- ID de l'analyse :
- 1ac38012-28ec-4388-845a-abbc106b2c2dTerminée
- URL soumise :
- https://skillcorner.com/blog/progressive-passing
- Fin du rapport :
Liens : 3 trouvé(s)
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| https://twitter.com/skillcorner | |
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Variables JavaScript : 43 trouvée(s)
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| 0 | object |
| 1 | object |
| 2 | object |
| 3 | object |
| 4 | object |
| 5 | object |
| onbeforetoggle | object |
| documentPictureInPicture | object |
| onscrollend | object |
| __core-js_shared__ | object |
Messages de journal de console : 2 trouvé(s)
Messages consignés dans la console web
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| warning | other |
|
| warning | other |
|
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<h1><span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text">Progressive Passing: evaluating decision-making</span></h1>
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<div class="date">December 2024 </div>
<div class="author"><span class="author-name">Charles Myers</span></div>
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<a class="" href="/insights?sport=football&hsLang=en" rel="tag"><button>Football</button></a>,
<a class="" href="/insights?sport=game-intelligence&hsLang=en" rel="tag"><button>Game Intelligence</button></a>,
<a class="" href="/insights?sport=analysis&hsLang=en" rel="tag"><button>Analysis</button></a>
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<span id="hs_cos_wrapper_post_body" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="rich_text"><h2>How can find progressive passers by measuring and analysing opportunities and tendency?</h2>
<blockquote>
<p style="font-size: 16px;"><em>"I think 'passes completed' is the most useless statistic there is. I can have 96% pass completion playing sideways or backwards without being dangerous for the whole game. That means I haven't done anything. What's the point?" </em></p>
<!--more-->
<p style="font-size: 16px;">Kevin De Bruyne</p>
</blockquote>
<p style="text-align: justify; font-size: 14px;">Elite teams need midfield players capable of making passes that penetrate and unbalance the opposition, creating opportunities to score.</p>
<p style="text-align: justify; font-size: 14px;">This article will demonstrate how recruitment professionals can evaluate the specific aspects of player performance that matter most to their game model using SkillCorner’s Game Intelligence data. By relying on key information derived from SkillCorner's tracking data, analysts can create passing metrics that go beyond passer & receiver location and reveal key football context.</p>
<p style="text-align: justify; font-size: 14px;"> </p>
<p style="text-align: justify; font-size: 14px;"><strong>Adding context to passing analysis</strong></p>
<p style="text-align: justify; font-size: 14px;">Let's start by selecting pass types that can represent a player’s ability to make progressive passes. We will focus on player capability to penetrate an opposition defensive structure based on the movement of their teammates:</p>
<p style="font-size: 14px;">- Passes to 'Runs In Behind'</p>
<p style="font-size: 14px;">- Passes to 'Switch Play'</p>
<p style="font-size: 14px;">- Passes to 'Players in Space Between the Lines'</p>
<p style="font-size: 14px;">The charts below rank Premier League defensive midfielders based on counts for each metric (normalised per 30mins with their team in possession):</p>
<p style="font-size: 14px;"><img src="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=763&height=386&name=Image%201.png" width="763" height="386" loading="lazy" alt="Image 1" style="height: auto; max-width: 100%; width: 763px;" srcset="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=382&height=193&name=Image%201.png 382w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=763&height=386&name=Image%201.png 763w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=1145&height=579&name=Image%201.png 1145w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=1526&height=772&name=Image%201.png 1526w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=1908&height=965&name=Image%201.png 1908w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%201.png?width=2289&height=1158&name=Image%201.png 2289w" sizes="(max-width: 763px) 100vw, 763px"></p>
<p style="font-size: 14px;">However, the limitation with pure counts - even if they’re adjusted for possession differences - is that for a defensive midfielder, they can be attributed in part to team style or player role.</p>
<p style="text-align: justify; font-size: 14px;"> </p>
<p style="text-align: justify; font-size: 14px;"><strong>Evaluating Players: Passes Made vs Opportunities</strong></p>
