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Reassessing Goalkeepers (Again): How Much Do They Influence Shooting?

Reassessing Goalkeepers (Again): How Much Do They Influence/Affect Shooting?

Introduction

Goalkeeper metrics and advanced data are still far from perfect, that’s not entirely news. It wasn’t until a few years ago that basic metrics such as save percentage were the barometer for what determined good goalkeeping – Save a lot of shots, and voila! That’s a good goalkeeper. However, with a lot of basic aggregation statistics that provides tons and tons of issues for evaluating players. For example: Mark Flekken of Freiburg, currently has a higher save percentage than Manuel Neuer. While the Freiburg stopper is a good player, are we led to believe that Neuer is worse than he is? Even metrics like goals conceded per 90 are conditioned by tons of external factors (team strength for one) and are not indicative of individual quality. 

SInce these days, we’ve now moved onto more advanced metrics/models which help better grasp how goalkeepers perform: Post shot expected goals, goals saved above average, etc. are just some basic examples of these. They help contextualize more information on the goalkeeper (their position, habits in goal) as well as the information of the demands placed on them by the shot (shot height, end location of the shot, etc.) rather than simple “accumulation” statistics. With all this information in our grasp, and readily available for analysis, how do we link this higher level of data/analytics with traditional coaching habits and ideas on what constitutes good goalkeeping?

An example of how increased stability/sophistication surrounding goalkeeper metrics & data has given us newfound insight into how they rate within their position.

Growing up as a goalkeeper myself in the American college system, training with MLS/USL clubs as well as my brief coaching of goalkeepers before moving into performance analysis, I was always taught and taught myself about ways (particularly in 1v1s) about how the player can “make the opposition miss” Whether it be making sure your angles are spot on to take up as much of the goal as possible, to pretty much the fugazi idea of having an imposing presence to make opponents lose composure. While much has been done in improving how we evaluate goalkeepers in terms of saving shots, not much has been done in regards to putting objective findings behind how they affect misses. Simply put – Can good goalkeepers influence how often the opposition hit the target? That’s my end goal, and I think I’ve found some interesting findings thus far!

Before we dive into it, the data set: I’m using goalkeeper data (and by extension shooting data) going back to the beginning of 2018 up until the start of this season from the “Big 5” leagues in European football. While most of the data has been unedited, I’ve manipulated some of the data myself to help better provide situational context to the players as needed – This will become clear as you read on.

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Analysis In Action: Part Three – Mailbag!

Analysis In Action: Mailbag! Answering Your Question On Analysis “In The Field”

First off, to lead part three, I’d like to issue a thank you to everyone for reading this series thus far. When I first decided on sharing my experiences and writing this series, I didn’t think it would be as popular as it has been. I think it’s a testament to how important analysis in football has become, and how the growth in the community space online is reflected; I’m just happy that I get a chance to call it a “career’ and help people out in some small way.

Another thank you to everyone for joining me and Carl Edwards of Rotherham last week with our Analyst’s Q&A: We hope to turn it into a running segment, and if you read on to the bottom, I’ll have some more news to share on that front…

So, this part of Analysis In Action will be a bit of a mailbag to break up the general flow of my thoughts thus far. In addition, this series was created to help people understand what the day-to-day role looks like and how to improve their own skills – You’re naturally going to have questions, so it’s good for me to address them. 

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World Cup Qualifying – Honduras (1) – (4) USA: Tactical Analysis

 “Football is a tale of two-halves”, the old cliché couldn’t have been truer than when the USMNT travelled to Honduras for their 3rd round match of World Cup Qualifying. Trailing 1-0 at halftime, the USMNT looked lost. Without ideas in possession, and a poor and uncoordinated pressing structure out of possession. With three subs made at half time, and a change in formation from a 5-2-3 to a 4-3-3, the USMNT managed to turn a dyer first half performance into an extremely important 4-1 victory picking up all three points on the road.

The new formation was not the only halftime change, as the USA also began to pressure higher up the picture and more aggressively.  A xG increase from .49 in the first half to 1.54 in the second half helps to tell the story for the Americans offensive improvement, while Honduras first half xG of .82 dropped all the way to .34 in the second half.

The USA also managed to decrease their PPDA (passes per defensive action) from 9.9 in the first half to 7.7 in the second half; a clear indication of their increased pressure.

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Analysis In Action: Part Two – Organizational Structures & Effective Feedback

Providing Effective And Organized Feedback Amongst The Chaos:

Football is chaos – And your job as an analyst is to package/collect that chaos into something that is discernible and palatable. Fun, right? While that can be tough at times, having clarity in your organization juxtaposed with the game itself (not-organized/fluid, mostly) is a huge factor in supporting one’s ability to do just that. This manifests itself in a number of ways: 

  • Use of technology/applications to database information.
  • Clear processes in your workflow so you can “automate” (without going on autopilot) the day-to-day admin.
  • Knowing how and when to feedback necessary information.

In part two of the series, I’ll give my thoughts and tips on how to do exactly that. This can be a bit of a dry subject: The operations of performance analysis are much less fun than the actual analysis process (i.e. watching games/training, looking at data, interacting with other people, etc.) but your ability to do those things effectively are dramatically affected by said “administration.” Hopefully by giving examples of it in practice this will provide concrete ideas of how to improve your own efforts!

