Category Archives: English Premier League

Scoring ability: the good, the bad and the Messi

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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 penalties. Paul 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?

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Is shooting accuracy maintained from season to season?

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This is a short follow-up to this post using the same dataset. Instead of shot conversion, we’re now looking at shooting accuracy which is defined as the number of shots on target divided by the total number of shots. The short story here is that shooting accuracy regresses more strongly to the mean than shot conversion at the larger shot samples (more than 70 shots) and is very similar below this.

Comparison between shooting accuracy for players in year zero and the following season (year one). Click on the image or here for a larger interactive version.

Comparison between shooting accuracy for players in year zero and the following season (year one). Click on the image or here for a larger interactive version.

Minimum Shots Players year-to-year r^2 ‘luck’ ‘skill’
1 2301 0.045 79% 21%
10 1865 0.118 66% 34%
20 1428 0.159 60% 40%
30 951 0.214 54% 46%
40 632 0.225 53% 47%
50 456 0.219 53% 47%
60 311 0.190 56% 44%
70 180 0.245 51% 49%
80 117 0.305 45% 55%
90 75 0.341 42% 58%
100 43 0.359 40% 60%

Comparison of the level of ‘skill’ and ‘luck’ attributed to shooting accuracy (measured by shots on target divided by all shots) from one season to the next. The data is filtered by the total number of shots a player takes in consecutive seasons.

Essentially, there is quite a bit of luck involved with getting shots on target and for large-volume shooters, there is more luck involved in getting accurate shots in than in scoring them.

Is scoring ability maintained from season to season? (slight return)

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In my previous post (many moons ago), I looked at whether a players’ shot conversion in one season was a good guide to their shot conversion in the next. While there were some interesting features in this, I was wary of being too definitive given the relatively small sample size that was used. Data analysis is a journey with no end, so this is the next step. I collated the last 5 seasons of data across the top divisions in England, Spain, Germany and Italy (I drew the line at collecting France) from ESPN. An example of the data provided is available here for Liverpool in 2012/13. The last 5 seasons on ESPN are Opta provided data and matched up perfectly when I compared with English Premier League data from EPL-Index.

Before digging into the results, a few notes on the data. The data is all shots and all goals i.e. penalties are not removed. Ideally, you would strip out penalty shots and goals but that would require player-level data that I don’t have and I’ve already done enough copy and pasting. I doubt including penalties will change the story too much but it would alter the absolute numbers. Shot conversion here is defined as goals divided by total shots, where total shots includes blocked shots. I then compared shot conversion for individual players in year zero with their shot conversion the following year (year one). The initial filter that I applied here was that the player had to have scored at least one goal in both years (so as to exclude players having 0% shot conversion).

Comparison between shot conversion rates for players in year zero and the following season (year one). Click on the image or here for a larger interactive version.

Starting out with the full dataset, we have 2301 data points where a player scored a goal in two consecutive seasons. The R^2 here (a measure of the strength of the relationship) is very low, with a value of 0.061 (where zero would mean no relationship and one would be perfect). Based on the method outlined here by James Grayson, this suggests that shot conversion regresses 75% towards the mean from one season to the next. The implication of this number is that shot conversion is 25% ‘skill’ and 75% is due to random variation, which is often described as ‘luck’.

As I noted in my previous post on this subject, the attribution to skill and luck is dependent on the number of shots taken. As the number of shots increases, we smooth out some of the randomness and skill begins to emerge. A visualisation of the relationship between shot conversion and total shots is available here. Below is a summary table showing how this evolves in 10 shot increments. After around 30 shots, skill and luck are basically equal and this is maintained up to 60 shots. Above 80 shots, we seem to plateau at a 70/30% split between ‘skill’ and ‘luck’ respectively.

