No detailed statistics today – just a narrative to pass on a few tidbits as I prepare my End of Season analysis for Europe.
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.
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Aye… the NFL track ‘hurried throws’ – why doesn’t a Statistics agency involved in Soccer track “Hurried Passes”?
I’ll get to that but first I need to set some conditions.
If you’ve read my article on Expected Wins 2 (XpW) it seems reasonable that a teams’ Passing Accuracy in the Final Third has great value in working towards generating quality shots taken that are more likely to be on goal and (therefore) more likely to go in.
So what activities does the defense take to mitigate successful passes (i.e. generate Unsuccessful Passes)?
Before digging in, I’m not the only one looking into Defensive Statistics; Jared Young has put together an interesting article on Individual Defensive Statistics that may be of interest.
Similarities in our work come from collecting ‘like’ defensive activities; Tackles Won, Clearances, Interceptions, etc…
Additional twists in my efforts will be to fold my Opponent team attacking statistics in with my team Defense Activities to see what correlations might be present.
My data comes from the first 71 games in MLS this year (142 events) and my source is the MLS Chalkboard.
Bottom line up front (BLUF) – however this data plays out it needs to make sense so here’s my operating conditions on Team Defensive Activities in the Defending Final Third and which ones I will focus on that can be associated with an Unsuccessful Pass in the Final Third:
- Recoveries – usually associated with ‘loose balls’ generated from some other activity like a deflection, rebound, or perhaps an unsuccessful throw-in that hits a head and deflects away (uncontrolled) that another player latches on to and then makes a move showing control the ball. Therefore Recoveries are not counted as a specific defensive activity that would impede a successful pass – it is the resultant of another activity that impedes a successful pass.
- Clearances – one of the better examples of a defensive activity that impedes a successful pass – especially those generated from crosses but not necessarily called a blocked cross. Therefore Clearances will be counted as a specific defensive activity that impedes a successful pass.
- Interceptions – pretty much self explanatory – an interception impedes a successful pass – therefore Interceptions will be counted as a specific defensive activity that impedes a successful pass.
- Tackles Won – this is a defensive activity that strips the ball from an opponent – so it is a possession lost but not a defensive activity that impedes a successful pass. It won’t be counted as a defensive activity that impedes a successful pass.
- Defender Blocks – this is a defensive activity that blocks a shot taken not a successful pass; therefore it won’t be counted as a defensive activity that impedes a successful pass.
- Blocked Crosess – clearly it is what it is; and since a cross is a pass it will be counted as a defensive activity that impedes a successful pass.
To summarize – Blocked Crosses, Interceptions and Clearances will be counted as defensive activities that should impact the volume of Unsuccessful Passes.
So what are the correlations between those combined Defensive Activities versus Unsuccessful Passes after 142 events?
Final Third Defensive Activities to Unsuccessful Passes = .6864
Final Third Defensive Activities to Unsuccessful Passes when the Defending Activities’ Team Wins = .7833
Final Third Defensive Activities to Unsuccessful Passes when the Defending Activities’ Team Draws = .6005
Final Third Defensive Activities to Unsuccessful Passes when the Defending Activities’ Team Loses = .6378
It seems pretty clear that Teams who win have more Defensive Activities, that in turn increase their Opponents’ Unsuccessful Passes given the higher positive correlation than losing teams – in other words a team that wins generally executes more clearances, interceptions and blocked crosses to decrease the number of Successful Passes their Opponents make.
It also seems pretty clear that all those Defensive Activities don’t account for the total of Unsuccessful Passes generated by the Opponent. If they did then the correlation would be higher than .7833; it’d be near .9898 or so.
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.
To date I’m not aware of any statistics that log ‘pressure applied’ to the attacking team. A good way to count that would be tracking how many seconds the defending team gives an opponent when they recieve the ball and take action.
My expectation is that the less time, given the opponent, the more likely they will hurry a pass that simply goes awry without any other statistic event to account for that other than – bad pass due to being hurried.
So in other words; like the NFL tracks hurried passes, I think that the Soccer statistical community should also track “hurried passes”…
I’m not sure that completely closes the gap between those three Defensive Activities and Unsuccessful Passes but it does seem to be a relevant statistic that can attempt to quantify panic in an attacker while also quantifying good physical and spatial pressure by a defender. Two relevant items of interest to a coach in weighing the balance on who plays and who doesn’t and who they might like to add to their team or perhaps put on loan/trade elsewhere.
