One of the first challenges of the Mathematical Modelling of Football
lecturer series was to:
- Think of a player who you enjoyed watching at the recent Men’s or Women’s World Cups.
- What actions did they perform that were important and why?
- Plot the actions and describe how the data supports or contradicts your own analysis.
- Write a short text using at most two figures that illustrate your point.
I’ll address this challenge with a view on a player (or set of players as it turns out), and later in the piece I’ll also share some code snippets on how the information was extracted from the StatsBomb data.
Think of a player
The first challenge and I must admit I’m floundering a little already as I can barely remember the Mens 2018 World Cup, bar the eventual winners, the golden boot winner and the best player award.
However the one thing I do like in football is the player assisting goals.
So for this challenge I thought I would look at the one player that came out on top in this regard at the 2018 World Cup and to state the important actions they performed.
However on first glance it is the case that there is a 16 way tie for the most goal assists at the World Cup 2018, well according to Wikipedia, when I head to another site there is a 19 player tie.
So the first thing I’ve done is take the Team names for all the players listed as the top assist players, have a look at all the passes, and especially all the passes that lead to a goal (or more so a shot on goal) and try and find out the top three players that assisted shots on goal.
This lead to a surprise, for me anyway, because the top three that popped out from this exercise was:
- Kieran Trippier (England) : 25
- Neymar da Silva Santos Junior (Brazil) : 24
- Philippe Coutinho Correia (Brazil) : 13
What actions did they perform
Kieran Trippier had 25 passes that lead to a shot on goal.
Figure 1: Kieran Trippier passes that assisted a shot on goal
Now it’s time to look at the Expected Goal plot of the shots that happened, straight after Kieran Trippier gave in the pass for that shot. As far as I can tell Kieran Trippier could have had at least 2, if not 3, other assisted goals, however there are many of those passes that are into areas where the Expected Goal of that next shot is low.
Figure 2: Expected goals from Kieran Trippier passes
It is also worth noting that a larger number of Trippiers actions are from set plays (corners and the like).
So who was the top assistor at the 2018 World Cup, is it Neymar of Brazil ?
Neymar comes in second on the list gathered earlier, however he only assisted 1 goal also.
Figure 3: Expected goals from Neymars passes
Therefore it must be Philippe Coutinho, with 13 passes that assisted a shot on goal and 2 actual goal assists he is the top player in this category.
Figure 4: Expected goals from Philippe Coutinho passes
Or is he ? A little addendum, when I look to the positions that Neymars passes went into and the Expected Goal setting for each one of those shots taken after a Neymar pass, for me he should be the player of note.
FWC 2018 – Group E – SRB v BRA Neymar Jr
Figure 5: Scatter plot of expected goals from Top 3 player passes
A large number of Neymars assists for a shot are close to the goal (120 is the goal line) and central to the goal (40 is the centre of the goal).
Code snippets
I also want to share some of the code snippets that helped gather the data from Statsbomb in regards to this challenge.
Extracting data from competitions.json
I found it handy to extract the season_id from the competitions.json file and using this to find all the matches with teams I was interested in. This also helped to identify which matches in the events folder had to be picked up.
The Assisted Shot Id
When iterating through the passes finding the pass_assisted_shot_id was very handy, but there was a bit that stumped me for a while when a pass didn’t turn into an assisted shot then that id is set as NaN or not a number, which is a little off putting at first.
There are also times when there are duplicate entries for the pass_assisted_shot_id for related events which in one iteration of this code had Kieran Trippier (England) with 37 actions.
#Find passes with pass_assisted_shot_id set
if isinstance(passasid, str):
#If the pass_assisted_shot_id is a nan (Not a Number) then
# we don't want it. Usually the assisted_shot_id is some hex value
playersAsisstingShotsOnGoal.append(tpassplayerdir)Finding the Expected Goal data for the related shot from a pass
Given that the dataframe of shots for the matches set’s the index name as the Id of the event, well I took me a while to figure how to re-access that index name when doing a compare later on in the code. Of course it’s simple when you see how with the .name.
for shotOnGoal in matchesShotsOnGoal:
if (shotOnGoal.name in assistedShotId):
x=shotOnGoal['location'][0]
y=shotOnGoal['location'][1]Acknowledgements
Thanks to feedback from Eoin O’Brien, Eoin Slattery, Michael Kerley, Oliver Critchfield and David Sumpter as this has lead to a revision of the text and images.
You must be logged in to post a comment.