Category: Statistics in Soccer

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: 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.

Best,

CoachChrisGluck

 

Gluck – Clearances – Clearing the Air or Clear as Mud?

My after season team performance analysis continues.

This week I’m taking a look at Clearances.  A much used individual statistic that many rely on to rate the value of central defenders.  But does it add real value?  Are defenders with more clearances better than those with fewer clearances?

My research looks at clearances from two different perspectives:

  1. What is the relationship of clearances by the opponents’ relative to a team earning points, and
  2. What is the relationship of defensive clearances by your own team relative to your team earning points.

For Major League Soccer, 2016, the number of clearances each team had, and their opponents’ had, was counted per game.

I then did a simple correlation on the number of clearances (per game) relative to the points earned (per game).  Pictured below is a summary of the two perspectives relative to their correlation to points earned.

clearances

Diagram 1

Initial observations:

  • Diagram 1:  The average number of opponent clearances (per game – right part of the diagram) has a (-.30) correlation to points earned.
  • Diagram 1:  The average number of defensive clearances (per game – left side of the diagram) has a (+.30) correlation to points earned.
  • Diagram 1:  It’s pretty clear that the correlations vary (considerably) from team to team.

Average of opponent’s clearances per game versus points earned:

  • Sporting KC gained the most benefit from lack of opponent clearances throughout the season; their correlation was (-.48).  In other words Sporting KC were more inclined to earn points when the opponent had fewer clearances.  This seems reasonable, especially since Sporting KC offered up the second most crosses (19 per game) this year.  The less likely the opponent was in clearing those crosses the more likely Sporting KC had in converting those crosses to goals scored.
  • DC United got the least benefit from lack of opponent clearances; their correlation was (-.08).  In other words the number of clearances by their opponent’s, throughout the season, had little to no overall impact in DC United earning points.  This also seems reasonable since DC United offered up the third fewest crosses (14 per game) this year.  With not many crosses offered it seems reasonable that this mode of creating scoring chances was less likely to occur.

What’s that mean?

  • For me, I would offer it means the number of defensive clearances an opponent has, per game, isn’t really a strong team indicator.

Average of defensive clearances per game versus points earned:

  • San Jose gained the most benefit from defensive clearances (.57); meaning San Jose were more inclined to earn points when having more defensive clearances per game.  This seems reasonable as San Jose faced an average of 20.5 crosses and over six corners per game; tied for 8th most in each category across the league.  A higher volume faced should result in a higher volume of clearances.
  • New York Red Bulls gained the least benefit from defensive clearances (-.01); meaning the Red Bulls were just slightly more inclined to earn points when they had fewer defensive clearances (per game).  What is unusual with New York is they averaged a greater number of defensive clearances (21 per game) but faced fewer crosses and corners than San Jose.

What’s that mean?

  • For me, I would offer it means (again) the number of defensive clearances a team has, per game, doesn’t greatly determine the outcome of a game.

In conclusion:

  • If neither opponent defensive clearances per game, nor your own teams’ defensive clearances per game, don’t have a strong correlation to points earned then the individual player statistics – that make up those clearances’ statistics won’t have much value either.
  • If anything – given the wide variation in clearances’ value, relative to points earned, a players’ individual clearances (per game) should be weighted relative to that game – and that game only.  Recognizing that the ‘weight’ of those clearances is subject to change every single game.
  • Perhaps what’s really missing here is the volume of “clearances not made” instead of “clearances”?
  • Finally, as a ‘giggle check’ if-you-will, I did take a look to see if the correlation of clearances was over .50 relative to the number of opponent crosses and corners offered – it was.  The average correlation across the league was .71 – quite strong…  see Diagram 2 below.

    clearances-vs-opponent-crosses-and-corners

    Diagram 2

  • So our own common sense is supported by data analysis.
  • Said differently; “common data sense” shows the volume of clearances are related to the volume of crosses and corners.
  • Therefore… (in my view)
    • If “the common data sense” (shown in Diagram 1) does not show the volume of clearances having a strong relationship to earning points then our own common sense should follow that view.
  • Again reinforcing that individual defensive clearances, as an effective individual statistic, does not add real value at all.