Minimum Shots Players year-to-year r^2 ‘luck’ ‘skill’
1 2301 0.061 75% 25%
10 1865 0.128 64% 36%
20 1428 0.174 58% 42%
30 951 0.234 52% 48%
40 632 0.261 49% 51%
50 456 0.262 49% 51%
60 311 0.261 49% 51%
70 180 0.375 39% 61%
80 117 0.489 30% 70%
90 75 0.472 31% 69%
100 43 0.465 32% 68%

Comparison of the level of ‘skill’ and ‘luck’ attributed to scoring ability (measured by shot conversion) from one season to the next. The data is filtered by the total number of shots a player takes in consecutive seasons.

The results here are different to my previous post, where the equivalence of luck and skill was hit around 70 shots whereas it lies from 30-60 shots here. I suspect this is driven by the smaller sample size in the previous analysis. The song remains the same though; judging a player on around half a season of shots will be about as good as a coin toss. Really you want to assess a heavy shooter over at least a season with the proviso that there is still plenty of room for random variation in their shot conversion.

What is shot conversion anyway?

The past summer in the football analytics community saw a wonderful catalytic cycle of hypothesis, analysis and discussion. It’s been great to see the community feeding off each other; I would have liked to join in more but the academic conference season and the first UK heatwave in 7 years put paid to that. Much of the focus has been on shots and their outcomes. Increasingly the data is becoming more granular; soon we’ll know how many shots per game are taken within 10 yards of the corner flag at a tied game state by players with brown hair and blue eyes while their manager juggles on the sideline (corrected for strength of opposition of course). This increasing granularity is a fascinating and exciting development. While it was already clear that all shots aren’t created equal from purely watching the football, the past summer has quantified this very clearly. To me, this demonstrates that the traditional view of ‘shot conversion’ as a measure of finishing ability is erroneous.

As an illustrative example, consider two players who both take 66 shots in a season. Player A scores 11 goals, so has a shot conversion of 17%. Player B scores 2 goals, so has a shot conversion of 3%. The traditional view of shot conversion would suggest that Player A is a better finisher than Player B. However, if Player A took all of his shots from a central area within the 18-yard box, he would be bang in line with the Premier League average over the past 3 seasons. If Player B took all of his shots from outside the area, he would also be consistent with the average Premier League player. Both players are average when controlling for shot location. Clearly this is an extreme example but then again it is meant to be an illustration. To me at least, shot conversion seems more indicative of shooting efficiency i.e. taking shots from good positions under less defensive pressure will lead to an increased shot conversion percentage. Worth bearing in mind the next time someone mentions ‘best’ or ‘worst’ in combination with shot conversion.

The remaining question for me is how sustainable the more granular data is from season-to-season, especially given the smaller sample sizes.

Is scoring ability maintained from season to season?

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With the football season now over across the major European leagues, analysis and discussion turns to reflection of the who, what and why of the past year. With the transfer window soon to do whatever the opposite of slam shut is, thoughts also turn to how such reflections might inform potential transfer acquisitions. As outlined by Gabriele Marcotti today in the Wall Street Journal, strikers are still the centre of attention when it comes to transfers:

The game’s obsession with centerforwards is not new. After all, it’s the glamour role. Little kids generally dream of being the guy banging in the goals, not the one keeping them out.

On the football analytics front, there has been a lot of discussion surrounding the relative merits of various forward players, with an increasing focus on their goal scoring efficiency (or shot conversion rate) and where players are shooting from. There has been a lot of great work produced but a very simple question has been nagging away at me:

Does being ‘good’ one year suggest that you’ll be ‘good’ next year?

We can all point to examples of forwards shining brightly for a short period during which they plunder a large number of goals, only to then fade away as regression to their (much lower) mean skill level ensues. With this in mind, let’s take a look at some data.

Scoring proficiency

I’ve put together data on players over the past two seasons who have scored at least 10 goals during a single season in the top division in either England, Spain, Germany or Italy from WhoScored. Choosing 10 goals is basically arbitrary but I wanted a reasonable number of goals so that calculated conversion rates didn’t oscillate too wildly and 10 seems like a good target for your budding goalscorer. So for example, Gareth Bale is included as he scored 21 in 2012/13 and 9 goals in 2011/12 but Nikica Jelavić isn’t as he didn’t pass 10 league goals in either season. Collecting the data is painful so a line had to be drawn somewhere. I could have based it on shots per game but that is prone to the wild shooting of the likes of Adel Taarabt and you end up with big outliers. If a player was transferred to or from a league within the WhoScored database (so including France), I retained the player for analysis but if they left the ‘Big 5’ then they were booted out.

