Category: World Cup 2014

Gluck: Fourth Year Anniversary Edition

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

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

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

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

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

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

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

In Closing:

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

I wish you all the best for the new year.

Best,

CoachChrisGluck

 

Gluck: 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.

Separating winners from losers in Major League Soccer…

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:

  1. The 92 game mark of the MLS Regular Season (184 events).
  2. The 183 game mark of the MLS Regular Season (366 events).
  3. The same data points for World Cup 2014  (128 events).

For background details on Possession with Purpose click this here.

To begin…

A reminder of how things looked after 184 Events (92 Games)…

MLS EXPECTED WINS AFTER 184 EVENTS

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)…

MLS EXPECTED WINS AFTER 366 EVENTS

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…

WORLD CUP EXPECTED WINS AFTER 182 EVENTS

 

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:

MLS SIX STEPS OF PWP AFTER 184 EVENTS

 

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…

MLS SIX STEPS OF PWP AFTER 366 EVENTS

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???

WORLD CUP SIX STEPS OF PWP AFTER 128 EVENTS

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…

In closing…

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…

Best, Chris

Re-tweets appreciated…

COPYRIGHT, All Rights Reserved – PWP, Trademark

 

FIFA World Rankings – time for a change?

Although this article was written about 18 months ago – I still think it retains relevance; for two reasons:

  1. FIFA is embroiled in a huge scandle, and
  2. People seem to keep reading it almost 2 years after the fact.

As such here’s a redux on the primary headline with some added juice about the corrupt behavior of the organization, to date, and how the rankings REALLY do need  a re-look in how they are calculated!

I don’t claim that my suggested new way is THE way, but I do think it represents a considerably more open and objective ranking approach then how it’s currently done.

Finally, as with my latest on Moneyball 2 – I highly recommend you get a cup or pint of your favorite beverage before digging in.

To begin – here’s what I offered previously; later on I’ll add some additional thoughts not touched on in the original article; thanks in advance for your patience:

In order to offer up my comments/questions for consideration it’s appropriate for me to include the FIFA World Rankings as of 20 months ago and then the link on how it’s determined.

First the link and the diagram below showing the Top 30 as of June, 2014.

June 2014 FIFA Rankings - Coca-Cola Sponsored

June 2014 FIFA Rankings – Coca-Cola Sponsored

Now, here’s how it’s calculated

What follows is a direct lift from the link provided above:  FIFA explanations are offered in “bold” while my questions/comments will be offered in ‘italics’.

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

The basic logic of these calculations is simple: any team that does well in world football wins points which enable it to climb the world ranking. 

Well I’m not so sure it’s simple but it does provide what it says it does – a listing from best to worst organized by ‘points earned’.

A team’s total number of points over a four-year period is determined by adding: 

The average number of points gained from matches during the past 12 months; and the average number of points gained from matches older than 12 months (depreciates yearly).

  • Maybe it’s just me but I don’t see the relevance of using four years worth of history in ranking current teams.
  • My own personal view is that the last two years (which ensures including the lead up to the World Cup) has more relevance given the nature of players that appear and disappear, from year to year, on National Soccer teams.
  • I wonder what the bi-yearly turnover rate in player personnel is compared to the quad-yearly (is that a word?) turnover rate in player personnel?
  • And what about changes in Head Coaches; shouldn’t that impact a National Team Ranking? 
  • Most, I think, would agree that a change in Head Coach will not only drive a change in player selection it will also drive a change in how the team strategically and tactically attacks and defends.
  • When that change occurs is it really the same team?
  • In considering the four year life-span of the points I’m not sure I see the relevance of how a team performed three years ago, with perhaps a 50% change in player personnel, has any bearing on how a team might perform in the current year.
  • The same can be said for a team coached by someone else 3-4 years ago versus in the last year or so…
  • Perhaps? a team should be ‘reduxed’ when a new Head Coach arrives on scene?   Might using just two years worth of data help ‘quantify’ that redux?
  • Or, in other words previous performance is excluded and a new clean sheet is started?
  • Perhaps? a team should be ‘reduxed’ when over 50% of the player personnel change? 
  • In other words previous performance with a team that has over 50% of new players means a new clean sheet is started?
  • Maybe this keeps the FIFA World Cup rankings more up to the ‘now’ as opposed to the ‘then’?