<p style="text-align: justify; font-size: 14px;">Defensive midfielders in ball-dominant teams will naturally have more opportunities to get on the ball, a variety of teammates offering to receive the ball, and therefore more opportunities to play each of these types of passes. Those playing in a more transitional or direct team won’t have the same quantity of opportunity.</p>
<p style="text-align: justify; font-size: 14px;">Similarly, a defensive midfielder playing in a double pivot alongside a deep-lying playmaker may be instructed to recover possession and move it ‘sideways’ to that playmaker rather than playing forward themselves. </p>
<p style="text-align: justify; font-size: 14px;">All of the players ranked at the top for each metric were either playing in ball-dominant teams (Jorginho, Rodri, Enzo Fernandez) or a ball-dominant playmaker within their team (Oliver Norwood, Christian Eriksen, Billy Gilmour) - or in some cases, both.</p>
<p style="text-align: justify; font-size: 14px;">This begs the question: how can clubs separate a player’s passing output data from their team style and role in an evaluation context?</p>
<p style="text-align: justify; font-size: 14px;">SkillCorner Game Intelligence helps to overcome this issue by measuring the passes actually made by a player, while also using tracking and contextual data to measure the number of opportunities they had to make a progressive pass:</p>
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<p> </p>
<p style="text-align: justify; font-size: 14px;">This recognition of passing options enables clubs to measure a player’s tendency to play each category of pass by adjusting for the number of opportunities they had to play them.</p>
<p style="text-align: justify; font-size: 14px;"> </p>
<p style="text-align: justify; font-size: 14px;"><strong>Comparing Players across the 'Big Five' European Leagues</strong></p>
<p style="text-align: justify; font-size: 14px;">Frenkie de Jong, Joshua Kimmich and Rodri are elite defensive midfielders playing in three of the most ball-dominant teams in Europe; they’re all up in the high 80-90th percentile for most of the progressive passing analysed above.</p>
<p style="text-align: justify; font-size: 14px;">However, adjusting these counts as a percentage of the opportunities they had to play them yields some interesting results:</p>
<p style="text-align: justify; font-size: 14px;"><img src="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=3006&height=1467&name=Image%202.png" width="3006" height="1467" loading="lazy" alt="Image 2" style="height: auto; max-width: 100%; width: 3006px;" srcset="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=1503&height=734&name=Image%202.png 1503w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=3006&height=1467&name=Image%202.png 3006w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=4509&height=2201&name=Image%202.png 4509w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=6012&height=2934&name=Image%202.png 6012w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=7515&height=3668&name=Image%202.png 7515w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%202.png?width=9018&height=4401&name=Image%202.png 9018w" sizes="(max-width: 3006px) 100vw, 3006px"></p>
<p style="text-align: justify; font-size: 14px;">Conversely, João Palhinha and Éderson are below or around average on the pass attempt counts (again, even adjusting for team time in possession), but when their number of opportunities to play each type of pass is factored in, it shows that they’re taking a very high proportion of those opportunities to penetrate the opposition defensive structure. Of course, this comes with an element of risk/reward and the profiles of de Jong, Kimmich & Rodri are perhaps reflective of controlling play rather than actively taking risk.</p>
<p style="text-align: justify; font-size: 14px;"> </p>
<p style="text-align: justify; font-size: 14px;"><strong>Scouting for players that actively look to break lines</strong></p>
<p style="text-align: justify; font-size: 14px;">Clubs may use these metrics to identify other players with a tendency to play line-breaking passes when the opportunity arises as illustrated in the plot below:</p>
<p style="text-align: justify; font-size: 14px;"><img src="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=2368&height=1164&name=Image%203.png" width="2368" height="1164" loading="lazy" alt="Image 3" style="height: auto; max-width: 100%; width: 2368px;" srcset="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=1184&height=582&name=Image%203.png 1184w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=2368&height=1164&name=Image%203.png 2368w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=3552&height=1746&name=Image%203.png 3552w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=4736&height=2328&name=Image%203.png 4736w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=5920&height=2910&name=Image%203.png 5920w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%203.png?width=7104&height=3492&name=Image%203.png 7104w" sizes="(max-width: 2368px) 100vw, 2368px"></p>