The Official statistic that would get tracked for attacking players is ‘Hurried Passes’ and the statistic that would get tracked for defensive players is ‘Passes Hurried’.
In addition – an increase in hurried passes can become a training topic that drives a Head Coach to develop tailor made passing or turning drills to minimize Hurried Passes (make space) while also providing a Head Coach statistical information to generate tailor made defensive drills that look to increase Passes Hurried. I’d expect the level of the training drills to vary given the level of skill/professional development as well.
So how might someone define a “Hurried Pass”? I’m not sure; there are plenty of smarter people out there in the soccer community than me – if I had to offer up a few suggestions it might be a pass that goes out of bounds given defensive pressure, or maybe a through-ball that goes amiss given pressure from a defender – in other words the timing of the delivery looked bad and given defensive pressure it was off-target.
However defined if judgment can be applied when identifying a pass as a key pass then it stands to reason that judgment can be applied to identify a bad pass as being bad because the defender hurried the attacker.
More to follow…
A more to follow is this recent article entitled New Statistics Open Pass and Open Shot.
If you’ve been following me the last 2-3 years, through Columbian Newspaper, out of southern Washington, you’ll know that I’ve been researching data with the intent of creating some Indices to analyze “team performance” in Major League Soccer.
The initial version of my Possession with Purpose approach was published on 10 February, 2013 on the Columbian Newspaper Portland Timbers Blog Site (here).
This revised version was published on the 15th of January, 2014. I retain COPYRIGHT on all materials published in association with Possession with Purpose (PWP) TM
My intent has been to develop a simplified (Strategic) set of team performance indicators that may help others better understand soccer and how the outcome of a game may be better understood based on the primary inputs to the game.
Data for presentation originally comes from documenting and analyzing all 646 MLS Regular Season games in 2013; my research in beginning to develop Possession with Purpose, as it is known today, began mid-season 2012; the pre-cursor articles on that effort can be found on my Columbian Newspaper blog site.
All future research will be published here… As things have progressed my research and efforts in Possession with Purpose led to an invitation to present my findings at the World Conference on Science and Soccer 2014; that presentation can be found through this link.
The source data originates with OPTA and is displayed on the MLS Chalkboard and the MLS Statistics Sheet found through www.mlssoccer.com.
With that here’s my introduction on Possession with Purpose…
To first understand the context, I offer that this is one of the End States of my effort:
Create a simplified approach and documented method for measuring team performance where the output is an Index that (while excluding points) comes close to matching results in the MLS League Table.
Beginning with that End State in mind here is the End State product:
Observations from the Diagram…
Note that 9 of the top 10 teams in this Index made the MLS Playoffs last year with the Houston Dynamo finishing 12th in the Index.
For comparison, in benchmarking whoscored.com their Index only had 8 of their top 10 teams make the Playoffs, while http://www.squawka .com matched my 90% success rating, but the team they missed in the top 10 (New England) finished 16th in the Index.
From a strategic standpoint, the End State objective has been met; create a simplified approach and documented method for measuring team performance where the output is an Index that (while excluding points) comes close to matching results in the MLS League Table.
Defining the PWP Attacking and Defending Processes…
Here are the six steps in the PWP Strategic Attacking Process:
- Gain possession of the ball,
- Retain possession and move the ball,
- Penetrate & create goal scoring opportunities,
- Take shots when provided goal scoring opportunities,
- Put those shots taken on goal,
- Score the goal.
Here are the six steps in the PWP Strategic Defending Process:
- Minimize opponent gaining possession of the ball,
- Minimize opponent retaining possession and moving the ball,
- Minimize opponent penetrating and creating goal scoring opportunities,
- Minimize opponent taking shots when provided goal scoring opportunities,
- Minimize opponent putting those shots on goal,
- Minimize opponent scoring the goal.
Every step is this process has an average success rate (percentage) based upon data gathered from all 646 MLS Regular Season games.
Understanding the context of these steps versus other conditions and activities that influence the outcome of a game…
In case you missed it I call these Processes and the Indices “Strategic” to separate their value/meaning relative to other things that can influence the outcome of a game.