Best, Chris

COPYRIGHT – All Rights Reserved.  Trademark: PMP

 

 

 

 

 

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 Squawka.com, Whoscored.com and MLSSoccer.com 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

@CoachChrisGluck

@MLS Total Soccer Index Power Rankings and More

The all star game happens this week so what better time to review where teams compare in the Total Soccer Index.

After every team had played 17 games; (halfway through the season).

Top team with 17 games in, points wise, was not Atlanta it was FC Dallas; Red Bulls of New York showed most consistency in quality across the pitch.

  • With Jesse Marsch departing it will be interesting to see if Red Bulls can continue that run of quality – if they do they could be a great bet to win it all if the pundits stick with Atlanta given their edge in points earned.
  • I also feel vindicated, as a pundit, as I recognized Jesse Marsch as the best ‘domestic’ MLS Head Coach option for the US Men’s National Team last year; read it here:.
  • Of additional interest; my second choice to coach the US Men’s National Team would be Oscar Pareja… not Gregg Berhalter.

Oscar Pareja, like Jesse Marsch, shows greater consistency of purpose in earning points when his team plays with OR without the ball.

Worst were Orlando, Colorado, and Montreal…  more to follow on just how bad Orlando and Colorado are; even compared to Chivas USA!

Here’s how the teams stack up (in one big conference) for total games played.   The Eastern Conference rules the roost.

Best and worst playing at home; the Eastern Conference rules the roost:

Best and worst playing away from home; only LA Galaxy,, of the Western Conference, squeeze into the top 3:

The elite teams at home are pretty much the elite teams on the road; some of the worst teams on the road are also in the bottom third at home.

When you’re good, you’re good, when you’re bad you’re bad.

In closing:

I don’t live in Orlando and I don’t follow their team, but it seems to me there’s some significant issues with that club at ALL levels.

  • In any other league in any other country they’d be relegated.
  • Instead they’ll get extra money to make them ‘appear’ to be more competitive.
  • When you’ve got a bad front office and ownership group you’ll consistently have a bad team.

To give you an idea what I mean; here’s the Total Soccer Index for every team that’s played in MLS since 2014 (all games included in this analysis). 

Only one team (not Chivas USA) has shown worse overall (combined) team performance (2014, 2015, 2016, 2017, and now 2018) than Orlando…. San Jose!

As much as folks might blast Orlando City ownership there should be equal, if not more, attention sent the way of Colorado and San Jose, as well as Chicago (maybe?).

  • Relegation would have seen these teams probably sink to division 3 if US Soccer ran a proper organization.
  • Ironic that Chivas USA has actually shown better in team performance than Montreal, Chicago, Colorado, Orlando, and San Jose…
  • When is the last time you heard soccer pundits calling out those organizations like they did Chivas USA?
  • If ever an organization needed to hire a forward-thinking, team-performance statistical analyst it’s Orlando, San Jose, Colorado, or Chicago.  Give me a call. 🙂

While Atlanta have shown well their first two season, the two most consistent teams (across 5 years’ measurement) are Red Bulls and FC Dallas.

  • Just another piece of team performance data for US Soccer to analyze that confirms both Jesse Marsch and Oscar Pareja are far more consistent in having their teams ‘earn points’, while also showing consistent quality across the entire pitch, than Gregg Berhalter.
  • Oooh, that’s likely to be a snarky comment for some and a front loaded criticism of US Soccer if they decide to hire Gregg.  Note this isn’t me saying he’s not a good coach – he is – but is he a better selection than Pareja or some other guy who coaches domestic soccer in Europe?

Best, Chris

Gluck: Who should Head Coach the #USMNT in @USSoccer?

I’m sure there’s many ways to determine what Head Coach might best lead the US Men’s National Team out of darkness…    

I’ve narrowed my scope of who might fit best by limiting the selection pool to those who currently lead a team in Major League Soccer.  This obviously includes a broad band of candidates – you need to start somewhere.

In today’s environment, world class national and domestic teams are great at “controlling the ball” AND/OR great at “controlling the opponent when they don’t have the ball”.

I’ve taken that statement and converted it into measuring four categories of possession:  

  1. Points per game a Head Coach averages where their team has equaled or exceeded 55% possession,
  2.  Points per game a Head Coach averages where their team has possession greater than or equal to 50% possession but less than 55% possession,
  3.  Points per game a Head Coach averages where their team has possession greater than or equal to 45% possession but less than 50% possession, and
  4.  Points per game a Head Coach averages where their team has less than 45% possession.

