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:
- 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
- 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…
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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…
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…
Perhaps 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…
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:
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.
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:
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…
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Much has transpired in the world of soccer statistics over the past four years since I first published: Possession with Purpose – An Introduction and some Explanations.
- Three years ago I published my Possession with Purpose – Revised Introduction.
- In 2014 the concept was presented at the World Conference on Science and Soccer 2014.
- Last year the concept was published in Europe and just this year another part of Possession with Purpose was presented at the World Conference on Science and Soccer 2017 (Predictability).
- Now it’s time for a new update that hopefully brings more clarity and simplicity?
CLICK this link for my NEW simplified power point presentation update of Possession with Purpose the Total Soccer Index
- The .pdf version should make it easier to print and use as reference material.
Within you’ll find:
- Definition of TSI
- Purpose of TSI
- Premise of TSI
- Parts of TSI
- Leagues / competitions analyzed
- Application of TSI and its parts
- The data for leagues / competitions analyzed
- Observations & conclusions by league / competition as well as reviewing TSI across leagues / competitions
My thanks to all for your support and kind words throughout the years.
- The sum of the parts has greater correlation to points earned than the parts independent of each other.
- Player A, from Team A, within any given league, has a different correlation to points (performance/outcome) than Player B, Team B, Player C Team C, etc in that same league. In other words outcomes of individual player statistical analyses are NOT EQUAL from team to team and league to league.
- Said differently, clearances or crosses (used as a measurement in fantasy soccer) for one player, on one team, DO NOT have the same weight/value of clearances or crosses for a different player on a different team.
- Same can be said for passes or shots taken, etc.
- Therefore, Calculations such as Expected Goals are not an apples to apples comparison between teams within the same league. Yes, it’s a predictive tool, but flawed/
- The lower the overall correlation of the Total Soccer Index to points earned the greater the parity within the league or competition; this also intuits those are less predictable.
In 2014 I created an Index to measure team performance; my goal was to create one number (exclusive of points scored) that could help me tell a story about team performance that isn’t just about goals scored and goals against (Goal Differential).
While I can’t share the internal data points and algorithms anymore I can offer the Index is designed to capture the ‘primary bell curve’ of team activities on the pitch.
Here are diagrams of the Indices for each league/competition originally measured in 2014. The ‘r’ in each diagram offers the correlation of the Possession with Purpose Total Soccer Index (PWP TSI) to points earned in the league table.
Points earned is not a data point in the algorithm used to create the Index.
Since the TSI was first created in 2014 I’ve updated the algorithm to try and exceed the ‘r’ of Goal Differential to the league table – a long-time benchmark of accuracy and one of the primary reasons the statistic Expected Goals was created.
That logic follows the premise that it’s all about goals scored and goals against.
In 2018 I updated my algorithm and for Major League Soccer the TSI now has a greater correlation (r) to the league table than Goal Differential.
With World Cup 2018 nearly here I will be testing my Index again – and comparing the accuracy of my new algorithms to what was generated during World Cup 2014.
Here’s the World Cup 2014 Index showing the new TSI compared to the previous TSI and Goal Differential:
The green cell shows the Attacking half of the new TSI has a greater correlation to points earned than either the new Composite TSI or Goal Differential.
The days of using Expected Goals as a predictability model for scoring goals is over.
This should also convince Anderson and Sally that it isn’t all about preventing goals scored; at least not in the World Cup of 2014.
Look to my site if you want to see how your favorite team is comparing against the rest of the world.
Here’s a reminder of what the PWP TSI showed at the end of group stages in World Cup 2014.
Germany and Argentina (the two finalists) were 1st and 3rd in the Index.
If an American it should be pretty obvious the USA was punching way above their weight in making it past the group stages. If you want to know my thoughts (back then) on the future of Jurgen Klinsmann and Sunil Gulati – click here.
Perhaps this years’ Index will be as telling as the one in 2014?
No detailed statistics today – just a narrative to pass on a few tidbits as I prepare my End of Season analysis for Europe.
The European Season is ending.
- There’s the winners, the losers, and those that stay afloat to live another year.
- I’ll peel back the results on the English Premier League, Bundesliga, La Liga, and UEFA Champions League in the next few weeks.
- For now, in La Liga the PWP Composite Index has a .94 correlation coefficient (r) to points earned in the league table; the Bundesliga sits at .92, the English Premier League sits at .94, while the UEFA Champions League sits at .87.
- All incredibly strong and far stronger than MLS (.61) this year; last year MLS finished at .87.
- Speaking of MLS, does a league, where winners display more characteristics of counterattacking, versus just possession-based attacking, detract from predictability?
- In other words does the lower correlation support a League’s ability to achieve “parity” in professional soccer?
- If so, is that style/type of football attractive enough to continue to grow footy in the States?
- If not – does that mean the business model currently set up in the States won’t ever achieve a league “status” that matches the “prestige” most seem to attach to the top leagues in Europe?
- More to follow…
I think these two video presentations by Hector Ruiz and Paul Power, from Prozone, are worth listening to.
- In this video (tactical profiling) Hector, who attended my presentation at the World Conference on Science and Soccer last year, talks about his latest efforts that include breaking down the different types of possession in a much greater detail than I ever could with public data.
Of note is Hector substantiates my finding that a Head Coach’s tactical approach can be differentiated through tracking possession (passing characteristics) on the pitch.
