In my previous series on Expected Wins Four – probably more appropriately entitled “Expected Points” – I’d taken a look at how the general tendencies of four primary Leagues in Europe (England, Germany, Spain, as the UEFA Champions League) compare to Major League Soccer – Is European Football Really Higher Quality than Major League Soccer?
This time I’m focusing strictly on Europe and offering up how things stand in PWP with the season coming to a close soon. But before digging some things to share about PWP to date:
A reminder – PWP is about two things:
- The End State in that the final Index comes as close as possible to the League Table without using points earned in any of the calculations, and
- Recognizing that soccer is a game that is played in a free flowing environment – picture two amoeba fighting against each other in a confined space…. There is attempted control by the Head Coach that includes tons of preparation to set the stage for ‘an approach’ to earn three points – and then there is the game itself where there is but one time out (halftime) – no namby pamby huddles or official stoppages of play between possessions. Meaning these guys play a full-on, in your face (sometimes literally), non-stop, constantly thinking and reacting to the game that can literally see the ball go in any direction at any time… not purely random but close.
Given that, PWP attempts to tone down all that volatility and parse out general tendencies that fall within the bell curve of activities – it’s not perfect – but it’s bloody good… and yes – I have made a few mistakes along the way (if you don’t work you don’t make mistakes). The latest has been a technical mistake – the relationship of CPWP to the League Table is not an R Squared number (Coefficient of Determination) it is an R number (Correlation Coefficient).
For the stats followers that may be an issue… but even with the Modernized TSR (read here) the CTSR “R” is still generally lower (team to team) and certainly lower (table to table) than CPWP – meaning there still remains room for both statistical analytical approaches in a gmae that is played across the world…
Also, my thanks to some great research by Rob Lowe, a mate with the same passion for footy, who has asked to collaborate with me in the future. He has done some additional regression analysis on the data points of PWP with respect to goals scored and points earned. I should point out that his results show that not all six of the data points in the PWP equation independently-directly relate to goals scored or points earned. For me that is okay – and actually great news for a few reasons…
- Both of my two new statistics (Passes Completed in the Final Third per Passes Completed across the Entire Pitch – Step 3 of PWP) and (Shots Taken per Completed Pass within and into the Final Third – Step 5 of PWP) did statistically relate to Goals Scored and Points Earned (independently). Meaning those new statistics are relevant – both within the context of PWP and outside the context of PWP. It’s this statistical regression type information that should solidify these two new statistics in the world of soccer.
- For both Possession (Step 6 of PWP) and Passing Accuracy (Step 5 of PWP) – as you will see a bit later – those two derived data points were never supposed to directly (independently) relate to goals scored or points earned as a matter of course I have advocated for quite some time that they shouldn’t. PWP was built with the intention that the six derived data points only needed to relate to each other in a stair step relationship recognizing that in every game a team needs to possess the ball, move the ball, penetrate the opponent’s final third, take shots based upon that penetration, put them on goal, and score goals – all while preventing the opponent from doing the same thing.
- Another view on the outcome that Rob has noted – it’s unreasonable to analyze a game of soccer without taking those activities into account. Rob’s positive feedback was that both possession and passing accuracy act as a “smoothing agent” within the Index – I agree but with beginning to learn the nuance of writing an Academic Paper I would put it this way.
- Possession and Passing Accuracy stats have limitations when vewing overall regression analysis relative to goals scored and points earned – but those limitations actually give the overall analyst of soccer a much better understanding about the context of activities that occur when a team is performing better than another team.
- In addition, Passing Accuracy statistics provide a coach a great measurement tool for how well some players may develop and progress into higher levels of competition – to exclude data of this import really ignores some of the most fundamental training aspects a team needs to do in order to improve.
- Also, there is excessive volatility in the percentages associated with Shots on Goal versus Shots Taken and Goals Scored versus Shots on Goal – if I only look at those two things then evaluating a game is all about (pass-fail) – granted winning and losing is pass-fail. But to develop a “winning culture” a grading system perhaps more appropriate is A-B-C-D-F – in other words there are levels of success above and beyond pass-fail – especially when you are a team that isn’t at the very top of the league.
- By having Possession and Passing Accuracy in the equation you get a much larger (explanatory) picture on the culture of success – and as things appear to take shape, the Index itself, gives better clarity to that level of success for teams that are mid-table as opposed to bottom dwellers or top performers…
Now for the grist in Europe – first up – England:
Note that the first two diagrams (in each four diagram grouping) highlight where the highest quantity and highest quality occurs within each competition – after some growing pains (earlier Expected Wins measurements) all four competitions now see the teams that win having the highest averages, in all categories, for both quantity and quality… proving (for the most part) that more is better and more results in more…
All told the correlation, at this time, remains very strong – note that the “R” has replaced the “R2” in my third and fourth diagrams.
If I remove Possession and Passing Accuracy from the CPWP Index – the R value drops to .78 – statistically reinforcing that the Index, itself, better represents the standings in the League Table by including Possession and Passing Accuracy data. Proving yet, another way, that goals scored and shots taken simply do not provide adequate depth on what activities occur on a pitch relative to earning points in the League Table! And if you’ve read Moderning TSR this doesn’t mean ATSR/DTSR or CTSR doesn’t have value – it does…
As things stand today Chelsea take the League and since Man City, Man United, and Arsenal round out the top four (different orders) in both CPWP and CPWP-PI I’d offer it’s those four that advance to the UEFA Champions League next year. The bridesmaid looks to be a two horse race (Spurs supporters may argue that) between Liverpool and Southampton.
Note that Southampton edges Liverpool in CPWP but that Liverpool edges Southampton in CPWP-PI – meaning when excluding Goals Scored – Liverpool has better quality than Southampton – so for Liverpool it’s more about converting Shots on Goal to Goals Scored – while for Southampton it’s more about getting clean sheets and scoring at least one goal; at least in my view – others may see that differently?
In retracing the earlier discussion on the data within the six steps of PWP – as you can see in both the first and second Diagrams (for all competitions) the Exponential Curve (Diagram 1) and well as Power Curve (Diagram 2) the stair step relationship between the data – point to point – are incredibly high… Even more intriguing is how close those “R2” numbers are for both winning, drawing, and losing… really driving home the point, in my view, just how small the margin of error is between winning, drawing, and losing.
For goals scored (for or against) we really are talking about 5 or 6 standard deviations to the right of the bell curve…
Perhaps the most intriguing issue this year isn’t the FC Bayern story – it’s the lack of goal scoring in Borussia Dortmund – when viewing the CPWP Predictability Index clearly Dortmund is offering up all the necessary culture the team needs in order to succeed – with one exception – goal scoring…. wow!
