Expected Wins Five – Europe

In my previous series on Expected Wins Four – probably more appropriately entitled “Expected Points” – I’d taken a look at how the general tendencies of four primary Leagues in Europe (England, Germany, Spain, as the UEFA Champions League) compare to Major League Soccer – Is European Football Really Higher Quality than Major League Soccer?

This time I’m focusing strictly on Europe and offering up how things stand in PWP with the season coming to a close soon.  But before digging some things to share about PWP to date:

A reminder – PWP is about two things:

  1. The End State in that the final Index comes as close as possible to the League Table without using points earned in any of the calculations, and
  2. Recognizing that soccer is a game that is played in a free flowing environment – picture two amoeba fighting against each other in a confined space…. There is attempted control by the Head Coach that includes tons of preparation to set the stage for ‘an approach’ to earn three points – and then there is the game itself where there is but one time out (halftime) – no namby pamby huddles or official stoppages of play between possessions.  Meaning these guys play a full-on, in your face (sometimes literally), non-stop, constantly thinking and reacting to the game that can literally see the ball go in any direction at any time… not purely random but close.

Given that, PWP attempts to tone down all that volatility and parse out general tendencies that fall within the bell curve of activities – it’s not perfect – but it’s bloody good… and yes – I have made a few mistakes along the way (if you don’t work you don’t make mistakes).  The latest has been a technical mistake – the relationship of CPWP to the League Table is not an R Squared number (Coefficient of Determination) it is an R number (Correlation Coefficient).

For the stats followers that may be an issue… but even with the Modernized TSR (read here) the CTSR “R” is still generally lower (team to team) and certainly lower (table to table) than CPWP – meaning there still remains room for both statistical analytical approaches in a gmae that is played across the world…

Also, my thanks to some great research by Rob Lowe, a mate with the same passion for footy, who has asked to collaborate with me in the future.  He has done some additional regression analysis on the data points of PWP with respect to goals scored and points earned.  I should point out that his results show that not all six of the data points in the PWP equation independently-directly relate to goals scored or points earned.  For me that is okay – and actually great news for a few reasons…

  1. Both of my two new statistics (Passes Completed in the Final Third per Passes Completed across the Entire Pitch – Step 3 of PWP) and (Shots Taken per Completed Pass within and into the Final Third – Step 5 of PWP) did statistically relate to Goals Scored and Points Earned (independently).  Meaning those new statistics are relevant – both within the context of PWP and outside the context of PWP.  It’s this statistical regression type information that should solidify these two new statistics in the world of soccer.
  2. For both Possession (Step 6 of PWP) and Passing Accuracy (Step 5 of PWP) – as you will see a bit later – those two derived data points were never supposed to directly (independently) relate to goals scored or points earned as a matter of course I have advocated for quite some time that they shouldn’t.  PWP was built with the intention that the six derived data points only needed to relate to each other in a stair step relationship recognizing that in every game a team needs to possess the ball, move the ball, penetrate the opponent’s final third, take shots based upon that penetration, put them on goal, and score goals – all while preventing the opponent from doing the same thing.
  3. Another view on the outcome that Rob has noted – it’s unreasonable to analyze a game of soccer without taking those activities into account.  Rob’s positive feedback was that both possession and passing accuracy act as a “smoothing agent” within the Index – I agree but with beginning to learn the nuance of writing an Academic Paper I would put it this way.
  4. Possession and Passing Accuracy stats have limitations when vewing overall regression analysis relative to goals scored and points earned – but those limitations actually give the overall analyst of soccer a much better understanding about the context of activities that occur when a team is performing better than another team.
  5. In addition, Passing Accuracy statistics provide a coach a great measurement tool for how well some players may develop and progress into higher levels of competition – to exclude data of this import really ignores some of the most fundamental training aspects a team needs to do in order to improve.
  6. Also, there is excessive volatility in the percentages associated with Shots on Goal versus Shots Taken and Goals Scored versus Shots on Goal – if I only look at those two things then evaluating a game is all about (pass-fail) – granted winning and losing is pass-fail.  But to develop a “winning culture” a grading system perhaps more appropriate is A-B-C-D-F – in other words there are levels of success above and beyond pass-fail – especially when you are a team that isn’t at the very top of the league.
  7. By having Possession and Passing Accuracy in the equation you get a much larger (explanatory) picture on the culture of success – and as things appear to take shape, the Index itself, gives better clarity to that level of success for teams that are mid-table as opposed to bottom dwellers or top performers…

Now for the grist in Europe – first up – England: 

Note that the first two diagrams (in each four diagram grouping) highlight where the highest quantity and highest quality occurs within each competition – after some growing pains (earlier Expected Wins measurements) all four competitions now see the teams that win having the highest averages, in all categories, for both quantity and quality… proving (for the most part) that more is better and more results in more…

Barcleys Premier League PWP Data PointsBarcleys Premier League PWP Derived Data PointsEnglish Premier League CPWP IndexEnglish Premier League CPWP Predictability Index

All told the correlation, at this time, remains very strong – note that the “R” has replaced the “R2” in my third and fourth diagrams.