- He also helps begin to solve the riddle on measuring which players perform better or worse given those different styles of possession.
- A soap-box, for me, when looking at my article on ‘Moneyball relative to soccer’, is the inability of modern day soccer statistics to show real value on how well teammates actually influence an individual’s success or failure on the pitch relative to how the team actually plays (what style it works to).
- Here’s a direct lift from my article referenced above…
Modern day soccer statistics, for the most part, don’t measure the appropriate level of influence teammates, opposing players, and Head Coaching tactics – as such when I say I’m not a Moneyball guy when it comes to soccer it really means I don’t buy all that crap about tackles, clearances, goals scored, etc…
I value players relative to team outputs and I strongly feel and think the more media and supporters who understand this about soccer the less frustration they will (have) in blaming or praising one individual player over another player.
- In the next video (game intelligence) Paul takes a similar approach in analyzing team behavior like PWP – separating out defensive characteristics from attacking characteristics while also modeling a ‘defensive press’ that measures success or failure in passing based upon whether or not a defender is hindering the attacker.
- This topic has been one that I have also touched on last year – here’s a direct quote from my article on Hurried Passes.
So what is missing from the generic soccer statistical community to account for the void in Unsuccessful Passes? Is it another statistic like Tackles Won, Duals Won, Blocked Shots, or Recoveries?
I don’t think so – none of them generated a marked increase in the overall correlation of those three activities already identified. I think (it) is the physical and spatial pressure applied by the defenders as they work man to man and zone defending efforts.
- Likewise, Paul also touches on ‘passing vision’ (in my words it’s not the innate vision many of us think of for players) – it’s more a discussion and analyses (I think) on the ‘windows of passing lanes’ available to players and whether or not they have tendencies to play riskier passes versus safer passes in relation to what the defenders are doing.
- For me this simply means Paul has taken the same defensive pressure data and flipped it to view the success or failure of a player to find another player to pass to or create a shot given the defensive pressure (lanes/vision) that are blocked or open.
- In simplistic teams (with new event statistics) you can capture and intuit that success or failure by filtering passes as being ‘open or hindered’ and also apply that same filter to create ‘open or hindered’ shots. My article on this approach was also published some time ago – New Statistics in Soccer (Open Pass and Open Shot)
- Finally, Paul also speaks to a game of soccer resembling the behavior of a school of fish; I’m not sure I’m convinced that is the best analogy – especially when he talks about under-loading and overloading, but his view does closely resemble mine where the game of soccer perhaps is best represented by a single-cell Amoeba.
All told – two well crafted presentations that begin to open up and really reinforce some of my soap-box issues with soccer statistics since starting my research three years ago.
To be redundant – soccer is not just about scoring goals – there is more to the game than goals scored; these two presentations continue to support my view that the world of soccer statistics needs to continuously get better…
My back-yard / stubby pencil approach to team performance analysis is soon to be published through Rand.
- I want to express my sincere thanks to Terry Favero – my Co-Author – who helped me navigate the challenging waters of writing an Academic Paper.
- Terry added considerable value, as well, in researching other works to help set the stage on the differences of PWP versus other efforts developed and published across the globe.
- Finally, Terry provided superb editorial support – a challenge in that the writing styles one normally sees in a blog are completely unacceptable when writing an Academic Paper.
- Great fun and the first of at least two to three more.
Last but not least, the Women’s World Cup is beginning.
- Last year I applied the principles of PWP to the Men’s World Cup – with good order.
- I’ll refresh everyone on how that took shape and then begin to chart how PWP takes shape for the Women’s World Cup.
- I wonder what, if any, differences will show in comparing the women’s game to the men’s game?
- Will the data show the same trends in quality and quantity?
- Or will we see a reduction in quantity that may end up driving an increase in quality?
- More to follow.
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For me, it’s not the top two that peak my interest this week, it’s the prime movers from mid-table – downwards while looking at the League Table from Week 12 to Week 19.
Here’s how they stand comparing Week 19 to Week 12:
For those interested the CPWP Family of Indices continue to have strong correlation to the League Table without using Points Earned in the calculations. Here’s how the Indices show things from a team performance standpoint through Week 19:
For those new to the Indices here’s an explanation on how they are created. No other publicly created set of Indices comes any closer to the League Table – not even Expected Goals – a popular Predictability statistic.
I should point out that these Indices are not Predictability Indices – they are not built to predict the future based upon past data – but…….. this Index, developed from the PWP Process is a Predictability Index:
The caution I offer in using it as a forecasting tool is this – when developing a forecasting model you need at “x” amount of samples to reach 95% Confidence Level in your data and its ability to represent trends for the future.
The “x” amount of data needed for this Index is at least 15 games — since games is the primary sample point. The twist is that since teams behave, for the most part, somewhat differently at home versus on the road you need 15 games of data at home and 15 games of data away from home.
Since this is only Week 19 that threshold has not been reached to substantiate that this predictability portion of this Index hits the 95% Confidence Level limit…
But, you say, the R2 is .77 – agreed – so yes, I would venture that those who like to gamble might want to rely on this tool to help them pick a winner – I did a test run in Major League Soccer, where the home and away statistics are notoriously different and my test run varied in success – straight CPWP PI # of one team compared to another.
