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.
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 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 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
Liverpool beat Norwich City 5-0 at Anfield while posting some impressive passing statistics. I’ve previously used network analysis to assess Liverpool’s passing this season. It has been a while since I last posted something on this but now seemed a good time to get back to it.
Below is the passing network for both Liverpool and Norwich City. The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity e.g. Suárez’s position is shifted left-of-centre. 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 are shown in this instance.
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.
Liverpool: Jones (1), Johnson (2), Agger (5), Carragher (23), Wisdom (47), Lucas* (21), Gerrard (8), Henderson* (14), Suárez (7), Sturridge* (15), Downing (19)
Norwich: Bunn (28), Garrido (18), R Bennett (24), Turner (6), Martin (2), Johnson (4), Tettey (27), E Bennett* (17), Howson (8), Snodgrass* (7), Holt (9)
There is a stark contrast between how the two teams approached passing the ball. Looking at Jones, the back four and Lucas, there are a multitude of connections between them as Liverpool aim to build from the back. Furthermore, Henderson and Gerrard are heavily involved in this area as the team aims to recycle possession – look at the strong links between them, Lucas and the centre-backs. This is completely missing in Norwich’s network as they sought to be more direct – see the long link between Bunn and Holt for example. Norwich created relatively little during the game and it is clear from their passing network that Holt was fairly uninvolved. I’ll not delve into Norwich’s passing network any further.
Sharing the load
An important diagnostic for network analysis is a measure known as “closeness centrality”, which in this context is dictated by the number of passes played and received by a given player. The higher the value the better and this can be thought of as the “passing influence” that a player has on their team. The absolute values aren’t important in this instance* so the main thing to look at is the relative size of the circles for each team. One of the major aspects of Liverpool’s network is that all of the outfield players aside from Sturridge were heavily involved in the passing movements of the team. Sturridge’s lesser involvement isn’t a criticism as such, as he clearly combined well with Liverpool’s more advanced players. In some ways, strikers can be disadvantaged by such a measure as they have less opportunity to get involved with everyone in the team, which can also be the case for goalkeepers. A more even distribution of passing responsibilities allows a side to create multiple attacking angles/opportunities – notice the large level of criss-crossing of the networks for Liverpool’s attacking players. Liverpool’s front-five plus Glen Johnson had a large amount of interplay with able support from Wisdom and Lucas.
O Captain! My Captain!
However, there was clearly a stand-out performer in terms of passing influence as Steven Gerrard dominates the passing network for Liverpool. Gerrard was the hub of the team’s passing. This combined with the rest of the team stepping up to the (passing?) plate, meant that Liverpool delivered an excellent passing performance. Whether they can continue this level of performance over the coming games will be crucial.
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*At some point I want to put these measures into a more quantitative context, which will hopefully add further detail regarding how Liverpool’s passing develops.
Luis Suárez, the latest member of Liverpool’s one-man team, has been playing rather well this season. At the time of writing, he is 2nd in the top scorers list with 15 goals, while also boasting the most chances created from open play in the league. Even more impressively he manages to accomplish this while nefariously drowning kittens in his spare time*.
This increased rate of scoring compared with last season has been much needed due to Liverpool’s lack of attacking options. The question is, what has changed?
Just can’t score enough?
Firstly, Suárez is averaging a goal every 8.4 shots this season compared to 11.6 last season. Secondly, he is shooting more often this season as he shoots every 15 minutes on average compared with a shot every 20 minutes last season. The combination of these two features would naturally lead to an enhanced scoring rate. So far, so good but can we delve a little deeper into Suárez’s shooting data?
Below is a summary of Suárez’s shooting across the last two seasons in the league based on data collected from Opta’s chalkboard services. The data is aggregated positionally to examine how regularly Suárez shoots from a particular location and also how efficient his goalscoring is from these areas. This provides us with indicators of the quality of a shot i.e. the distance from the goal-line coupled with the angle from which the shot is taken. Other factors will impact the quality of the shooting opportunity such as the position of the goalkeeper, whether the shot is attempted with the foot or the head and the number of players between the shooter and the goal. This last point is probably especially important for someone like Suárez who tends to see a lot of his shots blocked.