Another surprise may be Wolfsburg I’d pick them, and Bayer Leverkusen to finish two-three in their League Table – both show pedigree in team performance both with and without considering goals scored…
Barcelona and Real Madrid are locked in for the top team battle – my edge goes to Barcelona. I’d offer more here but I’m simply not up on the La Liga as much as I’d like to be…
UEFA Champions League:
The top eight teams that advanced are identified above – given the general success of CPWP relative to the top eight I’d expect FC Bayern Munich, BArcelona, Real Madrid, and Juventus to advance to the semi-finals.
My first of at least 4-5 Academic Papers is soon to be published – my thanks to Terry Favero for helping me work through this new experience – his support, patience, and knowledge in navigating all the nuance associated with writing an Academic Paper has been superb!
All four European competitions show more gets you more – this was not the case for Major League Soccer last year:
When more gets you more in MLS then I sense MLS has reached the BIG TIME – until then I think it’s a great breeding ground for Head Coaches that simply can’t get a job with a soccer club that has huge pockets of money.
Put another way – and many may disagree… I think a Head Coach who really wants to challenge their intellectual grit against another Head Coach can have greater opportunity to do that in MLS than they can by Head Coaching most clubs in Europe.
Why? For at least one reason – a Head Coach in MLS really has to do more with less…
Errata – the first MLS slide indicates 654 events – the correct number is 646 events…
COPYRIGHT – All Rights Reserved. PWP – Trademark
Since the inception of Possession with Purpose one of my goals was to try and develop a strategic set of indicators that can be used to assess team performance in both attacking and defending.
The idea that it would garner the global interest that it has is unexpected – since publication the approach has been presented at the 2014 World Conference on Science and Soccer and the accompanying academic paper is scheduled for publication later this year through Routledge. Needless to say I’m pretty ‘chuffed’ with those results.
But here’s the thing – I didn’t create my analytical approach for publication, I created it to be used by those who teach/coach the game of soccer to our youth.
Bottom line for me is an approach like this is intended to reinforce two things – 1) soccer is more than a sport it’s a passion, and 2) there really is more to this team sport than simply scoring goals. And our youth will never – ever – get better if all they think about is being the one player who scores the goal!
So where am I going with this?
Over the course of the last three years I’ve been approached by three different youth organizations, or coaches who coach youth soccer. In those discussions the coaches wanted to take my approach and apply it to their team. Needless to say I was interested in how those efforts took place and offered that I would publish an article, at their behest, to document their observations (un-edited) on the approach and how they gained value from the approach.
So that said, Mr. Carr has provided me this feedback for your consideration. What follows below is a direct quote from his document he sent me today:
I’d been keeping rudimentary statistics for my son’s club teams since his last season of U9 Academy. At first it was something I did because of my interest in sports statistics, and it kept me occupied during games instead of getting too engrossed in the game like some parents get.
But the stats I was collecting weren’t telling me anything other than what was obvious: goals, shots, etc. Then I read Chris’ Possession With Purpose, specifically in his blog post, “Getting Better as a Youth Soccer Coach”. In my son’s second U10 season I began to track events in the game as stated in that article and was able to not only track more events during games, but was able to identify trends in our own team as well as the opponent for future reference.
I track each game live (no video review) so I may miss an event here or there, but it doesn’t really affect the overall trends. I share each game’s stats with the coach after each weekend, and also when I identify any trends that he might find useful in what he instructs. He loves the information and builds elements of it into his training plans.
For example, when I first started tracking I noticed we were letting too many pass completions in our defending third and he worked more on defensive positioning, anticipating passes and closing down defenders to some good results. He can also see how the stats correspond to what he observes during the game.
We don’t share the information with players because they’re too young to really grasp it yet, and he feels it interferes with them focusing on the important items of individual player development (touches, foot skills, patterns of play, etc.) For older youth players it may have more value to the players themselves. We mainly use it to identify points to work on and to establish a general style of the opponents we play for future reference.
It hasn’t been shared outside of our team yet because I wanted to get enough data first to see how it worked with our team. I do share with a couple of parents on our team who are stat junkies like me and they like what it shows. Sometimes it tells a story that contradicts what they saw at the game themselves. The great thing about PWP is that it’s team based — even though I track individual stats they aren’t the focus; it’s the team stats and trends that reveal the most about each game and season.
What I’ve been able to determine from our team over roughly 30 games is that total possession and passing accuracy don’t mean as much as you’d think in terms of determining a win versus a loss. For our team it’s final third penetration (pass attempts and completions in that third) as well as limiting too much possession in your own third. If your final third penetration (number of pass completions in final third divided by total pass completions) is 20% or above, you have a really good chance of getting a result in the game.
The former stats are important, as in you’d rather possess than not, but it’s not the tell-all stat that most think of when they watch halftime stats on TV. My son’s team has moved from a season of 6v6 at U10 to 8v8 at U11, but the overall trends are basically the same, even with the addition of two players on the field and larger field dimensions.
I’m hopeful that others will take the thoughts offered, and analytical approach used through Possession with Purpose, and build from it.
And while some may think the outputs stemming from Possession with Purpose can’t be used, at the very highest level of domestic soccer in the United States, be advised – it’s not true.
You can follow me on twitter @chrisgluckpwp
I also co-host the YellowcardedPod as well as the Rose City Soccer Show, and appear, monthly, on Soccer City PDX, the local Comcast Sports Northwest TV show covering the Portland Timbers.
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…
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 on goal:
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
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.
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.”
- “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.
- 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.
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:
- Anything I really need to know as a head coach in order to make my team better on the pitch, and
- 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.
Two weeks in and Manchester City pretty much throws the gauntlet down against Liverpool and walks away with a dominating win.
Three other teams have also begun the season with six points (Spurs, Swansea, and Chelsea) but do those four teams show the most consistency with purpose in possession, penetration and creation of shots taken that result in goals scored?
And, do those same four teams show the most consistency in preventing their opponents from doing the same thing to them?
What about the early season dogs (QPR, Burnley, Crystal Palace, and Newcastle) – where do they fit?
I’ll try to answer those questions without too much detail given the season is just two weeks old.
So to begin; here’s the Composite PWP (CPWP) Strategic Index after Week 2:
- A quick look at the table sees the top four in the Index as being the top four in the Table – not specifically in order but there it is.
- In looking at the bottom end of the Table the bottom four teams in the Index match exactly the bottom four in the Table.
- I doubt very much the level of accuracy will match the League Table that well throughout the year.
- Of note is that Arsenal, Hull and Aston Villa are next up in the Table but Villa seems to drift down a bit in the CPWP; perhaps the APWP or DPWP might explain that drift compared to Arsenal or Hull City?
