As mentioned in Part I of this multipart post, Luca Pappalardo prepared a video, for the Friends of Tracking channel in 2020, to talk about some elements of a paper related to an open Wyscout data set, and advanced statistics related to passing networks, flow centrality and player ranking.
For this post (Part II) I’m going to cover my take on the match evolution and spatial stats.
I’ve forked the “mapping-match-events-in-Python” repo into my mmoffoot area and created a new branch called ‘englanddata’ to cover the data set of English Premier League information for the 2017-18 season.
Spatial distribution of events
There are tons of events collated in the WyScout API Events from duels to fouls to interruptions, as explained in the WyScout API document. For passes there are 6 types:
- Pass = Hand pass,
- Pass = Head pass,
- Pass = High pass,
- Pass = Launch,
- Pass = Simple pass,
- Pass = Smart pass,
In this Notebook there’s an interesting set of images created which show the distribution of positions per event type. These kernel density plots show the distribution of the events’ positions during the match with the darker the green representing the higher number of events in a specific zone of the field.
The first image is of duels, and in the WyScout world “Duel” has a specific meaning,
A challenge between two players to gain control of the ball, progress with the ball or change its direction.
With a number of subtypes to consider too: Defensive duel, Offensive duel, Aerial duel, Loose ball duel, and Sliding tackle
For the moment I’m not going into the whys and wherefores of these subtypes, but it’s really interesting to review and compare the images and to see the difference of where the Italian league and the English league host their duels. Dare I say right-backs, left-backs and right wingers, left wingers should look closer if they are moving between the leagues.
A big summer move from England to Italy was Emre Can from Liverpool to Juventus, with Stephan Lichtsteiner coming in from Juventus to Arsenal maybe a view of these plots before the new 2018-19 season got underway might have been handy.
Of note there’s a 10,000 event sample size in here by default, so for the Italian & English league this represents about 6 matches worth of events, and so a larger sample size would be nice to see and compare against. Would also be nice to identify specific players (RB,LB and RW, LW) that were strong in those main duel locations, however that will have to be for another day.
Here are where the fouls happen.
And the shots.
Intra-match evolution of the events
Goals are the main stay of football and so when looking at the English and Italian leagues (season 2017-18), its pleasing to see the difference between the leagues, especially the 1st half goals.
Yellow cards and red cards are covered in the data set too, and displayed in the Jupyter Notebook but I’ll be honest and say I didn’t take too much time to analyse the results here, because I was fascinated by the Duel plots.
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