That success ran as high as 75% to as low as 30% week to week for about 8 weeks – your choice… By the way – the Predictability Index created from PWP is simply my Index outputs minus (missing goals scored for or against)…
Back to the movers in La Liga these last seven weeks…
Recall the teams Espanyol (+6), Real Sociedad (+7) (Nice one Moyes!!!), Cordoba (+6), Levante (-6), and Granada (-6)…
In reviewing the APWP Index for each team, from Weeks 1-12 and Weeks 13-19, only one team has seen their Attacking Index increase, Cordoba – all the other teams have seen their overall attacking performance drop slightly during those two time-frames.
Why has Cordoba shown an increase?
It’s down to improved accuracy in Scoring Goals based upon Shots on Goal – all others have experienced slight decreases in quality; either with respect to percentages of Shots on Goal, Shots Taken per Penetration, or Goals Scored from Shots on Goal.
In reviewing the DPWP Index for each team, from Weeks 1-12 and Weeks 13-19, two teams have seen their Defending Index decrease, Levante and Granada – all other teams have seen their Defending Index improve , with Cordoba seeing the most improvement by as much as 11%.
Cordoba’s improvement in Defending comes from Opponents having less quality in putting Shots on Goal from Shots Taken and Goals Scored from Shots on Goal.
Clearly Cordoba has improved on both sides of the pitch, while with the others it’s slightly more difficult to pin down a specific area…
A few interesting notes here are:
- Cordoba were bottom of the table, and even after having to play Barcelona, Villarreal and Eibar during this stretch they still gained 6 places, and
- The CPWP Index had Cordoba rated 12 best after Week 12, and that Index rating has not changed through Week 19 – meaning it is likely the CPWP Index really did a great job of accurately representing the true team performance of Cordoba compared to other teams in La Liga…
- Finally, the CPWP Predictability Index (PI) had Cordoba rated 12th best, after week 12 as well… (perhaps??) an independent data point to substantiate that the predictability nature of the CPWP PI has value???
Cordoba showed improved performance on both sides of the pitch while the others didn’t… (perhaps???) this means that some of the new positions, for these teams, are as much a function of how others have gotten better, or worse, as it is a function of how those teams have, themselves, gotten better or worse…
Meaning position in the League Table, even when seeing changes by as much as six or seven places, may not mean that individual team is playing better – it may mean that other teams, with less noticeable drops in position are playing worse…
Reinforcing again that predictability is not solely associated with goal scoring – it’s also a function of not scoring because some teams are doing better, however slightly, with improved defending but not improved attacking…
If you are a writer for any team in the Bundesliga, La Liga, Barcley’s Premier League, or Major League Soccer and you’d like to use outputs from my Possession with Purpose Family of Indices in your articles please let me know…
I can provide a broad range of support that may help you better tell the story, (explain) to your readers, what or how well your team is doing compared to others… or even itself given certain time-frames (before and after a coach gets sacked, player gets injured, etc…)
COPYRIGHT, All Rights Reserved. PWP – Trademark
You can follow me on twitter @chrisgluckpwp.
Republication/update: My intent, this past year, was to update my series on Expected Wins, with EW-5 – that has changed. After conducting research and analyses, and seeing my work published in London, England I’ve decided to rename this series Expected Losses instead of Expected Wins.
- Why, because my data analyses is beginning to show it’s easier to track and predict losses as opposed to ‘draws’ or ‘wins’.
But to sustain the integrity of the ‘thinking process’ I’m only going to edit the first part of this article and remind folks about the previous research published:
- Expected Wins Original – Published April 2014
- Expected Wins 2 – Published May 2014
- Expected Wins 3 – Published September 2014
What follows is the original, unedited post offered in November of 2014. I think if you read this article below you may find it striking given the current conditions with US Soccer and the US Men’s National Team!
Jurgen Klinsmann made a statement the other week about his preference that players working to make the USMNT play in Europe not in America.
Lots of hoo-haw followed with opinions being thrown out there by just about everyone.
As far as I know no-one has, as yet, come up with a way to quantitatively measure which league, leagues, or competitions are higher quality.
This is my attempt to do that using my Possession with Purpose Analysis.
Be prepared for a few charts – sorry – it is what it is and a statement like Klinsmann’s deserves to have some quantitative analysis thrown towards it.
Finally, if you missed Expected Wins 3 here is a link to give you some history on this quantitative analysis.
Now for the grist, first the array of Expected Wins 4 diagrams for each league/competition I cover, Major League Soccer, English Premier League, La Liga, Bundesliga, UEFA Champions League, and the World Cup of 2014.
Major League Soccer – End of Season:
English Premier League after 240 Events (120 Games):
La Liga after 238 Events (119 Games):
Bundesliga after 214 Events (107 Games):
UEFA Champions League after Round 5 – 160 Events (80 Games):
World Cup 2014 – End of Competition:
In each of the diagrams I highlighted in green the category that had the highest volume for all my PWP Data Points.
For example, just above, in the World Cup of 2014 the winning team had the highest volume of activity for every single PWP data point.
The same holds true for the UEFA Champions League, La Liga, and the English Premier League.
The conclusion here? Volume speaks volumes…
Greater numbers of passes both outside and within and into the Attacking Final Third (RESULT) in MORE Shots Taken, MORE Shots on Goal and MORE Goals Scored!