In the end I ended up with 115 players who had scored at least 10 league goals in one of the past two seasons. Only 43 players managed to score 10 league goals in both 2011/12 and 2012/13, with only 6 players not named Lionel Messi or Cristiano Ronaldo able to score 20 or more in both seasons. Below is how they match up when comparing their shot conversion, where their goals are divided by their total shots, across both seasons. The conversion rates are based on all goals and all shots, ideally you would take out penalties but that takes time to collate and I doubt it will make much difference to the conclusions.

Comparison between shot conversion rates for players in 2011/12 and 2012/13. Click on the image or here for a larger interactive version.

If we look at the whole dataset, we get a very weak relationship between shot conversion in 2013/12 relative to shot conversion in 2011/12. The R^2 here is 0.11, which suggests that shot conversion by an individual player shows 67% regression to the mean from one season to the next. The upshot of this is that shot conversion above or below the mean is around two-thirds due to luck and one-third due to skill. Without filtering the data any further, this would suggest that predicting how a player will convert their chances next season based on the last will be very difficult.

A potential issue here is the sample size for the number of shots taken by an individual in a season. Dimitar Berbatov’s conversion rate of 44% in 2011/12 is for only 16 shots; he’s good but not that good. If we filter for the number of shots, we can take out some of the outliers and hopefully retain a representative sample. Up to 50 shots, we’re still seeing a 65% regression to the mean and we’ve reduced our sample to 72 players. It is only when we get up to 70 shots and down to 44 players that we see a close to even split between ‘luck’ and ‘skill’ (54% regression to the mean). The problem here is that we’re in danger of ‘over-fitting’ as we rapidly reduce our sample size. If you are happy with a sample of 18 players, then you need to see around 90 shots per season to able to attribute 80% of shot conversion to ‘skill’.

Born again

So where does that leave us? Perhaps unsurprisingly, the results here for players are similar to what James Grayson found at the team level, with a 61% regression to the mean from season to season. Mark Taylor found that around 45 shots was where skill overtook luck for assessing goal scoring, so a little lower than what I found above although I suspect this is due to Mark’s work being based on a larger sample over 3 season in the Premier League.

The above also points to the ongoing importance of sample size when judging players, although I’d want to do some more work on this before being too definitive. Judgements on around half a season of shots appears rather unwise and is about as good as flipping a coin. Really you want around a season for a fuller judgement and even then you might be a little wary of spending too much cash. For something approaching a guarantee, you want some heavy shooting across two seasons, which allied with a good conversion rate can bring you over 20 league goals in a season. I guess that is why the likes of Van Persie, Falcao, Lewandowski, Cavani and Ibrahimovic go for such hefty transfer fees.

Newcastle United vs Liverpool: passing network analysis

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Liverpool defeated Newcastle 6-0 at St James’ Park. Below is the passing network analysis for Liverpool split between the first 75 minutes of the match and the rest of the match up to full time. I focussed just on Liverpool here. More information on how these are put together is available here in my previous posts on this subject.

The reason I separated the networks into these two periods was that I noticed how Liverpool’s passing rate changed massively after Steven Gerrard was substituted and the fifth goal was scored. During the first 75 minutes, Liverpool attempted 323 passes with a success rate of 74% and a 45% share of possession. After this, Liverpool attempted 163 passes with an accuracy of 96% and a 60% share of possession. Liverpool attempted 34% of their passes in this closing period. Let’s see how this looks in terms of their passing network.

The positions of the players are loosely based on the formations played, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. The starting eleven is shown on the pitch for the first 75 minutes, with Borini replacing Gerrard in the second network.

