Our prediction of the Eredivisie Winter Champion 19/20

As there is an 80% correlation between FBM Team Score and the ranking in the Eredivisie and a 90% correlation between FBM Team Score and points scored in the Eredivisie, we can predict what the ranking of the clubs and the points they score. As teams that risks to be regulated can buy players that make significantly difference in the team’s performance, we can really only predict the ranking and points on January 1st 2020. In principle the same goes for any player still bought before the close of the summer transfer window, but in practice this will make very little difference to our prediction, if at all.

So without further ado, here is our definite prediction for the Eredivisie season 19/20:

RankClubAverage FBM Team ScorePoints 1/1/2020Points end of 19/20 seasonAverage points previous 5 yearsDifference
7Willem II137.52549463


This is what the columns mean in the above table:

  1. Average FBM Team Score. FBM Team Score is calculated by adding all the values of the players in the starting XI to the team score at the end of each match. This includes all the data we have of the team from the previous two seasons and the start of the new season. Older matches have less weight and recent matches weigh more.
  2. Points 1/1/2020. This is the predicted number of points that team will probably have January 1st 2020.
  3. Points end of 19/20 season. This value is only provided so we can check whether the values are in line with the average of the last five years for each position. Due to changes in the team during the winter transfer period, these values are only a prediction if no team changes anything during the winter transfer period which is of course extremely unlikely.
  4. Average points in the previous 5 seasons. This is the average points for the rank not for the club. For example, the champion in the Eredivisie had 85 points , on average over the previous 5 seasons.
  5. Difference. This is the difference between the 5 year average and our projected points for the end of the season. This shows how likely it is that we overestimate or underestimate the club. So we are likely to underestimate the number two of the Eredivisie and if that it is PSV, which is also likely, then we are underestimating PSV. And we are probably overestimating number 18, which in all likelihood won’t be Heracles, but one of the other teams just above Heracles.


  1. In our projection the teams score 6 less points than the 5 year average which is less than 1% difference of the 849 total points scored in the 5 year average.
  2. Although the model predicts the top two in the same way as most people would do, there is an anomaly in the sense that the number two of the Eredivisie would score significantly less than on average. This could be the case as the favorite teams in the Eredivisie had a rocky start of the season. Nevertheless, it is more likely that our model underestimates PSV currently.
  3. In the same light, our model seems to underestimate the upper half of the table and overestimate the bottom half of the table.
  4. The other anomaly would be the regulation of RKC with 30 points. Although it is likely that RKC will be regulated, 30 point is quite a high number of points to still be regulated. So it is likely that our model overestimates RKC.
  5. If clubs are predicted to score the same or almost the same number of points then it is obvious that the smallest change might affect whether one club is on top of the other or vice versa. For instance, we predict ADO to be on top of Emmen, but it could very well be the opposite.
  6. As this is the first season we make this prediction we have to see how clubs that are promoted from the Eerste Divisie do in this prediction. We use a deflator for historical games in the lower league, but even with the deflator promoted clubs do quite well. So we might overestimate promoted clubs. Nevertheless, Twente and Sparta did have a great start to the season.


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