If you’ve read these two previous articles, Expected Wins and Expected Wins 2, you know I look at how teams perform, on average, (win, lose, or draw) with respect to my primary data collection points for Possession with Purpose.
What will be added, in Version 3 (V3), will be a compare and contrast between all the leagues I evaluate in my Family of Indices.
Results of looking at the diagrams and reading through my observations should help clarify analyses like (ABAC, ABCB) doesn’t really have relevance to teams that win, lose or draw – at least not this year. (Note – two links – two different sites published roughly the same analysis)…
Don’t get me wrong – I’m not taking a personal dig at the grueling work associated with the analyses.
It has great value, but more from a tactical viewpoint in how passing is executed, not from a (bell curve) – volume/success of passing rate – relative to possession and penetration into the Final Third, that helps a team create and generate shots taken leading to goals scored; or… when flipped, leading to goals not scored.
And as pointed out by a (shomas) on the article, that surfaced on MIT, if anything, it adds predictability to what a team will do – and the more predictable a team, the more likely the opponent can defend against them better…
For me – I would have thought the GREATER the variation in that cycle(ABAC, etc…) the better… others may view that differently?
In addition, I think there could be more value, to the information, if it was segregated by league – more later on that…
To begin – here’s a reminder of what Expected Wins looked like in Major League Soccer after 92 games (184 events):
The term ‘event’ is used, as opposed to game, to clarify that each team’s attacking data is include in this analyses – and that the greater the volume of data points the stronger the overall statistical analyses is; i.e. sampling 15 data-stream points is not the same as sampling 1000 data-stream points.
Biggest takeaway here is the strength of correlation these seven data points have to each other (i.e. their representation – in my opinion – of the primary bell curve of activities that occur in a game of soccer)…
In every case, in every diagram that follows, all the Exponential trends exceed .947; and in every case the relationship for the winning teams is higher than the relationship for losing teams… speaking to consistency of purpose and lower variation in my view.
In general terms. this is my statistical way of showing that a goal scored is tantamount to a 5th or 6th standard deviation to the right from the normal bell cuver of activities that occur in a game of soccer.
Said another way – I don’t evaluate the tail – when measuring the dog’s motion – I evaluate the dog; recognizing that the tail will follow, to some degree, what the motion of the dog will be… and… that even if the motion of the dog is somewhat different, the tail will normally behave in the same way.
Therefore, it’s not the tail that should be analyzed – it’s the dog… others may view that differently.
Here’s the same diagram for the MLS after 366 events:
Oh… the green shaded areas are meant to show those data points that are higher for those particular categories; in other words the Volume of Shots Taken for winning teams (after 366 events) was higher than that of losing teams – but the volume of passes completed in the Final Third was higher for losing teams than winning teams… more on that later.
Here’s the diagram after 544 events in MLS:
Note the shift – only the volume of Final Third Passes Attempted is now higher for losing teams – all other data categories see the winning teams with greater volume.
For me, what this reinforces is the issue of time and space as well as patience – three statistics never measured in soccer (publicly at least)… again, reinforcing, for me, that shot location only has value relative to the time, space, and patience of the team in creating that time and space for that shot.
Statistically speaking, what that means, to me, is that Expected Goals; a very popular (and worthy) statistical calculation, needs to be refined if it’s to have greater value as a predictive tool/model… I’d be interested to hear / read the views of those who work Expected Goals efforts…
Now here’s the European Leagues I’ve added to my PWP Family of Indices analyses; first up the English Premier League:
Note that the pattern, here, after 100 events, resembles the same pattern for MLS after 544 events… worthy.
Moving on to the Bundesliga:
A pattern similar to MLS after 366 events; will this pattern morph into something different as the league continues? Possibly – the MLS pattern has changed so perhaps this one will too?
Now for La Liga:
A completely new pattern has taken shape – here “volume” speaks volumes!
Is this unique? Nope… It also happens to be the same pattern as the World Cup 2014 pattern – below:
Will that pattern show itself in the UEFA Champions League? I don’t know but we’ll find out…
So what’s it all mean? The “so-what”?
Before attempting to answer that, here’s two different diagrams plotting these data points for winners and losers (in reverse order) for the leagues I evaluate:
Now the grist:
The red shaded areas are where the losing teams’ average exceeds the winning teams’ average in the volume of those activites – the green shaded areas are highlighted for effect. Green shaded areas for the volume of Shots on Goal and Goals Scored indicate that those numbers are virutally the same, for winning teams, in all the activities measured…
Now, back to the so-what and what’s all mean?
For me this reinforces that the “pattern” of passing (ABAC, ABCB, etc…) that gets you into the Final Third has no relevance to the volume of Goals Scored.
And, it also reinforces that different motions of the ‘dog’ will generate the same tail wagging outputs – therefore it’s the analysis of the dogs activities that drive greater opportunities for improvement.
The averages for winners in the activities measured all behave somewhat differently – granted some patterns might be the same but the volumes are different.
And when volumes change, the game changes, and when the game changes, the strategic or tactical steps taken will change – but… the overall target should still remain the same (on average) – put at least 5-6 shots on goal and you ‘should’ score at least two goals… getting to that point remains the hard part!
Bottom line here:
These leagues are different leagues – and the performances, of the teams, in those leagues are different when it comes to winning.
Therefore, I’d offer that comparing a striker’s ability to score in one league is completely different than an expectation an organization might have in how that striker may score in another league.
Said another way – a striker who scores 20 goals in the Bundesliga, a league that shows winning teams play to a more counter-attacking style, might not perform as well in a league like the EPL; which looks to offer that winning teams play a more possession-based style.
Perhaps??? another good example… a striker playing for a team that counter-attacks, is more likely to have greater time and space to score a goal, than playing in a possession-based team where time and space become a premium because the opponents play far tighter within their own 18 yard box.
But, as mentioned before – since no-one statistically measures (publicly) the amount of time and space associated with passing, and shot taking, we can’t peel that onion back further. I have suggested two new statistics that may help ‘intuit’ time and space – that article is “New Statistics? Open Shots and Open Passes”: here.
For the future… I’m interested in seeing how these analyses play out when separating out teams who show patterns of counter-attacking, and perhaps direct play, over teams that show patterns of possession-based football.
In addition, I’m also keen to see how these take shape when reversing the filter and organizing this data based upon whether or not a team is defending deeper, or more shallow.
The filter there will come from looking at the opponent averages for passing inside and outside the Final Third…
It seems reasonable to me (others may view this differently?) that the if a team lacks goal scoring they need to find the right midfielders and fullbacks that are good enough to create the additional time and space the strikers need in order to score more goals.
And that doesn’t even begin to address the issues in defending – which statistics continue to prove year in and year out as being more critical to winning than attacking.
Given all this information, I may have missed something – I’m always looking for questions/clarifications so please poke and prod the diagrams and analyses and comment as time permits.
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