Comparison between Luis Suárez’s shooting and goalscoring from the 2011/12 and 2012/13 seasons. The circles designate areas from which Suárez took a shot from and are sized by the number of shots taken from that area. The areas correspond to horizontal bands from 0-10, 10-20 and more than 20 yards from the goal-line. The grey dotted lines show where the 10 and 20 yard lines are situated. The vertical bands are ordered along the lines of the touchline, edge of the 18 yard box and the 6 yard box. The numbers within each marker correspond to the average number of shots attempted per goal scored in that area. Markers without a number mean that no goals were scored from that area.
The first thing to note about the goals Suárez scores is that across both seasons, the vast majority of his goals come from relatively central areas within the penalty area or just on the edge of it. Furthermore, we can see that Suárez appears to shoot a lot from locations where he doesn’t generally score from. His overall number of shots is similar across the two seasons, although there are still 16 matches still to play this season. There has been some change in the areas from which he has been shooting this season, with close to twice as many shots being taken from the central zone that is more than 20 yards from the goal-line. This has been compensated with fewer attempts from the less than 10 yard zone.
The main difference between the two seasons is that he is now scoring goals more within the 10-20 yards central area and at a reasonable rate. Suárez is now far more efficient in this zone in terms of goalscoring, with 1 goal from 34 shots last season compared with 5 goals from 29 shots this season. It is the goals scored from within this zone that have led to his increased goalscoring rate.
Slipping and sliding
So we can see that compared with himself, Suárez has improved this season. The question is how does he compare with his peers? I don’t have a large enough dataset to do a like-for-like comparison but we can contrast his numbers with data collected by the Different Game blog. The zones are slightly different here but for the central zone within the penalty area, Suárez averaged 7.5 shots per goal last season and 4.5 this season. So compared to the 6 shots per goal average over last season and this, he is better than his peers this season but underperformed last season. There are caveats here in that my figures include penalties, although after his penalty “attempts” last season, Suárez hasn’t been taking penalties this season (not that Liverpool have had many to take and he only took one penalty in the league last season). Furthermore, this is for all players taking shots and potentially you might prefer to compare to other strikers.
In general, we can see that Suárez has been more efficient this season in terms of his goalscoring and that his conversion compares favourably with his peers. The reasons for this are less clear and could be due to a multitude of factors including luck, his role within the team this season, Liverpool’s overall tactics and even less tangible factors such as “off-field distractions”. One thing that is clear from this analysis is that if you want Luis Suárez to score goals, he needs to be taking his shots from central areas. Brendan Rodgers has hinted at playing him as a wide-forward now that Daniel Sturridge has arrived; preserving Suárez’s current goalscoring record would be a challenge if he ends up taking more shots from more difficult angles, which may occur due to his natural position being out-wide. Over the last season and a half, Suárez has taken 103 shots from the wide positions for a return of 5 goals.
Based on this analysis and watching him play a lot, I would say that in certain circumstances, Suárez is a good finisher but that he is wasteful in terms of his decision making. Since the beginning of 2011/12, just over 40% of his shots were taken from areas out-wide where he rarely scores from, coupled with 36% of all of his shots being blocked (although this has improved this season). While the “scorer of great goals, rather than a great goal scorer” line has been an attractive label for Suárez during his Liverpool career, the analysis presented here indicates that he is more nuanced than that. Mind you, “a reasonably efficient goalscorer provided that he is in a central shooting position within approximately 20 yards of goal who is capable of scoring the odd goal that takes your breath away” is a bit more of a mouthful.
The perceived playing style of a football team is a much debated topic with conversations often revolving around whether a particular style is “good/bad” or “entertaining/boring”. Such perceptions are usually based upon subjective criteria and personal opinions. The question is whether the playing style of a team can be assessed using data to categorise and compare different teams.
WhoScored report several variables (e.g. data on passing, shooting, tackling) for the teams in the top league in England, Spain, Italy, Germany and France. I’ve collated these variables for last season (2011/12) in order to examine whether they can be used to assess the playing style of these sides. In total there are 15 variables, which are somewhat limited in scope but should serve as a starting point for such an analysis. Goals scored or conceded are not included as the interest here is how teams actually play, rather than how it necessarily translates into goals. The first step is to combine the data in some form in order to simplify their interpretation.
Principal Component Analysis
One method for exploring datasets with multiple variables is Principal Component Analysis (PCA), which is a mathematical technique that attempts to find the most common patterns within a dataset. Such patterns are known as ‘principal components’, which describe a certain amount of the variability in the overall dataset. These principal components are numbered according to the amount of variance in the dataset that they account for. Generally this means that only the first few principal components are examined as they account for the greatest percentage variance in the dataset. Furthermore, the object is to simplify the dataset so examining a large number of principal components would somewhat negate the point of the analysis.
