Category: Major League Soccer

MLS Playoffs – Predictions with Purpose (Updated)

To the chase…  my PWP Predictability Index leveraging my Possession with Purpose Analysis.  Click here for my latest revision or click here to read the initial revision.

NOTE:  Updates for the Red Bulls v DC United and Sounders v Dallas match are at the end of the article.

The Predictability Index itself is the CPWP Index data minus Goals Scored / Goals Against and is split into two diagrams – Home Predictability versus Away Predictability.

Here’s the CPWP Strategic Predictability Index for teams at Home:


Here’s the CPWP Strategic Predictability Index for teams Away from Home.


Note the significant differences in how the teams are predicted to perform at home versus on the road; four teams really sucked at home this year, while four teams were expected to perform quite well on the road.  

Here’s how it works; I will compare the two digit number of the home team with the two digit number of the away team.

Whichever number is higher it’s that team which is predicted to win… again… based upon their history of team performance in overall attacking and defending, exclusive of goals scored; this year.

And now the PWP Predictions:

FC Dallas versus Vancouver Whitecaps matchup.  FC Dallas at Home (0.00) while Vancouver on the Road (-.11)  FC Dallas wins.

FC Dallas key indicators are ceding possession and creating quick counter-attacking scenarios that use time and space created by Vancouver being too aggressive in attack.

Vancouver key indicators are maintaining patience in possession and not losing position in defending – they are one of the top defending teams in MLS; they will need to be at their best to beat Dallas.

Next up; New York Red Bulls versus Sporting Kansas City.  New York at Home (0.10) while Sporting Kansas City on the Road (0.05) New York wins.

New York key indicators are their attack from a number of different angles.  They are simply one of the top attacking teams in all of MLS – they need to attack, attack, attack, and hope, with all their hope, that they can keep Sporting KC from scoring a goal.

Sporting KC key indicators are their ability to defend; they are still one of the best defending teams in MLS.  If they can control the wide open attack, I’d expect from New York, and their propensity for fouling in their own defending final third, I can see some individual talent from Zusi or some set-pieces giving them the edge to win.

Columbus Crew versus New England Revolution.  Columbus Crew at Home (0.06)  while New England on the Road (-0.08).  Columbus wins game 1.  Columbus Crew on the Road (0.06) while New England at Home (0.23) -> New England wins game 2.  I offer Columbus advances over New England on away goal difference.

Columbus key indicators include being one of the most consistent teams in overall attacking and defending team performance in MLS – with this being a two game set I’d imagine consistency in attacking and penetration as well as consistency in defending the danger spaces will see them through.

New England key indicators are slightly changed with Jones on the pitch – his leadership may give the edge to a Revolution team who are, in my opinion, outgunned in almost every other category.  They are a big under-dog in my opinion but not everybody rates Columbus as strongly as I do…

Real Salt Lake versus LA Galaxy.   Salt Lake at Home (0.33)  while LA Galaxy on the Road (0.12).  RSL wins game 1.   LA Galaxy at Home (0.19)  while Salt Lake on the Road (-0.01).  LA Galaxy wins game 2.  I offer LA Galaxy advance over Real Salt Lake on away goals difference.  

Salt Lake key indicators include, as noted, a stingy defense at home and a propensity to win in Rio Tinto.  They also have pedigree not unlike LA Galaxy, and perhaps an even more veteran line-up when it comes to big games.  Lest we forget Salt Lake could have done much better last year and didn’t – they will have added energy that might surpass the emotions LA bring with them in pushing to help Donovan raise the Cup once more.

LA Galaxy key indicators are pace, possession, penetration and all around purpose that operated at peak performance for almost the entire year.  It should be noted that they didn’t collect the silverware last week and in all likelihood they could stumble here as well as they may look past Real and consider the Cup is theirs…  So arrogance is an enemy as is the continued lack of mental awareness by Gonzalez…

More to follow after the games midweek after seeing who qualifies to play Seattle and DC United…

As for my own personal predictions I can see New York advancing as well as FC Dallas but the Vancouver defense is very good as is the Sporting KC defense.

I will go with Sporting over New York and Vancouver over FC Dallas because I think those team defenses are better – and for me it’s all about defense.

With respect to Columbus – I agree with my PWP Prediction model for that game as well as the game between LA and RSL…  and in this case I also happen to think the defenses for Columbus and LA are better.

More to follow:…

Seattle Sounders at Home (.22) while Dallas on the Road (-.20).  Seattle wins when playing at Home.  FC Dallas at Home (.00) while  Seattle on the Road (-.04).  FC Dallas wins at home.  Seattle advances on away goals difference.

