Soccer Analytics Panel – MIT Sloan Sports Analytics Conference 2015 #SSAC15


I havent posted anything on this blog in more than 2 years. Life has been hectic both professionally and personally ever since I took the Sounders job.

In February I was invited to be on the Soccer panel at the MIT Sloan Sports Analytics Conference #SSAC15. It was a great experience to be a part of the panel. Not only that it is a recognition of some of the work we have been doing at the Seattle Sounders but also because attending this conference in 2012 (thanks to @onfooty for encouraging me to go) was one of the origins and catalysts of my career switch. This back-story made it even more special to be on the #SSAC15 soccer panel.

SSAC have uploaded the video of the panel. Posting the video to this blog represents a small “coming full-circle” moment for this blog and for me personally.

I hope you enjoy it and looking forward to hear your thoughts and feedback.

Details of the panel and members 

Watch the full video of the panel

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MIT Sloan Sports Analytics Conference 2013: Soccer Analytics Panel


Panelists : Chris Anderson, Albert Larcada, Blake Wooster, Jeff Agoos

The makeup of this year’s panel is very different from that of Soccer Panel at SSAC 2012. Dominated by “club insiders” last year, this year’s panel had a mix of “outsiders” like Chris Anderson, Blake Wooster from Prozone represented the data companies, Albert Larcada from ESPN coming from media and Jeff Agoos, a former player and Technical Director of MLS.

The Ballroom was almost full and this was a bigger ballroom than that of the last year’s conference. (My totally un-scientific method of measuring crowd sizes puts “almost full” > “75% full of last year”)

Marc Stein moderated the panel as was the case last year.

soccerpanel

This year’s panel took a completely different track compared to the panel from last year. Last year it surrounded around how analytics is used, where it is useful and the importance of context and trust. The biggest challenge cited in 2012 was the availability of good data to work with.  This year it centered more around how much analytics is used by managers, metrics, visualizations – use of analytics in the media and trust (a repeat theme from year).

The panel started off with a historic perspective of soccer analytics from Chris. I thought it was a very good beginning that gave the audience an idea of how far we have come. Albert’s examples of using heatmaps of Messi and Ronaldo in the build-up to the “Clasico” in SportsCenter showed how data is being used to tell a story. The challenge in a scenario like SportsCenter is that the announcers need to be able to explain the graphic and tell the story in less than 10 seconds. Choosing the right type of visualization is key. I also liked the idea of “visualization is analytics” quote from Albert. All visualizations are an approximation of the raw data. If done right, they can tell a story not just in media but also inside a soccer club.

The big questions

  1. How much analytics is being used by the club managers?
  2. What are the challenges in making the coaches use analytics?
  3. Metrics – what do we have today?

How much analytics is being used by the club managers?

Chris tried to answered this based on his experience working with clubs as a consultant but the viewpoint of an “insider” (like an analyst at a club) citing examples where analytics has been used by a manager successfully would have been a good counter-balance.  It is always tough balancing act to not reveal confidential details and have a frank discussion but I felt that the club angle was addressed better in last year’s panel. West Ham United’s manager Sam Allardyce talking about how he uses analytics is a good example.

What are the challenges in making the coaches use analytics?

There was some great discussion on this one.  Coaches have a lot at stake and they may not be willing to use something new unless they know (with a high degree of confidence) or trust that it will help them but they won’t know for sure if it will be useful unless hey use it. The classic chicken and egg. A great example was the importance of survival in a promotion-relegation environment and how that pushes managers and front-offices more towards short-term thinking and immediate results.

Daryl Moorey, the GM of Houston Rockets and a co-chair of the SSAC brought up a great point in the “Revenge of the Nerds” panel about how the front office needs to support and persist with analytics. Managers may not trust it right away but if they find it useful over a period of time they will eventually come around.

Blake had a point when he stressed that the onus is on the analyst to communicate the value of analytics to the coach. If the coach doesn’t see value in it, it is probably because the analyst didn’t do a good enough job of conveying the message.

In the talk “Why we don’t understand Luck” by Michael Mauboussin one of my favorite talks of the conference, Michael stressed the importance of evaluating things based on the process (which is very hard to do) rather than the outcomes (which is easy and what we do normally).