Passing networks for Liverpool for the first and second halfs against Swansea City from the match at Anfield on the 17th February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. Players with an * next to their name were substituted. Click on the image for a larger view.

Passing networks for Liverpool for the first 75 minutes and up to full time against Newcastle United from the match at St James’ Park on the 27th April 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. Click on the image for a larger view.

Liverpool’s passing was quite balanced for the first 75 minutes of the match, with a varied passing distribution. There was a stronger bias towards the right flank compared with the left flank as Gerrard drifted right to combine with Johnson and Downing. The passing influence scores were also evenly distributed across the whole team with Gerrard and Lucas being the top two. A contrast with some previous matches is the lack of strong links along the back line, which indicates less reliance on recycling of possession in deeper areas. Instead, Liverpool were seeking to move the ball forward more quickly and played the ball through the whole team.

He makes us happy

After Gerrard and Lucas, the next most influential player was Coutinho, who put in a wonderfully creative performance as the attacking fulcrum of the team. He linked well with all of Liverpool’s forward players and threaded several dangerous passes to his team-mates including an assist and a ‘second goal assist’ (defined as a pass to the goal assist creator) for the second goal according to EPL-Index. His creative exploits thus far have been hugely promising during his first 10 appearances.

Sterile domination

The final period of the match saw Liverpool really rack up the passing numbers as mentioned earlier. Clearly, this is easier to do when 5 or 6 goals clear but it is still potentially illustrative to see how this was accomplished. The main orchestrator’s of this were Lucas and Henderson who were 28/28 and 35/35 for passes attempted/completed during this period. Henderson was 21/24 from the first 75 minutes, so this was quite a rapid increase with his shift in role after Gerrard went off and the state of the game.

Your challenge should you wish to accept it

Admittedly Newcastle were very poor in this match but Liverpool took advantage to enact a severe thrashing. This was accomplished without Suárez, which leads to obvious (premature?) questions about whether his absence improved Liverpool’s overall balance and play. Assuming that Suárez doesn’t leave in the summer, one of Bredan Rodgers’ key tasks will be developing a system that gets the best out of the attacking talents of Suárez, Coutinho and Sturridge. It could be quite tasty if he manages to accomplish this.

Is playing style important?

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I’ve previously looked at whether different playing styles can be assessed using seasonal data for the 2011/12 season. The piece concentrated on whether it was possible to separate different playing styles using a method called Principal Component Analysis (PCA). At a broad level, it was possible to separate teams between those that were proactive and reactive with the ball (Principal Component 1) and those that attempted to regain the ball more quickly when out of possession (Principal Component 2). What I didn’t touch upon was whether such features were potentially more successful than others…

Below is the relationship between points won during the 2011/12 season and the proactive/reactive principal component. The relationship between these variables suggests that more proactive teams, that tend to control the game in terms of possession and shots, are more successful. However, the converse could also be true to an extent in that successful teams might have more of the ball and thus have more shots and concede fewer. Either way, the relationship here is relatively strong, with an R2 value of 0.61.

Blah.

Relationship between number of points won in the 2011/12 season with principal component 1, which relates to the proactive or reactive nature of a team. More proactive teams are to the right of the horizontal axis, while more reactive teams are to the left of the horizontal axis. The data is based on the teams in the top division in Germany, England, Spain, France and Italy from WhoScored. The black line is the linear trend between the two variables. A larger interactive version of the plot is available either by clicking on the graph or clicking here.

Looking at the second principal component, there is basically no relationship at all with points won last season, with an R2 value of a whopping 0.0012. The trend line on the graph is about as flat as a pint of lager in a chain sports bar. There is a hint of a trend when looking at the English and French leagues individually but the sample sizes are small here, so I wouldn’t get too excited yet.

Playing style is important then?