For me, I can see Seattle beating FC Dallas at home and on the road.  Dallas may be a bit tired for game 1 and the Predictability Index hasn’t been built to address ‘tired legs’…

At the end of the day this should be a clean sweep for the Sounders…

DC United at Home (.03) while New York on the Road (-.03).  DC United wins at Home.  New York at Home (.10) while DC United on the Road (-.08).  New York wins at Home.  New York advances on away goals difference.

For me I can see a clean sweep here as well – it may be surprising but I can see New York, riding the wave of Phillips and, most likely, the last season for Thierry Henry, all the way into the Finals.  This is not intended to diss DC United.

They are a very good team but somehow I don’t see the ‘tired legs’ syndrome impacting the Red Bulls as much as Dallas… too much at stake for a team that has invested huge money in their players and program.

Best, Chris

COPYRIGHT, All Rights Reserved.  PWP – Trademark.



Redefining and Modernizing Total Shots Ratio

For many years Total Shots Ratio has plodded along as a good indicator of team shooting performance, not overall team performance, but shooting performance.

It’s a good enough indicator that its found its way into generic match reports for professional soccer teams and has good visibility on Opta – a well recognized soccer statistics company now owned by Perform Group.

But with all that publicity and ‘useability’ that doesn’t make it ‘right’!

Why do I say that?

Within a game of football there are always two teams playing against each other – so team performance statistics should not only take into account what the attacking team is doing – they should also take into account what the opponent is doing to the attacking team.

So what do I mean about modernizing TSR.  Most define TSR has simply the volume of shots one team takes versus the volume of shots another team takes.  That’s okay but the end state is excluded – the result – a goal scored.

So my new vision of TSR centers around the end state as well as the volume – in other words the equation for Attacking TSR (ATSR) now becomes Goals Scored/Shots Taken and then Defending TSR (DTSR) becomes the percentage of your opponent’s Goals Scored/Shots Taken.

Finally, in looking at how well Composite Possession with Purpose correlates to Points Earned in the League Table I would create Composite TSR (CTSR).

Before getting to the numbers – some history first:

I built Possession with Purpose using this philosophy and if you’ve been following my efforts for the last two years you know that my correlations to points earned in the league table are extremely high…  To date:

  • MLS 2014 = .86
  • Bundesliga = .92
  • English Premier League =.92
  • La Liga =.91
  • UEFA Champions League =.87

So let’s peel back the regular way TSR correlates to Points earned in last year’s MLS – when viewing the old way (Total Shots only as a percentage for both teams) the Correlation Coefficient “r” for the entire league was .32.

My new way of calculating CTSR with the End State of Goals scored has a correlation coefficient “r” of .75

Far higher…  now for some data.

Here’s the correlation of the my new TSR Family of Indices shows with respect to Points Earned in the League Table – the same analyses used with respect to CPWP above:

  • MLS 2014 ATSR .74) DTSR (-.54) CTSR (.75)
  • Bundesliga ATSR (.53) DTSR (-.41) CTSR (.68)
  • EPL ATSR (.86) DTSR (-.35) CTSR (.76)
  • La Liga ATSR (.88) DTSR (-.77) CTSR (.92)
  • UEFA ATSR (.64) DTSR (-.40) CTSR (.65)

Like CPWP the correlations vary – in four of five competitions the CTSR has a better correlation to points earned in the league table – while in one case (the EPL) ATSR has the best correlation.

So how do the numbers stack up for some individual teams when evaluating ATSR, DTSR, CTSR, and CPWP compared to those teams points earned throughout the season?

In other words what do the correlations look like (game to game) through the course of a season for sample teams within each of those Leagues?


In almost every sample TSR (now ATSR) has a lower, overall correlation to a teams’ points earned in the League Table than CTSR (Borussia Dortmund and Barcelona being the exception) – this pattern follows the same pattern seen with CPWP almost always having a higher correlation than APWP and Goal Differential almost always having a higher correlation than Goals Scored.

I’ve also taken the liberty of highlighting which Composite Index has the best correlation to points earned between all four categories – in every instance either CTSR or CPWP is higher than TSR.  But, as can be seen, sometimes CTSR is higher than CPWP…

What this proves is that there simply isn’t one Index that is far better or far worse than the other – it shows that different teams show different styles that yield better relationships to points earned in different ways —> meaning there is not only room for improvement in current TSR statistics but room for the inclusion of PWP principles within the Industry standard.

I would offer – however – that even when you create CTSR the backbone of that data can’t offer up supporting analyses on how a team attacks or defends.  It’s still only relevant to the volume of shots taken and goals scored.