My intent is to try and quantify/qualify three basic styles of play:

  1. Possession-based with controlled possession starting from the back,
  2. A mixture of controlled possession and controlled counter/direct -attacking, or
  3. A team relying solely on “controlling the opponent when they don’t have the ball” and offering counter/direct attacking as a method of penetration.

My relationships between the four measured categories of possession and three styles of play are:

  • #1 with #1,
  • #2 & #3 with #2, and
  • # 4 with #3.

Its’ not perfect, but then again, soccer isn’t perfect either.

Note:  Prozone has identified ~ 7 styles of play – I try to keep things simple.

I’ve made a list of five Head Coaches for consideration:

  • Gregg Berhalter,
  • Oscar Pareja,
  • Caleb Porter,
  • Peter Vermes, and
  • Jesse Marsch

Why didn’t I include Jason Kreis?

He’s been relieved of coaching duties twice and failed to make the playoffs with Orlando City.  Something, somewhere isn’t working…  nothing personal.

Here’s their initial PPG by each category from 2014 to 2017 (excluding the final two games):

The cells highlighted in green show which Head Coach had the highest PPG (per year) in the four categories listed.

It’s pretty clear those five coaches having varying strengths in earning points relative to the four categories of possession.

Here’s their average PPG over the last three years in an attempt to quantify/qualify their “consistency of purpose” – a phrase usually associated with Dr. Deming:

So how do their teams perform against conference opponents?

An attempt to measure how well each coach’s team performs against a “known quantity”; similar to the US Men’s National team playing “known” CONCACAF opponents…

Note the 2017 data excludes the last two games of this season.

Finishing Touches:

Jesse Marsch shows best (“consistency of purpose”) in:

  • Earning points per game in three of four possession categories over the last three years,
  • The Total Soccer Index versus “known” opponents over the last three years,
  • Goal differential versus “known” opponents over the last three years,
  • Earning points versus “known” opponents over the last three years.

Who’s your choice?

 

Best Chris

You can follow me on twitter @CoachChrisGluck

Gluck: It’s not just @USSoccer that needs to “wipe the slate clean”

I’m not on the bandwagon of blasting US Soccer, USSF, Sunil Gulati, the Coaching Staff, or the Players anymore – that’s old news for me; especially since the rest of our soccer media has finally caught up to what I was thinking after the US Men’s team performance in World Cup 2014. 

Here’s my summary of issues back then that STILL REMAIN today:

  • They lack on field leadership.
  • They lack the ability to possess the ball with any sense of conviction.
  • They lack the ability to penetrate with any sense of continuity in possession leading to that penetration.
  • They are predictable.
  • They lack “controlled aggression”.
  • Their team passing statistics are horrible.
  • They lack a pure #9, #8, #7, and #6 in the traditional sense of soccer.
  • They lack ‘shut-down’ fullbacks.
  • They lack center-backs who can not only possess the ball, but control space in and around their own 18 yard box with pace and fortitude.
  • They have a great goalkeeper.
  • Some of the players are really-really fast, many are slow or really slow.
  • Some of the players have a great first touch, many don’t.
  • Both Head Coaches have shown an inability to use the right tactics against opponents.

So am I personally surprised by the result?

No… and I don’t know why other guys who’ve played at that level are!

Anyway, since I’ve already lambasted US Soccer and Sunil Gulati, many times over three years, my target for today is mainstream soccer media.

Yes…  in the last two days mainstream media has blitzed Sunil Gulati and US Soccer/USSF given the horrendous result against T&T.  In a way, rightly so, but in a way…. very disappointing.

Why disappointing?

It’s disappointing because there’s nothing here you shouldn’t already know if mainstream news/TV media had done their job of informing/educating our country in HOW soccer is played and what statistics should be used to quantify or qualify results.

 

Hmmm…  you sure about this Chris?

Yes… here’s why.

Throughout the course of US World Cup qualifications mainstream media has quantified and qualified good or bad performances of players and coaching decisions based on the use of “event-based” statistics.