In the case of the Bundesliga (an oft mentioned counter-attacking league) it’s the losing teams that offer MORE Possession and MORE overall Passes but when it comes to the Attacking Final Third it’s the winning teams who do MORE with MORE!
With respect to the MLS – a contrast to be sure. MORE Passing outside and within, and into, the Attacking Final Third gets you LESS when it comes to Shots Taken, Shots on Goal, and Goals Scored.
Why is that?
I’d offer it’s down to playing a game that has less overall ball control from the players – in other words there is less quality on the pitch to take advantage of the MORE for MORE systematic outputs we see from all the other leagues/competitions; others may have a different view.
For me, this is an early indicator that what Jurgen Klinsmann offered is quantitatively accurate!
Before moving on here’s how all the leagues and competitions compare to each other in one diagram for winning teams:
The UEFA Champions League leads all competitions/leagues in the average volume of Passes Attempted, Passes Completed, Passes Attempted within and into the Final Third, Passes Completed within and into the Final Third, Shots Taken, Shots on Goal, and Goals Scored.
If volume of activity (were?) to be a quantitative measure of quality then it’s pretty clear the UEFA Champions League HAS the highest quality of all these competitions.
And what teams comprise the UEFA Champions League? Teams from Europe…
But there is more to Possession with Purpose than just volume; here’s how the PWP Data Relationships show:
In looking at the percentages here’s where it gets interesting – and reinforces what I’ve felt and thought all along, patience in creating time and space in the Attacking Final Third has value.
In terms of Possession Percentage, Passing Accuracy across the Entire Pitch, and percentage of Penetrating Possession within and into the Attacking Final Third the UEFA Champions League, again, exceeds all other competitions.
Where the patience virtue comes in is when it comes to the percentage of Shots Taken per Penetrating Possession – the UEFA Champions League is lowest (14.98%).
So in returning back to the volume of Shots Taken per penetrating possession.
The UEFA Champions League has the highest volume of Shots Taken but the lowest percentage rate.
So even with the third worst percentage of Shots on Goal per Shots Taken and the second worst percentage of Goals Scored per Shots on Goal this competition still has the highest volume of Shots on Goal and Goals Scored.
For me this is another quantitative means to substantiate what Jurgen Klinsmann offered about encouraging Americans to get better by playing in Europe.
Is it better to play on a winning team in a league where there is less overall control of the ball, on the pitch for 90 minutes?
Or is it better to play on a losing team (for 90 minutes), against top quality players, in a league where there is superb control of the ball across the entire pitch for 90 minutes?
Which competition forces you to concentrate more recognizing that the smallest positional error will completely punish your team?
In other words…
If you were a good player and you wanted to get better, would you prefer to play in a league where there are fewer passes and a more wide open play that doesn’t stretch your talent to control the ball?
Or…. would you rather play in a league where the ball is zipping about (by over 100 to 300 passes more) forcing you, in turn, to zip about yourself to try and better manage that game yourself with your teammates?
If I were a player in today’s market there is simply no need to consider answering that question any further – I’d play in Europe OR at least strive to play in Europe!
How about you?
If you’re new to Possession with Purpose and this analytical approach read here for an introduction.
By the way – even if you feel or think you don’t need this type of data to substantiate which leagues or competitions are better today – it will provide a great benchmark in looking at how the future takes shape in MLS.
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You can follow me on twitter @chrisgluckpwp
My next installment on La Liga takes a look at Week 12 and compares how things have progressed or digressed for teams since Week 6.
If you’ve not followed my Possession with Purpose analyses in the past here’s a quick link to an Introduction.
Some movers to be sure, so to give the Index some context here’s a quick look at the League Table after Week 12 alongside Week 6:
On the down side both Almeria and Granada CF dropped six places, while Real Sociedad (and their new Head Coach David Moyes) dropped four, and Celta de Vigo, with Espanyol dropped three.
Now for the CPWP Strategic Index after Week 12; followed by how things looked at Week Six:
Now Week Six:
As a reminder, the CPWP Index does not react as quickly as team changes in the League Table – it’s a wee bit slower and more subject to change based upon a consistent change in team performance.
That being said Malaga (who moved up eight positions in the League Table) is 6th in the CPWP Index after Week 12 compared to 11th after Week six.
On the negative side Almeria (who has dropped six places) was 10th after Week six but is now 16th in the Index.
A solid indicator, again, that the Index will keep up with team changes in the League Table.
So what has happened, PWP wise, for Malaga and Almeria that may help us better understand their significant moves in the League Table?
To narrow the scope here’s the APWP Strategic Index for Week 12 followed by Week Six:
A shift of ten positions – it’s likely some attacking indicators have improved – but I’ll check the DPWP too before choosing which to peel back.
As for Almeria, Week six shows them 13th best, while Week 12 shows them 16th best.
Not that much of a change, so perhaps it’s the DPWP key indicators? Let’s see.
DPWP Strategic Index after Week 12 followed by Week Six:
For Almeria they were 8th best in Week six and are now 14th best, for me that means it’s their DPWP key indicators that have taken a nose dive.
