Category: Bundesliga

Redefining and Modernizing Total Shots Ratio

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

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

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

Why do I say that?

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

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

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

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

Before getting to the numbers – some history first:

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

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

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

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

Far higher…  now for some data.

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

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

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

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

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

Samples ATSR DTSR CTSR CPWP

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

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

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

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

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

In Closing:

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

Best, Chris

COPYRIGHT, All Rights Reserved.  PWP – Trademark

Expected Wins Five – Europe

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

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

A reminder – PWP is about two things:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Improving Possession with Purpose

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

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

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

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

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

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

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

Why?

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

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

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

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

Major League Soccer 2014:  (Before and After)

 

English Premier League: (Before and After)

 

Bundesliga: (Before and After)

 

La Liga: (Before and After)

 

 

Major League Soccer 2015: (Before and After)

 

 

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

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

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

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

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

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

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

My analysis shows:

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

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

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

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

Composite Possession with Purpose (CPWP) Indices:

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

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

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

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

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

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

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

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

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

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

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

Data arrays:

Total Games

Total game observations for consideration:

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

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

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

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

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

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

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

Away Games

Away game observations for consideration:

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

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

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

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

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

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

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

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

Home Games

Home game observations for consideration:

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

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

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

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

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

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

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

Summary:

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

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

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

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

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

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

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

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

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

Conclusions:

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

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

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

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

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

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

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

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

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

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

Best, Chris

You can follow me on twitter @chrisgluckpwp

COPYRIGHT: All Rights Reserved.  PWP Trademark

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

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

Much has transpired in the world of soccer statistics over the past four years since I first published: Possession with Purpose – An Introduction and some Explanations.

CLICK this link for my NEW simplified power point presentation update of Possession with Purpose the Total Soccer Index

  • The .pdf version should make it easier to print and use as reference material.

Within you’ll find:

  • Definition of TSI
  • Purpose of TSI
  • Premise of TSI
  • Parts of TSI
  • Leagues / competitions analyzed
  • Application of TSI and its parts
  • The data for leagues / competitions analyzed
  • Observations & conclusions by league / competition as well as reviewing TSI across leagues / competitions

My thanks to all for your support and kind words throughout the years.

In Summary:

  • The sum of the parts has greater correlation to points earned than the parts independent of each other.
  • Player A, from Team A, within any given league, has a different correlation to points (performance/outcome) than Player B, Team B, Player C Team C, etc in that same league.  In other words outcomes of individual player statistical analyses are NOT EQUAL from team to team and league to league.
      • Said differently, clearances or crosses (used as a measurement in fantasy soccer) for one player, on one team, DO NOT have the same weight/value of clearances or crosses for a different player on a different team.
      • Same can be said for passes or shots taken, etc.
      • Therefore, Calculations such as Expected Goals are not an apples to apples comparison between teams within the same league.  Yes, it’s a predictive tool, but flawed/
  • The lower the overall correlation of the Total Soccer Index to points earned the greater the parity within the league or competition; this also intuits those are less predictable.

Best, Chris

@ChrisGluck1

Gluck – Predicting Team Standings in Professional Soccer

CAN IT BE DONE?

Over the last four years I’ve conducted research on various professional soccer leagues and competitions.  To include Major League Soccer, the English, German, and Spanish Premier Leagues, as well as the UEFA Champions League and the Men’s World Cup of 2014.

Here’s my latest analyses on how the Possession with Purpose Index can be used to predict which teams will make the playoffs, qualify for the UEFA Champions League, or make the semi-finals of the World Cup..

Before beginning here’s a rerun on a few important items of interest about Possession with Purpose:

Intent:  Develop a simplified, strategic set of performance indicators to better understand the outcome of a game based upon primary inputs.

End State:

  • A documented method for measuring team performance from those indicators.
  • An index that ranks teams for their performance based on this method.
  • The index, while excluding points, comes close to matching results in the MLS league table.
  • Bonus – unexpected outcome – a tool to predict teams making the MLS Playoffs.