Here’s some you may be familiar with:

Expected Goals, Expected Passes; numbers of Clearances, Tackles, Recoveries, Crosses, Missed Chances, Key Passes, Goals Scored, Shots Taken, Save Percentage, Blocked Shots; or Composite indices like the Audi Player Index or Castrol Index plus countless other ones too many or unworthy to name.

This information is well-intentioned but if you KNOW and understand HOW soccer is played NONE of these statistics have value UNLESS the author or TV pundit qualifies the data based upon how the opponent influenced those outcomes.

So… in EVERY instance (EVERY article and EVERY TV broadcast) mainstream media uses these event-based soccer statistics they facilitate ignorance of the mainstream soccer audience.

In other words, in modern terminology all that info they use is “fake news”…

But wait, there’s more… what is an article (in today’s environment) without including at least one tweet.

Last week the most popular @MLSSoccer.com writer, Matthew Doyle, tweeted about Paul Arriola, a good player who brings “energy” but world-class…. no.  My response, however harsh, is included.

 

Finishing Touches:

Matthew Doyle, from MLS Soccer, offers this quote in a recent article.

“Second is that, at the end of the video from last night, you can see me pleading for you (yes you, the one reading this) to get involved, specifically as a coach or a referee.”

I am involved.  I am a coach, I have coaching qualifications both here and from the United Kingdom.

I’m also a soccer analyst who’s been published in London and my statistical analysis has been presented at both the 2014 and 2017 World Conference on Science and Soccer.

So I have standing in what I offer as criticism to you and mainstream soccer media.

Finishing Touches:

I won’t prejudge MLS but I will offer some suggestions for MLSSoccer.com given my standing:

  • Stop the incessant use of individually tracked event-based statistics without qualifying what they mean relative to how the opponent played…
  • Stop advocating the Audi Player Index…
  • Stop advocating Expected Goals…
  • Stop advocating an MLS Best XI that excludes fullbacks or offers up a 3-4-3 when roughly 86% of teams in Major League Soccer play with four defenders, not three…
  • Stop advocating a Major League All Star starting squad that doesn’t account for ‘all’ the primary positions on the pitch, that means fullbacks, center-backs, wingers, central attacking and defending midfielders, forwards, and out-and-out strikers.
  • Stop advocating that a throw-back player of the 1990’s actually fits into a modernized 2022 US Men’s National Team.
  • Find writers/analyst who KNOW HOW soccer is played – not just guys who write articles that are offered simply as “click bait”!
  • Enforce that all academies (and all affiliated soccer clubs to those academies and the parent organization) are no longer “pay-to-play” this includes health insurance and travel.

Bottom line at the bottom:

Mainstream media organizations MUST take responsibility and retrain or sack writers/analysts and stop sponsor-ships with people/organizations that advocate/use “statistical disinformation” (fake news).

A reminder of where the US Men’s National Team finished in the Total Soccer Index for World Cup 2014 and why I’m not surprised they didn’t qualify for WC 2018.

By the way; just because I didn’t write an article about sacking Sunil Gulati, or other things like “pay to play” doesn’t mean I disagree with great articles like this one by Neil Blackmon.

I don’t see a guy like Neil being “mainstream soccer media”…

Best, Chris

You can follow me on twitter @CoachChrisGluck

 

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

Moneyball 2 – Soccer Statistics – Taking it to the next level

Probably the biggest leader in soccer analytics (Prozone Sports) have released two products in the last year, or so, that should begin to change the landscape of soccer statistics.

I applaud their actions but I’m still not convinced they scratch the right itch, or at least my question; what are the best statistics available to better understand team performance and how individual players influence the outcome of team performance?

Warning – before settling down to read this, get a cup or pint of your favorite beverage.

My reason for wanting to know the answer to this question is three fold:

  1. The first is knowing, or having a better idea, of what information I would like, as a senior leader/owner, to best understand my teams’ performance relative to my opponents/competitors.
  2. The second is knowing, or having a better idea, on what types of players I would need to buy/sell in order to improve my team.
  3. The third is knowing, or having a better idea, on what the true value of a player is when looking to negotiate a contract.

