Category: UEFA Champions League

Expected Wins Five – Europe

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

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

A reminder – PWP is about two things:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Germany:

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

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

Spain:

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

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

UEFA Champions League:

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

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

In Closing:

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

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

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

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

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

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

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

Best, Chris

COPYRIGHT – All Rights Reserved.  PWP – Trademark

Gluck: What adds more value? Goal Scored or Goal Prevented?

With soccer statistical analysis growing daily, a longer headline might be: 

What do the tea leaves show about team performance measurements in Major League Soccer?  Does the goal prevented show greater value, relative to points earned, than the goal scored?

Even that’s a bit wordy though… maybe it’s…

Soccer Statistics:  What does “right” look like now?

If you read The Numbers Game: Why Everything You Know About Soccer Is Wrong – July 30, 2013 by Chris Anderson (Author), David Sally

There is a section called “On the Pitch, which explains how the game is a balance of strategies.  Preventing a goal is more important to earning points than scoring one, the game is about managing turnovers, and the game can be controlled by both tiki taka as well as keeping the ball out of play longer than the average team does.”  Sourced from this article written here https://www.forbes.com/sites/zachslaton/2013/07/30/everything-we-know-about-soccer-is-wrong/#686a7ab47831

My analysis shows:

Goals scored have more value (relative to points earned) than goals prevented.

Furthermore, I don’t just see the game as a balance of strategies, I see it as a balance of team statistics driven by team operations, strategies and tactics.

In the last four years the balance between how well a team attacks, versus how well the opponent attacks against that team, has more value (relative to points earned) than simply goals scored or prevented.

Finally, what shows as a valuable (balanced) team performance measurement for one team does not hold true as a valuable (balanced) team performance measurement for all teams; either home or away.

Composite Possession with Purpose (CPWP) Indices:

The CPWP index is generated by subtracting team attacking statistics (APWP) from opponent team attacking statistics (DPWP).  This is my way of ensuring I capture a teams’ balanced performance (with and without the ball).

Intimate details on my PWP formulas can be seen in my academic paper published with the International Research Science and Soccer II, published in 2016.   “Possession with Purpose: A Data-Driven Approach to Evaluating Team Effectiveness in Attack and Defense C. Gluck and T. Favero”.

Breaking News:  An abstract on the use of Possession with Purpose Index as a tool for predicting team standings in Professional Soccer has just been approved for presentation (as a poster) at the World Conference on Science and Soccer – Rennes, France 2017.

General information and other relevant articles published, stemming from my research include:

Over the last four years I’ve measured these leagues/competitions using PWP analysis:

  • Major League Soccer 2013, 2014, 2015, 2016,
  • English Premier League 2014,
  • Bundesliga 2014,
  • La Liga 2014,
  • UEFA Champions League 2014,
  • Men’s World Cup 2014, and
  • Women’s World Cup 2015.
  • The lowest correlation this index has had, to the league table, was in MLS 2016 (.75).  The highest correlation this index has was for the EPL and La Liga of 2014 (.94).
  • I’d put the lower correlation in MLS 2016 down to increased parity across the league, but I’ll leave how my index can be used to measure parity, in a league, for another day.

In this analysis I’ve evaluated 18 MLS teams that have played 34 (17 home and away) games in each of the last three years (2014, 2015, and 2016).  This equates to 1003 games of data or 2006 total game events for home and away teams.

My analysis excludes New York City FC, Orlando City FC, Chivas USA, Minnesota United FC, and Atlanta United FC as these teams have not played 34 games in each of the last three years.

Data will be presented in three separate categories, total games, away games, and home games.

In addition to evaluating team performance using my standard PWP Indices I have added three additional families of indices to my analyses.  They are:

  • Composite Possession with Purpose Indices Enhanced with Crossing Accuracy (CPWP-CR),
  • Composite Possession with Purpose Indices Enhanced with Clearances (CPWP-CL),
  • Composite Possession with Purpose Indices Enhanced with Crossing Accuracy and Clearances (CPWP- CR/CL), and
  • My benchmark for passing the common sense ‘giggle check’ is, as always, Goal Differential.

Data arrays:

Total Games

Total game observations for consideration:

In every instance goal differential had the strongest correlation to points earned in the league table.