- As a reminder – the End State of the Index is to provide an objective view of team performance indicators that don’t include Points in the League Table – in other words it’s a collection of data points, that when combined, can provide value in what team activities are occurring that are directly supporting results on the pitch – sometimes results on the pitch don’t match points earned…
- In leveraging this Index last year in the MLS it was very accurate in reflecting why certain Head Coaches may have been sacked – in a League like the EPL (where everything is expensive) perhaps this Index might have even more value to ownership?
- Movement in the Index – in the MLS, this last year, I have seen teams move up as many as 12 places and down as many as 11 places – after the 4th week – so the Index is not likely to stay constant – there will be changes.
I do not quantify Index outputs specific to individual player acquisition or performance – there is no intent to do this. It’s my belief, good or bad, that even with individual star performances a team is a team is a team – you win as a team and you lose as a team… but this Index isn’t intended to stop others from doing that.
I leave that individual analyses for others who are far better at digging into the weeds than I – for the EPL I’d imagine many folks gravitate to @statsbomb or other @SBNation sites – I respect their individual analyses as I hope they respect my team analyses.
Whether the consistency of value shows itself in assessing team performance in the EPL like it has in Major League Soccer I have no idea – we will follow that journey, in public, together…
Now for Attacking PWP (APWP):
- In recalling Villa’s drift (it is still early) perhaps it’s an early indication that Villa are playing slightly more direct (given past indications analyzing Major League Soccer) – or with a greater lean towards counter-attacking and quick transition?
- In taking a quick look at their average volume of passes per game (305) compared to the rest of the EPL (456) it would seem to indicate Villa are playing more direct football.
- The team with the highest APWP while falling below the average number of passes attempted, per game, is Leicester City; they average 308 passes per game compared to the 456 average of EPL. For me that’s an early indicator that they are making the best use of a direct attacking scheme – others may have a different view?
- The team with the lowest APWP while showing higher than the average number of passes attempted, ~(500 per game), is Stoke City – that might indicate the Potters are looking to possess the ball more with the intent to possess it as opposed to penetrating with it. Folks who follow Stoke a bit closer might be able to add to that as I’ve yet to see them play this year.
- In terms of early form, relative to the six team performance indicators, Chelsea are tops with Everton, Arsenal, and Man City close behind.
- With respect to bottom feeders QPR are bottom in CPWP and bottom in APWP as well; most figured they’d be early favorites for relegation – the PWP Indices seem to lean that way already as well…
- Perhaps the early surprise in APWP is Newcastle? Not sure about that one – last time I lived in England Alan Shearer was their striker and probably the best one in the country at that time… others will no better about what Alan Pardew is up to…
Next up Defending PWP (DPWP):
- Leaders here include Spurs, Man City, Swansea and Newcastle – is this an early indicator that Newcastle has experienced bad luck already? Not sure but three of the bottom dwellers here are three of the four bottom dwellers in CPWP.
- Although not real clear here it might be easy to forget that Arsenal had a blindingly great first game and then eked out a draw against Everton in the last ten minutes; in considering that this data still just represents two games…
- Recall Stoke City – and the potential view that they might be possessing the ball with an intent to possess more-so than penetrate – even with just 1 point in the League Table their DPWP exceeds West Ham, Liverpool, and others who are further up the table.
- Man City showed great nous last year in winning the League and it reaffirmed for many of us the importance of defending – Liverpool were close last year given an awesome attack – players have changed but it’s likely the system/approach has not varied that much. And after two games Liverpool are embedded firmly in the middle of the DPWP pack.
- Can they push higher up the DPWP? And if so, will that climb in the DPWP Index match a climb in the League Table; or vice versa?
Far too early to look for trends but these first few weeks will provide a baseline for future trends.
As noted in my most recent articles on Possession – the more accurate soundbite on whether or not a team is more likely to win has more relevance with respect to Passing Accuracy (>77% in MLS usually means a team is more likely to win) and not Possession.
The margin of winning and losing in MLS is far to muddied when looking at Possession – so as the EPL season continues I will also make it a point to study what ‘soundbite’ has more relevance; Passing Accuracy or Possession.
Other links that may be of interest to you include:
My presentation at the World Conference on Science and Soccer
New Statistics (Open Shots and Open Passes)
Thanks in advance for your patience.
COPYRIGHT, All Rights Reserved. PWP – Trademark
In my passion to better understand how soccer is statistically tracked I’ve come across what I would call is an oddity about the general characterization of “passing” in the world’s greatest sport.
Here’s the deal – go to Squawka.com, whoscored.com, reference the “Stats” tab on mlssoccer.com, or review Golazo information, and you’ll notice they all provide passing information.
My intent is not to dig deep into passing details – not yet, anyway. We’ll get there in another article to follow after I get permission from OPTA to reference their F-24 definitions within their Appendices. For now here’s a simple question I have as a statistical person working on soccer analysis.
What is the number of passes I should use for teams and which denominator is the right number for total passes by both teams to help determine possession percentages?
In the MLS Chalkboard you can clearly see and count passes – here’s an example from a game this past week.
An important filter to note – the major term ‘Distribution’ is not to be clicked in creating this filter – all that is clicked is ‘successful pass and unsuccessful pass’; note also that some details are provided on the types of passes – we’ll get there in another article.
Bottom line is that the MLS Chalkboard identifies 309 successful passes and 125 unsuccessful passes for a total of 434 passes attempted.
On the MLS Stat sheet – one tab over but linked here the number of passes for Chivas = 369; that number doesn’t match the Chalkboard in either total, unsuccessful or successful.
For Golazo, for that same game here’s their total: 369 Passes total with 75% accuracy meaning the total successful passes was 277 and unsuccessful passes totaled 92. Not the same either.
For Squawka.com here’s their total:
Successful = 270 /// headers (8), throughballs (2), passes (239), long balls (21) and supposedly crosses (0)
Unsuccessful = 86 /// passes (52), headers (14), long balls (20), no unsuccessful crosses or throughballs logged here?! Yet the MLS chalkboard indicates 26 unsuccessful crosses!
All told that is 356 passes; those figures don’t match the other data sources.
For whoscored.com here’s their total: Short ball = 323, Long ball = 52, Through ball = 2, Cross = 35, for a total of 412 passes – again that figure doesn’t match the other data sources.
So what’s the right total? Here’s a table to compare showing the source of data and the total passes submitted for statistical folks like us to leverage in our analysis.
|Golazo (same as MLS Stats)||369|
I have no idea what ‘right’ looks like here but here’s what I’ve done to work through this issue.
I chose one source, the MLS Chalkboard, to gather and analyze statistics on passing and possession and all other things available from that data source – where other information is not offered there I reference the MLS Stats tab and Formation tab.