And while the volume of shots on goal and goals scored appears to be a constant across most competitive leagues (average greater than 5 and 2 respectively for teams winning on a regular basis) the average of shots taken for winning teams is not as constant… (Expected Wins 4)  —> why I favor PWP over TSR – nothing personal – just my view…

In Closing:

I’m not sure I did a good job of comparing what I view as the old way to calculate TSR (the way that ignores the End State of Scoring a goal) and how an update to it can help tell a better story that actually correlates better to the complexities of soccer.

Best, Chris

COPYRIGHT, All Rights Reserved.  PWP – Trademark

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…


 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…


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

Improving Possession with Purpose

Throughout this three year effort I have always wanted to take time to make time to review the process and look for ways to improve the output while retaining the integrity of the End State (create an Index that matches, as close as possible, the League Table without using points earned).

A critical part of this has always been to ensure that the data points used within the Index had relevance (made sense) to how the game is played.

For three years my data points within Possession with Purpose have been:

  1. Passes Attempted across the Entire Pitch
  2. Passes Completed across the Entire Pitch
  3. Passes Attempted within and into the Final Third
  4. Passes Completed within and into the Final Third
  5. Shots Taken
  6. Shots on Goal, and
  7. Goals Scored

My new and improved PWP Family of Indices will continue to leverage these relevant data points but I am making a modification with respect to the measurement of quality given those data points.  The new modifications end up seeing the overall measurement of PWP being:

  1. Possession Percentage
  2. Passing Accuracy across the Entire Pitch
  3. Passing Accuracy within and into the Final Third
  4. Percentage of Passes Completed across the Entire Pitch versus Passes Completed within and into the Final Third
  5. Shots Taken per Passes Completed within and into the Final Third
  6. Shots on Goal per Shots Taken, and
  7. Goal Scored per Shots on Goal (times 2)

The two categories making up the new Index composition are highlighted in boldface font…


Well for me – in how PWP has developed – I don’t think I quite captured the mroe significant intent of a team to penetrate (given any style of attack – direct, counter, or short pass type of engagement given conditions on the pitch) nor do I think I really captured the considerable value of a goal scored – in any fashion (be it in run-of-play or via set-piece).

I don’t think this violates the integrity of the general tendency of teams and their behavior – I think it actually better represents the importance (weight) of a goal scored as well as the considerable advantage some teams show in being mroe accurate (in passing) as space on the pitch diminishes.

Finally, in making this adjustment I don’t violate the integrity of the original data points collected – I just am finding a better way to translate that quantity of information into a different output relative to quality.

So how do these changes manifest themselves in the data outputs?  I’ll let the diagrams and Correlation of Coefficient (R) speak for themselves.

Major League Soccer 2014:  (Before and After)


English Premier League: (Before and After)


Bundesliga: (Before and After)


La Liga: (Before and After)



Major League Soccer 2015: (Before and After)



Gluck: Fourth Year Anniversary Edition

My thanks to everyone who has supported my web site the last four years!

It’s been a learning experience for me and, I hope, for you too.

As the new year starts I’ve got at least five new articles planned; here’s a quick synopsis on what to expect:

  • Following up on Coaching Youth Soccer Part I and Coaching Youth Soccer Part II, I’ll be offering Coaching Youth Soccer Part III – digging into which team statistics to use, why, when, and how to use them.  For those who don’t know me these three articles highlight my coaching philosophy into one three word catchphrase “muscle memory mentality“.
  • Two new individual soccer statistics:   This (may?) be controversial – My intent is to submit two new, professional level, individual, soccer statistics that could transform the player market value system.

Said differently; are private statistics companies, like Prozone Sports, OPTA, and InStat (along with player agents) manipulating the player market value system by ignoring what might be the most logical, intuitive, individual soccer statistics ever?

  • Expected Points – An updated version of my previously created Expected Wins series of articles.  A follow on to what was offered at the World Conference on Science & Soccer 2017, Rennes, France.
  • Expected Goals – A new way to calculate this over-hyped soccer statistic that brings it a bit closer to reality.
  • World Cup 2018 Total Soccer Index; to include predicting the winners after round one is complete.

For now, in case you missed one or two, here’s my rundown on the top five articles in each of the last four years.