Calculation of points for a single match:

The number of points that can be won in a match depends on the following factors:

Was the match won or drawn? (M)

How important was the match (ranging from a friendly match to a FIFA World Cup™ match)? (I)

How strong was the opposing team in terms of ranking position and the confederation to which they belong? (T and C)

  • Results are qualitative based not quantitative based; if the FIFA Rankings are intended to be used to “quantify”/”deem” which teams are better or worse, in overall performance, relative to placement in future tournaments, is it better to rank those teams using a quantitative or qualitative analyses?  
  • I’d offer it’s better to use a quantitative analytical approach.
  • Friendlies have absolutely no bearing on whether or not a team is good or bad – why? 
  • Because they are experiments that Head Coaches use to evaluate players for when it really matters; to attach a value to a friendly, that exceeds the ‘intent’ of the Friendly, and (brutal facts) violates all the common sense logic of a statistical based ranking system.
  • How is the strength of one Confederation compared to another? 
  • The percentages are provided further below but no additional explanation is offered to go with that…
  • If teams only meet in the World Cup, outside of Friendlies, from different Confederations, what is the value of one FIFA World Ranking System; isn’t it simply more relevant to create a FIFA World Ranking after all the Confederations have completed their elimination tournaments?
  • And then, perhaps, that listing is leveraged when the seeded teams from each Confederation are matched up to the other Confederations for the World Cup?
  • If a quantitative statistical approach were used it would be easier as you’d be comparing ‘apples to apples’…
  • And if Friendlies are not included in the analyses, then the only time the real Rank has value is right before and right after the World Cup.
  • And after the World Cup it could be used to seed teams for Confederation tournaments; or is that devolving the FIFA World Ranking of too much influence?
  • Will the hog butcher itself?

These factors are brought together in the following formula to ascertain the total number of points (P).

(P = M x I x T x C)    The following criteria apply to the calculation of points:

M: Points for match result

  • Teams gain 3 points for a victory, 1 point for a draw and 0 points for a defeat. In a penalty shoot-out, the winning team gains 2 points and the losing team gains 1 point.
    • Again, when in a Friendly, this places a value of ‘worth’ in winning, when in fact there is no value in winning a Friendly.
    • The intent of a Friendly is for the Head Coaches to see how their players perform and the players get a feel for what it’s like to work in that coaches system with other teammates.
    • If FIFA has the approach of awarding Ranking Points for teams who win in Penalty Shoot-outs than why have draws as a part of the game at all?
    • In a knock-out competition draws can’t happen; so why can they happen in regular competition?
    • Why not just have every game that ends in a Draw result in a Penalty Shoot-out where the winner gets 2 points in the League Table and the loser gets one point in the League Table?
    • Might this approach also help players better train for crucial PK competitions in the World Cup?
    • Put another way; is the “consistency of purpose” missing when it comes to FIFA and how games are ended?

I: Importance of match

  • Friendly match (including small competitions): I = 1.0
  • FIFA World Cup™ qualifier or confederation-level qualifier: I = 2.5
  • Confederation-level final competition or FIFA Confederations Cup: I = 3.0
  • FIFA World Cup™ final competition: I = 4.0
    • What is a “small competition”?
    • Why is the value of a FIFA World Cup match any different than the value of any other specific competition that is not a Friendly?
    • All of those other competition types (excluding Friendlies) can and do see players rotating in and out of National Team squads; so the teams are not the same teams all the time.
    • In addition, there are numerous changes in Head Coaches between World Cup events; therefore does it seem reasonable that all the Competition levels have different values/levels of importance?

T: Strength of opposing team

  • The strength of the opponents is based on the formula: 200 – the ranking position of the opponents.
    As an exception to this formula, the team at the top of the ranking is always assigned the value 200 and the teams ranked 150th and below are assigned a minimum value of 50. The ranking position is taken from the opponents’ ranking in the most recently published FIFA/Coca-Cola World Ranking.

