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.
CAN IT BE DONE?
Over the last four years I’ve conducted research on various professional soccer leagues and competitions. To include Major League Soccer, the English, German, and Spanish Premier Leagues, as well as the UEFA Champions League and the Men’s World Cup of 2014.
Here’s my latest analyses on how the Possession with Purpose Index can be used to predict which teams will make the playoffs, qualify for the UEFA Champions League, or make the semi-finals of the World Cup..
Before beginning here’s a rerun on a few important items of interest about Possession with Purpose:
Intent: Develop a simplified, strategic set of performance indicators to better understand the outcome of a game based upon primary inputs.
- A documented method for measuring team performance from those indicators.
- An index that ranks teams for their performance based on this method.
- The index, while excluding points, comes close to matching results in the MLS league table.
- Bonus – unexpected outcome – a tool to predict teams making the MLS Playoffs.
Key events to date:
- Objective index developed in 2013
- Results presented at the World Conference on Science and Soccer 2014
- Approach published in the book – International Research Science and Soccer II – Routledge, Taylor, and Francis 2016
- Leagues/Competitions evaluated
- MLS 2013, 2014, 2015, 2016
- English Premier League 2014
- Bundesliga 2014
- La Liga 2014
- European Championship League 2014
- Men’s World Cup 2014
Major League Soccer 2013 – The Maiden Year for PWP:
- Nine of the top ten teams in the CPWP Index made the MLS Playoffs in 2013
- Internal outputs from team performances showed that teams who cede possession (have lower than 50% possession) can be ranked within the top ten so the index is not biased towards teams that possess the ball greater than 50%
- This doesn’t even include all the internal evidence on the various tactical styles of play each coach advocated.
- Three of the bottom four teams replaced their head coaches as well.
- It’s the initial results here that provided me compelling information to investigate deeper into what the outputs of the index might offer.
- Each subsequent index shows a gold and red star – indicating which team finished first and last in the league table.
English Premier League 2014:
- Winner of the League, Chelsea, finished 2nd in the index.
- All four of the top four teams in the index advanced to the UEFA Champions League; those teams with green bars.
- By week 16, of 38 weeks, the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
- Three of the bottom four teams in the index were relegated in 2014; those teams with red bars.
Germany Premier League 2014:
- Winner of the League, Bayern Munich, finished 1st in the index.
- All four of the top four teams in the index advanced to the UEFA Champions League; green bars.
- By week 21 the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
- Augsburg and FC Schalke, who advanced to Europa League, finished 6th and 8th, respectively, in the index (light green bars).
- For those teams relegated (red bars), SC Paderborn, finished worst in the league table and index, while Freiburg was 7th worst in the index and Hamburger SV was 3rd worst in the index.
Spanish Premier League 2014:
- Winner of the League, Barcelona, finished 1st in the index.
- All four of the top four teams in the index advanced to the UEFA Champions League; green bars.
- By week 14 the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
- Sevilla and Villarreal, the two teams advancing to Europa League finished 5th and 6th, respectively, in the index; light green bars.
- The three teams relegated in 2014 were Cordoba, Almeria, and Eibar. They finished 2nd worst, 3rd worst, and 4th worst (respectively) in the index; red bars.
- Of note; Levante, who finished worst in the 2014 CPWP Index finished last in the 2015 La Liga Standings.
UEFA Champions League 2014:
- Winner and top team in the Index – Barcelona
- Four of the seven top teams in the index advanced to the semi-finals
- Barcelona 1st, Real Madrid 3rd, FC Bayern Munich 5th, and Juventus 7th; green bars.
- By the end of round one the top four teams to make the semi-finals were all in the top 10 for the index; with Barcelona 1st, Bayern Munich 3rd, Real Madrid 4th, and Juventus 9th.
- Poor performers, APOEL Nicosia and Galatasaray finished 2nd and 4th worst (respectively) in the index; red bars.
Men’s World Cup 2014:
- Winner of the World Cup. Germany, finished 1st in the index, with 2nd place finisher, Argentina 5th best in the index.
- Four of the top seven teams to reach the semi-finals finished 1st, 2nd, 5th, and 7th in the index; green bars.
- By the end of round one, the four teams to make it so the semi-finals were all in the top six of the CPWP Index; with eventual winners, Germany 1st, Argentina 3rd, Netherlands 5th, and Brazil 6th.