In Closing:

  • I called for Jurgen Klinsmann to be sacked after WC 2014 because his tactics and in-game adjustments weren’t up to snuff.  Three years later the rest of the american mainstream soccer media world agreed and Klinsmann was sacked.
  • I called for Sunil Gulati to be ‘ousted’ after WC 2014 because his leadership in helping youth development and head coach selection weren’t up to snuff. Three years later the rest of the american mainstream soccer media world agreed and Gulati is out.
  • In hindsight – I wonder where we’d be in youth soccer development if we’d have made those decisions three years ago?
  • No, I do not favor Caleb Porter as the next US Men’s National Team head coach.  I like Caleb, he’s a stand-up guy and always took time to share and listen.  That said, in my opinion, he’s not (consistently) good enough at reading in game situations and making tactical adjustments that lead to better performances; the exact same issue I had with Jurgen Klinsmann.  .
  • I’m hopeful either Eric Wynalda or Steve Gans are elected as the next United States Soccer Federation President; electing Kathy Carter is a NO-GO in my view as there’s perceived ‘collusion’ between MLS and SUM.  As a retired Air-Force veteran perception is reality until proven otherwise – some may disagree?

I wish you all the best for the new year.




Will 2015/2016 MLS Champs be Chumps by the End of this Season?

The Portland Timbers have opened their season no different than the four previous seasons under Caleb Porter – on their back foot.  But is there something different about this years’ team that may cause one to wonder how this season ends?


Here’s why – and yes it’s down to statistics.  At no time in the previous history of the Timbers have they started so low when it comes to statistical team performance.  Evidence for your consideration is provided below:


Note this is big picture – what I feel and think the senior leaders should be viewing to get a feel for how the Timbers are working, as a team, versus the quality and quantity behind those numbers.  Have no fear I’ll get there too..  Let’s not kid ourselves – the Timbers have access to this information and much more – so this shouldn’t be new news to the Timbers front office; it should be an early warning sign of a potential earthquake that could shake the foundation of this team.

For now let’s take a look at what this data offers…

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So with those big picture stats offered – here’s some deeper grist for grinding the teeth if you’re a Timbers supporter:


Passing volume in total:

Passes outside the attacking final third:

Passes within and into the attacking final third:

Shots taken:

Shots on goal:

Goals scored:



Possession percentage:

Passing accuracy:

Percentage of passes within and into the attacking final third:

Percentage of shots taken per completed pass within and into the attacking final third:

Percentage of shots on goal per shots taken:

Percentage of goals scored per shots on goal:



I don’t dig into this part of possession with purpose too much as it’s more relative to betting than anything else.  But I do think it’s worthy to show others what the Timbers predictability index offers.

As a reminder the PWP Predictability Index is the PWP Index (minus) all activities relative to a goal scored – a real prediction model does not use the projected end-state data to predict the future end-state – it uses the data leading up to the end-state to predict the future end-state.  So all those who track Expected Goals – it’s not a prediction model at all…


Now the tough questions:

Or………  Is Caleb Porter really just tinkering as he prepares the Timbers for CCL and the stretch run through the hot part of the season?

Or………  Is Caleb Porter human, like the rest of us, and he’s scratching his head as much as we are about what isn’t working this year that worked previously?

As a previous youth head coach and general manager I think it’s a little of both – there are times, early in the season, at any level, where it’s worthy to try out different things.  An offshoot on doing that is the team gets to gel and work out kinks that are likely to help them take more points as the season progresses – or in the case of the Timbers – not only help them make the top six in the Western Conference but also help them in CCL.

That said I do think it’s worthy to bring up one point about this year versus last year – Jorge Villafana is missing.

I don’t say this to personally dig anyone this year – instead two diagrams for your consideration – on how I think last year is different from this year:

Left fullback area in red for last year – a no go spot for most teams in attack – i.e. where Portland was inordinately strong in defending.  Ther ewere games last year where Jorge Villafana had virtually no defensive touches in a game – this year the left fullback position cannot say the same.

So with the opponent now having a complete width of the pitch to use the Timbers defense is stretched – not unnaturally compared to any other team – but unnaturally compared to last years’ team…

And that’s why I think their is considerable cause for  concern this year – the Timbers simply don’t have the shut down capability on either wing to decrease the size of the attacking space the opponent has available.  And with that normal size of space the opponents are now getting better shots on goal.

Path forward – with Jorge Villafana out I am stead


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

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.


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


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: Do “Expected” Soccer Statistics add Value?

From an general viewpoint, and adding awareness to the ever-growing soccer public of the United States, yes…  Expected Passes and Expected Goals add value. 

They’ve made their way into the #soccer #stats genre and most recently Expected Goals has appeared in many United States soccer national TV productions.