    • Given that the method for ranking teams is more qualitative than quantitative this statistical calculation is highly suspect and open to significant interpretation/influence outside the bounds of objectivity.
    • And we’ve already seen how objectivity can be manipulated with the selection of Qatar hosting the 2022 World Cup.
    • If no values are attached to Friendlies then this strength of Opponent has no relevance until the World Cup; the only time teams meet in a competition that actually has real value…

C: Strength of confederation

When calculating matches between teams from different confederations, the mean value of the confederations to which the two competing teams belong is used. The strength of a confederation is calculated on the basis of the number of victories by that confederation at the last three FIFA World Cup™ competitions (see following page). Their values are as follows:

  • UEFA/CONMEBOL 1.00
  • CONCACAF 0.88
  • AFC/CAF 0.86
  • OFC 0.85
    • How were these percentages developed and when, and how often, are they updated?
    • Again, to be redundant here, because I think it’s important to minimize internal/external influence in judging the effective performance of a team, this category, in the calculation gives the impression of adding a ‘fudge-factor’.
    • A more quantitative approach would eliminate the need for this “strength of Confederation”…
    • The less subjective influence FIFA has on the Confederation and World Ranking systems the better…

Final thoughts on the current FIFA approach:

  • As much as there are ‘numbers’ involved, this approach really is tainted with subjectivity.

Moving on to my Possession with Purpose Index – specifically the one resulting from the 2014 World Cup:

CPWP INDEX JULY 9TH 2014 WORLD CUP

There are considerable differences, even without the final two games being played…

  • The most glaring difference between the two Indices/Rankings is the inclusion of Ukraine, Denmark, Slovenia, Scotland, Romania, and Serbia in the FIFA Top 30, while Nigeria, Korea, Ghana, Cameroon, Iran and Australia are excluded.
  • Note, since the date of the FIFA Rankings is June 2014 there was plenty of time for FIFA to ask themselves why teams that made the World Cup did not make the Top 30 and teams that didn’t make the World Cup made the Top 30.
  • Is it really a relevant Ranking system if there are teams in the top 30 who didn’t make the World Cup and teams outside the top 30 that did make the World Cup?
  • If a team is strong enough to qualify, from within their Confederation, then shouldn’t they, by rights, be in Top 30 of the FIFA World Rankings?
  • Is there supposed to be a ‘good feeling’ for a Nation to have a team in the Top 30 that didn’t make the World Cup?
  • What is the intent of the FIFA World Rankings anyway?  If it’s strictly for “seeding purposes” wouldn’t it be reasonable that the teams competing in the tournament are the only teams to appear in the Top 30/32?
  • And why a Top 30; why not a top 32?
  • If you exclude Friendlies from the calculation what does the FIFA World Ranking Index look like?

I wonder how quickly the table adjusts from month to month?

  • If the FIFA World Ranking system does not react quickly to changes in new Head Coaches, or major shifts in player personnel, how effective is it in dropping or raising teams based upon the World Cup?
  • I think, in this day and age, the ability to adjust the ranking of teams should be quicker and have less influence based upon past performance and more influence based upon current form; especially with changes in formations, styles, players and Head Coaches.

Finally, it’s worth mentioning again, if FIFA can appear to be ‘bought’ (that’s no longer “an appearance” – it’s FACT) when selecting Qatar for the World Cup in 2022 how reliable (really reliable) is their Index as calculated today?

  • Based on a win/draw (qualitative analyses),
  • Influenced by games that mean nothing (Friendlies), and
  • Influenced by games played four years ago where neither the team nor the Head Coach might be the same?

In Closing:

  • There’s no question that corruption existed, and probably still does, in some fashion or another – when that type of environment exists EVERY path forward should be reviewed to cleanse and objectify rankings for the future.
  • My approach has been published – it is reasonable – accurate – (in some cases extremely accurate) and the rankings in my Indices can show movement up and down the ladder when head coaching changes are made.
  • How a team did three years ago, under one coach, says absolutely nothing about how a team will do under another head coach, three years later.
  • If a national team changes their head coach the team ranking should be scrubbed and reviewed with a new start point somewhere outside the top 30-40… at least that’s an idea…
  • My Index is quantitative – there is no qualitative measurement (judgment) involved – therefore the politics of FIFA will never-ever influence a teams ranking.