The video below gives a good explanation of how PCA might be applied to an everyday object.
Below is a graph showing the first and second principal components plotted against each other. Each data point represents a single team from each of the top leagues in England, Spain, Italy, Germany and Italy. The question though is what do each of these principal components represent and what can they tell us about the football teams included in the analysis?
Principal component analysis of all teams in the top division in England, Spain, Italy, Germany and France. Input variables are taken from WhoScored.com for the 2011/12 season.
The first principal component accounts for 37% of the variance in the dataset, which means that just over a third of the spread in the data is described by this component. This component is represented predominantly by data relating to shooting and passing, which can be seen in the graph below. Passing accuracy and the average number of short passes attempted per game are both strongly negatively-correlated (r=-0.93 for both) with this principal component, which suggests that teams positioned closer to the bottom of the graph retain possession more and attempt more short passes; unsurprisingly Barcelona are at the extreme end here. Total shots per game and total shots on target per game are also strongly negatively-correlated (r=-0.88 for both) with the first principal component. Attempted through-balls per game are also negatively correlated (r=-0.62). In contrast, total shots conceded per game and total aerial duels won per game are positively-correlated (r=0.65 & 0.59 respectively). So in summary, teams towards the top of the graph typically concede more shots and win more aerial duels, while as you move down the graph, teams attempt more short passes with greater accuracy and have more attempts at goal.
The first principal component is reminiscent of a relationship that I’ve written about previously, where the ratio of shots attempted:conceded was well correlated with the number of short passes per game. This could be interpreted as a measure of how “proactive” a team is with the ball in terms of passing and how this transfers to a large number of shots on goal, while also conceding fewer shots. Such teams tend to have a greater passing accuracy also. These teams tend to control the game in terms of possession and shots.
The second principal component accounts for a further 18% of the variance in the dataset [by convention the principal components are numbered according to the amount of variance described]. This component is positively correlated with tackles (0.77), interceptions (0.52), fouls won (0.68), fouls conceded (0.74), attempted dribbles (0.59) and offsides won (0.63). In essence, teams further to the right of the graph attempt more tackles, interceptions and dribbles which unsurprisingly leads to more fouls taking place during their matches.
The second principal component appears to relate to changes in possession or possession duels, although the data only relates to attempted tackles, so there isn’t any information on how successful these are and whether possession is retained. Without more detail, it’s difficult to sum up what this component represents but we can describe the characteristics of teams and leagues in relation to this component.
Correlation score graph for the principal component analysis. PS stands for Pass Success.
The first and second components together account for 55% of the variance in the dataset. Adding more and more components to the solution would drive this figure upwards but in ever diminishing amounts e.g. the third component accounts for 8% and the fourth accounts for 7%. For simplicity and due to the further components adding little further interpretative value, the analysis is limited to just the first two components.
Assessing team playing styles
So what do these principal components mean and how can we use them to interpret team styles of play? Putting all of the above together, we can see that there are significant differences between teams within single leagues and when comparing all five as a whole.
Within the English league, there is a distinct separation between more proactive sides (Liverpool, Spurs, Chelsea, Manchester United, Arsenal and Manchester City) and the rest of the league. Swansea are somewhat atypical, falling between the more reactive English teams and the proactive 6 mentioned previously. Stoke could be classed as the most “reactive” side in the league based on this measure.
There isn’t a particularly large range in the second principal component for the English sides, probably due the multiple correlations embedded within this component. One interesting aspect is how all of the English teams are clustered to the left of the second principal component, which suggests that English teams attempt fewer tackles, make fewer interceptions and win/concede fewer fouls compared with the rest of Europe. Inspection of the raw data supports this. This contrasts with the clichéd blood and thunder approach associated with football in England, whereby crunching tackles fly in and new foreign players struggle to adapt to the intense tackling approach. No doubt there is more subtlety inherent in this area and the current analysis doesn’t include anything about the types of tackles/interceptions/fouls, where on the pitch they occur or who perpetrates them but this is an interesting feature pointed out by the analysis worthy of further exploration in the future.