So the grist for Malaga in the APWP Key Indicators:
Week 12 48.46% / Week 6 48.84% (no real change)
Week 12 73.76% / Week 6 72.09% (about 2% increase in Passing Accuracy)
Week 12 27.53% / Week 6 25.46% (about a 2% increase in Penetrating Possession)
Shots Taken per Penetrating Possession:
Week 12 16.65% / Week 6 19.07% (about a 3% decrease in Shots Taken per Penetrating Possession)
Shots on Goal per Shots Taken:
Week 12 31.02% / Week 6 25.68% (about a 6% increase in Shots on Goal per Shots Taken)
Goals Scored per Shots on Goal:
Week 12 37.40% / Week 6 16.67% (about a 17% increase in Goals Scored per Shots on Goal)
I’d offer Malaga are playing shorter, quicker passes in order to gain penetration – while at the same time they are taking a wee bit more time to be selective in their shots taken.
The resulting outputs clearly indicate that this tactical change has led to more shots on goal and far more goals scored!
And given that the percentage of possession has not changed – I’d offer they have not dropped deeper to cede possession – they’ve simply decided to be more patient in their penetrating attack.
It will be interesting to see if this pattern continues to hold true through the next six weeks.
In moving on to Almeria – a team I’ve looked at a few times this year; here’s how their DPWP key indicators show what’s changed:
Opponent Possession Percentage:
Week 12 53.89% / Week 6 52.95% (about 1% increase in opponent Possession)
Opponent Passing Accuracy:
Week 12 76.35% / Week 6 77.92% (about a 1% decrease in opponent Passing Accuracy)
Opponent Penetrating Possession:
Week 12 24.34% / Week 6 23.96% (about a 1% increase in opponent Penetrating Possession)
Opponent Shots Taken per Penetrating Possession:
Week 12 19.40% / Week 6 20.44% (about a 1% decrease in opponent Shots Taken per Penetrating Possession)
Opponent Shots on Goal per Shots Taken:
Week 12 33.96% / Week 6 28.20% (about a 5% increase in opponent Shots on Goal per Shots Taken)
Opponent Goals Scored per Shots on Goal:
Week 12 33.43% / Week 6 22.69% (about an 11% increase in opponent Goals Scored per Shots on Goal)
There are minor changes in how the opponent performs against Almeria leading up to Shots on Goal and Goals Scored – at that point the success rate of the opponent jumps 5% and then 11%.
While that might not seem like that much of a change, leading up to Shots on Goal and Goals Scored, there are many times in this game where it only takes four or five more passes, that are accurate, to change the outcome.
Given that I’ll also take a look at the volume of opponent activity as well.
What stands out to me is this:
- In the last six games the opponent has averaged five more completed passes within and into the Almeria Defending Final Third.
- In turn, even with the exact same number of Shots Taken per Penetrating Possession, it’s led to the opponent averaging 5.67 Shots on Goal compared to 4.17 Shots on Goal the first six games.
- That change in volume of Shots on Goal as led to 1.50 Goals Against in the last six games compared to .83 Goals Against in the first six games.
- Another example, like what we’ve seen in Expected Wins, where the difference between winning and losing can be very small indeed.
Of course, what hasn’t helped is playing Barcelona two weeks ago – that being said – Almeria also gave up two goals to Elche, Villarreal, and Levante in this same six week span – so it’s not just down to playing Barcelona!
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In a recent article published about Major League Soccer there were clear indications (read here) where Home teams pretty much dominated the win column.
Here’s a few quick takeaways from that article to give a starting point for La Liga – whether or not the two leagues behave the same – I don’t know – we will see how that plays out together. In Major League Soccer:
The home team won 151 times this year, had 89 draws, and 77 losses.
Home teams winning at home averaged 2.33 goals per game in those wins – while away teams winning away had to average 2.47 goals per game to win the game.
Home teams losing at home averaged .82 goals per game versus away teams losing away averaged .54 goals per game. So even while losing, home teams still averaged nearly one goal per game.
Of the 77 games won by the away team this year only 15 were games won 1-nil.
When losing, (at anytime) the home team was only shut out 21 times, and when gaining a draw the home team was shutout just 18 times. That’s just 39 times, out of 317 games played, where the home team was shut-out.
Meaning, on average, the home team scored at least one goal 88% of the time.
In addition, when adding up the percentages of winning (47%) and drawing (28%) – Home teams had a 75% chance of taking points in home games this year…
In other words – playing at home pretty much meant the home team started the game 1 – nil.
Now for La Liga – and yes it’s just 11 Weeks in – but for the most part each team has had roughly 5 or 6 games each way. What may be surprising is seeing how the team performances change between the two – I’ll touch on that as well.
For now my standard Composite PWP Strategic Index for La Liga after Week 11:
No surprise for who’s on top and who’s bottom but to set the stage:
Home teams have Won 44 games, Drawn 30 games, and Lost 35 games.
Not that large of a difference in this league, yet, and really not enough to consider an overall league difference.
But for some teams there may be a few differences.
Hence the next two diagrams with one team picked out who performs better at home and one who performs better on the road.
Time to move on to the CPWP Strategic Index filtered only for home team performance:
Caveats to begin:
The bright blue bar represents a team whose performance dropped at least nine places between home and away games.
The light green bar represents a team whose performance increased by eight places between home and away games.
I’ll also check to see what their Points per game (PPG), Goals per game (GPG), and Goals Against per game (GAPG) are to see what differences are shown their as well.
If nothing significant pops out I”ll peel back a bit on the PWP Key indicators to see if they tell a story.