I suppose this is a good thing as they offer some interesting graphics (eye-candy) to help new followers begin to learn the game but for those of us who understand the game they’re more #fakenews than anything else; kinda like the Audi Player Index.

A bit of flash/click bait that offers something but really nothing; more harshly offered: #fakenews.

So why am I publicly lambasting some pretty exception statistical modeling by some very smart guys?  Here’s why:

From a personal standpoint, over four years ago, when I first started developing Possession with Purpose analysis I sat down with Caleb Porter for nearly an hour to discuss the value of statistics.

He imparted to me there’s plenty of information out there – the goal is to filter through the gloss and come up with analytical tools coaches can use (not only in the off-season, but during the season) that will lend value to what gets trained week-in and week-out as you prepare for each game.

Listening to what Caleb Porter offered was good enough for me, but if you need other (statistical/technical) reasons to know why Expected Passes and Expected Goals are flawed – not only from a coaching perspective but from a general knowledge perspective read on.

Expected Passes:

In my first article on passing statistics  (May 2014) I provided clear evidence that global soccer statistic web-sites, like, and all identify AND publish different passing totals for the same games.

In my example (for the same game) the MLS chalkboard showed one team had completed 434 passes, while MLS Statistics indicated 369 completed passes, versus Squawka indicating 356 completed passes, and Whoscored indicated 412 completed passes.

That inconsistency appeared time and again between these three web-sites even though all those web-sites use the F-24 data thread developed/provided by OPTA.

The reason for the numerical differences is how those web-sites define ‘passes’.

Do they include headers, through-balls, throw-ins, and crosses as passes (F-24 tracks those separately); some web-sites include some of those and others don’t.

For me, ALL of those actions are passes, and the reason why is they are used to ‘define’ movement of the ball from one location to another location without dribbling.

When quantifying ‘expected passes’ are ALL types passes used, if yes, are they weighted equally?  I’d offer it’s far easier to make a successful throw-in from anywhere on the pitch than it is to offer a successful cross.

And, if using different web-sites, for different leagues analyzed, are the same equations used and are the exact same types of passes included in one web-site the same types of passes counted in the other web-site?

What about passes that are made simply for the sake of opening up the defensive unit?

As a soccer coach I instruct/direct players to make passes, knowing they will be unsuccessful, in order to stretch the back four or relieve up-pitch pressure.

ALL of those passes are made “knowing” ahead of time they are unlikely to be completed.

When quantifying ‘expected passes’ (a statistic built on successful passes) the calculation penalizes players for unsuccessful passes even though there was specific intent in offering those passes.

Soccer is not a game where one team plays on the pitch “without” being impacted by the opponent – it’s two teams trying to gain possession, keep possession, move the ball, and score a goal…

Meaning passes attempted are a function of what the opponent gives as much as what you try to take as a team.

  • If the opponent plays a low-block passes outside the attacking final third are inherently easier to complete than those within the attacking final third.
  • If an opponent plays high pressure – passes outside the attacking final third are inherently harder to complete than when playing a team who bunkers.
  • You get my drift, yes?

When quantifying ‘expected passes’ the calculation ignores the defensive ‘team’ alignment of the opponent.

What about taking into account the location of the opponent in relation to where the pass is offered?

Soccer statistics don’t qualify whether or not the player completing the pass was being hindered (closely marked by a defender) versus in open field.

When quantifying ‘expected passes’ the calculation ignores the position of the opponent relative to the player making the pass.

What about taking into account the field conditions?

Recall the game against Costa Rica a few years ago in Colorado – the pitch was covered in snow.

How about a game played on field turf, or a narrow pitch, or an extremely wide pitch.

What about a game played with high winds, or a water-logged pitch, or a game played in excessive heat?

When quantifying ‘expected passes’ the calcuation ignores the pitch condition and how those conditions impact movement of the ball.

Statistics have value when measured in a completely controlled environment. 

While there are parts of the game that are controlled, the majority of a soccer game, when played within the rules of law, are uncontrolled.  Its’ non-stop, in your face action, split into 45+ minutes halves.

I’ve heard many head coaches, and offered these thoughts too, after a game:.

  • “We controlled the game, there were times where the opponent had a bit of control, but at the end of the day we got our three points because we controlled more of the game than they did.”
  • Or…
  • “Although we didn’t control the entire game we came away with a draw, and when playing a team of that caliber, or an away game in this atmosphere, a draw was almost as good as a win; I’m happy with the result.”

When quantifying ‘expected passes’ the calculation ignores whether a team controlled or failed to control a game.