- With Brazil giving up seven goals to Germany in the semi-finals they dropped from 7th to 18th in the index.
- France, Colombia, Belgium, and Costa Rica are the teams who made it to the quarter finals; light green bars.
- All three teams that failed to earn a point in the World Cup finished worst (Australia), 2nd worst (Honduras), and 4th worst (Cameroon); red bars.
Side note about the Men’s World Cup:
- USA finished 5th worst in the index (blue bar).
- At that time I called for Jurgen Klinsmann to be sacked. Why?
- My two most compelling reasons were:
- Omitting Landon Donovan from the squad (huge reduction in squad mentality/leadership without his presence – plus he was simply the best striker/forward in the USA).
- Replacing Graham Zusi with Omar Gonzalez late on in the game against Portugal – that replacement (a huge tactical error) created a vacancy in the area where Graham Zusi was defending; the exact same area where Ronaldo delivered his killer cross from.
- Two years later, after numerous tactical and mental leadership errors, Jurgen Klinsmann was finally sacked.
- I wonder where our team would be (NOW) if Sunil Gulati would have had the backbone to sack Jurgen Klinsmann back then?
- I’m not afraid to say I told you so Sunil Gulati…
Major League Soccer 2014:
- Four of the top ten teams, after week 1 CPWP Index, made the playoffs; with SSFC, eventual Supporter Shield winners in third. After week 13 Seattle never fell further than 3rd in the Index.
- Eventual Cup winners, LA Galaxy, were 11th after week one. By week 8 they were 1st in the Index and did not fall out of the top two after week nine.
- Slow starter award goes to DC United, who were bottom of the Index until the end of week 5; when they finally breached the top ten.
- It was here, along with seeing FC Dallas, at the top of the Index, that reinforced the Index was not overly influenced by teams who have high amounts of possession.
- In other words, the Index would, and does, rank teams in the top ten even when they cede possession and play more direct/counter attacking football.
- Although the first four weeks of the Index didn’t predict more than four of the top ten teams making the playoffs by week eight the Index showed nine of the top ten teams making the playoffs.
- The level of accuracy, from week eight, going forwards never dropped below 70% and reached (and sustained 90% accuracy) by week 25 for the remainder of the year.
- Accuracy in predicting the top ten teams making the playoffs was no worse than 40% (the first four weeks) and no less than 70% throughout the remainder of the year with 90% accuracy first attained by week eight – and sustained by week 25.
Major League Soccer 2015:
- Seven of the top ten teams, after week 1 CPWP Index, made the playoffs; with NYRB, eventual Supporter Shield winners in ninth.
- Eventual Cup winners, Portland, were 8th after week one.
- Slow starter award goes to New England, who started at bottom after week one, but had breached the top ten by week seven.
- At no time did the CPWP Index have less than seven eventual playoff teams in the top ten. And by week seven nine of the top ten teams in the Index were bound for the playoffs.
- Accuracy in predicting the top ten teams making the playoffs was no worse than 70% at any given time – and as high as 90% accurate by week seven.
Major League Soccer 2016:
- Seven of the top ten teams, after week 1 CPWP Index, made the playoffs; with FCD, eventual Supporter Shield winners in first.
- For those who were surprised by the Colorado Rapids this year – you shouldn’t be. By week four, the CPWP Index had Colorado Rapids as third best in MLS; and they didn’t move out of the top four, in the Index, the rest of the year.
- Slow starter award goes to New York Red Bulls; it wasn’t until week 12 that the Red Bulls breached the top four, but by week 14 they found their place at the top of the Index.
- At no time did the CPWP Index have fewer than six of the eventual playoff teams out of the top ten. And by week 25 nine of the top ten teams in the Index were bound for the playoffs.
- Accuracy in predicting the top ten teams making the playoffs was no worse than 60% at any given time – and as high as 90% accurate by week 25.
- The CPWP Index, and the sub-indices for team attacking and defending, show great value in looking to understand where failure/success may be occurring relative to team results.
- It’s evidence – one piece of evidence – that shareholders should pay attention to when looking to make changes – it is not a substitute for what the eye sees or the gut feels.
- I know more can be offered in drilling down into individual statistics relative to these team statistics.
You can follow me on twitter @Chrisgluckpwp.
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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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I’ve been a bit busy lately so apologies for not offering up any research on Possession with Purpose; lots going on with it at the moment while all five competitions I measure are still going at full speed.