If I remove Possession and Passing Accuracy from the CPWP Index – the R value drops to .78 – statistically reinforcing that the Index, itself, better represents the standings in the League Table by including Possession and Passing Accuracy data.  Proving yet, another way, that goals scored and shots taken simply do not provide adequate depth on what activities occur on a pitch relative to earning points in the League Table!  And if you’ve read Moderning TSR this doesn’t mean ATSR/DTSR or CTSR doesn’t have value – it does…

As things stand today Chelsea take the League and since Man City, Man United, and Arsenal round out the top four (different orders) in both CPWP and CPWP-PI I’d offer it’s those four that advance to the UEFA Champions League next year.  The bridesmaid looks to be a two horse race (Spurs supporters may argue that) between Liverpool and Southampton.

Note that Southampton edges Liverpool in CPWP but that Liverpool edges Southampton in CPWP-PI – meaning when excluding Goals Scored – Liverpool has better quality than Southampton – so for Liverpool it’s more about converting Shots on Goal to Goals Scored – while for Southampton it’s more about getting clean sheets and scoring at least one goal; at least in my view – others may see that differently?

In retracing the earlier discussion on the data within the six steps of PWP – as you can see in both the first and second Diagrams (for all competitions) the Exponential Curve (Diagram 1) and well as Power Curve (Diagram 2) the stair step relationship between the data – point to point – are incredibly high…  Even more intriguing is how close those “R2” numbers are for both winning, drawing, and losing… really driving home the point, in my view, just how small the margin of error is between winning, drawing, and losing.

For goals scored (for or against) we really are talking about 5 or 6 standard deviations to the right of the bell curve…

Germany:

 Bundesliga PWP Data PointsBundesliga PWP Derived Data PointsGerman Premier League CPWP IndexGerman Premier League CPWP Predictability IndexPerhaps the most intriguing issue this year isn’t the FC Bayern story – it’s the lack of goal scoring in Borussia Dortmund – when viewing the CPWP Predictability Index clearly Dortmund is offering up all the necessary culture the team needs in order to succeed – with one exception – goal scoring…. wow!

Another surprise may be Wolfsburg I’d pick them, and Bayer Leverkusen to finish two-three in their League Table – both show pedigree in team performance both with and without considering goals scored…

Spain:

La Liga Premier League PWP Data PointsLa Liga Premier League PWP Derived Data PointsSpanish Premier League CPWP IndexSpanish Premier League CPWP Predictability Index

Barcelona and Real Madrid are locked in for the top team battle – my edge goes to Barcelona.  I’d offer more here but I’m simply not up on the La Liga as much as I’d like to be…

UEFA Champions League:

UEFA Champions League PWP Data PointsUEFA Champions League PWP Derived Data PointsUEFA Champions League CPWP IndexUEFA Champions League CPWP Predictability Index

The top eight teams that advanced are identified above – given the general success of CPWP relative to the top eight I’d expect FC Bayern Munich, BArcelona, Real Madrid, and Juventus to advance to the semi-finals.

In Closing:

My first of at least 4-5 Academic Papers is soon to be published – my thanks to Terry Favero for helping me work through this new experience – his support, patience, and knowledge in navigating all the nuance associated with writing an Academic Paper has been superb!

All four European competitions show more gets you more – this was not the case for Major League Soccer last year:

Major League Soccer Expected Wins FourWinners Expected Wins PWP Data Relationships Four

When more gets you more in MLS then I sense MLS has reached the BIG TIME – until then I think it’s a great breeding ground for Head Coaches that simply can’t get a job with a soccer club that has huge pockets of money.

Put another way – and many may disagree… I think a Head Coach who really wants to challenge their intellectual grit against another Head Coach can have greater opportunity to do that in MLS than they can by Head Coaching most clubs in Europe.

Why?  For at least one reason – a Head Coach in MLS really has to do more with less…

Errata – the first MLS slide indicates 654 events – the correct number is 646 events…

Best, Chris

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