Why did I choose the Chalkboard? Because it provides additional detail that shows more clarity on all the other types of passes that occur in a game.
For example; if you scroll down on the Chalkboard link and select Set-Pieces you’ll see that Throw-ins are included in the successful passing totals – by definition a Throw-in is a pass as it travels from one player to another.
So my recommendation, if interested, is to track Major League Soccer statistics using the MLS Chalkboard first – it’s harder but seems to be the best one at this time.
I’m not sure why the MLS Chalkboard, Golazo, Whoscored and Squawka all had different team passing statistics; given that it is likely they all have different individual player statistics as well… but in asking a representative from OPTA about that – their response was provided below:
“The difference between the different websites could be down to a few things. Either they take different levels of data from us, or they take the same feed but only use a chosen set of information from each feed to display their own take on each game.”
By the way – I did try to find a reasonable definition of what a pass is defined as for soccer; here’s some of that information before final thoughts… note: they are all different and Wikipedia proves, by its definition, why it’s a pretty useless source for information… for them a pass in soccer must travel on the ground – no kidding – here’s their definition up front:
“Passing the ball is a key part of association football. The purpose of passing is to keep possession of the ball by maneuvering it on the ground between different players and to advance it up the playing field.”
Other definitions get pretty detailed – it is what it is apparently – complicated…
Passing Definition: About.com World Soccer.
When the player in possession kicks the ball to a teammate. Passes can be long or short but must remain within the field of play.
Soccer Dictionary: Note there are numerous definitions provided in this link so offering up a specific link is troublesome so I will cut and paste those definitions below:
Cross, diagonal: Usually applied in the attacking third of the field to a pass played well infield from the touch-line and diagonally forward from right to left or left to right.
Cross, far-post: A pass made to the area, usually beyond the post, farthest from the point from which the ball was kicked.
Cross, flank (wing): A pass made from near to a touch-line, in the attacking third of the field, to an area near to the goal.
Cross, headers: 64% of all goals from crosses are scored by headers.
Cross, mid-goal: A pass made to the area directly in front of the goal and some six to twelve yards from the goal-line.
Pass, chip: A pass made by a stabbing action of the kicking foot to the bottom part of the ball to achieve a steep trajectory and vicious back spin on the ball.
Pass, flick: A pass made by an outward rotation of the kicking foot, contact on the ball being made with the outside of the foot.
Pass, half-volley: A pass made by the kicking foot making contact with the ball at the moment the ball touches the ground.
Pass, push: A pass made with the inside of the kicking foot.
Pass, sweve: A pass made by imparting spin to the ball, thereby causing it to swerve from either right to left or left to right. Which way the ball swerves depends on whether contact with the ball is made with the outside or the inside of the kicking foot.
Pass, volley: A pass made before the ball touches the ground.
Passing: When a player kicks the ball to his teammate.
Through pass: A pass sent to a teammate to get him/her the ball behind his defender; used to penetrate a line of defenders. This pass has to be made with perfect pace and accuracy so it beats the defense and allows attackers to collect it before the goalkeeper.
Ducksters.com offers up a Glossary and Terms for Soccer; here’s what they define a pass as being… this one is geared more towards teaching players about various types of passes they will need good skill in order to execute them.
Direct Passes – The first type of soccer pass you learn is the direct pass. This is when you pass the ball directly to a teammate. A strong firm pass directly at the player’s feet is best. You want to make it easy for your teammate to handle, but not take too long to get there.
Passes to Open Spaces – Passing into space is an important concept in making passes in soccer. This is when you pass the ball to an area where a teammate is running. You must anticipate both the direction and speed of your teammate as well as the opponents. Good communication and practice is key to good passes into space.
Wall Passes (One-Twos) – Now we are getting into more complex passing. You can think of a wall pass as bouncing a ball off of a wall to yourself. Except in this case the wall is a teammate. In wall pass you pass the ball to a teammate who immediately passes the ball back to you into open space. This helps to keep the defense off balance. This is a difficult maneuver and takes a lot of practice, but the results will make it worth the effort.
Long Passes – Sometimes you will have the opportunity to get the ball up the field quickly to an open teammate. A long pass can be used. On a long pass you kick the ball differently than with other shorter passes. You use an instep kick where you kick the soccer ball with your instep or on the shoelaces. To do this you plant your non-kicking foot a few inches from the ball. Then, with your kicking leg swinging back and bending at the knee, snap your foot forward with your toe pointed down and kick the ball with the instep of your foot.
Backward Pass – Sometimes you will need to pass the ball backward. This is done all the time in professional soccer. There is nothing wrong with passing the ball back in order to get your offense set up and maintain control of the ball.
Now that’s probably not ‘every’ definition available but they pretty much say the same thing apart from ‘on-the-ground’ by Wikipedia – a pass is a transfer of the ball from one player to another…
As noted earlier – I’m not really sure what right looks like but I remain convinced that all these organizations are well-intentioned in offering up free statistics for others to use, be it for analysis, fantasy league or simply to check it out.
In my own effort to develop more comprehensive measurements and indicators a standardized source of data for the MLS would be beneficial – if the intent for MLS is to endorse OPTA then there remains a conflict as Golazo clearly does not use the same data filters as the Chalkboard.
My vote, is and will remain, keep the Chalkboard and then, MLS, consider ways, as OPTA (Perform Group) is now, to improve it for more beneficial analysis.
Here is Part II – where I peel back a wee bit more – consider these phrases, successful crosses, launches, key passes, through-balls, throw-ins and more, as ASA continues its venture into Soccer Analysis in America.
Here’s a few paraphrased thoughts from other folks who offer up articles on ASA about this issue on passing statistics:
Jared Young – The massive difference in pass data between sites is troubling and disturbing; I’ve been primarily using whoscored.com and golazo for my numbers so I may have to explore other options.
Cris Pannullo – Major League Soccer should take an initiative and define what pass means in their league; it is surprising that they haven’t given how popular things like fantasy sports are; people eat statistics up in this country.
All the best, Chris
You can follow me on twitter @chrisgluckpwp
We are past the halfway point in Major League Soccer this year and if you recall from this previous article I promised I would revisit my Expected Wins analysis again at about this stage.
To continue to chart the progress of PWP, to include the data points behind the calculations, I am offering up some diagrams on what the data looks like after:
- The 92 game mark of the MLS Regular Season (184 events).
- The 183 game mark of the MLS Regular Season (366 events).
- The same data points for World Cup 2014 (128 events).
For background details on Possession with Purpose click this here.
A reminder of how things looked after 184 Events (92 Games)…
Trends indicated that winning teams passed the ball more, completed more passes, penetrated the final third slightly less but completed more of their pass attempts in the final third.