For me I have two other ways to classify information that can influence the outcomes in those steps. I have Operational conditions and Tactical metrics; provided below are some examples of each:
- Operational conditions: Scheme of maneuver a team uses in setting up their system, such as flat-back four, flat-back three, double-pivot midfield, single-pivot midfield, bunkering with counterattacking, pressing high, direct attacking, possession-oriented attacking, weather conditions, location of the game (home/away), conference foe, non-conference foe, etc…
- Tactical metrics: Locations of shots taken, shots on goal, and goals scored; penalty kicks, free kicks, crosses, headers won/lost, tackles won/lost, interceptions, clearances, blocked crosses, blocked shots, etc.
The diagram below shows the PWP Strategic Attacking Process with the average percentage of success rate in MLS for 2013. A more detailed explanation of each step is provided below the diagram.
Step 1: Gain possession of the ball: The intent behind this basic step should be clear; you can’t win the game if you don’t possess the ball to some extent. A second consideration about this step is that the more you possess the ball the less your opponent possesses the ball.
- From a defensive standpoint there are any number of ways a team can work to gain possession of the ball; they include, but are not limited to, tackling, intercepting, clearing the ball, winning fifty-fifty duels on the ground or in the air, or simply gathering a loose ball based upon a deflection or bad pass.
- For this Process the measurement of success is the percentage of possession a team has in a given game; note that in Soccer, the primary method for measuring possession is to add up the number of passes made in a game and divide into that the amount of passes one team makes (create a ratio percentage of possession); the opposing team has the difference between 100% and the percentage of possession that the other team has.
- It’s not perfect but it provides a simplified ratio to compare one team versus another…
Step 2: Retain possession and move the ball: It shouldn’t be a secret to many that in most cases the team possessing the ball will need to move the ball in order to penetrate the opponents Defending Third and score a goal.
- This is not to say a team has a minimum number of passes they need to complete to score a goal; for teams winning possession deep in the opponents Defending Third there may be times where the only thing needed is a quick shot on goal.
- By and large, however, most teams – when they gain possession of the ball – do so in their own Defending Third and then move the ball (eventually forward) in a position where a teammate can create a goal scoring opportunity for another team member to take a shot.
- For this process, the measurement of success is the team’s passing accuracy percentage across the entire pitch; passes completed divided into passes attempted.
- It’s not perfect, but it provides a simplified ratio to compare one team versus another; statistically speaking there are weaknesses in how this percentage is measured by the big data folks.
- Throw-ins, for example, move the ball across the pitch from one player to another yet they are not officially counted as passes.
- Successful crosses are also not counted as a successful pass even though the ball moves successfully from one player to another.
- Oddly enough, when evaluating the data provided on the MLS chalkboard, an Unsuccessful cross is included as a Pass attempted (?!)
- For the purposes of this analysis I had to count all successful crosses as successful passes; therefore my final pass completions totals will be slightly higher than what Opta provides. It is what it is…
- I should also point out here that there are occasions when a team wins possession of the ball and takes a shot where no pass was completed. Like I said, this measurement method is not perfect but it is ‘equal’ in ignoring that exception for all teams.
- Therefore the measurement itself has value in tracking the majority (bell curve) of activities that normally occur in a game of soccer. And as a reminder, these are Strategic steps in PWP; by definition a Strategic step will not measure to a level of granularity; that is where Tactical metrics come into play based upon an Operational condition where the team is applying pressure higher up the pitch.
Step 3: Penetrate and create goal scoring opportunities: Most know that a pitch is divided into three parts; the Defending Third, Middle Third, and Attacking Third. For the purposes of this effort, Penetration is associated with entering the opponent’s Defending Third with the intent to score.
- For this Process, penetration is measured by dividing the volume of passes a team completes within the opponent’s Defending Third into the volume of passes a team completes across the entire pitch.
- It’s not perfect but it creates a ratio that treats all teams fairly, and given the overall accuracy of the End State Index (90%), it’s a reasonable way to measure this step.