The substantial gulf in quality between the top two sides in La Liga from the rest is well documented but this analysis shows how much they differed in style with the rest of the league last season. Real Madrid and Barcelona have more of the ball, take more shots and concede far fewer shots compared with their Spanish peers. However, in terms of style, La Liga is split into three groups: Barcelona, Real Madrid and the rest. PCA is very good at evaluating differences in a dataset and with this in mind we could describe Barcelona as the most “different” football team in these five leagues. Based on the first principal component, Barcelona are the most proactive team in terms of possession and this translates to their ratio of shots attempted:conceded; no team conceded fewer shots than Barcelona last season. This is combined with their pressing style without the ball, as they attempt more tackles and interceptions relative to many of their peers across Europe.
Teams from the Bundesliga are predominantly grouped to the right-hand-side of the second principal component, which suggests that teams in Germany are keen to regain possession relative to the other leagues analysed. The Spanish, Italian and French tend to fall between the two extremes of the German and English teams in terms of this component.
All models are wrong, but some are useful
The interpretation of the dataset is the major challenge here; Principal Component Analysis is purely a mathematical construct that doesn’t know anything about football! While the initial results presented here show potential, the analysis could be significantly improved with more granular data. For example, the second principal component could be improved by including information on where the tackles and interceptions are being attempted. Do teams in England sit back more compared with German teams? Does this explain the lower number of tackles/interceptions in England relative to other leagues? Furthermore, the passing and shooting variables could be improved with more context; where are the passes and shots being attempted?
The results are encouraging here in a broad sense – Barcelona do play a different style compared with Stoke and they are not at all like Swansea! There are many interesting features within the analysis, which are worthy of further investigation. This analysis has concentrated on the contrasts between different teams, rather than whether one style is more successful or “better” than another (the subject of a future post?). With that in mind, I’ll finish with this quote from Andrés Iniesta from his interview with Sid Lowe for the Guardian from the weekend.
…the football that Spain and Barcelona play is not the only kind of football there is. Counter-attacking football, for example, has just as much merit. The way Barcelona play and the way Spain play isn’t the only way. Different styles make this such a wonderful sport.
Liverpool began their season with a disappointing result against West Bromwich Albion at the Hawthorns. Much has been made since Brendan Rodgers’ appointment about his passing philosophy, so the focus here will be upon analysing how Liverpool passed the ball against West Brom.
Passing network analysis
One method of analysing passing by a football team is network analysis, which I’ve used previously to assess Liverpool’s passing against FC Gomel. The idea with network analysis is that the connections between players are analysed to look at passing patterns in the team and to identify key players in the network in terms of passing. The number of passes played and received by each player is collated according to the player they passed to and who they received from respectively. The data for passes played and received is taken from the Stats Zone application, which was kindly provided by the excellent Anfield-Index. One caveat to note is that throw-ins are included, which boosts Johnson and Kelly’s passes completed in particular.
Below is the passing network for Liverpool and shows completed passes only. The larger and darker the arrow is, the greater the number of passes played by one player to another. The positions of the players are based on their average positions during the match provided by WhoScored, although Lucas and Allen are slightly separated horizontally for clarity as their average positions were practically next to each other. It is important to note that these are the average positions, which will not always be representative of where a player passed/received the ball. Also, only the starting 11 is shown as the substitutes had a fairly limited impact upon the game in terms of passing.
Passing network for Liverpool from the away match against West Bromwich Albion on the 18th August 2012. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The position of each marker is based upon their average position and the size of each marker is related to their closeness centrality, which is described in the text below. Asterisk indicates players who did not play the full match. Only the starting eleven is shown.
The main features in the above network are the reciprocal passes played between the defenders and the criss-crossing of passes in the midfield zone. Liverpool clearly kept the ball efficiently in deeper areas as the back four plus Lucas and Allen retained the ball well. The main issue for Liverpool was getting the ball to their attackers further up the pitch. Borini and Downing received the ball just 31 and 33 times respectively, with Downing in particular tending to pass the ball back to players in deeper areas; Downing completed a pass to Suárez twice and Borini once. Borini tended to combine with Johnson and Suárez in the main, passing to both of them on 7 occasions. Liverpool did effectively get the ball to Suárez, as he received the ball on 51 occasions and he was Liverpool’s main attacking outlet. Suárez tended to receive the ball from players in wide areas and from Lucas and Allen, whereas against Gomel the main link was with Gerrard and the quick interchanging of passes between them was less in evidence sadly.
Where you gonna pass to now, where you gonna go?
