{"version":"1.0","provider_name":"MRKT Insights - Football Consultancy Services","provider_url":"https:\/\/mrktinsights.com","title":"Passing failures are as important as successes - MRKT Insights - Football Consultancy Services","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"F1fJHaWle5\"><a href=\"https:\/\/mrktinsights.com\/index.php\/2019\/09\/05\/passing-failures-are-as-important-as-successes\/\">Passing failures are as important as successes<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mrktinsights.com\/index.php\/2019\/09\/05\/passing-failures-are-as-important-as-successes\/embed\/#?secret=F1fJHaWle5\" width=\"600\" height=\"338\" title=\"&#8220;Passing failures are as important as successes&#8221; &#8212; MRKT Insights - Football Consultancy Services\" data-secret=\"F1fJHaWle5\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/**\n * WordPress inline HTML embed\n *\n * @since 4.4.0\n * @output wp-includes\/js\/wp-embed.js\n *\n * Single line comments should not be used since they will break\n * the script when inlined in get_post_embed_html(), specifically\n * when the comments are not stripped out due to SCRIPT_DEBUG\n * being turned on.\n *\/\n(function ( window, document ) {\n\t'use strict';\n\n\t\/* Abort for ancient browsers. *\/\n\tif ( ! document.querySelector || ! window.addEventListener || typeof URL === 'undefined' ) {\n\t\treturn;\n\t}\n\n\t\/** @namespace wp *\/\n\twindow.wp = window.wp || {};\n\n\t\/* Abort if script was already executed. *\/\n\tif ( !! window.wp.receiveEmbedMessage ) {\n\t\treturn;\n\t}\n\n\t\/**\n\t * Receive embed message.\n\t *\n\t * @param {MessageEvent} e\n\t *\/\n\twindow.wp.receiveEmbedMessage = function( e ) {\n\t\tvar data = e.data;\n\n\t\t\/* Verify shape of message. *\/\n\t\tif (\n\t\t\t! 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The traditional response to passing percentage statistics is &#8220;anyone can pass the ball under no pressure to an unmarked teammate&#8221;. That is true when you reach the professional level. It is also true that the players with the highest pass percentage success rate are generally centre backs and deep midfielders. They generally play short passes, in less crowded areas to unmarked players. So what is the state of play now? The standard player level data collection output contains:Number of passesSuccess rate of passesPasses forwards, sideways, backwardsSuccess rate of each type of passThrough balls (not sure on definition)Key passesPasses into box (open play and all)Long ballsFinal third passes (forwards, backwards, sideways) This is all great to have but you can start to pick holes in almost all the definitions and come up with counterexamples straight away. For example, what is a long ball? A Beckham 70 yard switch into space is different from an aimless hoof. You want a player with lots of forwards passes in the final third? OK you&#8217;ve lost Sterling and Sane then who play lots of backward passes, these are also called cutbacks and are one of the best ways to score. The biggest problem with this aggregate data is that it still doesn&#8217;t really tell you if you are making the right pass. We might want forward passes but a forward pass to a player under loads of pressure, even when complete, might be worse than one to a teammate in space to your side. For that we probably need to know more details about where you are on the pitch when you are making these passes and where they are going to. Paul Riley has produced a good passing model which I covered recently. That looks at pass success from X\/Y coordinate to X\/Y coordinate. This is a good step forward as you can the types of passes a player is making, whether they are above or below average difficulty and if the player is making them at above or below average completion rate. I really like the simplicity of the model, it is a big step forward and passes the &#8220;smell&#8221; test. What it doesn&#8217;t adjust for, which I think is important, is the relative dominance and skill of the player&#8217;s team. Take one player from a poor team that plays directly and put them on a possession-based team of superstars and their passing statistics will change dramatically. Opponents play differently against Manchester City to Cardiff. Yes, John Stones is a much better passer than Sol Bamba but he also has much better options to pass to, a manager who wants him to pass out from the back and probably more space to play into as opponents hunker down for a low block. A good step forward for this is Statsbomb&#8217;s pressure index which includes the ability to look at how many passes players play under pressure and changes in their passing style when under pressure. Again the results from this are all up for interpretation, for example, I would expect a dominant possession based team would play more passes under pressure higher up the pitch than a counter-attacking team. But it should help with finding defenders and midfielders who can break a press by retaining their ability to play out from the back. So now we can fairly confidently tell, on aggregate level data the type of passes a player typically makes, whether they do this at better or worse than average levels and how well they play under pressure. Plus it all passes the smell test, the names at the top of the list are the names I would expect to be at the top of the list. But are those names at the top of the list because they are at clubs who are dominant or because they are the best passers? Likely both. Can we, therefore, find a way of picking out good passers on bad teams and bad passers on good teams using the data that we have? Other things to consider: Passing is as much about the options available to you as the mechanics of kicking a ball where you want it to go. If you have great movement ahead of you the options increase as does your chance of making a successful pass, this doesn&#8217;t mean you are a better passer. You also need to consider the skill of the player you are passing to. Some of those Xavi passes hit very fast into feet in a crowded penalty box only worked because they were pinged into Messi, Villa or Eto&#8217;o. Most players would not have the deftness of touch to control them. So would Xavi have to alter his passing game if he were on a League 2 side? He would still be the pass master but I suspect it would involve lots more space and he wouldn&#8217;t have to demonstrate the same skills. A pass to feet isn&#8217;t always what you want. Busquets often passes the ball into the area he sees space in front of his teammate allowing them to turn and move forward at the same time. At OptaPro the Barcelona developer gave a&nbsp; talk about measuring the percentage of time players were orientated to receive a pass. This would be a good option for seeing which players were making themselves available to receive a pass and whether players were spotting this when selecting a pass. It involved full panorama cameras so isn&#8217;t suitable for match data but could be useful for training games as a coaching point. Failures With passing data, I think we look too much at the successes. Could we learn more about a player by looking at the failures? Defenders and deep midfielders tend to be passing the ball 50-100 times a game"}