If you think it’s time for a change in how FIFA calculates world rankings retweet this article – I’m not saying it’s THE answer but there are more ways (objective ways) to rank teams that completely ignore the almighty dollar bill.

Best, Chris  @chrisgluckpwp

 COPYRIGHT, All Rights Reserved.  PWP – Trademark

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

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

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

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

So am I personally surprised by the result?

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

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

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

Why disappointing?

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

 

Hmmm…  you sure about this Chris?

Yes… here’s why.

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

Here’s some you may be familiar with:

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

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

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

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

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

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

 

Finishing Touches:

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

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

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

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

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

Finishing Touches:

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

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

Bottom line at the bottom:

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

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

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

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

Best, Chris

You can follow me on twitter @CoachChrisGluck

 

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

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

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

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

Within you’ll find:

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

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

In Summary:

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

Best, Chris

@ChrisGluck1

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

FIFA World Rankings – Redux – Time for a Change?

FIFA is embroiled in a huge scandal and the time couldn’t be better to review and consider new ways to rank National teams across the World.

There are two parts to this for your consideration.

  1. Details on how the FIFA World Cup Power Rankings occur and my own thoughts on how they are calculated, and
  2. My own FIFA Possession with Purpose Power Rankings and my closing thoughts on the effort as a whole.

Here’s the Top 30 during the FIFA World Cup of June, 2014.

June 2014 FIFA Rankings - Coca-Cola Sponsored

June 2014 FIFA Rankings – Coca-Cola Sponsored

How the FIFA Rankings are currently determined: 

The basic logic of these calculations is simple: any team that does well in world football wins points which enable it to climb the world ranking.

A team’s total number of points over a four-year period is determined by adding:

The average number of points gained from matches during the past 12 months; and the average number of points gained from matches older than 12 months (depreciates yearly).

Calculation of points for a single match; the number of points that can be won in a match depends on the following factors:

  • Was the match won or drawn? (M)
  • How important was the match (ranging from a friendly match to a FIFA World Cup™ match)? (I)
  • How strong was the opposing team in terms of ranking position and the confederation to which they belong? (T and C)
  • These factors are brought together in the following formula to ascertain the total number of points (P).

Here’s how it’s calculated.   (P = M x I x T x C)    The following criteria apply to the calculation of points:

M: Points for match result:  Teams gain 3 points for a victory, 1 point for a draw and 0 points for a defeat. In a penalty shoot-out, the winning team gains 2 points and the losing team gains 1 point.

I: Importance of match

Friendly match (including small competitions): I = 1.0

FIFA World Cup™ qualifier or confederation-level qualifier: I = 2.5

Confederation-level final competition or FIFA Confederations Cup: I = 3.0

FIFA World Cup™ final competition: I = 4.0

T: Strength of opposing team:  The strength of the opponents is based on the formula: 200 – the ranking position of the opponents.

An exception to this formula, the team at the top of the ranking is always assigned the value 200 and the teams ranked 150th and below are assigned a minimum value of 50.

The ranking position is taken from the opponents’ ranking in the most recently published FIFA/Coca-Cola World Ranking.

C: Strength of confederation

When calculating matches between teams from different confederations, the mean value of the confederations to which the two competing teams belong is used.

The strength of a confederation is calculated on the basis of the number of victories by that confederation at the last three FIFA World Cup™ competitions (see following page). Their values are UEFA/CONMEBOL 1.00; CONCACAF 0.88; AFC/CAF 0.86; OFC 0.85.

My thoughts on the current method:

When viewing match results – there is no value in winning a friendly and any calculations using those results amplify the subjectivity of the ranking.

In addition, if FIFA has an approach of awarding ranking points for teams who win Penalty Shoot-outs why have draws as a part of the game at all?  In a knock-out competition draws can’t happen; so why can they happen in any other regular competition?  Why not just have every game that ends in a Draw result in a Penalty Shoot-out where the winner gets 2 points in the League Table and the loser gets one point in the League Table?