Expected Goals:

  • Some shots taken ARE taken for the sake of taking a shot to ‘test the keeper’.
  • Some shots are taken ‘early on’ simply to ‘show’ that a player isn’t afraid to take a shot from outside the box when given the time and space to do so.
  • Some shots are taken in ‘heavy traffic’.
  • Some shots, taken from the exact same location as those previously taken in ‘heavy traffic’, are taken with no defenders near by.
  • Some shots, taken from the exact same location, are taken with the left foot of a right footed player, or vice versa, or with the instep, or laces, or outside part of the boot, or head, or chest, or knee, or hand….????
  • Some shots, taken from the exact same location, are taken on a water-logged pitch or some other weather condition that impacts ball movement.
  • Some shots are taken, late on, that have absolutely no value relative to the score-line at that time.  In other words a goal scored when down 4-nil or up 4-nil really doesn’t matter with five minutes left..

Last, and certainly not the least, but perhaps THE most important – not all teams show the best correlation (r) of goals scored relative to points earned – some teams show shots taken, or shots on goal, or my Total Soccer Index as having the best correlation to points earned.

So, when quantifying ‘expected goals’ it completely fails to recognize that not all teams behave/perform the same way on the pitch – therefore one ‘event-based’ statistic simply CANNOT be relied upon to predict every teams’ future results.

Finally, The only shot taken that is ALMOST exactly the same (truly repeatable), with respect to player positioning, is a Penalty Kick. 

  • And even those can be deceptive given the pressure a player feels if the PK comes during the World Cup versus a domestic game that has little value to points in the league table.

Every weakness offered about ‘expected passes’ applies to ‘expected goals’ with one exception – all soccer statistic web-sites accurately count a goal scored the same way. 

In closing:

When quantifying any ‘expected statistics’ if those statistics don’t account for all those conditions, offered above, they are dangerously flawed.

Bottom line at the bottom.

Expected statistics don’t tell me:

  1. Anything I really need to know as a head coach in order to make my team better on the pitch, and
  2. Anything I probably don’t already know about the players on my team and whether or not they are good at passing or shooting.

In the old days these would probably be classified as ‘red herrings’…

In modern day terminology I’d offer they are #FakeNews.

It’s shameful national TV stations use statistics like these when they really aren’t anything more than background noise.

Good advice is often ignored – that doesn’t mean it shouldn’t be offered.

Best, Chris


Gregg Berhalter to lead #USMNT?

After an exhausting period of time (almost un-ending) I’m pretty sure US Soccer will announce Gregg Berhalter as our next US Men’s National Team Coach.

Is this a good decision?


Earnie Stewart set the tone in Philadelphia by standardizing a system of play and directing the development of players (across the entire organization) to match that system of play…. the 4-2-3-1.

A good piece, by Philly Soccer Page, highlighting tendencies of Earnie Stewart, can be found here.

Some might call him steadfastly stubborn, I tend to think of him as being stubbornly steadfast and predictable.

As a youth soccer coach in both England and America the single greatest weakness I’ve seen in American international and domestic soccer is our predictability.

Below is a heat map between Crystal Palace (left side) and Tottenham Hotspur (right side).

Tottenham, who offered 581 passes to 306 for Crystal Palace, makes far more use of the entire pitch than Crystal Palace.

Spurs players pass, turn, dribble, use their first touch and alter their facing/movement in far more congested areas than their opponent.  Doing more of this intuits two things:

  1. Players who are asked to work more areas of the pitch must have a higher soccer IQ as they have to learn to make decisions (with and without the ball) across the entire pitch.
  2. Players who are asked to work more areas of the pitch are less predictable – the more space you use the more space your opponent must plan to defend.

From a different perspective – passing distribution England vs the United States (3-nil to England):

The US Men’s National Team use virtually no space, with the ball, atop the 18 yard box…

England offered up 691 passes with > 60% possession while the Americans offered 450 passes and failed to reach 40% possession.  The US Men’s National Team ball movement was predictable and they used far less of the pitch than England.

This passing diagram, for the US Men’s National Team, is the norm not the exception.

Here’s the two most recent games played by Columbus against New York Red Bulls in the Major League Soccer Playoffs; Game 1 on the left – Game 2 on the right:


In game one Columbus pushed down the right side – in game two they pushed down the left side.

Their ball movement was predictable and lacked worthy penetration/movement, with the ball, atop the 18 yard box.

Possession – controlled possession.  

In the highest echelons of professional soccer teams that possess the ball more – earn more points on a regular basis.