One of the useful tools of network analysis is that you can derive measures that indicate which players in the team are the most influential in terms of passing. One of these measures is known as “closeness centrality”, which in this context is dictated by the number of passes played and received by a given player. The key aspect of this measure is that it is greater when the passes that the player plays and receives are distributed more evenly across the team. If a hypothetical player makes 100 passes in a match and receives the ball 100 times, they would have a greater closeness centrality if they passed and received the ball 10 times to and from each team-mate compared to if they simply passed the ball back and forth to just 1 team-mate. Players with a larger closeness centrality score are interpreted as being a greater influence upon the passing of the team as they dictate the movement of the ball within the side.
In the figure above, the size of the player markers is dictated by their closeness centrality score. Joe Allen was Liverpool’s stand out player as he dictated Liverpool’s passing play. He generally received the ball from his centre-backs and Johnson prior to playing his passes. He linked well with Johnson and Borini on the left, his midfield partner Lucas and Suárez further forward. A feature of Allen’s play was his movement to make himself available for a pass and he received a pass on 62 occasions, more than any other player.
Skrtel had the next highest closeness score, although he was some way behind Allen. Agger was far less effective compared to the Gomel match, partly due to the sending off but also due to his passing recipients being lesser in scope as he favoured passes to Johnson, Skrtel and Allen. Lucas was also less of an influence, again partly due to not playing the full game but also due to being less central to the teams passing. Johnson was more effective than Kelly from full-back and was probably Liverpool’s most influential attacking force as he played high up the pitch on the left and created 3 scoring opportunities according to the EPL-Index Stats Centre. Downing and Borini’s involvement was very limited compared to their team-mates (only Reina was less involved). The involvement of Suárez and Gerrard was also disappointing. Overall, the lack of involvement of Liverpool’s front-4 was a hindrance over the course of the match, as most of the play was contained in the defensive and midfield zones.
Hey Joe
Liverpool’s passing against West Brom was reasonable, particularly in the 1st half and there were definite signs of Brendan Rodgers’ philosophy bedding in. However, the lack of involvement of the front-4 and in particular, Borini and Downing was disappointing. The major bright spot was the performance of Joe Allen, who dictated the passing play of the team to good effect. Unfortunately, Lucas wasn’t up to his usual level, which may be due to his ongoing recovery from injury and also this match being the first time he started with Allen. Hopefully future games will see this partnership blossoming as they begin to complement each other in terms of their roles within the team. Such a partnership could be crucial in implementing the control that Brendan Rodgers desires.
In my two previous posts, I’ve investigated crossing frequency and crossing efficiency from both open and set-play. Much of those posts focussed upon Liverpool and their apparent addiction to crossing in 2011/12. One of the major questions surrounded whether this apparent crossing strategy was a phenomenon that had evolved from Liverpool’s transfer business last summer as the club sought to provide aerial service to Andy Carroll.
So the question is: did Liverpool cross more often in 2011/12 compared with 2010/11 under Kenny Dalglish?
Crossing comparison
Overall, Liverpool averaged 17.4 attacking half passes prior to an open-play cross in 2010/11, compared to 14 in 2011/12. Such a difference is statistically significant at the 99% level. Consequently, it would appear that Liverpool did indeed cross more in 2011/12 than in 2010/11 under Dalglish. However, this isn’t the whole story as the plot below investigates. Liverpool’s crossing frequency tended to fluctuate from game-to-game, although this is to be expected. In general, during 2010/11, they were above the average from last season. Conversely, during 2011/12, they were below the average.
The average number of passes attempted in the attacking half by Liverpool prior to an open-play cross in Premier League games while managed by Kenny Dalglish. Each bar is coloured according to whether Liverpool won, drew or lost the game. Dark grey background is for games in 2010/11 and lighter grey background is for games in 2011/12. The dashed black line is the average number of attacking half passes attempted prior to an open-play cross for all teams in the 2011/12 season. Data is provided by EPL-Index.
A complicating factor of the comparison between the two seasons is that Liverpool’s record in terms of wins and points won was much better pro-rata in 2010/11 than in 2011/12. Over the whole of Dalglish’s second tenure, Liverpool averaged 17.2 attacking half passes per open-play cross in games which they won, 14.4 in those that they lost and 12.8 in games which they drew. Combining those in which they failed to win (draws plus losses), they averaged 13.7. Limiting the analysis to just 2011/12, Liverpool averaged 16.2 during a win,14.1 during a loss and 12.1 during a draw. In losses and draws combined, they averaged 13.1. It would appear that score effects played a role in Liverpool’s crossing strategy, although this analysis is limited to just the final score (ideally you would investigate the crossing frequency as a game unfolds and the score changes).