Before digging into what might scratch my itch here’s a summary on what Prozone Sports has done of late:

Styles of play (Possession) = (tactical profiling).   Dr. Hector Ruiz and Prozone Sports have identified eight styles of play (what I almost call eight styles of possession).  They are:

  • Direct
  • Counter-attacking
  • Maintenance
  • Build-up
  • Sustained threat
  • Fast tempo
  • Crossing
  • High pressure

Each of these styles of play have definitions and video examples on what they mean and how they are calculated.  It’s a great presentation and opens the eyes on many different ways we can view the game.

Now my own thoughts on Tactical Profiling:

  • When I first met Dr. Hector Ruiz, at the 2014 World Conference on Science and Soccer we, Hector, myself and Ben Knapper (lead statistical analyst for Arsenal FC) had some enlightening discussions on what new ideas might come forward after seeing how team performance analysis, using Possession with Purpose (compare and contrast the attacking team to the defending team), had shown trends that could help categorize team behaviors.
  • Their tactical profiling approach begins to do that but it mostly ignores the defending team and what actions defenders take that may influence those seven different styles of attacking play/possession.
  • Note that I offer ‘mostly ignores’ defensive actions.
  • In truth, Tactical Profiling offers seven styles of play/possession not eight.
  • “High Pressure” is not an attacking style of play/possession, it’s a defensive tactic – and it belongs on the other side of the equation.
  • If Prozone Sports were to build a comprehensive style of play listing then they would need to include the low-block, bunkering-in, a high block, a single pivot, a double pivot (central defending midfielder schemes) or any other type of block involving three, four, five, six, seven, eight, or nine players.
  • They don’t, and I think they saw that flaw; hence their creation of Game Intelligence. A way to begin to measure the un-measured statistics in soccer.

Game Intelligence; A product developed by Prozone Sports that is similar to what has been offered in the NBA (gravity).  They have identified eight of them; here they are with their brief definitions provided by Prozone Sports:

  • Defensive Balance: Automatic recognition of imbalances created by the attacking team
  • Numerical Dominance: Identify overloads and underloads
  • Defensive Press: Pressure applied by a defender to the attacker in possession
  • Player Vision: Number of passing options available
  • Player Attraction: Number of defenders synchronised to an attackers movement
  • Player Space: Estimated region of the field a player has ownership over
  • Time On Ball: Time of nearest defender to intercept attacker
  • Offensive Channel: Quality of access an attacker has to goal

Now my own thoughts on Game Intelligence:

In short, this product is an attempt to address the weaknesses in current statistics – there isn’t a great way to measure the un-measurable. In particular the unmeasured statistics on the defensive side of the pitch.

Like Tactical Profiling I believe Prozone Sports have taken a great leap forward in doing this; but it still lacks.

A good example on why it lacks is the statistic called “Player Attraction”.

  • Prozone defines “Player Attraction” as the number of defenders synchronised to an attackers movement.
  • I would submit this isn’t a realistic metric as it assumes all defenders are, or can be synchronized, to defend against just one player.
  • Even if they were, I would find it very hard to believe a Head Coach would be willing to offer that tactical approach publicly.
  • So in returning to what Hector, Ben and myself discussed I would think a more appropriate title would be “Gravitational Pull”.
  • And the definition would be ‘how to measure the space and time a player creates elsewhere’.
  • This way that statistic isn’t prescribing a pre-determined coaching decision – it is only prescribing a team behavior that may be seen relative to one or multiple players versus another player.
  • A more accurate (team) metric, working from this, is one that answers the question —> “how effective is the team in using that time and space to create or take a shot that winds up on target and in the back of the net”?
  • I would call this metric (“Push-Pull). And the higher that metric the better.
  • In other words the individual metric is “gravitational pull”; and the team performance metric is “push-pull”. Where the value of “gravitational pull” only has relevance where the team metric “push-pull” is high.
  • After all – the true value of a player with high “gravitational pull” only adds value if the team is great at executing “push-pull”..

In conclusion:

  • None of these new statistics adds value in tracking team performance unless the output from those individual statistics shows a positive or negative correlation to possession or penetration that then translates to a positive or negative correlation relative to shots taken, shots on goal, and goals scored.
  • And like my analysis has shown in the article on Busting the myth of Moneyball statistics in Soccer – those correlations may differ from team to team and league to league – meaning one size does not fit everyone.
  • For me this is the biggest fatal flaw of these two new approaches by Prozone Sports.