In every instance a CPWP index had the second and third highest correlation to points earned in the league table.

Best, in order of frequency for correlation to points earned, is provided below:

  • Goal Differential – 18 times 1st *benchmark
  • CPWP Index – 14 times 2nd or tied for 2nd
  • CPWP-CL Index – 6 times 2nd or tied for 2nd
  • CPWP-CR Index – 3 times 2nd or tied for 2nd
  • CPWP-CRCL Index – 3 times 2nd or tied for 2nd

Teams not fitting the norm (PWP Index solely being 2nd best) were: Colorado Rapids, Columbus Crew, LA Galaxy, Montreal Impact, New England Revolution, Portland Timbers, Real Salt Lake, San Jose Earthquakes, Sporting Kansas City, Seattle Sounders, and Toronto FC.

When viewing the DPWP, seven teams showed stronger correlations to points earned (preventing the opponent from scoring goals).  They were:  Chicago Fire, Colorado Rapids, Houston Dynamo, Montreal Impact, New York Red Bulls, Philadelphia Union, and Sporting Kansas City.

Meaning 11 teams showed the APWP indices as having higher correlation to points earned; i.e. scoring goals was more important than preventing goals scored.

Away Games

Away game observations for consideration:

In every instance, but one, goal differential had the strongest correlation to points earned in the league table.  The outlying team, where goal differential was not the best correlation to points earned, was Colorado Rapids.

  • I think this exception is worth noting.
  • For me, goal differentials’ correlation to points earned has been THE benchmark in determining whether or not my team performance indices ‘make sense’.
  • Exceeding the benchmark, even once, confirms for me as a soccer analyst, that my approach adds value when looking for ways to help explain the game better.

In every instance a CPWP index had the second and third highest correlation to points earned in the league table.

Best, in order of frequency for correlation to points earned, is provided below:

  • Goal Differential – 17 times 1st *benchmark
  • CPWP Index – 9 times 2nd
  • CPWP-CL Index – 7 times 2nd
  • CPWP-CR Index – 2 times 2nd or tied for 2nd
  • CPWP-CRCL Index – 1 time 2nd or tied for 2nd

Teams not fitting the norm (PWP Index solely being 2nd best) were: Colorado Rapids, Columbus Crew, Chicago Fire, FC Dallas, Houston Dynamo, Montreal Impact, New England Revolution, Portland Timbers, Real Salt Lake, San Jose Earthquakes, and Toronto FC.

When viewing the DPWP indices, ten teams showed stronger correlations to points earned (preventing the opponent from scoring goals).  They were: Columbus Crew, Chicago Fire, Colorado Rapids, FC Dallas, Houston Dynamo, Montreal Impact, New York Red Bulls, Portland Timbers, Philadelphia Union, and Toronto FC.

Meaning ten teams showed the APWP indices as having a higher correlation to points earned; i.e. scoring goals was just as important as preventing goals scored.

Home Games

Home game observations for consideration:

In every instance goal differential had the strongest correlation to points earned in the league table.

In every instance a CPWP index had the second and third highest correlation to points earned in the league table.

Best, in order of frequency for correlation to points earned, is provided below::

  • Goal Differential – 17 times 1st *benchmark
  • CPWP Index – 10 times 2nd
  • CPWP-CL Index – 7 times 2nd
  • CPWP-CR Index – 2 times 2nd or tied for 2nd
  • CPWP-CRCL Index – 1 time 2nd or tied for 2nd

Teams not fitting the norm (PWP Index solely being 2nd best) were: Colorado Rapids, Columbus Crew, Chicago Fire, FC Dallas, Houston Dynamo, Montreal Impact, New England Revolution, Portland Timbers, Real Salt Lake, San Jose Earthquakes, and Toronto FC.

When viewing the DPWP indices six teams showed stronger correlations to points earned (preventing the opponent from scoring goals).  They were: Chicago Fire, Colorado Rapids, Montreal Impact, Philadelphia Union, Sporting Kansas City, and Vancouver Whitecaps.

Meaning 12 teams showed the APWP indices as having a higher correlation to points earned; i.e. scoring goals was more important than preventing goals scored.

Summary:

The CPWP indices are not perfect but they do show very strong, consistent, correlation to points earned in the league table.

