Scoring ability: the good, the bad and the Messi

Identifying scoring talent is one of the main areas of investigation in analytics circles, with the information provided potentially helping to inform decisions that can cost many, many millions. Players who can consistently put the ball in the net cost a premium; can we separate these players from the their peers?

I’m using data from the 2008/09 to 2012/13 seasons across the top divisions in England, Spain, Germany and Italy from ESPN. An example of the data provided is available here for Liverpool in 2012/13. This gives me total shots (including blocked shots) and goals for over 8000 individual player seasons. I’ve also taken out penalties from the shot and goal totals using data from TransferMarkt. This should give us a good baseline for what looks good, bad and extraordinary in terms of scoring talent. Clearly this ignores the now substantial work being done in relation to shot location and different types of shot but the upside here is that the sample size (number of shots) is larger.

Below is a graph of shot conversion (defined as goals divided by total shots) against total shots. All of the metrics I’ll use will have penalties removed from the sample. The average conversion rate across the whole sample is 9.2%. Using this average, we can calculate the bounds of what average looks like in terms of shot conversion; we would expect some level of random variation around the average and for this variation to be larger for players who’ve taken fewer shots.

Shot conversion versus total shots for individual players in the top leagues in England, Italy, Spain and Germany from 2008/09-2012/13. Points are shown in grey with certain players highlighted, with the colours corresponding to the season. The solid black line is the average conversion rate of 9.2%, with the dotted lines above and below this line corresponding to two standard errors above the average. The dashed line corresponds to five standard errors. Click on the image for a larger view.

On the plot I’ve also added some lines to illustrate this. The solid black line is the average shot conversion rate, while the two dotted lines either side of it represent upper and lower confidence limits calculated as being two standard errors from the mean. These are known as funnel plots and as far as I’m aware, they were introduced to football analysis by James Grayson in his work on penaltiesPaul Riley has also used them when looking at shot conversion from different areas of the pitch. There is a third dotted line but I’ll talk about that later.

So what does this tell us? Well we would expect approximately 95% of the points to fall within this envelope around the average conversion rate; the actual number of points is 97%. From a statistical point of view, we can’t identify whether these players are anything other than average at shot conversion. Some players fall below the lower bound, which suggests that they are below average at converting their shots into goals. On the other hand, those players falling above the upper bound, are potentially above average.

The Bad

I’m not sure if this is surprising or not, but it is actually quite hard to identify players who fall below the lower bound and qualify as “bad”. A player needs to take about 40 shots without scoring to fall beneath the lower bound, so I suspect “bad” shooters don’t get the opportunity to approach statistical significance. Some do though.

Only 62 player seasons fall below the lower bound, with Alessandro Diamanti, Antonio Candreva, Gökhan Inler and (drum-roll) Stewart Downing having the dubious record of appearing twice. Downing actually holds the record in my data for the most shots (80) without scoring in 2008/09, with his 2011/12 season coming in second with 71 shots without scoring.

The Good

Over a single season of shots, it is somewhat easier to identify “good” players in the sample, with 219 players lying above the two standard error curve. Some of these players are highlighted in the graph above and rather than list all of them, I’ll focus on players that have managed to consistently finish their shooting opportunities at an above average rate.

Only two players appear in each of the five seasons of this sample; Gonzalo Higuaín and Lionel Messi. Higuaín has scored an impressive 94 goals with a shot conversion rate of 25.4% over that sample. I’ll leave Messi’s numbers until a little later. Four players appear on four separate occasions; Álvaro Negredo, Stefan Kießling, Alberto Gilardino and Giampaolo Pazzini. Negredo is interesting here as while his 15.1% conversion rate over multiple seasons isn’t as exceptional as some other players, he has done this over a sustained period while taking a decent volume of shots each season (note his current conversion rate at Manchester City is 16.1%).

Eighteen players have appeared on this list three times; notable names include van Persie, Di Natale, Cavani, Agüero, Gómez, Soldado, Benzema, Raúl, Fletcher, Hernández and Agbonlahor (wasn’t expecting that last one). I would say that most of the players mentioned here are more penalty box strikers, which suggests they take more of their shots from closer to the goal, where conversion rates are higher. It would be interesting to cross-check these with analysts who are tracking player shot locations.

The Messi

To some extent, looking at players that lie two standard errors above or below the average shot conversion rate is somewhat arbitrary. The number of standard errors you use to judge a particular property typically depends on your application and how “sure” you want to be that the signal you are observing is “real” rather than due to “chance”. For instance, when scientists at CERN were attempting to establish the existence of the Higgs boson, they used a very stringent requirement that the observed signal is five standard errors above the typical baseline of their instruments; they want to be really sure that they’ve established the existence of a new particle. The tolerance here is that there be much less than a one in a million chance that any observed signal be the result of a statistical fluctuation.

As far as shot conversion is concerned, over the two seasons prior to this, Lional Messi is the Higgs boson of football. While other players have had shot conversion rates above this five-standard error level, Messi has done this while taking huge shot volumes. This sets him apart from his peers. Over the five seasons prior to this, Messi took 764 shots, from which an average player would be expected to score between 54 and 86 goals based on a player falling within two standard errors of the average; Messi has scored 162! Turns out Messi is good at the football…who knew?


Assessing forward involvement

One of the more interesting innovations from an analytical standpoint at the current European Championship has been the measuring of the amount of time that a player spends with the ball per game. This measure of player involvement has in particular been applied to forward players, such as Mario Gomez. Gomez managed to score 3 goals from 6 shots in 2 games despite only having the ball for 22 seconds, according to Prozone. This contrasted with Robin Van Persie, who was seemingly more involved in general play, scoring 1 goal from 10 shots in 106 seconds.