So what’s next?

I think the number of styles of play/possession by Prozone are excessive. Its’ simpler than that. For me there are only two styles of play (possession). They are:

  • Possession with the intent to possess, and
  • Possession penetration into the attacking third.

What does Possession with the intent to possess mean?

  • This style of possession does not have an objective end state of penetration; the objective end state is to control the ball so the opponent doesn’t control the ball.
  • This style of play occurs outside the attacking final third and many call it defending with the ball – I do too.
  • Two Tactical Profiling statistics fall into this category – maintenance and build-up.
  • I suggest four things can influence success or failure of this style:
    1. Opposing players who apply pressure in a certain area,
    2. The Head coach of the team with the ball,
    3. The Head Coach of the team without the ball, or
    4. Attacking players making in-game decisions based upon how they read the opponent’s actions.
  • For me this is where newer statistics like Game Intelligence and Tactical Profiling come in – and where there are high positive or negative correlations of that data relative to this style of possession I then begin to narrow down what individual statistics best support those higher correlations.
  • Given my Moneyball analysis (link provided earlier) I expect those correlations to vary from coach to coach, team to team, league to league, and year to year.
  • In other words one size of statistic, team or individual, does not fit all teams in all leagues, year in and year out.

What does Possession Penetration into the Attacking Final Third mean?

  • This style of possession has one objective – score a goal.  If you aren’t penetrating there is no real intent to score a goal.
    • In looking at what Prozone Sports has developed I think five of the other six styles of play (possession) fit here; direct, counter-attacking, sustained threat, fast tempo, and crossing.
    • For me, the same four things that influence Possession with the intent to Possess influence possession penetration into the attacking final third.
    • I should note here that PWP does not include dribble penetration.  Why?
    • Because most statistics show that a player only travels with the ball about 180 meters per game – versus traveling without the ball almost 6-8 kilometers per game.
    • Meaning, on a histogram, ~85%-95% of the game involves passing; and since I don’t have big data I choose to ignore dribble penetration.  It is what it is and if I had access to big data I probably would include it.
    • Anyhow, the critical point here is determining what team statistics have a higher positive or negative correlation to successful penetration that results in more goals scored.
    • This is where newer statistics like Game Intelligence and Tactical Profiling come in; where those next level of statistics show higher correlations to goals scored then you can begin to narrow down what individual statistics best support those higher correlations.
    • As noted in my article on Moneyball statistics in soccer – those are expected to vary from coach to coach, team to team, league to league, and year to year.
    • Again reinforcing that one size of statistic, team or individual, does not fit all teams in all leagues, year in and year out.

Back to my original question:  

What are the best statistics available to better understand team performance and how individual players influence the outcome of team performance?

For me, the first statistics to help answer this question are those used in Composite PWP:  The difference between how the opponent attacks against you and how you attack against the opponent.  With these team statistics being the most important:

  • Overall possession percentage
  • Passing accuracy across the entire pitch
  • Percentage of passes outside the attacking final third versus those within the attacking final third,
  • Shots taken per completed pass within and into the attacking final third,
  • Shots on goal per shots taken, and
  • Goals scored per shots on goal.

Put differently – any or all of those Tactical Profiling or Game Intelligence statistics should be evaluated for how well they correlate (influence) these team statistics.  If there is a strong negative or positive correlation to any of my PWP team performance indicators then they add value.

Next – what current individual statistics, in this list below, show a consistently strong negative or positive correlation to the team statistics above?

  • Dual’s won/lost
  • Tackles won/lost,
  • Aerials won/lost
  • Successful/unsuccessful dribbles,
  • Successful/unsuccessful passes,
  • Successful /unsuccessful crosses,
  • Passing accuracy,
  • Fouls,
  • Yellow cards,
  • Red cards,
  • Total passes,
  • Blocked shots,
  • Interceptions,
  • Recoveries,
  • Off-sides,
  • Clearances,
  • Headers,
  • Shot location (seriously – every one knows the closer you are to goal the more likely you will score – the same basic logic applies to basketball as well)
  • Key passes,
  • Assists,
  • Shots taken,
  • Shots on goal, and
  • Goals scored.

In reality – none of them show strong consistency from team to team.  