For shooting; winning teams shot slightly less by volume but were far more successful in putting those shots on goal and scoring goals.
For details you can enlarge the diagram and look for your specific area of interest.
As for how the trends show after 366 Events (183 Games)…
Winning teams now average less pass attempts and complete slightly fewer passes.
There is a marked decrease in pass attempts into the opposing final third and slightly fewer passes completed within the final third.
In other words – teams are counter-attacking more and playing a style more related to ‘mistake driven’, counter-attacking, as opposed to positive attacking leading into the opponents final third.
As for shooting; winning team are now taking more shots, with more of those shots being on goal and more of those resulting in a goal scored.
In my opinion what is happening is teams are taking advantage of poor passing accuracy to generate and create turnovers .
In turn those turnovers are generating cleaner and clearer shots given opponent poor positional play on the transition.
My expectation is that more teams will now begin to focus on bringing in newer players that have better recovery skills and can defend better.
In contrast, here’s how these same data points look after completion of the World Cup of 2014… there is a difference…
Winning teams average more passes attempted and far more completions than losing teams.
In addition winning teams penetrated far more frequently than losing teams, and that increase in penetration also translated to an increase in passes completed within the final third.
With respect to shooting; winning teams shot more, put more shots on goal, and scored far more goals.
Clearly what we see here is that quality in player skill levels also translated to an increase in quantity.
That should become even more apparent in looking at the PWP outputs for MLS and World Cup Teams…
Here they are for MLS at the 184 Events point this year:
A quick review of the data outputs shows winning teams averaged 51% possession and are 2% points better in overall passing accuracy.
That passing accuracy advantage also carried into the final third but when taking shots losing teams averaged more shots taken, per penetration, than winning teams.
Bottom line here is that winning teams had those fewer shots taken generate more shots on goal and more goals scored than losing teams.
After the 366 Event point this is how those same outputs look…
Like the indicators, in the PWP Data points, the percentages here are beginning to reflect the counter-attacking style of football taking over as the norm.
Winning teams now, on average, possess the ball less than their opponents… wow… mistake driven football is taking hold across the MLS.
As for Passing accuracy within and outside the final third…
Winning teams continue to be better in passing – and that level of accuracy is driving a large increase in shots taken, per penetration, by winning teams compared to losing teams (almost 2% different).
That is a marked difference (4% swing), from earlier, where losing teams shot more frequently, per penetration, than winning teams.
In addition that increase in shots taken, per penetration, also results in more shots on goal, per shot taken, and more goals scored, per shot on goal.
The margin between winning teams, and losing teams, for goals scored versus shots on goal, at the 184 Event point versus 366 Event point, still remains > 29%.
So how about teams in the World Cup???
Like earlier, winning teams not only passed the ball more frequently they possessed the ball more, by 5% (52.56% to 47.89%).
So contrary to what others might think – tika-taka is not dead, it’s just been transformed a wee bit…
With respect to passing accuracy…
I’m not sure it can be any more clear than this – winning teams averaged 82.40% and losing teams averaged 80.46%.
What makes these outputs different from MLS is that the level of execution is far higher in passing accuracy; by as much as 6%.
To put that in perspective; if a team looks to attempt 500 passes in MLS that equals 380 passes completed – compared to 412 passes completed by World Cup teams; clearly the level of execution is much higher.
That difference of 32 passes completed can have a huge impact when penetrating and creating opportunities within the final third.
What makes it even tougher is that the quality of defenders is significantly higher at the World Cup level as well.
With respect to penetration and creation within the final third…
World Cup winning teams averaged 2% greater penetration per possession than winning teams in the MLS.
By contrast World Cup winning teams generated fewer shots taken per penetration than those in the MLS.
Does this speak to better defending? I think so…
What I think is happening is that quality gets the team into the box, but then the quality of the defenders and goal keepers, in that confined space, is taking over.
This should be evident, even more so, when seeing that winning teams in the World Cup also put fewer shots on goal per shot taken than winning teams in MLS.
And that also translated to goals scored for winning teams in the World Cup also scored fewer goals scored per shot on goal…
All told, winning teams in the World Cup displayed slightly different (average percentages) than winning teams in MLS with one exception – passing accuracy.
And given the importance of the tournament it’s no wonder…
Without having the data, yet, I’d expect that the better teams in the EPL, Bundesliga, and other top European Leagues that difference in passing accuracy would remain.
As for the difference in possession (winning teams clearly possessing the ball more than losing teams) I’m not sure – mistake driven football, if memory serves is an approach Chelsea have used in the past…
I’d imagine it’s a pendulum type effect – as more teams work towards mistake driven football more teams will strengthen their ability to recover and open the game up a bit with direct attack to force the opponent from pressing so high.
I’ll be looking for additional trends as the year progresses to see if direct play increases – perhaps a good indicator of that might be even fewer penetrations and more crossing?
With respect to statistical relevance of the data and the outputs generated…
In every case the relationships created, be them Exponential or 4th Order Polynomial all had correlations that exceeded .95.
In other words the variations are minimal and should really reinforce just how tight the difference is between winning and losing in a game of soccer…
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If you read my initial article on “Passing – An oddity in how it’s measured in Soccer Part I“; I hope you find this article of value as well as the onion gets peeled back a bit further to focus on Crosses.
To begin please consider the different definitions of passing identified in Part I and then take some time to review these two additional articles (Football Basics – Crossing) & (Football Basics – The Passing Checklist) published by Leo Chan – Football Performance Analysis, adding context to two books written by Charles Hughes in 1987 (Soccer Tactics and Skills) and 1990 (The Winning Formula). My thanks to Sean McAuley, Assistant Head Coach for the Portland Timbers, for providing these insightful references.
In asking John Galas, Head Coach of newly formed Lane United FC in Eugene, Oregon here’s what he had to offer:
“If a cross isn’t a pass, should we omit any long ball passing stats? To suggest a cross is not a pass [is] ridiculous, it is without a doubt a pass, successful or not – just ask Manchester United, they ‘passed’ the ball a record 81 times from the flank against Fulham a few weeks back.”
In asking Jamie Clark, Head Coach for Soccer at the University of Washington these were his thoughts…
“It’s criminal that crosses aren’t considered passing statistically speaking. Any coach or player knows the art and skill of passing and realizes the importance of crossing as it’s often the final pass leading to a goal. If anything, successful passes should count and unsuccessful shouldn’t as it’s more like a shot in many ways that has, I’m guessing, little chance of being successful statistically speaking yet necessary and incredibly important.”