A desperate measure
It appears that Liverpool did cross more frequently in 2011/12 than in 2010/11 under Kenny Dalglish. This may well have been a result of the transfer business conducted in 2011. However, the change in style is somewhat conditioned by their poorer record in terms of wins and points gained. In games that they failed to win and particularly during home draws, Liverpool crossed more frequently. Was this a desperate measure as they attempted to force a result during these games? Cross after cross was sent into the area but generally yielded very little return.
The apparent willingness of Brendan Rodgers to sell Andy Carroll has been attributed to a perception that he won’t fit in with Rodgers’ possession-based style of play. Furthermore, it might be that Carroll is seen as too tempting a target for long balls and crosses from the rest of the team. Based on last season, Liverpool averaged 13.3 attacking half passes prior to a cross when Carroll started and played more than 60 minutes. When Carroll didn’t start, Liverpool averaged 14.9. This would suggest that Liverpool did cross more frequently when Carroll played, although such “with or without you analyses” are notoriously difficult as compounding factors can sway the results. One such compounding factor is that Liverpool’s win record was better when Carroll started and we already know that Liverpool crossed less when they won.
In summary, Liverpool did cross more during 2011/12 than in 2010/11 but this may have been somewhat skewed by the poorer record during the former. Possibly the more concerning aspect is that Liverpool tended to cross more when they were losing or drawing, which brought very little return. This seemingly desperate tactic led to much frustration and likely contributed to the loss of points over the course of the season. Ultimately, this poor points return cost Kenny Dalglish his job.
In my previous post, I looked at how Liverpool seemingly focussed upon crossing last season and how it was on the whole unsuccessful, at least in open-play. One thing that I noted was that crossing from set-pieces appeared to be more successful in terms of goals scored than crosses in open-play.
The average number of crosses per goal scored last season was 79 in open-play and 28.3 from a set-piece. Crossing accuracy is also higher for set-pieces (33.9%) compared with open-play (20.5%). This demonstrates that crossing is more effective from set-pieces than in open play.
So the question is: Which teams were particularly efficient at scoring from set-play crosses and how did this contrast with their open-play performance?
Crossing efficiency
As with the crosses from open-play analysis, there are several under and over-performers in terms of crosses from set-pieces. Furthermore, some teams score a large proportion of their goals from crosses.
Relationship between the number of crosses in open-play required to score a goal from a cross in open-play and the number of crosses from a set-play required to score a goal from a cross at a set-play for English Premier League teams in 2011/12. Note that both scales are logarithmic and that they are reversed as a larger number is worse. The horizontal dashed black line indicates the average number of open-play crosses required to score a goal from a cross in open-play across the league, while the vertical dashed black line indicates the number of crosses from a set-play required to score a goal from a cross at a set-play. The teams are coloured by the percentage amount of goals they scored from all crosses, relative to their total number of goals. Data is provided by Opta and EPL-Index.
Stoke conformed to their stereotype here as they led the way marginally from Chelsea, as they required 15.4 crosses per goal at a set-piece compared to Chelsea’s 15.6. Chelsea scored 14 goals in total from set-pieces, while Stoke scored 10. Other notable performers were Blackburn (16.9), Norwich (17.6) and Everton (17.7). Norwich were probably the most efficient crossing team in the league last season, as they scored frequently compared to their peers from both open-play and set-pieces. In fact, 46% of their goals came from crosses last season, ahead of Stoke (39%), QPR (37%) and Chelsea (37%). Whether such numbers will be sustainable next season could be crucial for Norwich under Chris Hughton.
Aston Villa (180), Newcastle (138) and Swansea (85.5) were the clearest under-achievers, as they only scored 4 goals from a set-piece cross between them. In contrast to their severe under-performance in the open-play crossing analysis, Liverpool were about average as far as set-piece crosses were concerned. Indeed, Liverpool scored 9 goals from set-piece crosses last season, which was joint third with Blackburn, Everton and West Brom.
Getting to the byline
One of the aspects of crossing that I find curious is the poor success rate of crosses in terms of their accuracy. At first glance, the accuracy of crosses appears to be uniformly low; 23.4% for all crosses, with Arsenal posting the lowest (21.5%) and Norwich having the highest (27.3%). Accuracy is even worse in open-play, where it drops to 20.5% on average last season. Norwich are again the highest (24.8%), while Bolton had the lowest (17.2%). The overall crossing accuracy figures are skewed by the greater accuracy from set-piece crosses, which on average were accurate 33.9% of the time. Newcastle had the lowest accuracy with 23.9%, which was far lower than any other side (Liverpool were next lowest with 29.1%). Such a low accuracy goes some way to explaining their poor efficiency from set-piece crosses. The contrast to this is Aston Villa, who amazingly had the highest set-piece cross accuracy with 41.7% but could only score 1 goal from a set-piece cross all season.