Key events to date:

  • Objective index developed in 2013
  • Results presented at the World Conference on Science and Soccer 2014
  • Approach published in the book – International Research Science and Soccer II – Routledge, Taylor, and Francis 2016
  • Leagues/Competitions evaluated
  • MLS 2013, 2014, 2015, 2016
    • English Premier League 2014
    • Bundesliga 2014
    • La Liga 2014
    • European Championship League 2014
    • Men’s World Cup 2014

Major League Soccer 2013 – The Maiden Year for PWP:

mls-2013

  • Nine of the top ten teams in the CPWP Index made the MLS Playoffs in 2013
  • Internal outputs from team performances showed that teams who cede possession (have lower than 50% possession) can be ranked within the top ten so the index is not biased towards teams that possess the ball greater than 50%
  • This doesn’t even include all the internal evidence on the various tactical styles of play each coach advocated.
  • Three of the bottom four teams replaced their head coaches as well.
  • It’s the initial results here that provided me compelling information to investigate deeper into what the outputs of the index might offer.
  • Each subsequent index shows a gold and red star – indicating which team finished first and last in the league table.

English Premier League 2014:

epl-2014

  • Winner of the League, Chelsea, finished 2nd in the index.
  • All four of the top four teams in the index advanced to the UEFA Champions League; those teams with green bars.
  • –By week 16, of 38 weeks, the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
  • Three of the bottom four teams in the index were relegated in 2014; those teams with red bars.

Germany Premier League 2014:

bl-2014

  • Winner of the League, Bayern Munich, finished 1st in the index.
  • All four of the top four teams in the index advanced to the UEFA Champions League; green bars.
  • –By week 21 the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
  • Augsburg and FC Schalke, who advanced to Europa League, finished 6th and 8th, respectively, in the index (light green bars).
  • For those teams relegated (red bars), SC Paderborn, finished worst in the league table and index, while Freiburg was 7th worst in the index and Hamburger SV was 3rd worst in the index.

Spanish Premier League 2014:

spl-2014

  • Winner of the League, Barcelona, finished 1st in the index.
  • All four of the top four teams in the index advanced to the UEFA Champions League; green bars.
  • By week 14 the four teams who advanced to 2015 UEFA Champions League were the top four teams in the Index; and they didn’t move out of the top four the rest of the season!
  • Sevilla and Villarreal, the two teams advancing to Europa League finished 5th and 6th, respectively, in the index; light green bars.
  • The three teams relegated in 2014 were Cordoba, Almeria, and Eibar.  They finished 2nd worst, 3rd worst, and 4th worst (respectively) in the index; red bars.
  • Of note; Levante, who finished worst in the 2014 CPWP Index finished last in the 2015 La Liga Standings.

UEFA Champions League 2014:

uefa-cl-2014

  • Winner and top team in the Index – Barcelona
  • Four of the seven top teams in the index advanced to the semi-finals
  • –Barcelona 1st, Real Madrid 3rd, FC Bayern Munich 5th, and Juventus 7th; green bars.
  • By the end of round one the top four teams to make the semi-finals were all in the top 10 for the index; with Barcelona 1st, Bayern Munich 3rd, Real Madrid 4th, and Juventus 9th.
  • Poor performers, APOEL Nicosia and Galatasaray finished 2nd and 4th worst (respectively) in the index; red bars.

Men’s World Cup 2014:

mwc-2014

  • Winner of the World Cup. Germany, finished 1st in the index, with 2nd place finisher, Argentina 5th best in the index.
  • Four of the top seven teams to reach the semi-finals finished 1st, 2nd, 5th, and 7th in the index; green bars.
  • By the end of round one, the four teams to make it so the semi-finals were all in the top six of the CPWP Index; with eventual winners, Germany 1st, Argentina 3rd, Netherlands 5th, and Brazil 6th.
  • With Brazil giving up seven goals to Germany in the semi-finals they dropped from 7th to 18th in the index.
  • France, Colombia, Belgium, and Costa Rica are the teams who made it to the quarter finals; light green bars.
  • All three teams that failed to earn a point in the World Cup finished worst (Australia), 2nd worst (Honduras), and 4th worst (Cameroon); red bars.

Side note about the Men’s World Cup:

  • USA finished 5th worst in the index (blue bar).
  • At that time I called for Jurgen Klinsmann to be sacked.  Why?
  • My two most compelling reasons were:
    • –Omitting Landon Donovan from the squad (huge reduction in squad mentality/leadership without his presence – plus he was simply the best striker/forward in the USA).
    • Replacing Graham Zusi with Omar Gonzalez late on in the game against Portugal – that replacement (a huge tactical error) created a vacancy in the area where Graham Zusi was defending; the exact same area where Ronaldo delivered his killer cross from.
  • Two years later, after numerous tactical and mental leadership errors, Jurgen Klinsmann was finally sacked.
  • I wonder where our team would be (NOW) if Sunil Gulati would have had the backbone to sack Jurgen Klinsmann back then?
  • I’m not afraid to say I told you so Sunil Gulati…