It’s always tempting when looking at scatter plots with nice trend lines and reasonable R2 values to reach very steadfast conclusions without considering the data in more detail. This is likely an issue here as one of the major drivers of the ‘proactive/reactive’ principal component is the number of shots attempted and conceded by a team, which is often summarised as a differential or ratio. James Grayson has shown many times how Total Shots Ratio (TSR, the ratio of total shots for/(total shots for+total shots against)) is related to the skill of a football team and it’s ability to turn that control of a game into success over a season. That certainly appears to play a roll here, as this graph demonstrates, as the relationship between points and TSR yields an R2 value of 0.59. For comparison, the relationship between points and short passes per game yields an R2 value of 0.52. As one would expect based on the PCA results and this previous analysis, TSR and short passes per game are correlated also (R2 = 0.58).

Circular argument

As ever, it is difficult to pin down cause and effect when assessing data. This is particularly true in football when using seasonal averaged statistics as score effects likely play a significant role here in determining the final totals and relationships. Furthermore, the input data for the PCA is quite limited and would be improved with more context. However, the analysis does hint at more proactive styles of play being more successful; it is a challenge to ascribe how much of this is cause and how much is effect.

Danny Blanchflower summed up his footballing philosophy with this quote:

The great fallacy is that the game is first and last about winning. It is nothing of the kind. The game is about glory, it is about doing things in style and with a flourish, about going out and beating the other lot, not waiting for them to die of boredom.

The question is, is the glory defined by the style or does the style define the glory?

Liverpool vs Swansea: passing network analysis

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Liverpool defeated Swansea 5-0 at Anfield. Below is the passing network analysis for Liverpool for both the first and second half. Usually I compare with the opposition but I think it is more interesting here to compare across each half. More information on how these are put together is available here in my previous posts on this subject.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. The starting eleven is shown on the pitch for the first half, while I have shown Henderson rather than Coutinho for the second half as he came on after 60 minutes.

Passing network for Liverpool and West Brom from the match at Anfield on the 11th February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team. The player markers are coloured by the number of times they lost possession during the match, with darker colours indicating more losses. Only the starting eleven is shown. Players with an * next to their name were substituted. Click on the image for a larger view.

Passing networks for Liverpool for the first and second halfs against Swansea City from the match at Anfield on the 17th February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. Players with an * next to their name were substituted. Click on the image for a larger view.

Looking at the passing networks for each half, there are clear differences for Liverpool. During the first half, there was much less interplay between Liverpool’s attacking players, central midfielders and full backs. There are many more stronger linkages between players in the second half passing network than the first as more passing triangles are built up. This is borne out by the general passing data from Squawka as Liverpool attempted 242 passes in the first half (83% completion rate) compared to 306 passes in the second (89% completion rate). The scoring of the penalty in the first half and early second goal after half time likely meant Liverpool were more patient in their approach coupled with tactical switches/substitutions. This also shows up somewhat in the shots data, as Liverpool attempted 22 shots first half, compared with 13 in the second.

Quietly effective

I tweeted the full passing network after the match having not watched it and commented that it looked like Gerrard had once again been influential. Gerrard’s performance was described as quietly effective by the Liverpool Twitterati, which seems like an apt description. While he was the most influential player for Liverpool in the first half (narrowly ahead of Lucas), he really dictated things in the second half. While the scoreline likely played a role here, Mihail Vladimirov pointed out a subtle tactical shift also, where Gerrard received the ball in deeper areas during the second half compared with the first. This likely allowed Gerrard more time/space to dictate play from deep.

Almost the whole team increased their passing influence scores in the second half, aside from Lucas and Suárez, who were both similar across both halves. Liverpool’s attacking players really came to the fore during the second half as they were all more involved. Furthermore, Henderson was impressively influential considering he only played 30 minutes and played quite a different role to Coutinho, as pointed out on the Oh you beauty blog.

Liverpool’s performance in this match, particularly in the second half was impressive even with the mitigation of Swansea fielding a weakened team. The key for the rest of the season will be recreating such performances against full-strengh sides and without the benefit of such a comfortable lead.