Once you’ve taken the time to read through those articles, and mulled over the additional thoughts from John Galas and Jamie Clark, consider this table.
|Stat||Golazo/MLS STATS||Squawka||Whoscored||MLS Chalkboard||My approach||Different (Yes/No)?|
|Total Passes||369||356||412||309+125 = 434||309+125+9=443||Yes|
|Total Successful Passes||277||270||305||309||309 + 9 = 318||Yes|
|Passing Accuracy||75%||76%||74%||NOT OFFERED||71.78%||Yes|
|Possession Percentage||55.30%||53%||55%||NOT OFFERED||55.93%||Yes|
|Final Third Passes||141||NOT OFFERED||NOT OFFERED||FILTER TO CREATE||140||Yes|
|Final Third Passing Accuracy||89/141= 63.12%||NOT OFFERED||NOT OFFERED||FILTER TO CREATE||92/140 = 65.71%||Yes|
|Total Crosses||35 vs 26 (MLS Stats)||NOT OFFERED||35||35||35||No|
|Successful Crosses||35*.257=9||NOT OFFERED||9||9||9||No|
|KEY PASSES||NOT OFFERED||7||9||6||6||Yes|
|* NOTE: MLS Chalkboard includes unsuccessful crosses as part of their unsuccessful passes total but does not include successful crosses as part of their total successful passes; it must be done manually.|
For many, these differences might not mean very much but if looking for correlations and considering R-squared values that go to four significant digits these variations in datum might present an issue.
I don’t track individual players but Harrison and Matthias do, as does Colin Trainor, who offered up a great comment in the Part I series that may help others figure out where good individual data sources might come from.
My intent here is not to simply offer up a problem without a solution; I have a few thoughts on a way forward but before getting there I wanted to offer up what OPTA responded with first:
I (OPTA representative) have has (had) a word with our editorial team who handle the different variables that we collect. There is no overlay from crosses to passes as you mentions, they are completely different data variables. This is a decision made as it fits in with the football industry more. Crosses are discussed and analysed as separate to passes in this sense. We have 16 different types of passes on our F24 feed in addition to the cross variable.
So OPTA doesn’t consider a cross a pass – they consider it a ‘variable’?!?
Well I agree that it is a variable as well and can (and should) be tracked separately for other reasons; but for me it’s subservient to a pass first and therefore should be counted in the overall passing category that directly influences a teams’ percentage of possession. Put another way; it’s a cross – but first and foremost it’s a pass.
(Perhaps?) OPTA (PERFORM GROUP now) and others in the soccer statistics industry may reconsider how they track passes?
I am also hopeful that OPTA might create a ‘hot button’ on the MLS Chalkboard that allows analysts the ability to filter the final third consistently, from game to game to game, as an improvement over the already useful ‘filter cross-hairs’…
My intent is not to call out any statistical organizations but to offer up for others, who have a passion for soccer analyses, that there are differences in how some statistics can be presented, interpreted and offered up for consideration. In my own Possession with Purpose analysis every ball movement from one player to another is considered in calculating team passing data.
Perhaps this comparison is misplaced, but would we expect the NFL to call a ‘screen pass’ a non-pass and a variation of a pass that isn’t counted in the overall totals for a Team and Quarterback’s completion rating?
Here’s a great exampleon how Possession Percentage is being interpreted that might indicate a trend.
Ben has done some great research and sourced MLS Stats (as appropriate) in providing his data – he’s also offered up that calculating possession is an issue in the analytical field of soccer as well.
In peeling back the data provided by MLS Stats he is absolutely correct that the trend is what it is… When adding crosses and other passing activities excluded by MLS Stats the picture is quite different and lends credence to what Bradley offers.
For example–when adding crosses and other passing activities not included by MLS Stats–the possession percentages for teams change, and the R-squared between points in the league table comes out as 0.353, with only 7 of 8 possession-based teams making the playoffs. New York, with most points, New England and Colorado all had possession percentages last year that fell below 50%, and only one team in MLS last year that didn’t make the playoffs finished with the worst record (16 points) DC United.
For me, that was superb research – a great conclusion that was statistically supported. Yet, when viewed with a different lens on what events are counted as passes, the results are completely different.
All the best,
You can follow me on twitter @chrisgluckpwp
Lots going on to share with you as Major League Soccer gets set for this weekend. In particular order:
- Major League Soccer Total Soccer Index (TSI)
- Eastern Conference
- Western Conference
- Jay Heaps gets the heave-ho from New England; why?
- Quality in MLS – has it got better since 2014?
- If so, where?
- Predictions for this weekend.
- Closing thoughts on Expected Goals
As a reminder – I called out Expected Goals and Expected Passes this week. Positive response from my European readers has been tremendous; so far my readers in the United States have remained quiet.
In case you missed it the explanation about what the Total Soccer Index is, is here.
Major League Soccer TSI:
This is how the league looks in a single table format; of course it’s pear-shaped from the start because the league doesn’t play a balanced schedule for everyone.
- The hammer identifies teams who have sacked their Head Coaches this year; are Jim Curtin, Ben Oslen, and/or Jason Kreis on the block too?
- The correlation of TSI to points earned is .82 this year; that’s an increase from the last two years.
- Offering, in my view, parity is decreasing.
- More to follow when we look at quality across MLS a bit later…
The Eastern Conference has been the more predictable conference all year even though MLS has an unbalanced schedule.
- Teams that usually possess the ball more, penetrate more, while showing greater patience in shot creation, end up with more goals scored.
- This pattern, across all the categories in Possession with Purpose, more closely matches European League performances measured in the past.
- Is this an indicator parts of Major League Soccer are growing closer to European Soccer in terms of tactics and how those general tactics drive similar results?
- More to follow when looking at quality across the entire league.
Who finishes as Eastern Conference Champion? Toronto.
I’m not sure anything is settled in the wild-wild west.
- We’ve seen musical chairs in almost every position of their conference table.
- About the only thing remaining constant is the poor play of Colorado Rapids, Minnesota United, and LA Galaxy.
- The greatest surprise may be the demise of FC Dallas, we’ve seen them swoon in late season before, does it happen again this year?
- If any one team has been consistent this year it’s Sporting KC – but that’s the case every year.
- With US Men’s National Team looking for a new Head Coach, after WC 2018, has Peter Vermes put himself in pole position over someone like Oscar Pareja?
Who finishes as Western Conference Champion? Sporting KC
Who wins the MLS League Championship? I have no idea.
Jay Heaps gets the heave-ho by New England, why?