In every instance the balance of a teams’ success in possession, passing accuracy, penetration, shot creation, shots taken, shots on goal, and goals scored AND preventing the opponent from doing the same, exceeds either APWP (scoring goals) or DPWP (preventing goals scored).

The same CPWP index was not the best CPWP index for every team relative to points earned in the league table.

Teams playing in away games had different CPWP indices (showing greater correlations to points earned) than games played at home.

The DPWP indices did not, consistently, have a greater correlation to points earned than the APWP indices.

Colorado, Columbus, Montreal, New England, Portland, Real Salt Lake, San Jose, and Toronto consistently showed CPWP-CR and CL indices had greater correlation than the standard CPWP index.

Correlation of all indices, to points earned, differed between home and away games.

Final correlations to points earned for all teams measured (combined) the last three years in MLS were:

  • Goal differential =  .87
  • APWP   = .53 // DPWP = -.51 // CPWP = .74
  • APWP-CR = .52 // DPWP-CR = -.50 // CPWP-CR = .72
  • APWP-CL = .49 // DPWP-CL = -.46 // CPWP-CL = .66
  • APWP-CR/CL = .49 // DPWP-CR/CL = -.47 // CPWP-CR/CL = .66
  • Goals Scored = .63

Conclusions:

The balance of attacking, versus stopping the attack of the opponent, has more value in measuring team performance (relative to points earned in the league table) than goals scored or prevented.

Goals scored, on average, (APWP) have more value (relative to points earned in the league table) than goals prevented (DPWP).

The correlation of team measurements, relative to points earned, varies from team to team, both home and away.

Therefore the value of individual player statistics (used to create those team statistics) varies from player to player, both home and away..

For example:  The CPWP-CR and CPWP-CL indices showed 2nd best for correlation to points earned for Colorado, Columbus, Chicago, FC Dallas, Houston, Montreal. New England, Portland, Real Salt Lake, San Jose, and Toronto (in away or home games) over the last three years:

Therefore, the players who play on those teams should have their individual statistics (for crosses and/or clearances) weighted differently than players who play on the other teams; because the value of their successful crosses/clearances had greater weight relative to those teams earning points.

Last but not least, what the other leagues/competitions offered after one season/competition:

  • EPL // APWP = .92 // DPWP = -.88 // CPWP =.94
  • La Liga // APWP = .93 // DPWP = -.90 // CPWP = .94
  • UEFA Champions League // APWP = .74 // DPWP = -.66 // CPWP = .81
  • Bundesliga // APWP =.89 // DPWP = -.84 // CPWP = .93
  • Men’s World Cup 2014 // APWP = .58 // DPWP =-.77 // CPWP = .76
  • Women’s World Cup 2015 // APWP = .63 // DPWP = -.77 // CPWP = .76

Both the Men’s and Women’s World Cup competitions saw the value of the goal prevented greater than the goal scored.  In all other instances the balance between the two showed greater correlation.

Anderson and Sally weren’t wrong at all; it’s more about what right looks like depending on what league/competition is being evaluated.

Best, Chris

You can follow me on twitter @chrisgluckpwp

COPYRIGHT: All Rights Reserved.  PWP Trademark

NOTE:  All the data used in my analysis is publicly available with the exception of the Women’s World Cup 2015 data; my thanks to OPTA for providing me that data last year.

Gluck: Updated Possession with Purpose and the New Total Soccer Index

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.

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.

In Summary:

  • 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.

Best, Chris

@ChrisGluck1

Gluck – Predicting Team Standings in Professional Soccer

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.

End State:

  • 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:

mls-2013

  • 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:

epl-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:

bl-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:

spl-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:

uefa-cl-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:

mwc-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:

mls-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:

mls-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:

mls-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.

Closing Thoughts:

  • 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.

Best, Chris

You can follow me on twitter @Chrisgluckpwp.

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UEFA Champions League – Who moves on Barcelona, Bayern, Madrid, or Juventus?

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:

PWP v TSR v GDNote that I’ve highlighted – in green – where the top 4 teams (FC Bayern Munich, Barcelona, Real Madrid, and Juventus) fit into each of the correponding Indices (if you will).

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.

In Closing:

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.