<p style="text-align: justify; font-size: 14px;"> </p>
<p style="text-align: justify; font-size: 14px;"><strong>Line-Breaking Pass Impact: Difficulty vs Execution</strong></p>
<p style="text-align: justify; font-size: 14px;">The combination of on-ball event data with player tracking data also enables the measurement of the difficulty and impact of a line-breaking pass. For example, this plot displays the percentage of a player’s line-breaking passes that were difficult, along with the number of opponents bypassed:</p>
<p style="text-align: justify; font-size: 14px;"><img src="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=2368&height=1166&name=Image%204.png" width="2368" height="1166" loading="lazy" alt="Image 4" style="height: auto; max-width: 100%; width: 2368px;" srcset="https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=1184&height=583&name=Image%204.png 1184w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=2368&height=1166&name=Image%204.png 2368w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=3552&height=1749&name=Image%204.png 3552w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=4736&height=2332&name=Image%204.png 4736w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=5920&height=2915&name=Image%204.png 5920w, https://26560301.fs1.hubspotusercontent-eu1.net/hub/26560301/hubfs/Image%204.png?width=7104&height=3498&name=Image%204.png 7104w" sizes="(max-width: 2368px) 100vw, 2368px"></p>
<p style="text-align: justify; font-size: 14px;">As with any single datapoint, these measurements can only form part of an assessment of a player, and still need to be placed in context of the player’s individual role, and the style and situation of their team. However, the additional depth and context of these metrics can generate interesting signals for recruitment professionals to identify potential or tendencies missing in surface level data.</p>
<div class="hs-video-widget" data-hsv-embed-id="a0a43b68-8cde-4c87-b76c-01ea03149cca">
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<p> </p>
<p> </p>
<p style="text-align: justify; font-size: 14px;"><strong>About SkillCorner Game Intelligence</strong></p>
<p style="text-align: justify; font-size: 14px;">SkillCorner’s Game Intelligence product combines and synchronises event and tracking data to create integrated physical, technical and tactical metrics, forming the world’s broadest and deepest football performance dataset for both on-ball and off-ball analysis.</p>
<p style="text-align: justify; font-size: 14px;"><em>On-ball: Understand passer tendencies and evaluate passing decisions</em><br>- Line breaking passes / opportunities<br>- Dangerous / difficult passes<br>- Execution under different levels of pressure</p>
<p style="text-align: justify; font-size: 14px;"><em>On-ball: Identify players that create movement on-ball</em><br>- Initiate give and goes<br>- Receive backward and play forward<br>- Possession retention when under different levels of pressure</p>
<p style="text-align: justify; font-size: 14px;"><em>Off-ball: Find players who support the ball carrier effectively </em><br>- Options between lines, in space<br>- Off-ball runs broken down by type and threat<br>- Off-ball runs that lead to shot and/or goals</p>
<p style="text-align: justify; font-size: 14px;"><em>Off-ball: Combine tactical and physical insights</em><br>- Speeds, trajectory, angles for different types of movements<br>- Sequences of off-ball runs to create threat<br> <br>Game Intelligence provides these objective insights and more - for any player across 100+ global competitions – bringing an unprecedented depth of insight in player recruitment and match analysis.<br> <br>SkillCorner is the leader in tracking data and analytics for football based on a single-camera video source.<br> <br>Its AI-powered technologies provide rich and accurate player tracking data at scale, enabling teams to efficiently identify and benchmark players from all over the world.<br> <br>More than 200 clubs, leagues and federations around the world trust SkillCorner data to help them make smarter decisions in scouting, player evaluation and analysis.</p>
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<p style="text-align: center; font-size: 12px;"><span style="color: #00c800;"><a href="/blog/introducing-game-intelligence?hsLang=en" rel="noopener" style="color: #00c800;">FIND OUT MORE ABOUT SKILLCORNER GAME INTELLIGENCE</a></span></p></span>
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