Goalkeepers. A position that is arguably the hardest to truly analyse in football due to the unpredictability of the job. What makes a good goalkeeper? How do we know if a goalkeeper is performing well or is just blessed with a talented defence? In turn, how do we know the struggling goalkeeper is not just being let down by those in front of him? As the former England international Joe Hart once said. “As a goalkeeper, you can’t come off the bench for 10 minutes to prove your worth – it’s either you’re in or you’re out.” As an elite goalkeeper, you are not only tasked with stopping shots but also being the beginning piece to an attack and being a reliable passing option.
So what are some metrics to consider? How do we determine what we – as viewers, analysts, or trainers – should expect from an elite goalkeeper? Thanks to data from WyScout and Football Reference, we can break it down into two main categories; shot-stopping and distribution. Under the shot-stopping category, we can look at save percentage and Expected Conceded Goals Difference, or xCG Difference. xCG difference is calculated by taking the real average goals conceded per match and subtracting it by xCG, leaving a difference that shows over/underperformance.
For example, as of November 16th, 2019, Sheffield United goalkeeper Dean Henderson has an xCG Difference of +0.23, meaning he is overperforming. On the other hand, Burnley goalkeeper Nick Pope has an xCG Difference of -0.17, meaning he is underperforming. These two aspects can be looked at to help form a baseline on what goalkeepers in Europe’s top five leagues should be expected to be performing at. The second category is distribution. Within distribution, the average received passes, long passes, and short passes completed per match will be examined. Considering 98 clubs play in Europe’s top five leagues, an average can comfortably be established that accounts for sides who are kept under pressure (meaning a goalkeeper is more likely to have to launch it forward) and for sides who are more comfortable in possession using each club’s starting goalkeeper. Without further adieu, this tactical analysis will use statistics and data analysis to determine some baselines for shot-stopping.
Shot-stopping
When looking across the top five leagues of Europe, a few things stand out for Expected Conceded Goals Difference. When looking at the top five individual scores, as of November 16th, 2019, one player from each of the leagues represents. Coming in first with a difference of +0.5 is 28-year-old Aitor Fernandez. The Spanish goalkeeper from Levante is expected to concede an average of 1.65 goals per match but massively outperforms that, only conceding an average of 1.15 goals per match. Following Fernandez is Serbia’s Predrag Rajkovic with a difference of 0.47. At Reims, he is expected to concede 0.91 goals per match and is shockingly only conceding 0.44. Coming in third is perhaps the most surprising of all, Roberto Jimenez. The Spanish backup at West Ham has faced heavy criticism in his time filling in for Lukasz Fabianski, but actually has a difference of 0.38. It is worth noting that Jimenez also faces the highest expected conceded goals average in all of Europe at 2.33, but is only actually conceding an average of 1.95. Placing fourth in the rankings is Marco Silvestri. The Hellas Verona goalkeeper scores a difference of 0.33 (xCG Average 1.19, Actual Average 0.86) which is closely followed by Pavao Pervan of Wolfsburg, who scores a difference of 0.32 (xCG Average 1.25, Actual Average 0.93).
Looking at the other end of the rankings, the bottom five are not nearly as league-diverse, with four playing in the Bundesliga and one playing in the Premier League. Coming in as the fifth-worst performer in Europe is Southampton’s Angus Gunn, offering a -0.61 difference while conceding an average of 2.32 goals per match despite only being expected to concede 1.71 per match. Closely following Angus Gunn is respected duo Roman Burki of Borussia Dortmund (Difference -0.63, xCG Average 1.08, Actual Average 1.71) and Werder Bremen’s Jiri Pavlenka (Difference -0.64, xCG Average 1.4, Actual Average 2.04). With a reasonable gap come the bottom two, both representing newly-promoted Bundesliga sides. Once a top prospect, Köln’s Timo Horn has fallen from grace with a difference of -0.78, an xCG Average of 1.2, and an Actual Average of 1.98. Finishing 98th out of 98 goalkeepers is Paderborn’s new transfer Jannik Huth. The former German youth international rounds off the rankings with a difference of -0.79, an xCG Average of 1.9, and an astonishing Actual Average of 2.69.
So what is an average difference? Well, looking at the top five leagues, only two leagues sees an average that is positive. The league with the best average is Italy’s Serie A with an average difference of +0.067. Following Serie A is France’s Ligue Un with an average difference of +0.025. Barely falling onto the other side of 0 is the English Premier League, with an average difference of -0.009. Following the Premier League is Spain’s La Liga with an average difference of -0.031. Coming in last, largely due to 22% of the league finishing in the bottom four spots, is the Bundesliga with a disappointing average of -0.1639. Overall, the expectation of a goalkeeper in the top five leagues of Europe is to meet or exceed an xCG difference of -0.019. Below you can see how your club’s starting goalkeeper performed in relation to the rest of Europe’s best.
Along with xCG Difference, we can look at save percentage as a metric to evaluate goalkeepers. While the downside of save percentage is it does not account for those shots that a goalkeeper realistically has no chance of being able to stop, there is still a noticeable correlation to save percentage and xCG Difference.
