New Statistics in Soccer (Football)? Open Pass and Open Shot

Over my two year course of analyzing/researching team performance in soccer I’ve come across a number of general issues I have with modern day statistics in this game and think there is room for improvement.

That’s not to say most of the current statistics have weaknesses – they don’t; the majority of them have function and purpose but in considering recent discussions with others at the World Conference on Science and Soccer, plus my own analyses and that of others I sense (think and feel) there needs to be a better way to answer this issue.

Sometimes what doesn’t happen on the pitch has more value than what does happen.  And my previous article entitled with that phrase is the impetus for this follow on article.  Another article previously written (Hurried Passes) also attempts to capture more background on these potential improvements.

To begin:

  • Much of what doesn’t happen in this game is as much, if not more critical, to the outcome/result, as what does happen.
  • Question – Is there a way to deductively or intuitively measure what doesn’t happen?  I think so.
  • Question – Do you feel or think it would be better to know the consistency of a striker scoring from ‘open shots’ versus a ‘hindered’ (not open) shots? This gives you two separate attacking data points for strikers – their success rate in scoring goals from open shots as well as their success rate in scoring goals from ‘hindered shots’. I do.
  • Question – Do you feel or think it would be better to know the successful consistency of a team/ individual player being able to make an ‘open pass’ or a ‘hindered pass’? This gives you two separate attacking data points for everyone – their success rate in making open passes as well as their success rate in making passes while being ‘hindered’. I do.
  • Question – Do you feel or think it would be more helpful to better quantify and qualify team and individual defending statistics that can be used to support what did and didn’t happen? I do.
  • If you answered ‘yes’ to any of these questions please read on…

Potential new statistics for soccer (football):

  • Open Shot – An open shot is when the striker can take a shot, directly towards goal, with no defender within a square yard or so that can hinder, or impeded the pure technical ability of a striker to strike the ball.  This offers a number of ways to analyze a condition – 1) the team has had the skill necessary to generate and create a clean shot on goal.  2) the defending team has been put into a condition where that open shot clearly indicates they were out of position, and 3) the defending team, when ‘not’ giving the opponent an Open Shot has done their best to be in a position to deny a clear shot on goal; in other words this is a deductive way of measuring ‘what didn’t and did happen’ with respect to good/poor team / individual defending and good/poor team/individual attacking.
  • Open Pass – An open pass is when a teammate can pass a ball, directly towards any other teammate, where no defender, within a square yard or so is present to hinder that pass.  This offers a number of ways to analyze a condition 1) the team has had the skill necessary to generate and create a clean pass to a teammate, 2) the defending team has either made a consicious decision not to challenge a pass from that area (note the location of the pass needs to be graphed) or the team was not in a good defensive position to hinder that pass, 3) the defending team, when ‘not’ giving the opponent an Open Pass has done their best to be in a position to deny a clear pass to another teammate; in other words this is a deductive way of measuring ‘what didn’t and did happen’ with respect to good/poor team / individual defending and good/poor team / individual attacking.

In closing…

I truly believe more effective and efficient analysis can come from defining passes and shots as being ‘open’ versus ‘hindered’ and by doing this it creates a more effective way to filter and help better determine what statistically doesn’t happen versus how current approaches are taken to measure what does happen.

And with this approach, other ‘did happen’, statistics like tackles, interceptions, blocked crosses, blocked shots can add additional clarity on the ‘did happen’ while the what ‘didn’t happen’ can now be more precisely graphed and plotted to better track good/bad zonal defending schemes versus good/bad man-to-man defending schemes – further identifying individual performance indicators that plot strengths and weaknesses of individual performance as as well as tactical coaching performance.

From an operational standpoint it merely means adding two new statistical categories (Open Shot and Open Pass) – the current statistical categories (Shot and Pass) would merely be redefined as being as the terms associated with shots and passes that are ‘hindered’.

And yes, it will be slightly judgmental (nothings perfect and even the refereeing in this game still remains judgmental) but with modern day technology I’m sure the video analysis programs can be tuned to generate that statistic based upon the physical presence of a data dot (of the player) relative to the other player making the pass or shot… – especially with the advent of GPS.

For more explanations on this concept read here (about 2/3rds of the way through the article)…

Best, Chris

If you’re in the world of soccer statistics and you think or feel these improvements add value please retweet.  In addition, under any circumstances 🙂 please add comments, as appropriate.