- In order to measure this step I first had to manually filter, for all 646 games, every pass attempted and completed using the MLS Chalkboard; my thanks to MLS and OPTA for providing us ‘stats’ guys the opportunity to do that. With Golazo stats now available, that task will be easier next year. As a stats guy, it would have been inappropriate to switch measurement methods ¾’s of the way through the year.
Step 4: Take shots when provided goal scoring opportunities: This is, by far, the hardest indicator to measure, given how current data sites really lack granularity in how they identify/define ‘created goal scoring opportunities.
- I define a ‘created goal scoring opportunity’ as any pass, successful or not, that may have ended with another teammate taking a shot. That’s hard to quantify, but an example, if you will:
- A fullback overlapping down the right side puts in a wicked cross that gets cleared at the last minute by a center-back, with his head. With OPTA and other data companies that wicked cross, though unsuccessful, is not quantified as a goal scoring opportunity created; it’s merely tracked as a clearance and an unsuccessful pass.
- I disagree; the fullback did their job in putting in that wicked cross – what really happened is the defender also did their job in clearing it – therefore a “potential” for the attacking team to complete a created goal scoring opportuinty and take a shot was denied.
- Both the attacking team and defending team should be statistically credited for doing what they are expected to do. Others may disagree…
- But as a Head Coach, I would put to memory that the fullback did what was supposed to happen; create the chance – therefore in my books that player created a goal scoring opportunity.
- For this Process, the step is measured by counting the number of Shots Taken compared to the number of completed passes in the opponent’s Defending Third.
- It’s not perfect, but it’s measured in an unbiased manner for every team, and there will be instances where a shot can be taken without a completed pass or originate from a defensive error.
- In going back to the example, as a Head Coach I would call that effort a “failed assist.” I think there is value in knowing the number of “failed assists” as much as there is in knowing “assists.”
- By tracking “failed assists” it provides a pure, statistical way, to track individual player performance (tactical metric) that can influence team performance.
- Bottom line on this one, as contentious as it may be for some, recall the End State of this Final Index… create a simplified approach and documented method for measuring team performance where the output is an Index that (while excluding points) comes close to matching results in the MLS League Table.
- Given the accuracy rating of 90% in matching the top 10 Playoff teams this year I feel and think the approach to measure this indicator works.
- If OPTA, or another data compilation agency starts to track “failed assists”, could an Index like this reach 100% accuracy?
Step 5: Put those Shots Taken on Goal:For the most part this is an individual statistic that is added up to create a team performance indicator.
- For this process, the step is measured by dividing the number of Shots on Goal by the number of Shots Taken.
- It’s one of the easier indicators to measure, and if you watch any level of soccer, it’s pretty self explanatory – if the Shot can come anywhere within the dimensions of the Goal, it is considered a Shot on Goal. One of two things happens; it goes in or it doesn’t.
Step 6: Score the Goal: One critical objective of the game.
- I say ‘one’ because indications, I see, lead me to offer that this game is not all about scoring goals.
- In my research it appears to me that teams who defend better seem to take more points in games than teams that don’t defend very well.
- A recent example in my End of Season analysis of Vancouver: in Western Conference competition, they scored 35 goals and gave up 35 goals; all told they took just 26 of 72 possible points – clearly, in this example, scoring goals did not result in wins…
- Prozone, a noted professional sporting analysis company, offers the following in the article: “Using data from the last ten seasons of the Premier League, Anderson and Sally compared the value of a goal scored and the value of a goal conceded. They found that scoring a goal, on average, is worth slightly more than one point, whereas not conceding produces, on average, 2.5 points per match. Goals that don’t happen are more valuable than goals that do happen.”
- It’s not perfect, but it provides reasonable information in a reasonable format that has reasonable value when comparing the End State output to how the MLS League Table finished.
- For those interested the PWP Strategic Attacking Index and Defending Index are provided below:
- In looking at these two Indices, note the number on the left; the difference between the Index number in the Attacking Index and the Defending Index is the number that appears to the left in the Final Strategic Index at the beginning of this article.
- That may help explain why some teams finished above zero, as opposed to below zero in the Final Index.
- Teams finishing above zero had team attacking percentages that exceeded their team defending percentages; in other words they were better in their attack against the opponents than the opponent’s were in attacking them.
- Team success rates in these six steps will be used next year to begin to analyze how well the team is performing as the new season starts compared to performance the previous year.