Now for the CPWP Index for teams playing Away:
As noted; the light green bar shows which team performed better, in team performance, on the road versus at home – while the bright blue bar offers up what team performed better at home versus on the road.
Now for the grist on those two teams (Almeria & Cordoba):
(Away) PPG = 1.20 GPG = 0.80 GAPG = 1.20 (Home) PPG = 0.50 GPG = 0.83 GAPG = 1.33
There isn’t a considerable difference in the results based performance measures; perhaps some differences appear when peeling back the PWP Key Indicators in Defending?
In looking at possession, when at home, the opponent’s possess the ball 4% more.
But this is very deceptive as it includes two games against Atletico Madrid and Barcelona – where both those teams absolutely dominated the game.
So to better understand (see) what is going on I took out the game data for Atletico Madrid and Barcelona.
So now, when at home, the other fours games point towards Almeria having more possession, with their opponent’s being less accurate and with less penetration into the Almeria defending Final Third.
Sadly, that front-footed attack minded tactic, at home, actually ends up seeing the opponents’, with less possession and penetration, have increased percentages in shots taken, shots on goal that are more accurate and a significant increase in goals scored from shots on goal.
A 37% increase – shooting up from 23% for opponents when Almeria plays on the road to 60.42% when Almeria plays at home.
In away games, Almeria’s opponents are more accurate in their passing and they penetrate more – but – their shots taken, shots on goal and goals scored per shots on goal are all less.
In other words, Almeria’s tactical approach of playing a deeper line, yielding more space outside the Defending Final Third, results in less space and time for the opponent to offer up shots that actually produce fewer goals. This has also been a successful approach employed by West Ham, Portland Timbers and Philadelphia Union.
Bottom line here is the ‘front footed attacking scheme’ employed at home (playing not as deep in defending) has seen a marked increase in goals against (1.25) against lower ranked teams like Cordoba, Elche, Espanyol and Athletic Club.
Bottom line here is the ‘front footed attacking tactic’ employed at home, is less prudent and produces worse results than a more defensive-minded tactic adopted on the road.
Leading me, and perhaps others as well, to believe that in order for Almeria to be more successful this year they need to play games at home as if they were playing on the road.
(Away) PPG = .40 GPG = .80 GAPG = 2.20 (Home) PPG = .67 GPG .67 GAPG = 1.17
So now the opposite for Cordoba – they appear to perform better at home than on the road – what do the PWP Key Indicators offer here?
At Home Cordoba like to possess the ball (58%) therefore their opponents average possession is 42%; when away it’s almost exactly the opposite; the opponents average possession sits at 59%. Two completely different outputs.
The same can be said for passing accuracy as well; opponents, in away games for Cordoba have an 83% passing accuracy – versus 66.39% when Cordoba is at home.
What’s intriguing here is that’s where the differences end – when it comes to penetration, shots taken per penetration, shots on goal per shots taken and goals scored per shots on goal the overall percentages are nearly the same. (27% to 28%), (14% to 13%), (39% to 41%), and (36% yo 35%).
What’s that mean? Well this indicator may help – the opponent passing accuracy within and into the Cordoba Defending Final Third is 55% when Cordoba is at home and it’s 72% when Cordoba is away from home.
For me that speaks volume – in other words the percentages, for the most part, show matches – meaning the volume is the final determinant. And since La Liga is a volume driven league (Expected Wins 3) this shouldn’t come as a surprise.
When playing away from home the average volume of passes completed by the opponent is 424, with 16 shots taken, 6 shots on goal and 2 goals scored. In home matches those volumes are 230, 8, 4 and 1.17. A considerable difference.
In other words, when it comes to defending at home less is better – the less the opponent offers, regardless of overall percentage, the better. Put another way, the front-footed attacking tactic employed at home is not working on the road – i.e. even though they are ceding possession on the road – perhaps they are not ceding possession in the right place???
Perhaps Cordoba might do well to take the Almeria tactical approach on the road (i.e. playing deeper to cede possession and penetration by volume and percentage) in order to lure the opponent into a position where they can’t manage an effective Cordoba quick counter-attack. And since Cordoba has such a low goals scored average to begin with (5th worst in La Liga) they really ought to consider that type of approach to maximize time and space needed to score what few goals they can?
Cordoba shows ideal team performance outputs where their home advantage of playing at home works. That approach does not work for Almeria.
Almeria needs to employ their away tactic at home.
The away tactic for Cordoba is not working – it’s actually less effective than the approach taken by Almeria.
Cordoba should adopt a deeper line, like Almeria, and cede more than just possession, they should make it a point to cede penetration as well.
Not discussed in great detail has been the lack of goal scoring, as whole, for either team.
I’d imagine Cordoba would see good, positive, impact with a new striker, more quickly, than Almeria; especially in a quicker counter-attacking road tactic. I’d imagine Almeria will need more than just one striker to solidify more points on a regular basis.
Finally, I’d expect to see more granularity as the season continues – how much that differs, in comparison to Major League Soccer is unclear, but for now I’m hedging that we don’t see the stark differences in La Liga that we see MLS; especially since this league seems to support the ‘more is better’ outputs we already see in Expected Wins 3.
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Most of the Headlines speak to the Real Madrid victory over the vaunted Barcelona; mine obviously don’t.