A better definition should be provided for “small competition”; in my view, excluding friendlies there are no “small competitions”.

A calculation involving ‘”T” (strength of opposing team) looks to exacerbate subjectivity – in other words – if the original ranking profile is more subjective than objective the derived calculation stemming from that is amplified.

If no values are attached to Friendlies then this strength of Opponent has no relevance until the World Cup; the only time teams meet in a competition that actually has real value…

Why is the value of a FIFA World Cup match any different than the value of any other specific competition that is not a Friendly?

How and when were the percentages in “C” (strength of confederation) developed.  And how often are they updated?  Is there a hidden ‘fudge-factor’ of subjectivity here?  Would a more quantitative approach would eliminate the need for this “strength of Confederation”?

As much as there are ‘numbers’ involved, this approach is heavily tainted with subjectivity.

Moving on to my FIFA Possession with Purpose Index – specifically the one resulting from the 2014 FIFA World Cup:

CPWP INDEX JULY 9TH 2014 WORLD CUP

In Closing:

The most glaring difference between the two Indices/Rankings is the inclusion of Ukraine, Denmark, Slovenia, Scotland, Romania, and Serbia in the FIFA Top 30, while Nigeria, Korea, Ghana, Cameroon, Iran and Australia are excluded.

If the pre-FIFA World Cup rankings are to have relevance (be used to seed teams in the tournament) then the top 32 teams that make the World Cup should be the top 32 teams that are in the FIFA World Rankings.

I wonder what the FIFA Ranking looks like if friendlies are excluded?

Corruption exists in FIFA – when that environment exists EVERY path forward should be reviewed for improvement.

My index is quantitative (objective)  – not qualitative (subjective) – and the statistical correlation (r) to points earned in the FIFA was .83.

I’m not advocating that my approach is THE approach – but it has credibility, simplicity, and objectivity.

If not this one, certainly another way can be created with more objectivity than the current method.

Best, Chris  @chriswgluck

 COPYRIGHT, All Rights Reserved.  PWP – Trademark

Possession with Purpose – Prozone – and more…

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

The news:

The European Season is ending.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Best, Chris

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

I’ve been a bit busy lately so apologies for not offering up any research on Possession with Purpose; lots going on with it at the moment while all five competitions I measure are still going at full speed.

To catch up, using a picture first, here’s a look at how PWP compares to Total Shots Ratio as well as Goal Differential when viewing the UEFA Champions League:

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

As an added Index I’ve also included the PWP Predictability Index (an Index that EXCLUDES goals scored (for or against) in the overall calculations.  A reminder that from a pure predictability standpoint the Predictability Index remains the only Index that excludes goals scored when developing a prediction as to whether a team might earn points.

For the benefit of all I’ve also included how things take shape when teams play at home versus away from home; there are differences.

So what does this mean?

PWP, even in a format different than general league play shows better than TSR as it is known today (i.e not modernized).

While Goal Differential shows well with respect to the overall correlation coefficient (r) to average points earned it doesn’t show best when racking and stacking it as an Index compared to PWP —> when viewing how it ranks teams versus how well they have progressed into the final stages.

What I found intriguing was that the PWP Predictability Index (which excludes goals scored) actually racked and stacked the top 4 teams in the UEFA Champions League better than Goal Differential.

If you’re someone who likes to bet on games early indications show PWP Predictability (excluding goals scored) has FC Bayern ahead of Barcelona and Real Madrid ahead of Juventus.

Of course Arjen Robben has been injured, and given his considerable influence with FC Bayern Munich that predictability model pretty much goes out the door – or does it?

I’d say yes, because when adding goals scored (the PWP Index) Barcelona leap-frogs past FC Bayern; meaning it is highly likely we see Real Madrid and Barcelona in the Finals…. but you decide.

In Closing:

Awhile ago I wrote that FIFA needed to change how they rank teams across the World.

I remain stubbornly steadfast and steadfastly stubborn that the outputs from both the UEFA Champions League and World Cup PWP Indices lend credibility to the suggestion that FIFA revisit protocols on how they seed teams in their various competitions.

Best, Chris