Best, Chris

 

Control or Lack Thereof – MLS 2015

I originally posted this article on Stumptownfooty – an SB Nation blog site where I cover the Portland Timbers…

For over two years I’ve been researching team and league statistics to help paint a picture and perhaps? better explain what is happening on the pitch relative to points earned in a league table.  For the most part every competition measured, MLS 2014, English Premier League, Bundesliga, La Liga, and the UEFA Champions League have all shown a pattern of consistency.

This pattern isn’t relative to individual teams that win or lose (earn points) it’s relative to the behavior of the league/competition as a whole.

To give you an idea of what I mean here’s a diagram on how each of the six data statistics I track in Attacking Possession with Purpose correlate to points earned for all the teams in those leagues.

League_Control_Attacking_Possession_with_Purpose.0.jpg

By correlate (statistically speaking) it’s the correlation coefficient between each data point and points earned – for those using statistics that is called “r”.

The other technical point here is that the average percentage for each of these statistics doesn’t matter – they are different – but even when they are different their overall relationship (correlation) to points earned is very-very close…

Said differently — there is a consistent pattern of league behavior relative to all leagues measured for team attacking.

What’s kinda cool (for me) is the pattern of information shows up as a bird – with the brains (head) of the bird located in the center (v). And that center (v) in soccer usually represents where the asset of vision comes into play; the better the completed pass into a danger area (attacking final third) the more likely it is to create a shot taken that has a better chance of resulting in a goal scored.

Here’s a look at the same diagram with the MLS 2015 information (red line) and the Portland Timbers information (green line) in comparison to the other leagues:

League_Control_Attacking_Possession_with_Purpose_Plus_MLS_2015.0.jpg

No “Birds” here…

In looking at MLS 2015 (red line) Possession Percentage – there is virtually zero corrleation to points earned – meaning it simply doesn’t matter how much possession (control) you have in a game in MLS.

This isn’t true for Portland however, with Possession Percentage (green line) hovering around -.50 this means the less possession Portland has the more likely they are to earn points – said another way – the more direct the play (this year) the better the odds they take points.

Last year that number was .10 – in 2013 that number was .02.

A takeaway here, on the Timbers, is that they were able to take points in 2013 given any level of possession percentage. In 2014 their tendency was to earn points (more frequently) when having greater possession. This year it’s not only the opposite (so far) it’s actually the opposite by quite a large margin.

Some might say that means the tactical approach for the Timbers is far easier to predict this year than the two previous years….

A few other thoughts about the two diagrams…

When considering Passing Accuracy and Goals Scored versus Shots on Goal – for MLS (red line) that correlation is a bit lower than either the Timbers or the other leagues… for me that means the value of scoring a goal this year carries far less weight than other leagues or even MLS for 2014.

Said a different way – perhaps more goals are being scored this year as a result of individual mistakes instead of controlled, well placed, passes that create more effective shots that finish in the back of the net?

Even more apparent is the far lower difference between MLS 2015, and the others, for Shots on Goal per Shots Taken… in other words there is virtually NO correlation on how accurate a team is in having their shots taken wind up as shots on goal.

In Closing:

Composite_Possession_with_Purpose_Index_MLS_2015.0.jpg

The latest Composite PWP Index for MLS through Week 6.

What’s it mean?

For now, it appears that MLS 2015 is nowhere near the general level of consistency it showed in 2013 or 2014. And the league itself, is also far different from those measured in Europe.

While some may disagree I’d almost be willing to offer that it’s a complete crap-shoot on which teams win this year…

As a Timbers supporter I suppose that means at any given time, from any given angle, the Timbers could either get a goal or concede a goal… regardless of how good or bad their passing or penetration is…

If I were a Head Coach this bit of info might??? be interpretted a few different ways….

Either it doesn’t matter how much you plan, mistakes are going to happen and it’s anybody’s guess who makes those mistakes and when… or,

When the lads take to the pitch make sure you get the ball as far forward, as quickly as possible, so that when a mistake occurs it is more likely to occur outside your own defending final third, or

It doesn’t matter how much is spent on players – as long as we get guys who can strike the ball, in open space, (regardless of how it got there) we have a great shot at winning the game…

Enjoy the rollercoaster ride this year – I know I will!

Best, Chris

UEFA Champions League – Some great games coming!!!