To catch up, using a picture first, here’s a look at how PWP compares to Total Shots Ratio as well as Goal Differential when viewing the UEFA Champions League:
As an added Index I’ve also included the PWP Predictability Index (an Index that EXCLUDES goals scored (for or against) in the overall calculations. A reminder that from a pure predictability standpoint the Predictability Index remains the only Index that excludes goals scored when developing a prediction as to whether a team might earn points.
For the benefit of all I’ve also included how things take shape when teams play at home versus away from home; there are differences.
So what does this mean?
PWP, even in a format different than general league play shows better than TSR as it is known today (i.e not modernized).
While Goal Differential shows well with respect to the overall correlation coefficient (r) to average points earned it doesn’t show best when racking and stacking it as an Index compared to PWP —> when viewing how it ranks teams versus how well they have progressed into the final stages.
What I found intriguing was that the PWP Predictability Index (which excludes goals scored) actually racked and stacked the top 4 teams in the UEFA Champions League better than Goal Differential.
If you’re someone who likes to bet on games early indications show PWP Predictability (excluding goals scored) has FC Bayern ahead of Barcelona and Real Madrid ahead of Juventus.
Of course Arjen Robben has been injured, and given his considerable influence with FC Bayern Munich that predictability model pretty much goes out the door – or does it?
I’d say yes, because when adding goals scored (the PWP Index) Barcelona leap-frogs past FC Bayern; meaning it is highly likely we see Real Madrid and Barcelona in the Finals…. but you decide.
Awhile ago I wrote that FIFA needed to change how they rank teams across the World.
I remain stubbornly steadfast and steadfastly stubborn that the outputs from both the UEFA Champions League and World Cup PWP Indices lend credibility to the suggestion that FIFA revisit protocols on how they seed teams in their various competitions.
It’s been a bit since I last offered anything on Europe – sorry – just a whole lot going on to include putting together an Academic Paper, or five, on Possession with Purpose.
Since it’s been awhile here’s the primary Composite Index for all four areas covered – I’ll try to offer some more insight into the specific competitions a bit later – for now I appreciate your patience and hope this scratches the itch for a wee bit.
Oh – and a surprise at the end about Total Shot Ratio…
Does anyone really think anybody is going to beat out FC Bayern Munich? Probably not – but the other top three in the League Table are also the other top three here – the leader here appears to be Wolfsburg – and if a betting man that one seems a worthy gamble on them finishing second…
English Premier League:
I wouldn’t say it’s a runaway yet – still some games to be played but the real battle seems to be who finishes fifth – the bridesmaid as some say? As for Everton – well………. I’d be very surprised to see Martinez back next year – how can a team so dominant in possession completely lack the ability to finish? It’s called possession with no purpose – and it may not just be all about their strikers….
UEFA Champions League:
Was anyone not surprised with Monaco defeating Arsenal? In considering how things are developing it probably should have been foreseen a bit better…
I’ll try to offer up the Predictability Indices later this week in preparation for the Weekend.
By the way, when putting together the Academic Paper on PWP I have had to create two new Total Shot Ratio indicators…
As things have stood so far TSR has merely been an indicator viewed with attacking team data only and it’s never been flipped to see how the opponent behaves in TSR with respect to the other team.
Well I’ve fixed that, if you will.
Now like Attacking PWP, Defending PWP, and the Composite PWP I’ve taken TSR – renamed it to Attacking TSR (ATSR), and created Defending TSR (DTSR /// what the Opponent’s combined ATSR’s are against you), and CTSR – the difference between ATSR and DTSR…
I’ll be offering up more about that in my upcoming paper – you should know now that CTSR has a higher correlation (R^2) to Points Earned in the League Table than the old TSR…
In case you don’t know what TSR is – here’s an explanation pulled (DIRECTLY) from Statsbomb:
“TSR – Total Shots Ratio
A ratio to explain how teams fare against their average competition in the shots battle. Ex: If Manchester City has 20 shots in the match and Newcastle have 10, City’s TSR for that match is .67, Newcastle’s is .33.
James Grayson has written about this frequently on his website here. We care about TSR for teams because it has a reasonably strong correlation to points and goal difference.
In hockey, this is called Corsi.”