Major League Soccer 2014:

mls-2014

  • Four of the top ten teams, after week 1 CPWP Index, made the playoffs; with SSFC, eventual Supporter Shield winners in third.  After week 13 Seattle never fell further than 3rd in the Index.
  • Eventual Cup winners, LA Galaxy, were 11th after week one.  By week 8 they were 1st in the Index and did not fall out of the top two after week nine.
  •  Slow starter award goes to DC United, who were bottom of the Index until the end of week 5; when they finally breached the top ten.
  • It was here, along with seeing FC Dallas, at the top of the Index, that reinforced the Index was not overly influenced by teams who have high amounts of possession.
  • In other words, the Index would, and does, rank teams in the top ten even when they cede possession and play more direct/counter attacking football.
  • Although the first four weeks of the Index didn’t predict more than four of the top ten teams making the playoffs by week eight the Index showed nine of the top ten teams making the playoffs.
  • The level of accuracy, from week eight, going forwards never dropped below 70% and reached (and sustained 90% accuracy) by week 25 for the remainder of the year.
  • Accuracy in predicting the top ten teams making the playoffs was no worse than 40% (the first four weeks) and no less than 70% throughout the remainder of the year with 90% accuracy first attained by week eight – and sustained by week 25.

Major League Soccer 2015:

mls-2015

  • Seven of the top ten teams, after week 1 CPWP Index, made the playoffs; with NYRB, eventual Supporter Shield winners in ninth.
  • Eventual Cup winners, Portland, were 8th after week one.
  • Slow starter award goes to New England, who started at bottom after week one, but had breached the top ten by week seven.
  • At no time did the CPWP Index have less than seven eventual playoff teams in the top ten.  And by week seven nine of the top ten teams in the Index were bound for the playoffs.
  • Accuracy in predicting the top ten teams making the playoffs was no worse than 70% at any given time – and as high as 90% accurate by week seven.

Major League Soccer 2016:

mls-2016

  • Seven of the top ten teams, after week 1 CPWP Index, made the playoffs; with FCD, eventual Supporter Shield winners in first.
  • For those who were surprised by the Colorado Rapids this year – you shouldn’t be.  By week four, the CPWP Index had Colorado Rapids as third best in MLS; and they didn’t move out of the top four, in the Index, the rest of the year.
  • Slow starter award goes to New York Red Bulls; it wasn’t until week 12 that the Red Bulls breached the top four, but by week 14 they found their place at the top of the Index.
  • At no time did the CPWP Index have fewer than six of the eventual playoff teams out of the top ten.  And by week 25 nine of the top ten teams in the Index were bound for the playoffs.
  • Accuracy in predicting the top ten teams making the playoffs was no worse than 60% at any given time – and as high as 90% accurate by week 25.

Closing Thoughts:

  • The CPWP Index, and the sub-indices for team attacking and defending, show great value in looking to understand where failure/success may be occurring relative to team results.
  • It’s evidence – one piece of evidence – that shareholders should pay attention to when looking to make changes – it is not a substitute for what the eye sees or the gut feels.
  • I know more can be offered in drilling down into individual statistics relative to these team statistics.

Best, Chris

You can follow me on twitter @Chrisgluckpwp.

COPYRIGHT – All Rights Reserved.  PWP – Trademark

Major League Soccer – Can it truly reach the heights of top European Leagues by 2025?

What prompted this piece was the recent article published on Soccer By Ives about Don Garber’s vision for MLS by 2025 :  http://www.sbisoccer.com/2015/09/insists-worlds-leagues.html

It’s a good article and worth the read.

 

Editorial – In a follow up to this article (below) Stephen Brandt and I, on Yellowcardedpod, interview Brian Dunseth and Thomas Rongen on some of these topics: http://www.blogtalkradio.com/ycp/2015/09/22/wingers-and-bow-ties–mls2025

Before digging into some of my thoughts/questions on what else might be a part of this 2025 vision I’ll first ask this question – does MLS “need” to attain that level?  Mull that one over as I offer this caveat prior to digging in a bit deeper…

My thoughts offered are not intended to reflect that I don’t follow the league, support the league, or wish the business model of the league to fail. – I like (no) I love soccer – it’s been a passion of mine since the early 90’s and I continue to think and feel it is the greatest individualized-team sport in the World!