I believe that analytics is being used differently in different clubs (hardly surprising). It has a complimentary role as “another tool” in the overall toolkit for success.  The two key themes that resonated across the 2-day conference “communicating the message” and “winning the trust of coaches/decision-makers” are very important.

Metrics – what do we have today?

This is probably one of the most debated aspect of soccer analytics today. What metrics do we have? What is the equivalent of Baseball’s WAR in soccer? There is a lot to gain from knowing the process of how analytics and metrics work in other sports but every sport is unique and presents its own challenges. It is a fact that we haven’t been able to model soccer matches as well we would love to. Albert brought up a great point about how the paucity of scoring on soccer makes it much harder to model than almost every other team sport. We haven’t yet come up with a formula that tell us how to win a game or score a goal. That is because there are many ways to win a soccer game. Different coaches employ different systems. A metric/KPI valued highly in one system might not matter at all in another system. For example, speed on the ball and accurate long-balls might be very important in a counter-attacking system but may not be that important in a short passing system.

 

Charles Reep, probably the first soccer analyst, concluded that most goals were scored from fewer than three passes: therefore he concluded it was important to get the ball quickly forward as soon as possible.

While his statement might still hold true, the “How?” is the key question. The answer is not as straightforward. The quickest way to move the ball is for the goalkeeper to hoof balls upfield and we know that it doesn’t work most of the time.

I believe the availability of spatial XY-data + data from camera tracking systems (installed in most of the stadiums around the world today) in conjunction with video has helped in answering the “How” better. But we still have a long way to go.

As Chris pointed out at the very beginning of the panel – This is not a revolution but an evolution. We are constantly evolving.

A few more of my thoughts in a podcast with Richard Whittall  editor of the The Score Media’s Counter Attack blog

Suggestions for the next year’s panel

  1. I felt that the composition of the 2012 and 2013 panels were at the extremes in terms of “club insiders/outsiders”. A panel with a mix of the two groups would probably be more useful.
  2. Panel formats don’t lend themselves very well to 1-on-1 dialogue and interaction. An online Q & A chat session with the panelists during the conference is a good thing to try. I got similar feedback from a few others who attended the conference.
  3. Have Big Sam on the panel! – Seriously, having a manager who has used analytics and is willing to talk about it would elevate this panel to a whole another level. I am aware that EPL will be in season, but technology gives the option to have someone participate remotely.

I have another post summarizing my views on the other panels I attended coming up in a few days.

Links

Official website Sloan Sports Analytics Conference

Richard Whittall’s thoughts on the Soccer panel

Zach Slaton’s Summary of the SSAC 13

Mitch Lasky’s impressions of the conference

Sports Hack Day Project


I have been busy ever since I started working for the Seattle Sounders about a month ago. It has been great so far. We are less than a month away from the season kick-off. I am very excited, to say the least.

Coming up in a few weeks is the Sloan Sports Analytics Conference in Boston. Last years conference had a profound impact on me. More about that in another post.

This weekend I participated in the 1st ever Sports Hackday in Seattle. The idea of learning something new, meet like-minded people and a chance to avoid the endless Superbowl pregame show were enough motivation to sign-up. The Hackday was very well-organized. Kudos to the organizers and sponsors. We started off Friday night with introductions and forming teams. Our team “Submarino” constituted of Sarah, AdamMatt and I. We had a few ideas going into the Hackday. After a brief brainstorm we decided on looking at the impact of injuries to soccer clubs.

Sunday morning during the integration phase a few hours before the demo

Sunday morning during the integration phase a few hours before the demo

One of the coolest things about Sports Hackday was  that data providers like Sports Data LLC and platform companies like Google, Cloudant, Twilio etc., provided tools that ensured that we spent most of our time implement our idea and not worry about basic infrastructure and plumbing.

We used the Sports Data LLC‘s API to extract the injury information of English Premier League and broke them down based on teams, types of injuries, # of games missed due to these injuries. We built a fully working model of our idea using real data. It helped that we had an awesome team and that we did a very good job of decoupling the Frontend UI pieces and the backend database work which enabled us to work almost parallely. We had our hairy moments during the integration phase with the clock winding down to Noon, Sunday (the deadline for code-complete). However we were able get done most of what we wanted to do.