  • France, who mastered counter-attacking soccer to the nth degree this past World Cup, still played controlled-possession-based soccer.  Three of their wins saw them possess the ball greater than 55% of the time.  The 2014 winner, Germany, averaged over 60% possession.
  • The best teams in the EPL this year considerably out-possess their opponents on a regular basis; the same was true in 2014.
  • The best teams in UEFA CL this year considerably out-possess their opponents on a regular basis; the same was true in 2014.
  • Two of the top four teams in MLS this year out possessed their opponents on a regular basis.
  • There will always be exceptions; here’s a couple .
    • In 2014 WC Japan possessed the ball 59.22% of the time but earned just one point; need I remind about Spain?
    • In 2018 WC Germany possessed the ball 71.97% of the time but earned three points.

A good yardstick when measuring possession, that can intuit higher soccer IQ, is when teams regularly exceed 60%.

If a team regularly hits this target it’s usually accepted that the team plays controlled possession-based soccer and they use every inch of the pitch.

A few examples before historical info about Columbus since 2014:

  • Arsenal has exceeded 60% possession in all but one of their six wins this year.
  • Chelsea has exceeded 60% possession in all of their six wins this year.
  • Liverpool has exceeded 60% possession in four of their seven wins this year – in the other three wins their possession was under 50%; a trend matching France…  being able to win with and without the ball.
  • Manchester City has exceeded 60% possession in six of their seven wins this year.
  • Columbus Crew exceeded 60% possession three times in 2014; they won one, drew one and lost one.
  • Columbus Crew exceeded 60% possession six times in 2015, they won five.
  • Columbus Crew exceeded 60% possession six times in 2016; they won one, drew two and lost three.
  • Columbus Crew exceeded 60% possession five times in 2017, they won two, drew one, and lost two.
  • Columbus Crew exceeded 60% possession three times in 2018, they lost all three.

Columbus has never consistently dominated games through controlled possession; only six times out of 34 games (2015 and 2016) did they exceed the 60% target.

How about 55% possession?  Major League Soccer has a salary cap so perhaps they have a better track record in earning points when exceeding 55% possession.  

  • In 2014 Columbus exceeded 55% possession 14 times; in those games they won five, drew three and lost five.
  • In 2015 Columbus exceeded 55% possession 18 times; in those game they won nine, drew three, and lost six.
  • In 2016 Columbus exceeded 55% possession 18 times; in those game they won twice, drew six, and lost six.
  • In 2017 Columbus exceeded 55% possession 18 times; in those game they won five, drew once, and lost six.
  • In 2018 Columbus exceeded 55% possession 18 times; in those game they won five, drew twice, and lost five.
  • All told, Gregg Berhalter has lead Columbus to 26 wins, 15 draws, and 28 losses when his team has exceeded 55% possession.

While having more than half their games exceed 55% possession, in four of the last five years, Gregg Berhalter has not shown a tendency to win more games than he loses.

How about in the general sense of out-possessing their opponents? 

Gregg Berhalter has shown a history of out-possessing his MLS competitors, has this lead to more points on a regular basis the last five years?

  • 2014: 53.84% Possession, 52 points, +10 goal differential, 7th overall
  • 2015: 53.47% Possession, 53 points, +5 goal differential, 5th overall
  • 2016: 55.05% Possession, 36 points, -9 goal differential, 18th overall
  • 2017: 51.83% Possession, 50 points, +3 goal differential, 6th overall
  • 2018: 52.57% Possession, 52 points, -2 goal differential, 10th overall

While earning more points than most opponents in some years Gregg Berhalter does not show a tendency to earn more points year in and year out.

In closing:

If it’s reasonable to intuit playing possession-based soccer means players have a higher soccer IQ and make the game less predictable Gregg Berhalter teams don’t really do that.

  • So is US Soccer taking a bold step to change the style and direction (regularly looking to exceed 55% or 60% possession per game) of the US Men’s National Team by hiring Gregg Berhalter?
    • I’d say no… not yet.
  • Does it appear US Soccer are at least lending credence to changing the style of soccer to match that of teams who historically earn more points through controlled possession-based soccer that also includes the flexibility to play a brutal counter-attacking style of soccer?  (Liverpool and France)
    • I’d say no… not yet.
  • Does it appear they are looking to increase soccer IQ and make more use of the soccer pitch than previously?
    • I’d say no… not yet.
  • Does it appear the US Men’s National Team will be less predictable?
    • I’d say no… not yet.

For now, I’m not on the Gregg Berhalter bus; but then again I’m not on the sidewalk disparaging his selection either.

Time will tell; the greatest asset Gregg has going for him is his ability to organize a team that wins more than it loses.

Having the capacity and capability to select players without regard to salary cap should be highly beneficial.