- Their Attack:
- 2nd worst percentage in overall possession across MLS
- Mid-table in passing accuracy percentage
- 3rd highest percentage in overall penetration of final third
- 7th lowest percentage in shot creation
- 5th highest percentage in shot precision
- 8th lowest percentage in shot finishing
- Opponents Have:
- 2nd highest percentage of possession vs NER
- Highest percentage of Passing Accuracy in MLS vs NER
- Mid-table percentage in penetration vs NER
- Lowest percentage of shot creation in MLS vs NER
- Eighth highest percentage in shot precision vs NER
- Fourth highest percentage in shot finishing vs NER
- Their Attack:
- The team does not lack in attack.
- Shot creation is at a lower level relative to a higher level of penetration; usually a positive sign of patience in attack.
- That, coupled with being eighth highest in shot precision means when they create space there are putting shots on goal.
- What (may?) lack in attack is finishing…. but when you look at the stable of players and see Kamara on 11 goals, Nguyen on nine, and Agudelo on eight they are pretty good/versatile in attack.
- Their Defense:
- Lacks by a considerable margin compared to their opponents.
- Opponent’s are averaging over 80% passing accuracy; partly due to Revolution tactics of ceding space outside the final third in order to facilitate a better counter-attack.
- What is striking is their opponents are also eighth best in putting shots on goal and fourth best in finishing.
- That indicates Revolution opponents are gaining solid possession time BOTH INSIDE and OUTSIDE their defending final third.
- Is it the wrong players on the pitch?
- Is it the wrong defensive tactics on the pitch?
I’d say it’s both.
Quality in Major League Soccer:
It appears that quality has been roughly the same, year in and year out since 2014.
- But that’s deceptive. From 2014 through to 2017
- The difference between average passing accuracy for the best and worst has increased from 7.17% to 9.50%
- The difference between average penetration percentages for the best and worst has increased from 12.67% to 16.05%
- The difference between average creation percentages for the best and worst has increased from 6.67% to 11.48%
- The difference between average precision percentages for the best and worst has increased from 9.31% to 12.87%
- The difference between average finishing percentages for the best and worst has increased from 21.73% to 23.42%
- The gap between better teams and worse teams has widened.
- Another indicator parity has decreased, not increased.
Given the trends offered through PWP analysis it appears parity is on the decline in MLS.
When the season ends poor management will be rewarded with more money instead of being relegated; entitlement is alive and strong in Major League Soccer.
Predictions for this weekend:
As with most weeks, the home team is more likely to earn points.
- So far this year the home teams have earned 589 points versus 284 for away teams.
- That’s a pretty solid 2 to 1 margin in favor of the home team.
- Last year home teams earned 612 points compared to 300 points for away teams.
- In 2015 it was 624 points for home teams and 324 points for away teams.
- In 2014 it was 557 points for home teams and 323 points for away teams.
- Conclusion – even without using Expected Goals it’s pretty clear a novice in soccer can guess who will earn points in MLS games.
By the way, if using the TSI to predict who would have won the U.S. Open Cup the numbers show Sporting KC with an average TSI of .41 (at home) while the New York Red Bulls were .00 (away from home).
The final result was 2-1 Sporting KC. In hind sight the TSI predictor was accurate in predicting the U.S. Open Cup winner.
- Do you really need to know what Expected Goals are to predict which teams in Major League Soccer will earn points week to week? No….
- If you bet the home team you’ll be right roughly 66% of the time.
- Just another reason to debunk the value of expected goals.
- Oh… I’m hearing expected goals statistics are being used to predict results for the next year, using previous years data.
- And that those correlations are pretty solid from year to year.
- Well, they will be.
- You’re only using one event-based statistic to predict results in the next year and that number is notoriously low for every team; for room for error is minimal.
- I’m willing to bet a teams’ Expected Goals from two, three, or even four years ago will also have a pretty high correlation to the current year too…
- Because only one variable is being measured and the variation in that variable is low – very low.
- What makes that approach worse is it violates common sense.
- Teams change players and Head Coaches from year to year and while they may score the same amount of goals, year in and year out, their overall results may be different because they got better defenders or improved their defensive tactics.
- Parity in Major League Soccer has waned this year and it’s likely to get worse next year as LA adds another team.
With three more points in a home win, against the early season nomads of Toronto (first eight games on the road), Sporting KC is solidifying an early season position as the team to beat.
Not only in the Western Conference league table but in the MLS Composite PWP Index (through Week 3) at the end of this article.
Here’s the CPWP Index just for Week 3:
Rounding out the top five teams in overall performance this week:
- FC Dallas get the top honors this past weekend. Many have figured Dallas would be at or near the top all season and it’s reasonable to assume that Pareja was happy with those three points.
- A huge surprise, perhaps, is the appearance of Philadelphia Union in the top five. They had a solid 2-nil victory over a slumping Revolution and Curtin is sure to be pleased with CJ Sapong getting a brace. Ride that wave as long as you can Sons of Ben!
- For the first time this year Columbus appear in the top five; much of that may be down to their possession-based attack but I’d offer it’s more about their defensive tenor. It’s the Crews’ first point this year and none to soon.
- Colorado Rapids – Fourth best, and a team that many considered a cellar-dweller. It’s always a good thing but perhaps, in four to five weeks, its’ a two points lost and not one point one. DC United, as you’ll see later, are in an early season swoon. Another plus for the Rapids is the addition of Tim Howard.
- Last but not least – the first time this year the LA Galaxy break the top five in my weekly Index. Some folks observed that the Earthquakes defense is pretty paltry and gives up lots of shots in prime locations. That may be the case – but it’s reasonable to offer that two of the Galaxy three goals came from outside the prime hot-spot. I’d put that down to stellar attacking play – not poor defensive play on the part of San Jose.
Bottom dwellers for Week 3:
- If FC Dallas were best in Week 3 then it shouldn’t surprise many that Montreal were worst in Week 3. Across the board their team numbers suffered. They finished near bottom in Attacking PWP and mid-table in DPWP. More to follow a bit later.
- New England Revolution were next worse and Jay Heaps should be worried. They had a goal fest against Dynamo and only got one point. Question – if they have an early season slump, to go along with an expected mid-season slump, where exactly do they finish?
- Chicago Fire are third worst this week. Even so. I remain steadfastly stubborn, or stubbornly steadfast, that things will improve. Bottom line here is they ceded possession and weren’t able to score on the counter. When a team takes an approach like that it’s likely they’ll finish near bottom in CPWP. On the bright side – Defense first – and a shot out against last years’ Eastern Conference Champs is a good thing; at least for now.
- DC United are fourth worst even though they got a draw this weekend. Here’s the sad part – when looking at their volume of passes in the attacking final third they had 142 of them. A 66% completion rate with 19 shots taken. If you’re a Timbers supporter it’s likely those numbers sound familiar. Bottom line here – lots of shots and lots of penetration – but no patience…
- San Jose got beat in a number of ways this weekend by LA. If you’re going to cede possession it’s probably wise to be a bit tighter just atop the 18 yard box. They weren’t and they paid for it.