So What I’ve done is taken the same approach as what I did when creating Possession with Purpose – I’ve also created a Defending TSR (how the Opponent does against you) – so you have your teams Attacking TSR but also the Opponents Attacking TSR against you – called Defending TSR.
Composite TSR is created by subtracting DTSR from ATSR… note that CTSR is higher than ATSR with one exception – in other words the difference between the two TSR’s gives you a better picture and better correlation to points earned in the League Table than just plain TSR…
All told though – CTSR does not exceed the R^2 of CPWP – again with but one exception and that exception varies from week to week…
And TSR gives you no objective evidence on team attacking and defending behavior leading up to shots taken or goals scored… this is not to be critical of TSR – it simply points out the technical weakness in the ratio compared to PWP.
Here’s a snippet of what I mean:
CPWP to Pts Earned APWP to Pts Earned DPWP to Pts Earned ATSR to Pts Earned DTSR to Pts Earned CTSR to Pts Earned
0.85 0.79 -0.68
0.87 0.8 -0.76 0.64 -0.4
0.92 0.9 -0.85 0.86 -0.35
0.92 0.83 -0.81 0.53 -0.41
La Liga 0.91 0.88 -0.88 0.88 -0.77
The number in bold is the one with the highest R Squared to Points Earned – at least this week… and the numbers are those R Squared with respect to the League Table – not indicative of what the CTSR is for each team on a game to game basis – I will publish those a bit later this week… hope that clears up any confusion and appreciate your patience.
As of the 12th of March I have published that additional article speaking to TSR and the recommended changes to the overall effort… it’s here: Modernizing TSR
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No European team can match the league domination that Bayern Munich has shown this year in the Bundesliga. However, in spite of Die Bayern’s efforts to run away with the title, the German premier division is still awash with fascinating stories.
The race for the remaining Champions League spot could not be closer – five teams are separated by a mere two points. And no, that excludes Dortmund, who are floundering in the relegation zone.
To set the stage here’s the five teams vying for that third and final spot: Bayer Leverkusen; Augsburg, Monchengladbach, FC Schalke, and TSG Hoffenheim.
Here’s where they compare with each other in my Composite Possession with Purpose Index:
From this it would seem pretty obvious that Bayern Munich also stood out way above all others in the CPWP Index.
In addition, it’s good to see the Index also shows a marked difference, in overall team performance, between Wolfsburg and the other five teams battling for the final UEFA Champions League spot.
Of all the leagues I evaluate, using my Possession with Purpose Family of Indices, this League usually shows the best overall correlation.
Meaning, for some, it may be far more predictable – in other words perhaps the Bundesliga is a great league to bet on game results?
If you do that sort of thing here’s what the CPWP Predictability Index looks like:
A reminder – the CPWP Predictability Index was developed after I had some great discussions with folks at the World Conference on Science and Soccer 2014.
Myself, Ben Knapper (Arsenal FC Head of Stats) and others at PROZONE sports all agreed that the Index ‘could?’ have value as a predictability model if Goals Scored/Against data was removed.
The teams with Green Bars are the five teams battling for the third and final UEFA Champions League spot – the Purple Bar, Borussia Dortmund, is highlighted simply because they ‘should’ be winning – given their talent – but they aren’t!
But… could this be a model to actually reinforce Borussia Dortmund still remain a team who can make UEFA Champions League next year even though they are 13 points behind Bayer Leverkusen? I wonder what the odds are on that?
If you missed my presentation at the WCSS of 2014 here’s a link – in the seven months of this blog it has been my most viewed/read article.
Here again the top two teams are tops in the Index.
For those thinking the best in attack is what drives success it appears FC Schalke and then Bayer Leverkusen are best situated to push forward – while Augsburg slides way back towards Borussia Dortmund.
In taking a look at FC Schalke versus Bayer Leverkusen what separates them in this Index seems pretty interesting.
- Schalke average more total passes by volume (452 to 399) but within the Opponent’s Defending Final Third Leverkusen average more passes (155 to 120).
- To go with that, Leverkusen averages more possession (52% to 50%) but lower overall passing accuracy both within and outside the Opponent’s Defending Final Third (68%/57% compared to Schalke at 76%/61%.
- Meaning Schalke offer more passes, accurately, prior to entering the Final Third while also offering fewer, more accurate passes, once they’ve penetrated.
Looked at from a Leverkusen viewpoint – Bayer actually possesses the ball more – but is less accurate in that possession. In addition they also look to penetrate far more frequently than Schalke.