My hope is the US Men’s National Team learn to dominate the entire soccer pitch – when that happens the flood gates to create great soccer players in our country is limitless.

Best, Chris

You can follow me on twitter here:  ChrisWGluck


What’s Different About #Portland @Timbersfc this Year?

Caleb Porter left Portland Timbers at the end of 2017.  There were many rumors as to why that happened; I put (some) of my thoughts in writing here:  Porter Pulls out of Portland

I didn’t include everything and still won’t; what happens behind closed-doors should stay behind closed doors.

But here’s what I offered almost half-way through the season (last year) that led me to believe his departure would happen soon:  Getting Hot in Portland

During the off-season Merritt Paulson and Gavin Wilkinson interviewed some folks and selected Giovanni Savarese.

Many good articles and discussion sharing positive thoughts about Giovanni Savarese – none have been inaccurate so far and some, in my opinion, don’t go far enough in singing his praises.

In my five years of following/researching Portland Timbers soccer no head coach has shown a greater positive (team building) environment as well as a greater understanding of the tactical nous needed to earn points, consistently, in this league.

I would go so far as to say he’d be my first (domestic) choice to head coach the United States Men’s National Team now that Jesse Marsch has departed for Europe.

This is simply my way of offering up how good of a coach I think Giovanni Savarese is; others may disagree for one reason or another.


Anyhow, we’ve seen how the Timbers perform this year – most would categorize the Timbers as a top counter-attacking team and I’d agree.

What makes this team so special in counter-attacking is how well they ‘pack’ their defending final third while also having attackers with a great first touch.

Here’s some team performance statistics that may help tell this story a bit better.  The Timbers have:

  • Averaged less possession this year than in any other year from 2014.
  • Averaged the 2nd highest percentage of passing accuracy this year since 2014.
  • Averaged the 2nd highest percentage of penetration this year since 2014.

In other words they have less of the ball – but when they have less they do more with it.

Opponents have had:

  • Greater possession this year than in the past.
  • Greater passing accuracy this year than in the past.
  • Worse penetration this year than three of the last four years.
  • Worse creativity this year than in the past.
  • Worse precision this year than in the past.
  • Worse finishing this year than in the past.

So while the opponents have had more of the ball outside the attacking final third they’ve been less efficient with it.

Tactically the Timbers have offered:

  • Fewer crosses per game this year than in the past.
  • More shots taken than in three of the last four years.
  • More shots on goal than in three of the last four years.

From an attacking standpoint the Timbers aren’t quite as predictable in their approaches to penetrating this year than in the past.

In other words less predictability in how the team penetrates the 18 yard box has resulted in more shots taken and nearly more shots on goal than in any previous year.

Tactically the Timbers have:

  • Averaged fewer fouls per game than in the past.
  • Averaged fewer tackles per game than in the past.
  • Ceded more opponent passes within their defending final third than in the past.
  • Blocked more opponent shots this year than in the past.
  • Had fewer goals scored against them than in three of the last four years – only 2015 was lower.

It’s interesting to me the Timbers have had fewer tackles and fewer fouls while ceding more passes to the opponent.

One of my pet peeves is statisticians who offer that a high volume of tackles means a player is a great defender – I could easily argue the opposite – the fewer tackles a player has the more likely that player has not been caught out of position.

Tell the folks who created the Audi Player Index that. 😉

There is a tactical approach known as “packing and IMPECT” – an approach developed after analysis of team performance soccer statistics in Europe.

  • It was this approach that France used to great effect when winning the World Cup.
  • It should be noted France didn’t win the World Cup strictly through counterattacking – they also won three games by dominating possession too.
  • In a recent home game we saw Savarese switch to a more attacking style when playing San Jose.
  • The Timbers had 59% of the possession and earned three points.
  • This is the first time the Timbers earned three points at home while also exceeding 55% possession this year.

In closing:

While Giovanni Savarese is at the tip of the spear it’s worthy to say both Paulson and Wilkinson have done a great job in selecting him.

You only need to look at Colorado and San Jose to know hiring a new head coach can go horribly wrong.

Here’s a look where the Timbers ranked in the Total Soccer Index after Week 5:

Now. after Week 22:


Final thoughts:

I’m not joking when offering credit to Paulson and Wilkinson – you only need to pick out Colorado Rapids (CRFC) in these same two diagrams.

In Week 5 CRFC were 6th best, at Week 22, they are now 2nd worst.  The team with the other new head coach to start the season, San Jose, is 3rd worst in MLS.

I think bringing Gio Savarese in was a great move.

Best, Chris