In the third part of our miniseries, the focus shifts to Italy for the Serie A. After failing to drop a single point at home last season, Juventus currently haven’t dropped a single point in any of their six matches. Like in the Bundesliga, the title run might not be that contested, but behind the boys from Turin, all sorts of excitement and unpredictability arises.
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.
Like Bayern in Germany and Chelsea in England, Juventus dominate the Good-Lucky graph, but mostly so on the ExpG axis, which is a good thing for them. With an ExpG-ratio of 0.767 they set an unprecedented dominance.
Behind them is an interesting group of eight blue-ish teams, who seems clearly separated from the rest of the bunch. I write interesting, because apart from Roma and Sampdoria, this bunch had been more or less unfavourable in PDO. The best bet for PDO issues to resolves, and that means we should expect the like of Napoli (7th in the table) and Lazio (8th) to put up a good chase of 2nd placed Roma and 3rd placed Sampdoria, whose outcome seems partly PDO fueled.
Although Milan (5th in the table) would be deemed in less deep trouble than their rivals Inter (10th), it seems to be a matter of time before the ‘nerazzuri’ will catch up with the ‘rossonero’.
Down the ExpG axes it’s Chievo who find themselves in most trouble, with early season surprise package Udinese (4th in the table) the most likely candidates for a winter depression. Sassuolo, Parma and Palermo illustrate the fact that PDO rules early in the season, as these bottom three in PDO terms are also bottom three in the table with 3 points from 6 matches.
Points per Game
In the Serie A, just like in the Bundesliga, the connection between performance and points is quite direct. Holding a perfect 3 points per game spot high on top are Juventus, while this graph illustrate future drops and rises.
It’s quite easy to spot what’s going to happen at Udinese and Verona, who hold more points that their performance so far justifies. The reverse is true for Cagliari, and in decreasing order Parma, Palermo and Sassuolo.
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!