This greater range and contrast in crossing accuracy when they are broken down potentially points towards a level of granularity in the crossing data, that is not separated by the coarse definition of crosses used here. Ideally, the crosses would be separated by the position from which the cross originated, along with defending and attacking players positioning. EPL-Index include “byline crosses” in their crossing database, which is a start as it shows that such crosses are far more accurate on average (47.8%). If we assume that successfully crossing to a team-mate is the first stage in potentially creating a chance to shoot and subsequently scoring, then it would appear that byline crosses are a far better option than other crosses in open-play; open-play crosses excluding these byline crosses have an accuracy of 19.7%.
Sadly I don’t have enough information available to assess whether byline crosses are a more efficient means of scoring from a cross plus the sample size is relatively small compared to total open-play crosses on a team-by-team basis. Some teams essentially never get to the byline and cross the ball; Stoke attempted only 2 byline crosses all season. Only Manchester City (50), Arsenal (47), Liverpool (34) and Manchester United (31) really attempted enough to draw even tentative conclusions. However, it would make sense if such crosses were a more effective means of scoring from a cross as they are often attempted closer to goal, which may result in an easier chance for the receiver.
In the mixer
Based on last season, set-piece crosses are a more efficient means of scoring than open-play crosses. There are likely a multitude of reasons for this, one of which is possibly the superior crossing accuracy from set-pieces compared to those in open-play. The greater parity in numbers between attackers and defenders could be another reason plus the more specialised headers of the ball, such as centre-backs, could be used to greater effect at set-pieces. One potential method of extracting more value from crosses is to attempt them closer to the byline, where the accuracy is far greater than other open-play crosses but at present I don’t have enough data to fully explore this idea.
Overall, scoring from a cross does not appear to be a particularly efficient and direct method of providing goals. However, it could be argued that a goal may indirectly result from a cross; the “in the mixer” approach, although this is likely to be particularly subject to the vagaries of luck and is more applicable to set-pieces. Based on last season, a team will on average score a goal from a cross in open-play every 79 crosses. Even the best performers in the league needed 45 crosses on average to score a single goal. The average number of open-play crosses per game attempted by a team last season was 17, which suggests that over the long-term, a team can expect to at best score a goal from an open-play cross every 2-3 games. Crossing, especially in open-play, appears to be a low-yield method of scoring.
If Liverpool had been merely average last season, the 841 open-play crosses they attempted would have yielded an extra 8 goals. If they had been exceptional, they could have expected another 16 goals. The question is whether this is a good enough return to motivate basing your playing style upon over the long-term?
In some recent interviews, Simon Kuper has suggested that Liverpool established a data-driven style of play focussed around crossing last season. He theorised that Liverpool attempted to cater to Andy Carroll’s heading strengths by buying players with good crossing statistics, such as Stewart Downing and Jordan Henderson. Kuper then goes on to state that such an approach is flawed due to crossing being an inefficient means of scoring goals.
Earlier in the season, the Guardian’s Secret Footballer also suggested that statistical principles guided Damien Comolli towards a crossing focussed approach in the transfer market. Andrew Beasley conducted an excellent analysis for The Tomkins Times on whether the data indicated that such an approach (along with some others) was actually working.
So the question is: Did Liverpool really pursue a strategy based around crossing last season and to what extent was it successful (you can probably guess the answer to the second part)?
Noughts & Crosses
Firstly, Opta define a cross as:
A pass from a wide position into a specific area in front of the goal.
The basic numbers show that Liverpool attempted more crosses (1102) than any other team in the Premier League last season. Manchester United (1018) and Wolves (999) ranked second and third respectively. At the other end of the scale, Blackburn (610), Fulham (649) and Swansea (721) attempted the fewest. The average per team was 837.2 crosses attempted, which equates to just over 22 crosses per game.