For me Valencia is showing strong, and in my view, seems to have struck a great balance in attack and defense as they continues to impress. And even though this early season run of form might not last I do think it’s worthy to dig a bit deeper into their overall performance to see exactly why they are doing so well.
To begin – my standard Composite PWP Strategic Index:
Why are Valencia so high in their overall team performance?
Is it their overall team attacking or defending performance?
At first glance you may think it’s their Attack – to review that here’s the latest Attacking PWP Strategic Index:
Even higher than Barcelona – one of the best attacking teams in the World! Valencia are:
- 7th best in overall possession – 51%; a full 17% less than Barcelona
- 3rd best in overall passing accuracy – 85.97% – still less than Barcelona by 3%
- 17th best (4th worst) in penetration per possession -19.71% – a full 13% below Barcelona
- 9th best in Shots Taken per penetrating possession – 15.84% – this time ~6% higher than Barcelona
- 9th best in Shots on Goal per Shots Taken – 34.87% – roughly 5% lower than Barcelona
- Finally, and perhaps the single greatest graphic difference is Goals Scored per Shots on Goal; at this point Valencia have scored a HUGE 60.83% of the time they’ve put a Shot on Goal – by comparison Barcelona sit at 31.68%..
In a phrase – Valencia ‘are’ the best team in performing the key indicators in possession with purpose. They may not have the glitz and glamour of a Barcelona or Real Madrid but steady is good.
But before moving on to Defending I think it’s worthy to note their volume of activity not just the percentages above:
- They match the league average in passes attempted (410) what skews that average is Barcelona and Real Madrid. All told only six of the 20 teams in La Liga exceed the league average.
- As noted above their passing accuracy is 3rd best in the league – with that their total completed passes across the entire pitch is 5th best at 349.
- So by volume they are not what would be considered a dominating possession based team.
- And in looking at their overall penetration into the final third Valencia average 107 passes per game – 13th best.
- In other words they’re not really a possession based team, they are more of a counter-attacking team who simply wait for some extremely superb moments to take advantage of the opponent’s weaknesses in order to create ideal time and space conditions.
- And to reinforce this view they are slightly lower (10.78 per game) than the league average (11.45) in Shots Taken – but slightly lower in Shots on Goal (3.78) versus the league average of (4.03).
- And that ‘finishing touch’ sees them average 2.22 Goals Scored per game compared to the 1.34 for La Liga and just slightly lower than Barcelona’s average of 2.56 per game!
All told – Valencia are simply a team that is performing at an optimal rate.
But that’s not the complete answer for Valencia – here’s how they stand in the Defending PWP Strategic Index:
They are 3rd best in La Liga in defending team performance; here’s how the key indicators compare to others as well as Barcelona:
- Opponents average 48.68% possession – pretty much meaning the opponent has the ball as much as Valencia – opponents of Barcelona possess the ball just 31.18% of the time.
- Opponents average 77.62% passing accuracy – and I’d offer that is more down to the amount of space Valencia cede outside their Defending Final Third – we’ll take a look at that when reviewing the volume of opponent activity.
- In terms of penetration and shot creation from that penetration their opponents are 10th best at penetrating 24.09% of the time they possess the ball while also generating shots taken 16.21% of the time.
- All told that leads to an opponent accuracy shot rate on goal of 35.53% with 21.67% of those shots on goal scoring a goal.
- Bottom line here is that with average penetration (compared to others in La Liga) and average shots against, Valencia are 4th lowest in facing shots on goal and 4th lowest in seeing those shots on goal score goals.
It would appear they have a very organized defensive system and a very good Goal Keeper.
So how about the volume of attack faced from their opponents?
- At this stage they have faced, on average, the 8th fewest passes per game (388) compared to Barcelona at 300.
- In terms of overall penetration, the opponents have offered up 117 passes per game in the Valencia Defending Final Third – with that being the 10th most in La Liga.
- Statistics would seem to indicate that they do make it easier for their opponents to penetrate – which in turn appears to support what was offered up earlier.
- When it comes down to shots faced they are 9th lowest in that category – while translating that to just 3.78 shots on goal (tied 8th best).
- All told that added volume of penetration sees Valencia with a .89 goals against per game – 3rd best in La Liga.
Bottom line here – like what the percentages offer – Valencia cedes time and space outside the Defending Final Third while doing a great job of closing up shop as the opponent finally gains entry.
Is that the right mix to minimize the likes of Real Madrid, Barcelona, and Sevilla?
Hard to say at this time – but clearly – going into Week 10 against Villarreal it is likely they should get another three points.
Which brings me to my last Index – the CPWP Predictability Index.
In MLS this Index averaged a 55-65% accuracy in identifying the winner of upcoming games – at times the outputs were pear-shaped while others were spot on.
I have no idea how this will play out this year in Europe but here’s the Index itself and then a quick blurb on how to understand it:
As noted Valencia take on Villarreal this weekend – note that Valencia has a higher number than Villarreal – simply meaning, with the law of averages considered, and the teams perform as they have in the past Valencia should win.
So in looking up the schedule for next weekend; Getafe should edge Deportivo; Real Madrid should defeat Granada; Atletico Madrid should defeat Cordoba; Barcelona should beat Celta de Vigo; Real Sociedad should defeat Malaga; Athletic Club should beat Sevilla; Levante should lose to Almeria; Elche should lose to Espanyol; and Rayo Vallecano should beat Eibar.