In no particular order some topics I think are worthy of consideration as MLS looks to develop/implement reach their 2025 vision:

Unbalanced League Schedule:

Probably the single worst aspect of the current system is an unbalanced league schedule where some teams you play once a year, some teams you play twice a year, and some teams you play three times a year….

I get it – at least for now – but it seems reasonable to me that the vision of 2025 needs to include a ways and means to create a more balanced schedule.

And I find it very doubtful that a schedule looking similar to how things appear today will convince others, outside this country, that MLS is a premier league.

League and Conference Size:

I think most would agree that the league will continue to grow – the question remains on what is the ideal stopping point of expansion?

If MLS wants to compete against the likes of La Liga, Serie A, EPL, and Bundesliga I would offer the stop point is 36 to 40 teams with two distinct conferences of 18 to 20 teams each.

This not only facilitates a balanced league schedule it also facilitates the league taking on the best of the single-table model those other leagues use; while also taking what I sense is a great attribute of American sports – the Playoff system.

There is a down side to this in that it may eliminate teams like New York City visiting Portland during the regular season – or other big cross-country games – but with every strength there is a weakness.

A possible end-run on that weakness is to open up the US Open Cup and eliminate ‘geographical area’ match-ups?  And in the current conditions the US Open Cup is not ready for prime-time TV coverage – if eliminating geographical match-ups in the early stages of the US Open Cup occurs those matches might have greater value to the overall soccer TV audience…

However viewed, having a single table for each conference with a playoff scenario at the end of the season does set up MLS to get the best of both soccer worlds – and it might even convince those across the pond to set up a playoff system too?

Expansion – specifically the Expansion Draft:

I get it – the reality, at least at this time, seems reasonable to allow for an Expansion Draft but seriously, is it reasonable to continue penalizing strong organizations – who build depth for the ever-competitive season – by asking them to potentially sacrifice good players they’ve already invested time and money in only to see them disappear just when they begin to reach their potential?

My view is no – the sooner the Expansion Draft is stopped, in MLS, the sooner the league goes on record to reaffirm that if you are going to be a part of the “premier” league in America you need to have already developed an organization from within that will help you sustain that ‘permanent promotion’ to MLS.

Of course – when MLS reaches that final team number the Expansion Draft is toast anyway – so perhaps this potential 2025 vision doesn’t matter?

Free Agency: Maybe the most contentious (now):

A question if you will – when is the last time you heard about a company like Boeing, who has plants in every state of the United States, prohibiting that employee from seeking a greater wage packet, with Boeing, elsewhere?

The current lack of an expanded free agency system in MLS really does hinder the ability of this league to attain a top league status across the rest of the world.

If MLS is expecting to be a great league in 2025 then a more flexible Free Agency system is most likely needed to sustain that vision.

MLS College Draft:

While I understand the goodness and intent behind the College Draft I remain unconvinced that the hype and expectation of a player moving from college to the professional ranks is really a high-value proposition if MLS is to attain status as a top league across the world.

There are other angles to consider to include 1) NASL has no draft, 2) to who really ‘owns the player’ and 3) what rights the college player already has in other competitive leagues.

I figure it’d take a lawyer or two, like with Free Agency, to work out all those details – especially since the ‘college draft’ is a primary mechanism for other American Professional Sports to improve their organizations.

I’m not going to bet on this but I wouldn’t be surprised to see the glamour of the MLS College Draft decrease – especially if MLS is intent on being a top flight league across the world.

On the other hand, if the NCAA pulled their head out their arse and looked to attain full status as an Amateur League within the US Soccer system then a whole new vision could open up where the likes of Ohio State, and other Colleges could find themselves competing in the US Open Cup – now what sort of atmosphere might that create where a College Team finds themselves playing a Professional Team in a College Stadium that holds 100,000 supporters — for me that sort of atmosphere would be monumental – never-mind the financial and media interest it may draw from the commercial world of the United States!

The question, for me, then becomes – what is the vision of the NCAA for soccer in 2025?

Taking a greater leadership role in the development of Soccer in the United States (College Soccer continues to play ‘outside the lines’ of US Soccer and FIFA regulations):

While some College Head Coaches may disagree, from any number of reasons, those that I’ve interviewed seem to agree that – for the most part – the game of soccer played in college is not the same style, or of the same tactical nous, of professional soccer.

Count down clocks and substitution policies – along with Referees that are not FIFA qualified – place young, impressionable players – at the prime age of skill development, in an environment that comes no where close to matching the type of soccer environment they’ll potentially encounter in the professional ranks.

