In the second part of our miniseries, the focus shifts from the English Premier League to the Bundesliga. Bayern Munich have hardly been contested for the past two seasons, and already hold a ten point lead over Klopp’s Dortmund, but how do things look beneath the surface of the league table? Will Hamburger SV and Werder recoup their disastrous season starts? And Paderborn hipsters, anyone?
Using the recently explained Good-Lucky matrix, in a format adopted from Benjamin Pugsley, we can easily scan the league for the best performance teams (horizontal axis) and the most efficient teams (vertical axis). Anyone into football analysis will know that being highly efficient lasts only so long, and PDO levels tend to revert back to normal before you know it. Depending on team quality, normal is a PDO of 980-ish for poor teams and 1020-ish for good teams.
Dominating this matrix in both performance and efficiency, no surprise, is indeed the boys from München. Although they may regress a little bit in PDO, their solid 0.703 ExpG-ratio is undisputable league winning form.
Best of the rest are Leverkusen and Dortmund, although neither has been able to show that result-wise, due to extreme PDO depressions. This will revert, but in order to keep the trace of leaders Bayern, they will need and extreme PDO dip for their rivals to occur with unlikely PDO waves themselves. Not gonna happen.
Good performances are noted by Wolfsburg, Gladbach and Freiburg. Yes, 15th ranked Freiburg that is. They won’t likely make a run for the CL spots, but with any good PDO wave through their season, they may just knick one of the EL spots, if their current performance holds up. More on that later.
Disappointments are mid-table Schalke, who don’t look like moving up soon and Hoffenheim, whose early season run seems fuelled by an efficiency that can’t hold. Hamburg and Werder may be the bottom teams in the table, but are not in serious relegation form in ExpG terms.
That orange zone of relegation form, holds two teams that have been bailed out by an early season PDO wave – Köln and most notably Paderborn – and 16th ranked Stuttgart.
Points per Game
The Bundesliga already displays quite a direct connection between performance and outcome, indicated by the steep regression line. As to be expected given their underlying performance, Bayern has already distanced itself from the pack, with a nice trailing group of that will compete for 2nd to 7th place, as it seems. The models prefers Leverkusen for now, but with a very small margin, and it’s still early days.
Werder, Hamburg and Freiburg all hold quite low positions, and it’s easy to see them catching up as more matches will be played, and teams will tend to move towards the red line. The interesting case study here is Freiburg, whose ExpG-ratio is displayed as an amazing 0.554! Yet their prediction below is a sober bottom spot with just around 31 points. How come?
In the predictions, teams are evaluated according to their non-blocked non-penalty non-rebound shots. Freiburg has one of the highest percentages of blocked shots (30%) and one of the lowest percentage of shots blocked by their own defense (18%). That is not a good thing, and it seriously hurts their prediction. Furthermore, they have already been awarded 3 penalties, which means they’d need some 15 penalties to keep this pace until the end of the season. Not gonna happen, and the model knows that.
Here’s the ‘sticking my neck out’ part of this mini-series. Using ExpG as a basis, a pretty straightforward model can simulate the remaining part of the season and come to predictions for the final league table. I figured it would be more fun sharing these from time to time, for various leagues, and see what we can learn along the way towards the end of the season.
For this model I’ve limited ExpG to 11v11 or 10v10 situations, filtered out blocked shots (since shot blocking is a skill), filtered out penalties (since they are distributed pretty random and skew the numbers a fair bit) and filtered out rebounds. Furthermore, I’ve regressed the ExpG towards last season’s numbers, based on the R2 between ExpG’s on each particular match day to ExpG’s at the end of the season.
Without further ado, here’s the graph of predicted points, along with a box plot showing the spread and most likely number of points for each particular team. Enjoy!