Much like the Bundesliga representing four out of the five worst spots in xCG Difference, Ligue Un fills out four of the top five spots for save percentage. However, the highest save percentage (as of November 24th, 2019) is Worjiech Szczesny of Juventus, with a percentage of 0.837. Following Szczesny is a tie for second, with Dijon’s Alfred Gomis and Montpellier’s Geronimo Rulli with a save percentage 0.83. Behind the duo is a familiar face in Predrag Rajkovic with a save percentage of 0.816. Rounding off the top five is Paris Saint-Germain’s new transfer Keylor Navas, bringing with him a 0.808 save percentage.
On the other end of the perspective, we see just three Bundesliga goalkeepers in the bottom five this time. Kepa appears as fifth worst with a save percentage of 0.537. Behind is another tie with Timo Horn and Eibar’s Marko Dmitrovic with save percentages of 0.5. xCG Difference bottom-feeder Jannik Huth finishes second from last with a save percentage of 0.485. Finally, the worst of the worst, Borussia Dortmund’s Roman Burki. Burki’s save percentage this season is just a mere 0.469.
Now that the good and the bad has been discussed, what is the average? Ligue 1 tops the save percentage category with 0.725, followed by Serie A’s 0.703, then the Premier League with 0.688. Finishing fourth is La Liga, with an average save percentage of 0.677. Despite the noble efforts of Pavao Pervan (0.789) and Yann Sommer (0.776), the Bundesliga again falls to fifth in the rankings, bringing an average save percentage of 0.653 with them. Overall, the average save percentage of Europe’s top five leagues is 0.690. Pictured below is the correlation between save percentage and xCG Difference.
Distribution
In terms of goalkeeper distribution, it is important to stress the level of realistic subjectivity needed in evaluation. When we look at long passes completed, there is no poor number. A goalkeeper with a large number of long passes completed is not necessarily better than the goalkeeper with a lower or moderate number due to the fact that a goalkeeper’s requirements in distribution are dependent on a team’s quality and style of play. If the play is route one, naturally there will be more long balls played. If a relegation-battling side are facing a title-battling side, it is more likely that the goalkeeper on the relegation-battling side is going to be forced into sending the ball far rather than playing out of the back. On the contrary, a goalkeeper who is averaging 22.81 completed short passes per match (Yann Sommer, Borussia Monchengladbach) is more likely to be on a side enjoying reasonable success. When it comes to goalkeeper evaluation and the potential scouting of a replacement, it is crucial to be identifying the replacement that best fits your club’s realistic needs.
Looking at data collected on November 27th, 2019, we can see the highs and lows of short passing, long passing, and received passes. However, due to (as discussed in the previous paragraph) this being a reflection of team quality and style of play, we will only be discussing the high performers in each category and the averages. Starting with short passing, we finally see the Bundesliga at the top, taking all five spots for highest short passing averages. With the most in Europe comes Bayern Munich’s Manuel Neuer with 26.73 short passes per match. Neuer is followed by Bayer Leverkusen’s Lukas Hradecky (23.4) and ‘Gladbach’s Yann Sommer (22.81). Rounding out the top five is Paderborn’s Jannik Huth (20.44) and Hoffenheim’s Oliver Baumann (20.07). Despite the top five goalkeepers all averaging greater than 20 short passes per match, the average across the top five leagues drops to 11.237.
Shifting to long passes, we still see three Bundesliga goalkeepers in the top five. In the pole position, we see newly-promoted side Union Berlin’s Rafal Gikiewicz, who averages 13.8 long passes per match. Close behind is another newly-promoted side in Granada with Rui Silva, who averages 13.28 long passes per match. In third and fourth comes Eintracht Frankfurt’s Kevin Trapp (12.25) and Bayer Leverkusen’s Lukas Hradecky (12.02). Rounding off the top five is another newly-promoted goalkeeper in Sheffield United’s Dean Henderson, who averages 11.38 long passes per match. Europe’s average elite goalkeeper plays 7.424 long passes per match.
In order to pass the ball, you have to receive the ball. This means goalkeepers must be able to safely position themselves as a passing option for their team, who may have no other option but to drop back, or the side may just be looking to restart play. Given his appearance in both the top five of short passing and long passing, Lukas Hradecky finds himself at the top of the list for received passes with 27.72 per match. Following Hradecky is the sweeper-keeper himself, Manuel Neuer, with 23.94 per match. In third and fourth we see Yann Sommer (23.13) and Brighton’s Mat Ryan (20.38). Finishing fifth is Rafal Gikiewicz with 20.31. While these five top the 98-goalkeeper deep pack, the average goalkeeper is tasked with receiving 12.36 passes per match.
So what are the expectations?
Bringing it all back together, we know what the top five in each category produce. We know the worst shot-stoppers and we know the averages, so what would the most perfectly average goalkeeper in one of the top five leagues of Europe look like? What would the overall best be, or perhaps the overall worst?
Using the table above, we can see what it takes in the five aspects to be the best of Europe’s big five, along with being the worst or just simply meeting the expectations of a modern elite goalkeeper. However, these five metrics are not the only aspects that make or break a goalkeeper. Ranging from shot-stopping and distribution to communication and mental strength, the list of what makes a great goalkeeper goes on far too long and cannot be completely quantified. When kickoff comes, is your club’s goalkeeper meeting the expectations of a modern elite goalkeeper?
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