It’s time to offer up another revised version of my Possession with Purpose Analysis.

My intent here is to:

1. Provide an update that may help simplify this effort, and
2. Update new links to articles most have found to be of great interest in the last year.

To begin… Possession with Purpose (PWP):

The End State, as always this is good to know up front:

Create an objective Strategic Family of Indices, with publicly made available data, that has relevance and helps identify (explain) the strengths and weaknesses of team performance ‘outside’ the realm of Points in the League Table.

Of note; this analysis has been presented, and received with great interest, at the World Conference on Science and Soccer of 2014.  So it’s not a fly-by-night attempt to offer up analysis that can’t translate back to the soccer and science industry or help inform the general, or well educated, soccer community (both here and across the pond) about Footy…

The Intent:

Create a Family of Indices that measure the ‘bell curve’ of strategic activities that occur in a game of football (soccer); recognizing that in order to score goals the following activities usually need to occur:

1. Gain possession of the ball
2. Move the ball
3. Penetrate the opponents defending final third
4. Generate a shot taken
5. That ends up on target and,
6. Gets past the keeper

From a statistical (measurement) standpoint those activities are organized into these six categories:

1. Possession percentage
2. Passing Accuracy across the Entire Pitch
3. Passing Percentage within and into the Opponents Final Third compared to overall possession (i.e. = Penetration)
4. Shots Taken per Percentage of Penetration
5. Shots on Goal per Shots Taken
6. Goals Scored per Shots on Goal

It’s not a secret formula but I do retain Copyright.

The Family of Strategic Indices – there are three of them:

1. Attacking Possession with Purpose (APWP)
2. Defending Possession with Purpose (DPWP)
3. Composite Possession with Purpose (CPWP)

APWP Index:  How effective a team is in performing those six process steps throughout the course of a game.  Example:

DPWP Index:  How effective the opponent is in performing those six process steps, throughout the course of a game, against you.  Example:

CPWP Index:  The mathematical difference between the APWP Index and DPWP Index.  Example:

The Analysis:

Simply stated, the analysis stemming from this effort is a comparison and contrast between how a team performs (in the bell curve of these activities) relative to other teams in their league “without” including points in the league table.

Statistical Correlation:

Last year the CPWP Strategic Index Correlation (relationship) to Points in the Table, for Major League Soccer, was .77; this year, at the end Week 26, the R is .85.

In returning back to the End State:

“Create an objective Strategic Family of Indices, with publicly made available data, that has relevance and helps identify the strengths and weaknesses of team performance ‘outside’ the realm of Points in the League Table.”

Given the very high level of Correlation these Indices have, I’d say this Family of Indices has considerable statistical relevance; and I should point out that although the PWP approach is an Explanatory Model it can also be leveraged as a Predictability Model.

After speaking with a number of folks at the World Conference on Science and Soccer (2014) it was agreed that the most effective way to turn this into a Predictability Model is to remove Goals Scored (in both Indices) and ‘see’ how the Composite Index takes shape after that.

Here’s an example of what I mean:

A word or two of caution…

From a purely statistical viewpoint I do not see this as a Predictability Model that has direct relevance yet… why?

For the simple reason that there have not been 15 games played for all teams both Home and Away – teams show a tendency, for the most part to behave slightly different at home versus on the road…

Why the number 15?  I suppose it comes down to Confidence Level in the number of samples that are needed in order to forecast the future based upon the past…  with 34 games played in Major League Soccer you really need 15 games to reach that 95% Confidence Level limit in samples…

All that said, it is extremely inviting/inticing to see that even when Goals Scored (both for and against) are removed the CPWP Predictability Index still has a correlation (R) of .84…

Links to articles that have had extensive views over the last year and a way to get a taste of how PWP analyses might be able to help you, as a writer (through collaboration with me), better inform your audience about the nuance of soccer:

• Chicago Fire
• Portland Timbers
• Consistency of Purpose – Attacking Standard Deviations
• La Liga – Simana 2 ( I can offer translation of my articles from English to Spanish on special request)
• Bundesliga – Bayer Leverkusen (I can offer that translation request to German as well, on special request)
• English Premier League – Chelsea
• Colorado Rapids
• LA Galaxy
• Sometimes what doesn’t happen on the pitch has more value than what does happen
• New Statistics – Open Shots – Open Passes
• FIFA World Rankings – Time for a change?
• Expected Wins
• Passing – an oddity in how it’s measured (Part I)
• Passing – an oddity in how it’s measured (Part II)
• Expected Wins 3 – My deepest dive yet into the average performance of what winning teams do in Major League Soccer, the English Premier League, Bundesliga, LaLiga, and World Cup 2014.
• My original Introduction and Explanations (detailed) to Possession with Purpose Family of Indices
• 2014 End of Season Analysis – Houston Dynamo – Dynamic Dynamo Demagnetized as Dominic Departs
• West Ham and Aston Villa – EPL– Going in two different directions
• 2014 End of Season Analysis – Chicago Fire – Candle Burned at Both Ends
• Getting Better as a Youth Soccer Coach
• The Comforts of Home in Major League Soccer
• Seattle Sounders – Road Warriors in 2014 MLS Regular Season
• Portland Timbers End of Season 2014 – Defense Wins Games & Better Defending Leads to Better Attacking
• Valencia – Formula Won – La Liga
• Getting More From Less – Peeling back the statistical differences between teams that Direct Attack versus playing a Counter-Attacking Tactic as part of a Possession-based System.
• Expected Wins 4 – Is European Football Really Higher Quality than Major League Soccer?
• Seeing Red!!! Toronto FC
• World Cup – Two Best Teams?  You Bet!
• UEFA Champions League – Some Great Games Coming
• Busting the Myth of Moneyball in Soccer Statistics…
• Scintillating Saints of Southampton Stay Strong
• Hurried Passes
• Catching up with Europe (CPWP and initial discussions on TSR)
• Redefining and Modernizing TSR
• Expected Wins Five (Europe – Pucker Time is here)
• Passing – More from Less – Barcley’s Premier League after Week 30
• MLS 2015 – Control or Lack Thereof

In Closing:

Others in mainstream media sometimes offer up subjective opinions that may not be substantiated with objective data; I won’t do that.

Every shred of analysis offered here will include some sort of objective data to support an opinion or conclusion.

Like any other mainstream business; statistical analysis provides objective data as a tool to leverage when looking to make business decisions.  It is not a substitute for the seasoned leadership needed to make final decisions.

I don’t advocate that this analysis is the ‘answer’ or the only tool that substantiates one view – in a soccer match, with 40,000 supporters in attendance, I’ve learned that those 40,000 supporters have 40,000 sets of eyes that see things differently.

On this site, this information and analyses presented, is merely my view, from my eyes, in how I see the game – hopefully, in order to make my future articles of better value, others will add their comments, thoughts, and questions.

Finally, I’m not sure how this will develop but I’ve been approached to provide a manuscript for this analytical effort – for publication in a Sports Science Journal.   More to follow on how that goes.  

Best, Chris

COPYRIGHT, All Rights Reserved.  PWP – Trademark

NOTE:  All data used to generate this analysis stems from OPTA through a number of open/public websites across Europe and America.

My thanks to OPTA and all those open websites for helping to facilitate my own analysis and potential improvements that may arise from this effort.