The greatest professional team soccer competition continues…

But before digging into the Possession with Purpose Family of Indices some general news for consideration.

As the PWP Indices have shown such a strong correlation/relationship to the league tables without using points earned, in ALL competitions measured, I want to try a create a quantifiable way to measure “luck”.   More to follow…

Anyhow, here’s my updated Strategic Composite PWP Index after the Group Stages are completed:

CPWP Strategic Index Group Stages CompletedA few explanations to better understand the diagram – I have paired up the teams (color coordinated them) to show the match-ups for the knock-out stages.

This way others can get an idea of how their team CPWP, APWP, and DPWP Indices compare.

I won’t go so far as to say that the team expected to win has their color first but I would offer it may give you an inkling of who might be favored to advance.

Finally, the red bars represent those teams that did not move on to the knock-out stages.

The knock-out stage pairings:

Barcelona play Manchester City.

FC Porto play FC Basel.

FC Bayern Munchen play Shakter Donetsk.

Real Madrid play FC Schalke.

Chelsea play Paris Saint Germain.

Borussia Dortmund play Juventus.

Atletico Madrid play Bayer Leverkusen.

Monaco play Arsenal.

So here is how the teams compare in the Strategic Attacking Index:

APWP Strategic Index Group Stages Completed

A few observations…

I’m a firm believer that Defense wins Championships – but I’m also not willing to ignore how effective team attacking performance is in relationship to team defending.

With Barcelona, Bayern, Porto, Chelsea, Arsenal, and Real Madrid being slightly head-and-shoulders above the rest, in attack, it’s likely those teams will put on a great performance.

All told I’d offer those teams will work towards a possession based attack – especially seeing their opponents.  I’d also offer up that Juventus is likely to work towards that style as well.

Teams like Schalke, Dortmund, Donetsk, Leverkusen, Man City, and PSG can show willingness to possess the ball but I think they will cede somewhat more this stage than in the Group Stages.

Teams like Atletico Madrid, FC Basel, and Monaco are )highly) likely to continue to cede possession and play for a swift counter-attacking (almost direct attacking) style of soccer.

Now for the Strategic Defending PWP Index:

DPWP Strategic Index Group Stages Completed

FC Porto have been best, with Bayern, Monaco, Dortmund, Real Madrid, and Barcelona not far behind…

If there is an anticipated blow-out I’d offer Real Madrid is the team most likely to win big – with Porto next up against Basel and Atletico Madrid handling Bayer pretty easily.

As for the others – way too close to call in my opinion – and I’d imagine a huge audience on telly for the Man City/Barcelona match-up – and you can be sure I’ll be watching The Arsenal take on Monaco!

In Closing:

As I noted going into this competition – all these teams are good – what follows as pucker time nears – is the separation of good from great…

If you want a taste on my approach in measuring luck – consider this – Athletic Club had just seven points earned yet they were 7th best in team defending performance and 13th best in the overall Composite Index performance…  if any team was unlucky, in the Group Stages, it was Athletic Club.

On the flip side – if any team was lucky, in the Group Stages, it was FC Basel (who also had just seven points earned) – they were 16th best in team defending performance, 22nd best in team attacking performance, and 20th best in overall Composite Index performance.

Best, Chris

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You can follow me on twitter @chrisgluckpwp

UEFA Champions League – Bundesliga Throws down the Guantlet

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:

CPWP Strategic Index Group Stages Through Game 2

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:

DPWP Strategic Index Group Stages Through Game 2Like 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:

APWP Strategic Index Group Stages Through Game 2

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!

CPWP Strategic Index Week 9 La Liga

Okay – now a few extra diagrams for your consideration:

APWP Strategic Index Final Third Passes Greater Than 132 Through Game 2

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:

APWP Strategic Index Final Third Passes Less Than 132 Through Game 2

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.

In Closing:

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.

Best, Chris

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UEFA Champions League – Let the games begin…

Well, it’s started – the World Cup of League Football for most; at least in my eyes that is.

Who’s going to come out on top and who’s not?

Of course these teams are the best of the best (so-to-speak) and that means I won’t be using words/phrases like sucks, bottom dweller, or some other derogatory term to describe loser.

In other words no comparisons to Chivas USA, Newcastle (sorry lads and I did see Alan Pardew is under fire already), Levante or some other team not starting/doing well in regular season competition.