We did this cool  interactive visualization illustrating the breakdown of injuries in a team by category and the players. The thickness of the arcs depict the # of games they missed due to a particular injury.

We had 3 minutes to demo and it went well, although all of us were a bit nervous and very tired. We won two prizes. “Best data visualization” and the “Best overall data hack of the Hackday”.

Here is a piece on the Sports Hackday on Geekwire.
Local TV King 5‘s coverage of the event

Frankly, I did not expect to win the overall prize. We ended the evening very happy and very very tired.

Visuals

Manchester United had the highest # of player-games missed due to injuries so far this season. The 2nd visual highlights that muscle injuries is a team-wide issues and not just Nani who missed the most time due to muscle injuries.

This poses a new question : Is there something in the training regimen of Manchester United that is causing this? 

Manu Injury Breakdown

manu2

PS: I couldnt get the interactive part working on the blog due to javascript issues, if I ever figure it out, I will update.

Modeling 101


“Essentially, all models are wrong, but some are useful.” – George E.P. Box

from Empirical Model-Building and Response Surfaces (1987) co-authored with Norman R. Draper, p. 424.

Mr. Box’s quote is quite popular and used a lot these days. However, I am not sure if all those who quote it fully understand it. And that includes me.

What I took away from the quote.

A model is an approximation of a process that we don’t fully understand. We model processes to get a deeper understanding of the process itself. To build a model we use what we understand about a process and try to approximate (or sometimes ignore) what we dont know.

All models are wrong because of the inherent approximation. But some could be useful because what we have “approximated (or ignored)” is not important to understand or replicate the process.

“Relative simplicity” is an important virtue of a good model. By “relative simplicity” I mean the simplicity of a model compared to the actual process. For example, a “simple model” of Relativity theory for a physicist could still take me two lifetimes to understand.

Math lends itself very well to represent these models. We test the usefulness of a model against the real process by giving identical inputs and comparing the outputs. This is easy in case of some models and not so easy in case of others. ( like verifying the existence of Higgs-Boson particle using the Large Hadron Collider that cost an estimated $9b to build).

A model is useful if for a range of inputs, the output of model closely matches the real output of the process. (How close? – It is a matter of threshold and the +/- error range the user of the model is comfortable with.). To summarize, an useful model has some predictive power with

The game of football is a process. Over the years a lot of people have tried to improve our understanding of the game by building models based on what they understood with the data available to them. We have been collecting more and more information as we progressed. More information has led to the better understanding of the game and dispelling some of the myths. But it has also helped create “new myths” (or truths until they get proven wrong in due course of time).

There are a lot of models out there for football. Some based on past results, some based on what happens in a game (events like shots, goals, final 3rd passes etc) and so forth. I dont quite agree with all of them. Some I agree with more than the others.

What I look for in a model?

  1. A model should an understanding of the process; to be aware of what and how the process works. It doesn’t have to be comprehensive (because it is a model after all) but capture the essence of it.Example: If I want to build a model to predict the winner of a football game – I want my model to take into consideration how I win a game of football? By scoring more goals than the opponent. How do I score more goals? By taking a lot of (hopefully good) shots and not letting the opponent take good shots, How have the two teams have been doing in the run-up to the game and so forth. I can list 20-30 items and I am sure I still would have missed many things. The goal is to not to account for every factor but to identify the handful of factors that capture the essence or “signal” as Nate Silver calls it in his popular new book.

    I am skeptical of any model that does not understand the process that it is trying to be a model of.

  2. A model should factor in the nature of the underlying data from a process. For example: Based on Chris Anderson‘s analysis, number of goals scored in a game is not normally distributed. Something like that needs to be factored in if the model using goals scored as an input. This is probably more important for models making long term predictions because the inherent characteristics of data tend to manifest over a longer period of time than in a shorter period of time.Example: Probability of a getting 4 heads if you toss a coin 4 times is 6.25%. But if you repeat the experiment 5 times, you might get 4 heads once, twice, thrice, 4 times, 5 times or never. You might have to repeat the experiment thousands of times to see the probability of 4 heads converge to 6.25%.

Building a good model is not trivial and is an iterative process. But if the first version of a model doesn’t address the above, it might be time for a rethink.