And if college is supposed to provide a ‘learning environment’ isn’t it reasonable that that learning environment match, as closely as possible, the environment those same players will need to operate in as professionals?

To put this into perspective – there are roughly 1100 organized NCAA teams – meaning there’s roughly (20 X 1100) 22,000 players looking to hone their skills in an environment that doesn’t match professional soccer.

In addition, if growing Professional Referees is an objective then if there are 1100 teams that equates to roughly 550 crews of one Referee and 2 Linespeople also adjudicating games under rules different to those of FIFA.

Last but not least – Coaching staff – if MLS is to be truly competitive isn’t reasonable to expect that there also needs to be a pipeline for Coaches. With 1100 teams that’s 1100 Head Coaches and probably 2200 to 3300 Assistant Coaches all learning to manage a game that has little comparison to the types of tactics they’ll encounter when managing a professional side.

With all that said I remain unconvinced MLS will attain this lofty status when the single largest pool of Players, Referees, and Coaches, in the United States, plays outside the FIFA governing rules of soccer.

Professional Referee’s: 

This topic is probably a topic for every league in the World but if memory serves MLS has yet to completely close the loop on mandating that all Professional Referee’s be full-time for every league match.

Some opinions may vary on this but I do sense it is reasonable to believe that the level of adjudicating MLS matches, by full-time Professional Referee’s in this country, will be better in 2025 compared to now.

Perhaps we might even see FIFA decide to have two primary Referee’s adjudicate a game, just like the National Hockey League?  That may be helpful, for not only MLS, but World Soccer as a whole.

Perhaps another 2025 vision includes better use of video technology in support of Referees?

NASL – Where and how it fits – and if not what happens to those organizations:

There’s no question NASL run a competitive league but the money invested isn’t really on par with what teams leverage in MLS.

If it did then the business model that would best match and create a true environment like that of Europe – would be NASL’s.

So part of the MLS vision should probably consider two different possibilities.

1) Either NASL begins to fade away or 2) NASL merges with USL

Here’s the thing though – if soccer continues to grow in popularity for this country there is a risk to MLS that NASL could surpass MLS in league attendance given an influx of new owners that prefer the European Model of competition – the more you invest, the more likely you are to earn more, which in-turn means more attendance and media coverage, which in-turn drives a larger income, and so on….

Conversely – if MLS and USL are linked then it would seem reasonable, that in order to further strengthen a ‘lower league’, MLS needs to see USL merge with and absorb NASL – with ‘MLS Team 2 teams’ in USL getting relegated to a league division 3 status.

I wonder if that sort of consideration has been given as MLS looks towards a vision of 2025 – especially when looking at European leagues there are no ‘team 2 teams’ that can ever have an opportunity to directly compete against a ‘team 1 team’ – and as things stand today it is possible, not probable, but possible that LA Galaxy 2 could end up competing against LA Galaxy 1 in a US Open Cup Final – now what sort of bollocks would that be?

Before closing on this topic – a thought or two on mergers.  If you recall the AFL and NFL merged to create a new NFL.  The ABA and NBA merged to create a new NBA.  The World Hockey League and National Hockey League merged to create a new NHL.  And the American League and National League eventually tied the knot to create inner league play where both leagues still operate under slightly different rules.

Is it too far fetched to imagine, that by 2025, there will be MLS and then leagues operating directly below MLS where the business conditions are the same?  However viewed I believe a reasonable vision of MLS (and) US Soccer, in 2025, sees greater clarity on where NASL fits into the mix.

The business model of MLS (in America) compared to the business model (in Europe):

It’s ironic really – the business model of Europe sees a socialistic society operating a capitalistic business model (survival of the fittest) while a capitatlistic society (in America) operates a socialistic business model.

If you don’t follow I’ll put it this way – no team, in any top league across the World, is ever guaranteed the right to play in their countries highest level soccer league – they must prove, year in and year out that they are good enough to stay in their top league.

Whereas here, in America, if you join the MLS franchise you are always guaranteed (provided you are somewhat financially savvy) to always have a team in MLS.

Now some may offer this is a bit brutal but I think it is worth noting the word “entitled”; teams in Europe are not entitled to anything – just ask Leeds or Glasgow…

But anyway – I kind of digress here because the intent of using the word ‘entitled’ isn’t really about MLS it’s more about the overall tenor of youth soccer in this country.  Americans should never forget that the game of soccer is/and always has been, in other countries of the world, a game for the working class.

When parents are required to spend money, sometimes greater than $3000 per year, to support their child’s development in soccer they feel that their child is ‘entitled’ to play.