By the way – the Predictability Index is made up of all the PWP Data Point Relationships excluding ‘goals scored’ and ‘goals against’ – you really can’t develop a worthy predictability index using goals scored.
That should help explain why Celta de Vigo are higher up the prediction table than Valencia… based upon their overall run of play performances Celta should probably score more goals than they do.
All for now…
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FC Bayern Munchen and Borussia Dortmund take a big leap in working towards the knockout stages as each sit on six points, along with Real Madrid.
Others falling in line for a push into the knockout stages include Roma, Chelsea, Monaco, Paris Saint Germain (stunner that was), Zenit St. Petersburg and FC Porto.
In seeing those results here’s how the Possession with Purpose Strategic Composite Index (CPWP) shows:
Of the teams with six points – all three fall within the top five of the Index, For those on four points, each, only Paris Saint Germain falls in the negative end of the Index.
Clearly the statistical impact of playing Barcelona is painful – and the orange star above Nicosia also highlights how far down the Index they are after that 6-1 thumping in Game 1. Yet now they’ve won their second game and sit on three points…
From a statistical standpoint the CPWP Index, correlation to average points earned, (R2) is .69 – very reasonable given only two games worth of data.
Oddly enough; and this doesn’t happen very much – the DPWP Index R2 (-.60) was slightly stronger than Goals Against (-.53); normally it’s about 5 one-hundreth’s of a point lower.
The Goal Differential R2 is .76; still the single best indicator that reflects results but doesn’t tell you anything about the internal activities of the game like the PWP Family of Indices.
Moving on – Defending PWP first:
Like the other DPWP Indices for the other leagues I analyze – I’ve adjusted the Y axis to begin at 1.5, as opposed to 0, in order to magnify the differences between those teams that don’t perform well versus those teams that do.
Note both Borussia Dortmund and FC Bayern Munchen are 1 -2 in the DPWP Index – while Real Madrid are 11th best – is that an early indicator that Real’s attack (see below) isn’t going to get them past a much tighter defensive network offered by the two German clubs?
As for other observations – I’d say it’s pretty clear that Benfica, Ludogorets, and CSKA Moscow are toast – all three are 7th worst or worse in team defending… nevermind they all sit on nil-pwa.
Moving on to the APWP Index, with some additional diagrams to sweeten the observations:
As noted above, Real Madrid are much better in team performance for attacking versus defending – for the most part teams that defend better advance further in competitions like these. I’d imagine Real will need to play a whole lot tighter if they are to succeed.
And what about Barcelona?
Wow – it’s unlikely they don’t advance but it should be an electrifying wake up call that possession for the sake of possession is not going to cut it in the Champions League this year.
This league is a far cry more skilled than La Liga – a reminder on how Barcelona looks in overall CPWP for La Liga is below… you’re not in Kansas anymore Toto!
Okay – now a few extra diagrams for your consideration:
First off – here’s what the APWP looks like when you filter the teams based upon the volume of passes attempted in the Opponent’s Final Third; in this diagram here’s the teams who have exceeded (the average) of 132 passes attempted.
Those teams with red bars are those that sit on zero or one point; those with yellow bars are teams sitting on two or three points, while those with green bars have four or six points.
Of course it’s unlikely that Barcelona doesn’t advance – but the same can’t be said for Arsenal.
In this diagram Arsenal are 2nd best in APWP – when looking at the diagram for Final Third passes attempted below 132 note where Arsenal is -(last in APWP).
Clearly they perform much better when they attempt to penetrate more – that style of play where more is more in the EPL seems to translate to Arsenal doing better here too.
Whether that holds true for all teams in the Group stages is unclear – I’m sure we’ll see soon enough.
Before moving on; note that there are seven teams in this diagram who exceed 132 passes in at least one game – while four teams sit on one or zero points.
That’s not the case here where the APWP Index is filtered based upon teams/games where passes attempted in the Final Third fall below the average:
Only four teams here have four or six points – actually all four of them sit on four points.
I don’t know (yet) if this is more or less impacted by how the opponent dictates play – nor do I know if this is more or less impacted by how the attacking team dictates play… More to follow on that one.
Note the high volume of teams with red bars in the lower end of APWP when pass attempts in the Final Third fall below 132 – the lone wolf at the bottom end is Arsenal – kind of reaffirming the need for them to sustain a high passing volume game in order to maximize their team attacking talents.
All for now – only two games in and detailed statistical analysis really isn’t worthy at this time – for the most part it is what it is…
The teams not best suited to do well in this competition are beginning to appear – Game three begins 21 October – should be exciting and the special match-ups I see might not be yours.
Here’s the ones that intrigue me given the state of affairs today:
- Roma at home to FC Bayern Munchen
- Barcelolna at home to Ajax
- FC Schalke at home to Sporting Lisbon
- BATE Borisov at home to Shaktar Donetsk
- FC Porto at home to Athletic Club
- Atletico de Madrid at home to Malmo FF
- Liverpool at home to Real Madrid
- Beyer Leverkusen at home to Zenit St Petersburg
Exactly – that’s almost all the games – well you’re right 😉
Looking forward to that round and any upsets that might occur like Paris Saint Germain beating Barcelona 3-2.
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