Liverpool vs West Bromwich Albion: passing network analysis

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Liverpool lost to West Bromwich Albion 2-0 at Anfield. Below is the passing network analysis for Liverpool and West Brom. More information on how these are put together is available here in my previous posts on this subject.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. Only the starting eleven is shown on the pitch, as the substitutes weren’t hugely interesting from a passing perspective in this instance.

Passing network for Manchester City and Liverpool from the match at the Etihad on the 3rd February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team. Only the starting eleven is shown. Players with an * next to their name were substituted. Click on the image for a larger view.

Passing network for Liverpool and West Brom from the match at Anfield on the 11th February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team. The player markers are coloured by the number of times they lost possession during the match, with darker colours indicating more losses. Only the starting eleven is shown. Players with an * next to their name were substituted. Click on the image for a larger view.

There are some contrasting features between the two sides here. Liverpool’s standard recycling of possession in deeper areas is evident, with interplay between Reina, the back four and the midfield two of Lucas and Gerrard. West Brom showed some similar features, although the link between their centre backs is much weaker than the link between Agger and Carragher.

Mulumbu and Morrison were impressive for West Brom, linking well with the players around them. They formed some nice triangular passing structures with those around them, particularly with their midfield partner Yacob. Based on their passing network, West Brom passed the ball around well when they had it although Long wasn’t hugely involved (he did provide his usual nuisance value though).

One of the major differences is how both sides involved their respective centre forwards. Long generally either received the ball from deeper areas e.g. the long link between himself and Foster (although many of the passes were unsuccessful) or by linking up with Morrison, who was typically the most advanced of West Brom’s central midfielders. In contrast, the link between Shelvey and Suárez is almost non-existent. Given that these two were ostensibly Liverpool’s two most attacking players, the lack of interplay between them was disappointing.

Ineffectual width

With Henderson and Downing continuing on their “unnatural” sides, Liverpool’s fullbacks had plenty of space to move into down the flanks. This meant they were often a natural passing outlet for their team mates and this is highlighted by the high passing influence scores they both received. Unfortunately, much of the attacking impetus that Enrique and Johnson provided was highly wasteful. As noted on the Oh you beauty blog, their pass completion in the final third was woeful. Between them, Enrique and Johnson accounted for 30% of Liverpool’s total losses of possession. Enrique misplaced 9 passes within his own half also, as noted by WhoScored. Generally I’ve interpreted a higher passing influence score as being a good thing but perhaps in this instance this wasn’t the case.

That is why we like him

Aside from Enrique and Johnson, the main passing influence for Liverpool was Lucas. Lucas’ absolute and relative passing influence within in the team has been steadily increasing over recent matches, which is encouraging as he recovers from his injury issues. Unfortunately for Liverpool, Gerrard, Henderson and Downing had less influence than in recent weeks, which alongside the lack of partnership between Shelvey and Suárez, went some way to Liverpool struggling to open up West Brom.

Manchester City vs Liverpool: passing network analysis

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Manchester City drew 2-2 with Liverpool at the Etihad. Below is the passing network analysis for Manchester City and Liverpool. More information on how these are put together is available here in my previous posts on this subject.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. Only the starting eleven is shown on the pitch, as the substitutes weren’t hugely interesting from a passing perspective in this instance.

Passing network for Manchester City and Liverpool from the match at the Etihad on the 3rd February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team. Only the starting eleven is shown. Players with an * next to their name were substituted. Click on the image for a larger view.

Passing network for Manchester City and Liverpool from the match at the Etihad on the 3rd February 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team. Only the starting eleven is shown. Players with an * next to their name were substituted. Click on the image for a larger view.

In the reverse fixture, Yaya Touré and De Jong were very influential for City but Touré was away at the African Cup of Nations, while De Jong joined Milan shortly after that fixture. Their replacements in this game, Barry and Garcia, were less influential, although Barry had the strongest passing influence for City in this match, with Milner second. The central midfield two, Lucas and Gerrard, were very influential for Liverpool and strongly dictated the passing patterns of the team. They both linked well with the fullbacks and wider players, while Lucas also had strong links with Suárez and Sturridge. Certainly in this area of the pitch, Liverpool had the upper hand over City and this provided a solid base for Liverpool in the match.