In shifting to strictly Attacking PWP – here’s how the teams line up in attack:
Best of the best were:
- LA Galaxy – They completely dominated the attacking side of the game. The last two weeks I’ve offered that a poor performance by the Galaxy this year could see Bruce Arena end his stint as the Head Coach. In speaking with Wendy Thomas last week it may be Bruce’s last year anyway. Wendy offered Bruce may opt to move into the front office; guess we’ll see.
- New York Red Bulls had a goal fest against Houston. All told they got four and barely won the game. Lesson learned for future Houston opponents – you need to score, and score a lot, to beat Houston.
- Colorado Rapids were third best in overall attack. They got their goal and one point on the road. But as noted earlier – this might have been 2 points lost not 1 point won.
- Portland Timbers got two goals from 26 shots. Wow… Lots of possession and penetration. If Real Salt Lake doesn’t throw the game away, with two red cards, it’s quite possible the Timbers are on the back end of this Index. In recalling last year – the Timbers had a huge volume of shots taken but found it hard to find the back of the net – till late on. So this shouldn’t be a surprise this year. The challenge, I think, is how long does Caleb continue to stick with this horse (4-3-3) before showing some more flexibility in running a single or double pivot 4-4-2? Question – is it too early to think the opponents have had enough time to plan on defending the 4-3-3?
- Philadelphia Union were fifth best in attack this week. Getting three goals usually gets you in the top five. Unlike the one point, the Rapids got against DC United, the Union did not wind up with a draw and a potential loss of two points. This was a very good win for the Union!
Worst of the worst in attack:
- Chicago Fire – Figure this as a no-brainer now… If you don’t look to possess the ball that much, and you don’t execute the counter-attack that well, it’s likely you’ll finish quite low in this Index. Bottom line is they got a point against a very strong Columbus team (at least last year they were strong)!
- Montreal Impact were second worst. The more to follow from earlier…. Only 16% of their completed passes, in the attacking final third, ended up leading to a shot taken. And of those shots taken, only 33% were on goal and none of them hit the back of the net… It was an away game, and it was against FC Dallas. I wouldn’t panic about the Impacts’ result this week.
- Toronto FC – I’m still trying to figure out if this team needs Jozy Altidore. I saw a small part of this game, and granted he’s been injured, but it just seems reasonable to me that the attacking focus comes from Giovinco. And since Altidore isn’t really a passer I wonder if he’s more of a spare part that doesn’t really fit.
- New England – If you thought the Impact results were poor – get this. With nearly 57% possession, a 74% completion rate within the final third, (equaling ~150 passes completed) they generated just nine shots taken. That’s a 6% penetration to shots taken ratio – probably the lowest ratio I’ve seen in three years. Terrible – and a great indicator the Union defense was stellar!
- Columbus Crew – With such a vast amount of possession they too were paltry in shots taken per completed pass in the attacking final third. They finished on 8% compared to the Revolutions’ 6% – I’d say that speaks volumes about the Chicago Fire defense too.
Defense usually wins games – for this week three of the top five got three points.
- Columbus Crew – If your opponent doesn’t really possess the ball then it’s sometimes a good bet they won’t score either. For the Crew this week that was the case. Berhalter likes his possession-based game and he should feel good his team got a clean sheet. But – with Chicago running a 3-5-2 it kinda seems reasonable they wanted to get at least one point – so maybe the Crew finishing high here is more about what the Fire didn’t do than what the Crew did do?
- FC Dallas was second best this week in defending. That’s not usually the case with this team as their counter-attacking style usually cedes possession and means the opponent has a better passing accuracy percentage. That was not the case with Montreal this weekend. And it’s very timely given the five-nil loss against Houston last weekend!
- Sporting KC- Did I mention last week that both Nagamura and Feilhaber still haven’t played yet? I think I did, but at the risk of repeating myself Sporting KC sit on nine points with two of their best midfielders out! Hmmmmm…
- Philadelphia Union – Is three nil and three points a worthy output against the Revolution? You betcha – the question for me is are the Union really that good in defending or are the Revolution really crap in attacking? We should know more about that in five to six weeks.
- Chicago Fire – They employed their 3-5-2 last weekend and it got them one point against last years’ Eastern Conference Champions. Worthy result – if your happy and you know it clap your hands #CF97.
The worst of the worst:
- I’ve already talked about San Jose so let’s move on to Houston.
- Houston has been scoring goals left and right. They did this week too – but gave up four as well. If Coyle wants to continue to get points he either needs to have his team increase their three goals a game average or get his defense to improve. I’d expect him to work on the latter of the two.
- DC United – Does the swoon predicted in week 1 continue for DC United this year – like 2013 – or do they get better. First order of business is to get a Kitchen sink….
- Real Salt Lake – I put this down to two red cards and a complete lack of discipline. In watching this game from start to finish I’d offer Nick Rimando and company had the tactical game plan to win this game. They blew three points, and with the Western Conference expected to be very competitive this year – they may really regret those two red cards. On the other hand, as a Timbers supporter – brilliant! Even more brilliant is seeing that City up North at the bottom of the table!
- New England Revolution – A number of gaps in this team. They can’t score and they can’t really defend. Are the Revolution the new Chivas USA? Wow – never figured I’d offer that question for this team…..
So how do the teams stack up after three full weeks?
Is it any surprise Sporting KC are at the top and that city up north is at the bottom?
- Not really… for me the early season surprises are Colorado and Houston 2nd and 3rd – while DC United and New York Red Bulls are 19th and 18th respectively.
- A good thing to remember about my Index – there is no subjectivity… so what happened last year stays with last year!
Some fun facts after Week 3.
- The ‘r’ for Composite TSR is .56 – well below the CPWP Index.
- Average passing accuracy 75.55%
- Average shots taken per completed pass in penetration 16.14%
- Teams that win have a higher average passing accuracy than teams that lose
- Teams that win average slightly more possession than teams that lose.
- Away teams have taken 29 points and average 386 passes per game with 11 shots taken, 4 shots on goal, and 1.13 goals scored.
- Home teams have taken 53 points and average 438 passes per game with 15 shots taken, 6 shots on goal, and 1.87 goals scored.
- It still pays to play at home…
A new feature this year; what was the +/- for each team after Week 3 compared to Week 2?
Biggest movers are the plus side were FC Dallas, 6 places, while Colorado, LA Galaxy, and Columbus Crew moved up 3 places. Those dropping the most included San Jose losing 6 places, with New England dropping 5 places, and Toronto/Montreal each sinking 4 places.
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