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Coaching “Vision”: How to Turn Scanning Research into Training

The widely-held notion is, typically, that these players can see what others can’t, enabling them to then act on those esoteric opportunities. We call these people maestros or artists, in large part, due to their ability to create beauty out of what we might perceive as nothingness–but it’s often not the technical, execution-level ability, itself, that manifests in these stunning passes. It’s the amount of information they collect, and base their decisions off of, instead.

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Analysis In Action: Part One – Self Collected Data

If I were to write an article about explaining various metrics, “what is performance analysis?”, and other topics about what advanced analytics/analysis is, it wouldn’t get a lot of traction – People are still obviously interested in these: It’s not 100% part-and-parcel of the game yet, and there is still a lot of negative reaction when advanced statistics, specialization in coaching, and increased levels of objectivity are sought out. However, throughout my time working in football and since it became my full time job 4 years ago now, I have always tried to frame my work which can be summarized in one word: Actionable.

Ultimately, posting visualizations, videos, threads on tactical tweaks, etc. can get you picked out from the crowd and show people you have the technical skills to get a job. However the day to day of actually working in the game and making it usable is VERY different: Deadlines are more meaningful, ad hoc requests come out of the blue, and you need to work within a framework (ideally) of the coaching staff’s game model. The goal of this mini-series is to help people understand some basic tools I use (and those who are at level above me!) to translate technical skills to working at it. 

To do this, words are somewhat unhelpful: *Describing* how to work is just an extension of the articles I’m trying to get away from. Alongside this mini-series, I will be posting example data dashboards, presentations, and more! Hopefully it’ll give you a flavour of what I do. I cannot share all my trade secrets, of course, but it should serve as an inspiration and understanding for what analysis is like in the real world. Part one will look at “self collected” metrics.

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Shooters Gonna Shoot – Contextualizing Trends In Front Of Goal, And Finding Actionable Insights

This article is partly inspired by a conversation I had with Jamon Moore regarding our Where Goals Come From series on American Soccer Analysis – As we move into season two of the project, we wanted to more closely link xG to our progressive passing model: Something which anyone with even a passing interest in analytics will be aware of. Our discussion involved a look at an article written by ModernFitba (RIP) three years ago now regarding Miles Storey – Then of Partick Thistle, and now plying his trade for Inverness. To sum it up (you should read it too, of course) Jason talks about Expected Conversion Rate, a metric of xG divided by  shots (unblocked ones) – Essentially what % of shots should have found the back of the net. Unblocked shots can be additionally measured with “Fenwick-adjusted” Expected Conversion rate, a hockey concept, but that’s neither the time nor place for that.

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Breaking Down the Football warmup Part 1: Philosophy and Fitness

Within the game, physical preparation is essential and the first step of on-field practice both in training and matches. Whilst the on-field performance from a tactical perspective is heavily looked at, this article will explore how the warm up both in training and for matches can contribute to on-field success. This is part 1 of an article series as to how the warm up in football can be broken down, with this article analysing the fundamental principles to help maximise the movement efficiency of the players. Subsequently, a greater efficiency of the players’ movement, the better they are able to execute tactical movements, such as utilising hip rotation to switch the play and create overloads and so forth. This article will further underpin the execution of the warm up from the perspective of the practitioner, introducing motivational psychology theory and the terminology of “football coaching philosophy”.

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Tactical Theory: 4-4-2 Low-Block Principles

Tactical Theory: 4-4-2 Low-Block Principles

Out of possession, the 4-4-2 is perhaps the most traditional and common shape used in football. While teams and managers are always looking to be innovative and find new and exciting approaches to defending – solutions that offer unique styles of proactive defending in different shapes and different heights, many teams in professional football are reverting back to the 4-4-2. The renaissance of the 4-4-2 has seen clubs such as Juventus, Manchester United, Manchester City, Tottenham and many other top sides rely on this shape out of possession, though each club has their own way of utilizing it.

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How the creation of different superiorities generates opportunities to progress the ball

Fundamentally, football is a game that consists of 11 players on either team. The team in possession will usually have an 11v10 situation outfield, as the defending team’s goalkeeper can be excluded as they will look to stay in goal. As the teams organise in their respective structures, situations will arise in different areas of the pitch. For each situation, we can look to assign numerical values to each team depending on how many players they have within the situation that can be considered ‘active’. A player who is said to be ‘active’ is one which is either looking to make themselves directly available to a player in possession (for the team in possession) or looking to close off a pass (for the team not in possession). However, the space for interpretation of which players are considered active in the situation opens the potential to vary how we look at each situation. For instance, a player on the ball-far side of the pitch could be in space, available to receive a pass. For the purposes of defining the numerical values for the number of players in each situation, however, we should only look to consider players within a closer proximity to the player in possession. Doing so will allow us to determine areas of numerical superiority, equality, and inferiority. In this article, a standard 1-3-2-5 possession structure will be used against a 1-4-5-1 out of possession structure to demonstrate a variety of situations.

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