  • In some cases combinations of those individual statistics do, like combining clearances, with blocked shots, recoveries, tackles, and unsuccessful passes, may show better positive/negative correlations but they still ignore the influence of un-measured statistics.
  • In other cases, shots taken, or shots on goal, or goals scored, by individual players show good positive correlation but not on a consistently strong basis across all teams.
  • And there are instances where shots taken, or shots on goal against may show good negative correlations to points earned.  Meaning the higher the opponents’ statistics in those areas the more likely the other team is to earn points.  Translating to a team that sits back and cedes possession is more successful in earning points than if that same team played a more possession-based game.
  • All this circles back to the point about a need to understand both teams activities before trying to determine what individual statistics add or detract value in analyzing that team performance.

Here’s a recent example that may better describe what I mean.

  • The Audi Player Index, sponsored and offered on the MLSSoccer.com web page identified Goal Keeper, Bobby Shuttleworth  (New England Revolution) as the second best player in MLS last week even though their team lost 3-nil to Philadelphia Union.
  • When I offered, in a tweet, that this didn’t pass the giggle test a few people responded by saying – hey – Shuttleworth had two PK saves.
  • So I then asked – so what about the Union and the players who took those PKs?
  • Might the fact that Shuttleworth made those two saves come down to the Union players offering poor PKs as opposed to Shuttleworth being solely responsible for those saves?
  • The response (to paraphrase) was…  well the Union guys have a history of not taking good PKs…
  • So even though the Revolution lost their game 3-nil, and it’s likely at least one of those PKs was a poor attempt by a Union player, Shuttleworth still got individual recognition as being the second best individual player last week!?!?

In closing – this game is about two teams playing, not one; therefore all relevant team statistics, and subsequent individual player statistics must account for both teams activities, not just one.

  • In addition, many of those team statistics can first be influenced by in-game decision making (mentality) by any player, coach or assistant coach, unplanned injuries, time left in the game, score-line/game-state, pre-game tactical formations in attack and defense, weather, pitch condition, referees, or playing at home versus on the road.
  • So if I’m a senior leader/owner in a soccer organization don’t tell me the individual statistics my players have – tell me how the team statistics relate to the bottom line and then give me the relevant individual player statistics that directly correlate to those team statistics.
  • Then I’ll know how my team compares to other teams, what types of players I’ll need to buy or sell, and how much value I put in the purchase of new players to make my team better.
  • The un-offered fourth reason why I’d like to know this information – I’m an American, and I firmly believe taking this more in-depth approach will help our country compete at the international level.  There is nothing more disappointing to a soccer supporter than to see their country lose a World Cup game.
  • And seeing outputs from our most recent game against Guatemala it seems reasonable to me there are fatal flaws being made in tactical approaches as well as player selection and I wonder how much of that is due to misunderstanding the value of individual statistics relative to team performance.
  • Finally, for what it is worth, Prozone Sports do not own the intellectual rights to the ideas behind those new statistical approaches.

Best, Chris

COPYRIGHT – All Rights Reserved.  PWP – Trademark

Possession with Purpose – Prozone – and more…

No detailed statistics today – just a narrative to pass on a few tidbits as I prepare my End of Season analysis for Europe.

The news:

The European Season is ending.

  • There’s the winners, the losers, and those that stay afloat to live another year.
  • I’ll peel back the results on the English Premier League, Bundesliga, La Liga, and UEFA Champions League in the next few weeks.
  • For now, in La Liga the PWP Composite Index has a .94 correlation coefficient (r) to points earned in the league table; the Bundesliga sits at .92, the English Premier League sits at .94, while the UEFA Champions League sits at .87.
  • All incredibly strong and far stronger than MLS (.61) this year; last year MLS finished at .87.
  • Speaking of MLS, does a league, where winners display more characteristics of counterattacking, versus just possession-based attacking, detract from predictability?
  • In other words does the lower correlation support a League’s ability to achieve “parity” in professional soccer?
  • If so, is that style/type of football attractive enough to continue to grow footy in the States?
  • If not – does that mean the business model currently set up in the States won’t ever achieve a league “status” that matches the “prestige” most seem to attach to the top leagues in Europe?
  • More to follow…

I think these two video presentations by Hector Ruiz and Paul Power, from Prozone, are worth listening to.