When digging into the shots area – Schalke show more patience in taking fewer shots by volume and percentage but both teams end up with roughly the same volume of Shots on Goal and Goals Scored per Shots on Goal (36% for Leverkusen and 34% for Schalke).
- Put another way – each team shows different statistical trends in possession, accuracy, penetrating, creating, and taking shots but their overall results are the same.
- Reinforcing, at least in my view, there are a number of different systematic approaches that will get you to the same place.
Before moving on to Defending PWP I think there is value in taking a look at Augsburg. Earlier this week I did an article on Major League Soccer called “Getting More from Less“.
The intent was to see who did better last year, in MLS, in getting better results with lower team performance. My gut-check example to quantifying the results in MLS was West Ham and their Direct Attacking nature.
What I determined was a team who averaged fewer passes than the League Average (both within and outside the Opponent’s Defending Final Third) with less than 50% possession could be reasonably called a Direct Attacking Team.
In looking at Augsburg here’s their attacking data as it fits that mold.
Overall dead on average in Possession at 50%.
Passing Accuracy (entire pitch), 73% – less than the League Average of 74.25%.
Passing Accuracy within the Opponent’s Defending Final Third (56%) – less than the League Average of 57%.
In looking at volume – Total Average Passes for Augsburg was 413 – the League Average was 435
Total Passes within the Opponent’s Defending Final Third for Augsburg was 114 – the League Average 126.
So on the surface it would appear that Augsburg shows the tendency to play more Direct Attacking, as opposed to a Counter-Attacking ‘tactic’, within a Possession-based game.
For Augsburg – they’ve had eight games that have followed the mode of Direct Attacking – they’ve won five of those games. Pretty solid in getting more from less – but can they sustain that?
The West Ham review showed they have won 7 games out of 11 games where their team averages fell into the Direct Attacking mode.
It would seem Augsburg are almost as successful (percentage wise) in matching West Ham when it comes to winning games where their performance falls below League Average… (63.63% for West Ham versus 62.5% for Augsburg).
Augsburg, like West Ham, are pretty high up in the Defending PWP Index (Hammers are 6th best in the EPL DPWP Index versus Augsburg who are 4th best here).
So the value of a higher team performance in defending helps sustain success with the lower volumes offered up in attack.
Meaning the will of Augsburg rides more with a collaborative approach, in overall team play, than strictly an attack dominated performance.
Monchengladbach is next highest here, while TSG Hoffenheim doesn’t seem to shine in either Index.
I’d expect some long odds on TSG making that third and final UEFA Champions League spot…
So what separates Monchengladbach from TSG?
- Goals Against – for Monchemgladbach their GA is .94 – for TSG it’s 1.47 – is that down to Mochengladbach simply having a better Goalie?
- Maybe… their opponent’s actually average more Shots on Goal (5.35) compared to TSG, whose opponent’s average 4.5 Shots on Goal.
Opponents for both teams average total passes, both within and outside the Defending Final Third, greater than the League Average – so by and large most opponents are playing possession based attacking against these two sides.
Where it gets interesting is the volume of successful passes by their opponents after they’ve entered their Defending Final Third.
- In the case of TSG, the opponents average 20 fewer successful passes, with almost the same amount of shots taken and shots on goal.
- Meaning, to me, TSG are finding themselves out of position more often as the screws tighten – hence the greater Goals Against.
In other words one team may be playing more man-to-man while another team may be playing more zonal?
I’m not sure which – those with video or access to X,Y coordinates may know that better?
Anyhow – clearly the data points towards one team having a different defensive scheme that may also include Mochengladbach simply having a much better Goal Keeper.
Half the season remains and while Bayern is basically blowing the Bundesliga away there are others who are still making this league worthy to watch.
Will it be the West Ham of the Bundesliga (Augsburg)? Can Borussia Dortmund pull it back? How about the other challengers who appear more steady, like FC Schalke, Bayer Leverkusen, or Monchengladbach?
And does TSG Hoffenhein really have a chance as well? For some I bet UEFA Champions League is the goal for next year – but others might also be shooting for Europa too.
And this doesn’t even broach the topic about who gets relegated – Might that Borussia Dortmund ends up in that race instead? Wow…….
Jürgen Klopp would get clobbered if that happens!
More to follow…
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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