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  7. Pavlina

    Hi Chris,

    I find your blog quite useful and informative. Especially this article. I am starting my PhD with focus on soccer analytics and looking for some new ways of analyzing soccer data for more insights for coaches and managers.
    My data is sensor data for both legs of the players plus the ball and in the hands of the goalkeepers. It has timestamps, speed, sensor position, acceleration and position (x,y,z) data.
    I have some ideas but I am wondering if it makes sense to answer these questions and also if it is feasible to answer them based on the data I have:
    1. When should a coach remove a player?
    2. Which tactics / strategies lead most to shot on goal or goal?
    3. With this data we can have a timeline of events that happened, but we can answer the why and where this happened? -> the where is easy to answer here, but I am wondering about the why. Seems pretty tough to answer that.

    Well anyways, would love to hear your thoughts on this. And once again, thx for sharing these articles here.

    Best regards,


    • Chris Gluck

      Greetings Pavlina,
      Thanks for your kind words and great to hear you find value in reading what I offer.

      First off – congratulations on working towards your PhD! I respect all the hard work needed to get there and wish you the best.

      In turn I’ll try to do my best in offering up some thoughts that may have benefit – my apologies up front if they miss the mark.

      They are short questions but not necesarrily short answers 🙂

      1. When should a coach remove a player? I think and feel there is value in trying to answer your first question – in reading Soccernomics Arsene Wenger was noted, by Simon Kuper, for removing Dennis Bergkamp (Arsenal) at/or around the 60 minute mark because his performance began to lag after that. Other teams I’ve watched seem to have a habit of subbing players, for various reasons, around the 70-75 minute mark.

      In looking at all the game data I would be willing to bet that lactic acid build-up for some players begins at or around that time – so for physical performance reasons there’s probably an ‘average’ to work from. If your sensors can measure lactic acid build-up or heart rate then I think there is value (BUT) I’m not a performance specialist by any stretch and others may know much more about that than me.. 🙂

      As for general statistical analysis, with enough games, their may be a general bell curve you can create but that may have too many variables given different game states (score-lines)…

      2. Tactics /strategies leading to most shots on goal or goals? Okay for me Tactics mean one thing and strategies mean another – I don’t see them as the same. So Tactics first :)… I’d say it’s probably two things – one more clinical in nature and the other more general in nature…

      clinical = it’s no secret that the 6 yard box is the ideal place to strike a shot on goal – it’s no different than basketball; the closer you are to the rim the more likely the shot goes in… same logic applies – any tactics that can generate a shot taken inside the 6 yard box are good – crossing, dribbling, wall passes, etc…

      general = time and space –> any tactics that can generate time and space for a player to strike the ball are good (no matter what location is used) – as such the same above applies.

      Strategies – I see these as what approach is used to penetrate the final third; counter-attacking, direct attacking, 2nd level passes, short passing, deep crosses from up high, etc… In my two years of research so far there are differences in results based upon different ways of penetrating – particularly in Major League Soccer – I can’t say the same yet for the Bundesliga, La Liga, or EPL as I don’t have the data to substantiate a view yet. In MLS, usually, possession based teams do better – but this year that percentage differnce has gotten tighter. Good examples of possession based teams are LA Galaxy, Sporting, Seattle, and others – but note – Seattle can and will play counter-attacking…

      3. … answering the why and where… For me the why is easy to answer too 🙂 the why for me is ‘time and space’… now measuring that and proving that is incredibly difficult because public domain data doesn’t give you the necessary information to do that – nor would it appear does you sensor data. To be able to answer that question in your PhD I would submit your sensor data needs to ‘identify’ where the closest opponent is to the player being monitored… meaning that both teams would need to be monitored at the same time using the same equipment.

      I hope that helps Pavlina – and best wishes on your effort!
      Kind Regards,


      • Pavlina

        This helps a lot. Many thanks! Trying to boost my soccer knowledge at the moment to be able to understand what would be of value to coaches and what not and relate that to the data I have. Still a long way to go since I am only in the beginning. Will keep reading your blog posts 🙂



  8. Chris Gluck

    Thanks Pavlina and good luck with your effort 🙂 In two years time I will probably take my 3-4 years worth of data and put together my PhD as well – but of course, holidays and other things may get in the way of that ;). Best, Chris


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