This prompts the question: can we assess such player involvement on a wider level, with particular focus on forward players?

Without having access to the time in possession statistics, another measure is required. The number of passes per game should give a reasonable approximation of how involved a forward is in general play. Contrasting this with the number of shots attempted per game should provide a comparison between a forwards goal scoring duties and his overall involvement in play.

Top European League analysis

Below is a comparison of the number of shots a forward attempts per game vs the number of passes he attempts per game. The data is taken from and is for all players classified as forwards and have started 10 games or more in the top division in England, Spain, Italy, Germany and France. The graph includes players who have played in a non-forward role at some point in the season, as defined by WhoScored. For example, Cristiano Ronaldo is classified as playing as both a left-sided attacking midfielder and forward, although in this case the distinction is likely irrelevant. Including players who have at some point played outside of the forward line makes little impact upon the general trend and averages (see table below).

Relationship between number of shots attempted per game vs number of passes attempted per game by forward players in the top division in England, Spain, Italy, Germany and France. The points are coloured by the number of goals scored by each player. The vertical dashed grey line indicates the average number of passes per game by these players, while the horizontal dashed grey line indicates the average number of shots attempted by these players. The text boxes (Z1, Z2, Z3, Z4) designate the zones of interest referred to in the text. All data is taken from for the 2011/12 season. An interactive version of the plot is available here, where you can find any of the forwards included in the study.

Filter Players Shots/game Passes/game Goals
Forwards only 130 2.06±0.76 18.72±6.24 7.96±5.85
Mixed 135 2.10±1.01 24.67±8.82 8.23±7.57
All 265 2.08±0.89 21.75±8.21 8.10±6.77

Comparison of the different player position classifications prescribed by WhoScored. The mean and standard deviation for shots/game, passes/game and goals scored are given for each group. Mixed refers to players who have been classed as playing as both a forward and another position (generally as an attacking midfielder) at some point in the 2011/12 season.

In general, there is a weak positive relationship between shots attempted and passes attempted by forward players (correlation coefficient of 0.46 if you are that way inclined). The major feature though is that there is a great deal of variability across the forward players in terms of their involvement in player relative to their goal scoring attempts. An interactive version of the plot is available here, where you can find any of the forwards included in the study.

Players such as Mario Gomez and Jermain Defoe take an above average number of shots relative to the number of passes they attempt (Zone 1), with Gomez in particular being prolific for Bayern Munich with 26 goals in 30 Bundesliga starts. Other notable forwards with these traits include Antonio Di Natale, Robert Lewandowski, Edison Cavani, Mario Balotelli and Falcao who attempt a slightly below average number of passes but still attempt a large number of shots per game. Fernando Llorente and Andy Carroll also reside in this zone, with similar values for shots attempted and passes attempted. Players in this zone score 9.6 goals on average.

Several “star” forwards reside in Zone 2, where forwards take an above average number of shots and attempt an above average number of passes. The two extremes here are unsurprisingly Lionel Messi and Cristiano Ronaldo, who attempt the most passes and take the most shots respectively out of all of the forwards in the study. Messi ranks 34th for the number of passes across the top five European leagues, some 30 passes behind his Barcelona team-mate Xavi. Clearly, Messi’s false-nine role for Barcelona allows him to become extremely involved in general play and to even dictate it at times. He combines this with being Barcelona’s primary provider of shots on goal and indeed goals. Ronaldo is also involved significantly in Real Madrid’s play and incredibly attempts almost 7 shots per game. Several other notable forwards in this zone include Francesco Totti, Wayne Rooney, Zlatan Ibrahimovic, Raúl, Luis Suárez and Robin Van Persie with some of these forwards being more prolific than others. Clint Dempsey is an example of someone who generally plays outside of the forward line but is included here as he did play up-front for Fulham this season (scoring 5 goals in 5 games according to WhoScored). Players in this zone score 13.7 goals on average, although this is somewhat skewed by the exploits of Messi and Ronaldo (12.6 goals on average when excluding them).

Out of the 265 players included, 98 attempt both a lower than average number of shots and passes per game. In general, the number of goals scored in this group (Zone 3) is unremarkable, with the average goals scored per player being 5. However, there is one significant over-perfomer; Gonzalo Higuaín scored 22 league goals from 60 shots last season. In most squads, this would guarantee more games but he was up against Karim Benzema, who by comparison scored a paltry 21 goals from 100 shots. However, an added benefit of Benzema based on this analysis is that he is far more involved in general play.

The last group (Zone 4) includes players who take fewer shots than average but attempt more passes than average. Many of these players are more attacking midfield players than forwards, such as Dirk Kuyt. Again, a Fulham player is a good example of a player who rarely plays as a forward being included in the analysis, as Moussa Dembélé generally plays in midfield. Players in this zone score 5.3 goals on average, essentially the same as those in Zone 3.

Finishing the jigsaw

Clearly there is a large variation in how involved a forward player is in general play versus how often he attempts to score. Such differences are likely driven by both the individual player in terms of their skills and style of play alongside their tactical role within the team. Mario Gomez for instance has very similar numbers from the current European Championship for Germany as he does for his club side, although this could be a statistical quirk given the small sample size. It would be interesting to analyse how an individual performs by these measures across multiple games in multiple tactical systems.

There isn’t necessarily a better “zone” in this analysis but teams should bear these traits in mind when attempting to improve their squad. For example, Liverpool’s woes in front of goal last season led for calls for a simple poacher to be brought in who would simply “stick the ball in the net”. However, if by bringing in a poacher, Liverpool were to lose the passing and creativity provided by players in other areas, then you could end up exchanging one problem for another. Balance is key in such decisions; hopefully Brendan Rodgers can solve Liverpool’s goal scoring issues and at least maintain the quality of their chance creation next season.