On to the Family of Indices in Possession with Purpose – but before going there a few obligatory reminders, on things past, in a competition such as the Champions League.

In case you missed it I took my possession with purpose analytical approach to the World Cup this year and here’s a reminder on how things started and then how things ended

As a refresh, the Composite PWP Strategic Index diagrams are provided below for that prestigious event:  How it started…

CPWP After Game 2 Group Stages

CPWP After Game 2 Group Stages

 And… how it ended:

CPWP INDEX JULY 9TH 2014 WORLD CUP

CPWP INDEX JULY 9TH 2014 WORLD CUP

Notice that the trends after Game 2 seem to be pretty consistent (in terms of what teams performed better and worse) all the way through to the final.

The overall R2 (correlation to average points scored) to the Final CPWP Index was .82; Goal Differential was .89.  The DPWP Strategic Index R2 was -.81 and the APWP Strategic Index was .65.

The Goals Scored R2 to average points was .69 and the R2 for Goals Against was -.74.

To be sure I was a bit surprised on how well the Family of Indices played out.

I’m hopeful the relationship will be somewhat near the same for the UEFA Champions League competition.

So how do the CPWP, APWP, and DPWP Indices show after Game 1?  

Well, it’s a bit earlier than the World Cup Indices but the intent here is to 1) let you know I’m tracking the Champions League this year, and 2) all the Index outputs will be made available for consideration.

CPWP Strategic Index Group Stages Game 1

CPWP Strategic Index Group Stages Game 1

Seems pretty clear that FC Porto would be where they are given the 6-0 romp over Bate Borisov.

It’s still very early days so we’ll leave it at that and just note that their were five draws.

Here’s the Attacking PWP Strategic Index offering up the first to worst team performances in Attack:

APWP Strategic Index Group Stages Game 1

APWP Strategic Index Group Stages Game 1

Perhaps a surprise in seeing Roma ahead of FC Porto?  Why is that?  

A couple of reasons and the last one, in my opinion, is the most telling one on who may proceed a bit further:

  1. Roma had 91.07% passing accuracy compared to FC Porto’s 86.65%
  2. Possession was basically equal (~67% each)
  3. Roma was 55% accurate in scoring goals based upon shots on goal; while FC Porto was 50% accurate.
  4. Roma had a 69.23% accuracy rating in having their Shots Taken end up on goal, as opposed to FC Porto (also very high) who was 60% accurate.
  5. Now for the final difference, and most telling in my view — FC Porto generated 23.53% Shots Taken per penetrating possession – while Roma generated just 11.40%.

Why do I have that one last, when it also shows that FC Porto exceeded Roma by over 10%?

The reason why gets back to patience, along with time and space…

Roma was patient.  They statistically, give the appearance, that they waited for better opportunities to take shots (more time and space to shoot) and that reduced volume of shots, per penetration, ended up generating a 9.23% difference in goals scored.

This is type of pattern, that good teams continue to show in Possession with Purpose analysis, reinforces for me that the ‘unmeasured’ amount of time and space has as much, if not more value, than the location of the shot taken.

As a reminder – here’s three previous articles speaking to that in better detail…

On to the Defending PWP Strategic Index and the teams performing best/worst in that area:

DPWP Strategic Index Group Stages Game 1

DPWP Strategic Index Group Stages Game 1

Juventus take the top spot – even ahead of the possession and passing mad Barcelona, the biggest difference really comes down to one team defending statistic:

With Juventus, Malmo FF completed only 36% of  their passes within the Juventus Defending Final Third.

While APOEL Nicosia were able to complete 56% of their passes within the Barcelona Defending Final Third.

Perhaps this is down to how deep or how shallow the back four for each team lined up in the defending half?

Perhaps not?

However viewed it should be noted APOEL Nicosia had fewer passes attempted, in total (292) , than Barcelona had attempted in the Nicosia Final Third (303).

Wow…  Not unlike the same run of play that Barcelona sees in La Liga.  But is that indicative of a team that is going to win the Champions League?

It didn’t work last year… I guess we will see.

In Closing:

It’s only one game – and trends can never be seen with just one game.

They do, however, provide a starting point for a trend.

Best, Chris

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