While the raw numbers provide a guide, it is possible that the figures could be skewed by how much of the ball a particular team has on average. For example, Wolves had much less of the ball than Manchester United last season but attempted a similar number of crosses. This suggests that Wolves were keener to attempt crosses than Manchester United. Furthermore, set-plays should be isolated from the total crosses, as teams may have different approaches in open-play vs set-play. In order to account for this, I’ve calculated the ratio of attacking half passes to total open-play crosses in the graph below. This gives an indication of how keen a team is to attempt a cross during open-play. I limited the passing to the attacking half only as this is where most (if not all) crosses will originate from and it avoids the data being skewed by teams that play a lot of passes in their own half.
Similarly to this tweet by OptaJoe, I calculated the average number of open-play crosses that each team in the Premier League required to score a goal from an open-play cross last season. This is shown in the graph below versus the number of attacking half passes per open-play cross.
Relationship between the number of crosses in open-play required to score a goal from a cross in open-play and the number of passes in the attacking half by a team prior to an open-play cross for English Premier League teams in 2011/12. Note that the cross:goal ratio scale is logarithmic and that it is reversed as a larger number is worse. The horizontal dashed black line indicates the average number of open-play crosses required to score a goal from a cross in open-play across the league, while the vertical dashed black line indicates the average number of passes in the attacking half by a team prior to an open-play cross. The teams are coloured by the percentage amount of goals they scored from open-play crosses, relative to their total number of goals in open-play. Data is provided by Opta, WhoScored and EPL-Index.
The analysis indicates that Liverpool did indeed pursue a crossing strategy last season relative to their peers in the Premier League, as they attempted 14 passes in the attacking half prior to attempting a cross. Only Wolves, Stoke and Sunderland played fewer attacking half passes prior to attempting a cross last season. At the other end of the scale, Manchester City and Fulham were relatively sheepish when it came to crossing, attempting just over 21 passes in their opponent’s half prior to attempting a cross. Arsenal, Swansea and Spurs also stood out here, lying more than 1 standard deviation above the league average.
The major issue for Liverpool based on the above analysis was that their conversion from crosses was simply atrocious. They required a staggering 421 open-play crosses to score a single goal in open-play on average last season. This was the worst rate in the whole league, with Wigan the closest on 294. Contrast this with the likes of Manchester United (44.5), Norwich (45.1) and Arsenal (48.4) who were the only clubs to post a value below 50. Furthermore, only 8.3% of Liverpool’s goals in open-play came from an open-play cross. Norwich scored 53.3% of their goals in open-play from open-play crosses
Liverpool seemingly embarked upon a style of play that provided them with a extremely poor return in terms of goals (only 2 goals from an open-play cross all season).
Is crossing the ball an inefficient means of scoring?
The above analysis seemingly demonstrates that Liverpool did indeed pursue a style of play centred around crossing. Liverpool’s apparent quest to show that crossing is an extremely inefficient means of scoring last season (I’m personally still trying to forget those 46 crosses against West Brom at Anfield) potentially clouds the more general question of whether crossing is a tactic worth basing your team around. It could be that crossing can be an efficient way to score but Liverpool were just simply not very good at it.
According to WhoScored, 659 goals were scored in total from open-play, while 241 goals came from set pieces (excluding penalties). The data from Opta show that 166 and 128 goals were scored from open and set-plays respectively. Thus 25% and 53% of all goals in these categories came from crosses. The average number of crosses per goal scored last season was 79 in open-play and 28.3 from a set-piece. Crossing accuracy is also higher for set-pieces (33.9%) compared with open-play (20.5%). This demonstrates that crossing is more effective from set-pieces than in open play.
Crossing the divide
The above analysis demonstrates that Liverpool pursued a playing style overly focussed upon crossing, which yielded very meagre returns. Whether the poor return was a symptom or a contributing factor to their generally poor shot conversion isn’t clear at present and requires further analysis.
The more general question regarding whether crossing is an efficient means of scoring is difficult to assess without more analysis. This study shows that crossing at set-pieces is more efficient than in open-play but to fully answer this question requires comparison with other modes of scoring. The above analysis suggests that structuring your team around crossing in open-play is a very low yield method of scoring, which also results in the loss of possession close to 80% of the time.
Liverpool’s addiction to crossing appears to be a recent trend. In the 3 seasons prior to 2011/12, they averaged 16.4, 15.4 and 15.5 attacking half passes prior to an attempted cross. Swansea under Brendan Rodgers averaged 18.9 last season, which potentially suggests that next season Liverpool will try to kick the crossing habit.
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 WhoScored.com 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 WhoScored.com 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.
2+2=11