  • In this video (tactical profiling) Hector, who attended my presentation at the World Conference on Science and Soccer last year, talks about his latest efforts that include breaking down the different types of possession in a much greater detail than I ever could with public data.
  • Of note is Hector substantiates my finding that a Head Coach’s tactical approach can be differentiated through tracking possession (passing characteristics) on the pitch.
  • He also helps begin to solve the riddle on measuring which players perform better or worse given those different styles of possession.
  • A soap-box, for me, when looking at my article on ‘Moneyball relative to soccer’, is the inability of modern day soccer statistics to show real value on how well teammates actually influence an individual’s success or failure on the pitch relative to how the team actually plays (what style it works to).
  • Here’s a direct lift from my article referenced above…

Modern day soccer statistics, for the most part, don’t measure the appropriate level of influence teammates, opposing players, and Head Coaching tactics – as such when I say I’m not a Moneyball guy when it comes to soccer it really means I don’t buy all that crap about tackles, clearances, goals scored, etc…

I value players relative to team outputs and I strongly feel and think the more media and supporters who understand this about soccer the less frustration they will (have) in blaming or praising one individual player over another player.

  • In the next video (game intelligence) Paul takes a similar approach in analyzing team behavior like PWP – separating out defensive characteristics from attacking characteristics while also modeling a ‘defensive press’ that measures success or failure in passing based upon whether or not a defender is hindering the attacker.
  • This topic has been one that I have also touched on last year – here’s a direct quote from my article on Hurried Passes.

So what is missing from the generic soccer statistical community to account for the void in Unsuccessful Passes?  Is it another statistic like Tackles Won, Duals Won, Blocked Shots, or Recoveries?

I don’t think so – none of them generated a marked increase in the overall correlation of those three activities already identified.  I think (it) is the physical and spatial pressure applied by the defenders as they work man to man and zone defending efforts.

  • Likewise, Paul also touches on ‘passing vision’ (in my words it’s not the innate vision many of us think of for players) – it’s more a discussion and analyses (I think) on the ‘windows of passing lanes’ available to players and whether or not they have tendencies to play riskier passes versus safer passes in relation to what the defenders are doing.
  • For me this simply means Paul has taken the same defensive pressure data and flipped it to view the success or failure of a player to find another player to pass to or create a shot given the defensive pressure (lanes/vision) that are blocked or open.
  • In simplistic teams (with new event statistics) you can capture and intuit that success or failure by filtering passes as being ‘open or hindered’ and also apply that same filter to create ‘open or hindered’ shots.  My article on this approach was also published some time ago – New Statistics in Soccer (Open Pass and Open Shot)
  • Finally, Paul also speaks to a game of soccer resembling the behavior of a school of fish; I’m not sure I’m convinced that is the best analogy – especially when he talks about under-loading and overloading, but his view does closely resemble mine where the game of soccer perhaps is best represented by a single-cell Amoeba.

All told – two well crafted presentations that begin to open up and really reinforce some of my soap-box issues with soccer statistics since starting my research three years ago.

To be redundant – soccer is not just about scoring goals – there is more to the game than goals scored; these two presentations continue to support my view that the world of soccer statistics needs to continuously get better…

My back-yard / stubby pencil approach to team performance analysis is soon to be published through Rand.

  • I want to express my sincere thanks to Terry Favero – my Co-Author – who helped me navigate the challenging waters of writing an Academic Paper.
  • Terry added considerable value, as well, in researching other works to help set the stage on the differences of PWP versus other efforts developed and published across the globe.
  • Finally, Terry provided superb editorial support – a challenge in that the writing styles one normally sees in a blog are completely unacceptable when writing an Academic Paper.
  • Great fun and the first of at least two to three more.

Last but not least, the Women’s World Cup is beginning.

  • Last year I applied the principles of PWP to the Men’s World Cup – with good order.
  • I’ll refresh everyone on how that took shape and then begin to chart how PWP takes shape for the Women’s World Cup.
  • I wonder what, if any, differences will show in comparing the women’s game to the men’s game?
  • Will the data show the same trends in quality and quantity?
  • Or will we see a reduction in quantity that may end up driving an increase in quality?
  • More to follow.

Best, Chris

COPYRIGHT – All Rights Reserved.  PWP – Trademark