalstatistiek

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 65.875.000 euro and earned 56.000.000 euro for a loss of –9.875.000 euro. From 2021 on,players need to be born 2001 or later.

Of the 117 players on the list 77 has increased in valuation (73%), 6 have decreased in valuation (4%) and 34 have no change in valuation mainly due to still being too young. (23%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/Sold
64612-4-2018126 million transfer to RennesRW8.66Jérémy Doku26M
804422-4-20190.53CM6.8420022022
87398-6-20190.20.5CB6.7720022023
1261415-7-202000.7CB6.1620022023
1125817-9-20190.050.1CB6.052002unknown
1228918-5-202011.2CF5.9920022021
1112127-1-202000.25CM6.1320012021
1241422-6-20200.81RW5.620012021
1206325-1-20200.10.25CM7.7620002021
810727-1-20200.750.7CF7.2920002021
109571-8-20190.40.3AM7.1720002022
115137-3-20201.11.3RB7.0620002022
110465-3-20200.40.6DM6.8820002022
1165720-12-20190.10.25CB6.4720002023
74062-3-20200.70.635GK6.2420002021
823021-10-201934LB5.9720002024
1173230-10-20192.7530 million transferAM7.22Odilon Kossounou30M
118414-12-201905LB7.4220012024
122305-6-20203.53.6CF8.220022023
126652-7-20200.43DM7.520012024
1272121-7-20200.10.1LW6.52000unknown
1274922-7-20200.40.5RW62001unknown
1279023-7-20202.54LW5.720012022
1279325-7-20200.30.35CF720012023
1279425-7-20200.50.5CF6.520022023
1279525-7-20200.150.5CB620002022
1279625-7-20200.81CB620012022
1234326-7-20202.32.3AM620012025
1280426-7-20200.10.2CB5.820032022
1281026-7-20200.050.4RW5.720042023
1009328-5-20192.54.5CB7.420002024
128551-8-20200.20.2CF5.920022022
128562-8-202002CM5.620032021
128695-8-202001AM720002022
128745-8-20200.20.6LW6.320012023
128899-8-20200.92CB620002024
1290210-8-20200.10.1LW6.62003unknown
1290310-8-20200.150.1LW7.52003unknown
1291011-8-20200.250.5CM8.220012022
1292712-8-20200.20.8CF5.920042022
1292212-8-20200.151.5CF7.120012021
1292913-8-20200.50.8RW620012023
1034012-12-201800.5RW820002021
1035219-8-202004.5LB6.2520012024
1297519-8-202003CM5.520012021
1298322-8-20200.73CM6.1220022024
1302526-8-202000.1AM6.320052023
121033-3-20200.0252.2CB7.3120002022
814517-8-20190.50.55RB6.8520012022
73835-9-20200.50.575LW7.8220002021
130755-9-20200.30.4DM7.1320002021
563929-5-20190.20.3AM5.9520002022
130968-9-20200.20.4CB5.6820002023
81303-5-20190.10.7DM7.8920002022
804619-4-20190.250.6CM7.3920002022
1209412-9-20200.050.6CM7.1120002022
1316215-9-20200.42CB7.5620002023
1315915-9-20200.0511CB6.4320022025
132816-10-20200.050.075CB6.2220002022
1329914-10-20201.25DM7.520012024
1330018-10-202032.5CB5.720032023
1330122-10-20200.151.2CB5.8620002023
1334428-10-202034.5LW5.9620012023
133563-11-20200.10.8CM7.520022025
133573-11-20200.10.4CF720022025
134718-12-202013CF5.520022022
134729-12-2020011CF6.620012023
1349110-12-20200.30.3CM620012022
1344210-12-20200.20.4CM820002023
986310-12-20200.30.7RB7.620002024
985010-12-20200.10.4RW6.820002022
1312613-12-20200.51.2LB620012023
1127024-12-202023CB6.320022021
135493-1-20211.81.8AM620022024
135536-1-20210.7250.8CF620012023
135346-1-202100CM62004Unknown
135356-1-202100CM820052022
135366-1-202100AM82005Unknown
135376-1-202100CF720052023
135396-1-202100CF62004Unknown
135606-1-202100CF62004Unknown
135616-1-202100CF720042022
135626-1-202100CF720042022
135636-1-202100AM72005Unknown
1365211-1-202100GK6.220022022
1367714-2-202100RW7.520022022
1367914-2-202100LW6.92002Unknown
1368417-2-202100CF6.32002Unknown
1369419-2-202100CF6.420042021
1369519-2-202100AM6.720042022
1369619-2-202100RW72004Unknown
1399719-2-202100CF62004Unknown
1399819-2-202100CF5.720042021
1369919-2-202100LW5.820042022
1370019-2-202100AM6.92004Unknown
1370119-2-202100AM6.62004Unknown
1370220-2-202100CF6.820032021
1370324-2-202100.3RB7.920022026
1370625-2-20210.50.5GK620012024
1370827-2-202111CF5.720032023
137224-3-2021010CM620022025
1373415-3-20210.83RW720022023
1373516-3-20210.70.8CF620012024
1373616-3-202100CM7.520032021
1374822-3-202122DM6.520022022
1376924-3-202133CB5.920022021
1377125-3-202136CB7.120012024
1380630-3-20210.80.8CB72002Unknown
1381130-3-20210.10.2CB6.820012021
1383331-3-20210.50.5CM720032022
125256-4-202111.1CF620022025
138448-4-202101CM6.720022024
138428-4-202100.3LB5.820022024
1387016-5-20213.54CM6.320032024
1387117-5-20210.0750.4CB8.520012021
1387217-5-2021214RW720032022
1425913-7-202100CM72006Unknown

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.

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.