So while MLS may have a vision of top-flight status by 2025 I really believe that status will never be attained as long as the youth learning this game think they are entitled to play if they pay…  and with College Soccer facilitating that entitlement to play through their obtuse substitution rules that entitlement is reinforced!

In closing:

I’d offer there are a number of issues that may??? impact a ‘top-world-league’ vision for MLS 2025.

With some European teams operating on a budget the size of the US Department of Defense (just kidding) is it really reasonable to expect that an MLS franchise, with a salary cap, is going to be able to attract the worlds greatest footballers, in their prime, to the United States?

I’m not so sure. 

In case you missed it – Raheem Sterling was just sold to Manchester City for 49 Million Pounds – I think the purchase price for one player is greater than the team budget of most clubs in MLS…

Do we really expect a team, within the MLS Franchise business model, to pay 49 Million for one player?  Not likely…

So in going back to the first question.  Does MLS “need” to attain that level?

I don’t think so.

But I do think and feel there is value in seeing soccer attain top-flight status as a sport in America.

For me that means a vision where MLS is operating on an equal footing with Basketball and Football – the other Football…

As to the rest of the world I’m not sure it matters much to many folks – if we want to watch the EPL, the best league in the World, all we gotta do is turn on the TV and take it in.

Would I want to see a league table that mirrors the yearly expectations of the EPL here in America?

No.

I admit I don’t like the parity concept from a personal standpoint as I’d like to see the Portland Timbers win every game – but it’s not realistic so I understand and get the business model.

And being a stats guy, who’s analyzed team performance in MLS, La Liga, Bundesliga, and the English Premier League, I pretty much like the greater chances the format offers than the usual (same old teams) we see finishing atop those other league tables.

Best, Chris

Possession with Purpose Total Soccer Index What is it?

In 2014 I created an Index to measure team performance; my goal was to create one number (exclusive of points scored) that could help me tell a story about team performance that isn’t just about goals scored and goals against (Goal Differential).

While I can’t share the internal data points and algorithms anymore I can offer the Index is designed to capture the ‘primary bell curve’ of team activities on the pitch.

Here are diagrams of the Indices for each league/competition originally measured in 2014.  The ‘r’ in each diagram offers the correlation of the Possession with Purpose Total Soccer Index (PWP TSI) to points earned in the league table.

Points earned is not a data point in the algorithm used to create the Index.

 

Since the TSI was first created in 2014 I’ve updated the algorithm to try and exceed the ‘r’ of Goal Differential to the league table – a long-time benchmark of accuracy and one of the primary reasons the statistic Expected Goals was created.

That logic follows the premise that it’s all about goals scored and goals against.

In 2018 I updated my algorithm and for Major League Soccer the TSI now has a greater correlation (r) to the league table than Goal Differential.

 

With World Cup 2018 nearly here I will be testing my Index again – and comparing the accuracy of my new algorithms to what was generated during World Cup 2014.

Here’s the World Cup 2014 Index showing the new TSI compared to the previous TSI and Goal Differential:

The green cell shows the Attacking half of the new TSI has a greater correlation to points earned than either the new Composite TSI or Goal Differential.

The days of using Expected Goals as a predictability model for scoring goals is over.

This should also convince Anderson and Sally that it isn’t all about preventing goals scored; at least not in the World Cup of 2014.

Look to my site if you want to see how your favorite team is comparing against the rest of the world.

Here’s a reminder of what the PWP TSI showed at the end of group stages in World Cup 2014.

Germany and Argentina (the two finalists) were 1st and 3rd in the Index.

If an American it should be pretty obvious the USA was punching way above their weight in making it past the group stages.  If you want to know my thoughts (back then) on the future of Jurgen Klinsmann and Sunil Gulati – click here.

CPWP INDEX GROUP STAGES COMPLETED

Perhaps this years’ Index will be as telling as the one in 2014?

Best, Chris

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

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Control or Lack Thereof – MLS 2015

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

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

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

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

League_Control_Attacking_Possession_with_Purpose.0.jpg

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

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

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

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

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

League_Control_Attacking_Possession_with_Purpose_Plus_MLS_2015.0.jpg

No “Birds” here…

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

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

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

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

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

A few other thoughts about the two diagrams…

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

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

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

In Closing:

Composite_Possession_with_Purpose_Index_MLS_2015.0.jpg

The latest Composite PWP Index for MLS through Week 6.

What’s it mean?

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

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

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

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

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

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

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

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

Best, Chris