Tagged: Unsuccessful Pass

Passing – An oddity in how it’s measured in Soccer (Part II)

If you read my initial article on “Passing – An oddity in how it’s measured in Soccer Part I“; I hope you find this article of value as well as the onion gets peeled back a bit further to focus on Crosses.

To begin please consider the different definitions of passing identified in Part I and then take some time to review these two additional articles (Football Basics – Crossing) & (Football Basics – The Passing Checklist) published by Leo Chan – Football Performance Analysis, adding context to two books written by Charles Hughes in 1987 (Soccer Tactics and Skills) and 1990 (The Winning Formula). My thanks to Sean McAuley, Assistant Head Coach for the Portland Timbers, for providing these insightful references.

In asking John Galas, Head Coach of newly formed Lane United FC in Eugene, Oregon here’s what he had to offer:

“If a cross isn’t a pass, should we omit any long ball passing stats? To suggest a cross is not a pass [is] ridiculous, it is without a doubt a pass, successful or not – just ask Manchester United, they ‘passed’ the ball a record 81 times from the flank against Fulham a few weeks back.”

In asking Jamie Clark, Head Coach for Soccer at the University of Washington these were his thoughts…

“It’s criminal that crosses aren’t considered passing statistically speaking. Any coach or player knows the art and skill of passing and realizes the importance of crossing as it’s often the final pass leading to a goal. If anything, successful passes should count and unsuccessful shouldn’t as it’s more like a shot in many ways that has, I’m guessing, little chance of being successful statistically speaking yet necessary and incredibly important.”

Once you’ve taken the time to read through those articles, and mulled over the additional thoughts from John Galas and Jamie Clark, consider this table.

Stat Golazo/MLS STATS Squawka Whoscored MLS Chalkboard My approach Different (Yes/No)?
Total Passes 369 356 412 309+125 = 434 309+125+9=443 Yes
Total Successful Passes 277 270 305 309 309 + 9 = 318 Yes
Passing Accuracy 75% 76% 74% NOT OFFERED 71.78% Yes
Possession Percentage 55.30% 53% 55% NOT OFFERED 55.93% Yes
Final Third Passing Accuracy 89/141= 63.12% NOT OFFERED NOT OFFERED FILTER TO CREATE 92/140 = 65.71% Yes
Total Crosses 35 vs 26 (MLS Stats) NOT OFFERED 35 35 35 No
Successful Crosses 35*.257=9 NOT OFFERED 9 9 9 No
* NOTE: MLS Chalkboard includes unsuccessful crosses as part of their unsuccessful passes total but does not include successful crosses as part of their total successful passes; it must be done manually.

For many, these differences might not mean very much but if looking for correlations and considering R-squared values that go to four significant digits these variations in datum might present an issue.

I don’t track individual players but Harrison and Matthias do, as does Colin Trainor, who offered up a great comment in the Part I series that may help others figure out where good individual data sources might come from.

What’s next?

My intent here is not to simply offer up a problem without a solution; I have a few thoughts on a way forward but before getting there I wanted to offer up what OPTA responded with first:

I (OPTA representative) have has (had) a word with our editorial team who handle the different variables that we collect. There is no overlay from crosses to passes as you mentions, they are completely different data variables. This is a decision made as it fits in with the football industry more. Crosses are discussed and analysed as separate to passes in this sense. We have 16 different types of passes on our F24 feed in addition to the cross variable.

So OPTA doesn’t consider a cross a pass – they consider it a ‘variable’?!?

Well I agree that it is a variable as well and can (and should) be tracked separately for other reasons; but for me it’s subservient to a pass first and therefore should be counted in the overall passing category that directly influences a teams’ percentage of possession. Put another way; it’s a cross – but first and foremost it’s a pass.

(Perhaps?) OPTA (PERFORM GROUP now) and others in the soccer statistics industry may reconsider how they track passes?

I am also hopeful that OPTA might create a ‘hot button’ on the MLS Chalkboard that allows analysts the ability to filter the final third consistently, from game to game to game, as an improvement over the already useful ‘filter cross-hairs’…

In closing…

My intent is not to call out any statistical organizations but to offer up for others, who have a passion for soccer analyses, that there are differences in how some statistics can be presented, interpreted and offered up for consideration. In my own Possession with Purpose analysis every ball movement from one player to another is considered in calculating team passing data.

Perhaps this comparison is misplaced, but would we expect the NFL to call a ‘screen pass’ a non-pass and a variation of a pass that isn’t counted in the overall totals for a Team and Quarterback’s completion rating?

Here’s a great exampleon how Possession Percentage is being interpreted that might indicate a trend.

Ben has done some great research and sourced MLS Stats (as appropriate) in providing his data – he’s also offered up that calculating possession is an issue in the analytical field of soccer as well.

In peeling back the data provided by MLS Stats he is absolutely correct that the trend is what it is… When adding crosses and other passing activities excluded by MLS Stats the picture is quite different and lends credence to what Bradley offers.

For example–when adding crosses and other passing activities not included by MLS Stats–the possession percentages for teams change, and the R-squared between points in the league table comes out as 0.353, with only 7 of 8 possession-based teams making the playoffs. New York, with most points, New England and Colorado all had possession percentages last year that fell below 50%, and only one team in MLS last year that didn’t make the playoffs finished with the worst record (16 points) DC United.

For me, that was superb research – a great conclusion that was statistically supported. Yet, when viewed with a different lens on what events are counted as passes, the results are completely different.

All the best,


You can follow me on twitter @chrisgluckpwp


Hurried Passes – Could this be a new statistic in Soccer?

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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

In conclusion:

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.

In Closing…

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.

Best, Chris