No Silva lining

Something that Liverpool did particularly well was limit the involvement of David Silva, who posted his worst pass completion rate (73% via EPL-Index) this season. Usually, Silva completes a pass every 96 seconds this season, whereas against Liverpool it was every 162 seconds. While Mancini’s tactical change did bring Silva more into the game briefly, overall it had a negligible impact upon Silva’s influence when comparing the networks before and after the substitution. However, one of the few occasions where Silva was able to find some time and space, he combined well with James Milner to help create City’s first goal. Goes to show it is difficult to keep good players quiet for a whole match.

Moving forward

Similarly to the Arsenal game, Liverpool showed less of an emphasis upon recycling the ball in deeper areas. Instead, they favoured moving the ball forward more directly, with Enrique often being an outlet for this via Reina and Agger. Liverpool’s fullbacks combined well with their respective wide-players, while also being strong options for Lucas and Gerrard. Strurridge was generally excellent in this match and was more influential in terms of passing than in his previous games against Norwich and Arsenal, combining well with Suárez, Lucas and Gerrard.

At least based on the past few games, Liverpool have shown the ability to alter their passing approach with a heavily possession orientated game against Norwich, followed up by more direct counter-attacking performances against Arsenal and Manchester City. The game against City was particularly impressive as this was mixed in with some good control in midfield via Lucas and Gerrard, which was absent against Arsenal. How this progresses during Liverpool’s next run of fixtures will be something to look out for.

Arsenal vs Liverpool: passing network analysis

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Arsenal and Liverpool drew 2-2 at the Emirates, as Arsenal came back from two goals down. Below is the passing network analysis for Arsenal and Liverpool. More information on how these are put together is available here in my previous posts on this subject.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. The starting eleven is shown on the pitch, while Enrique and Santos, who came on as substitutes are shown on the sidelines.

Passing network for Liverpool and Norwich City from the match at Anfield on the 19th January 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. Only the starting eleven is shown.

Passing network for Arsenal and Liverpool from the match at the Emirates on the 30th January 2013. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each side. The starting eleven is shown on the pitch, with the substitutes on the sidelines. Click on the image for a larger view.

The contrast between the two teams approach is apparent, with Arsenal dominating possession (62% according to EPL-Index), which is reflected in their much stronger passing links across the team. Much of Arsenal’s play went through Aaron Ramsey, who played a similar role to that played by Mikel Arteta in the reverse fixture, although Arsenal saw more of the ball in this match. Arsenal’s midfield-three of Ramsey, Wilshire and Cazorla combined very well and dictated the passing patterns of the side excellently.

For Liverpool, the story was slightly different. The side was happy to counter-attack, which meant that the usual recycling of possession in deeper areas was less prevalent than for example against Norwich. Most of Liverpool’s play went through Henderson and Gerrard (again Liverpool’s major passing influence), with Johnson and Downing providing good support down the left and right flanks respectively. Daniel Agger was also able to influence the game from deeper positions, with his passing influence score being third behind Gerrard and Downing. Suárez was reasonably involved, combining well with Agger, Johnson and Henderson.

Hymns & Arias

In terms of passing influence, Ramsey was the undoubted star of the show. He conducted Arsenal’s play from deep beautifully, completing over 100 passes in the process. Obviously this was partially a result of Liverpool’s approach, which allowed him the time and space to dictate play but he combined well with Arsenal’s attacking players throughout the match. Gerrard was the major influence for Liverpool, while Jordan Henderson provided a passing option higher up the pitch and brought Downing, Suárez and to a lesser extent, Sturridge into the game. This was an important function in the team’s counter-attacking.

Liverpool delivered a different passing performance in this match. There are many parallels with the Everton match here, where Liverpool had a similar passing network and employed a more pragmatic counter-attacking style. It will be interesting to see if they use such tactics in the next match against Manchester City