MCFC Analytics blogposts Summary #9


In the past week, I found the following posts written using the #MCFCAnalytics data

  1. Some interesting stuff by @PedroAfonso85 building on some previous work  to breakdown the importance of ball possession and some discussion about the oft discussed yet hard to quantify, momentum.
  2. @MarkTaylor0 analyzed Blocked shots to find if blocking shots is a talent.
  3. @hpstats visualized points difference “with/without” a player in  the starting lineup. Also from the same blog is profiling players based on their shooting
  4. @SportsViz has a video with examples of 3D-visualization of passes using the data from Bolton vs. City game

Previous Summaries

Summary #8

Summary #7

Summary #6

Summary #5

Summary #4

Summary #3

Summary #2

Summary #1

MCFC Analytics blogposts – Summary #8


Here is the list of interesting posts I found in the past week

  1. An interesting post on home advantage and how it manifests itself into football stats by @FbPerspectives. The post also has a link to a detailed paper from 2009 on home advantage.
  2. Guardian Data blog has an interactive visualization of the Bolton – City game by @jburnmurdoch. The viz has a pitch map + a radial diagram that captures the pass direction and length.
  3. The man in the yellow shirt – an analysis of the refs by @PedroAfonso85
  4. An interactive visualization of the direction of a player’s passes by @alekseynp . Some of the outliers are very interesting.
  5. Momentum in Bolton – City game. by @SoccerStatistic . This is a different approach from the previous attempts on visualizing momentum using this data set.

I did not publish anything last week, although I did start writing. Hopefully I will publish something later this week.

Previous Summaries

Summary #7

Summary #6

Summary #5

Summary #4

Summary #3

Summary #2

Summary #1

If I missed any, please post them in the comments section or tweet them to me!

MCFC Analytics – blogposts summary #7


I did not see too many new posts in the past week. I didn’t publish any as I was busy with a different project.

  1. An interactive viz of Bolton – Manchester City  match data by @JBurnMurdoch on @GuardianData blog
  2. @HPStats attempts at defining metrics to be able to cluster players based on their style. Here is a good first step on Passing
  3. @shots_on_target made a summary of vital stats regarding goals, shooting accuracy, penalties etc..
  4. Scouting report on Tim Howard by @footballfactman
  5. An interactive visualization of the full dataset by @PhilyB1976 I posted this in one of the first few summary posts but there is additional information on the site. worth revisiting!
  6. An

Previous Summaries

Summary #6

Summary #5

Summary #4

Summary #3

Summary #2

Summary #1

If I missed any, please post them in the comments section or tweet them to me!

Sunderland – Opposition Analysis


This is an “Opposition analysis” of Sunderland, City’s opponent on Saturday 2012/10/6 at the Etihad. I used the #MCFCAnalytics Lite data set to do this analysis.

Disclaimer: The analysis is primarily based on data from 2011-12 season with some data points from the first six games of 2012-13 season.

Sunderland – Offense

Goals 1.13 per game – 12th (excluding own goals)
Strong on direct free kicks 5 goals – 1st
% of Open play goals 76% – 4th
% of goals from inside the box 69.7% – 19th
% of goals from outside the box 30% – 2nd
Shots on Target 3.71 per game – 18th
Efficiency: Goals/shots On + off Target 7th
Efficiency Inside the box 16th
Efficiency Outside the box 3rd
Assists per Goals scored 18th
Poor from inside the box 19th in proportion of goals from inside & 16th in efficiency
Strong from outside the box 5th most goals and 3rd most efficient from outside
Final 3rd completions / comp % 16th / 14th
Poor in the final third and opposition box 18th in final 3rd touches & 19th in touches in opp. box
Poor in short passingcompletions / comp % 15th /15th
Good in long balls:  # of successful / success % 9th / 8th
Good in Open play crosses 7th in # of crosses & crossing accuracy
Very few Through balls 18th – less than 1 through ball per game
Other Sunderland just took 6 short corners all season, fewest in the league.

Sunderland – Key attacking players (2011-12)

Goals Bendtner – 8, Larsson & Sessegnon – 7 each,

McClean – 5

Shots On Target Bendtner – 23, Sessegnon – 21, Larsson – 17
Efficiency Larsson – 23%, McClean – 17% and Bendtner – 16. %
Assists Sessegnon – 9, Bendtner – 5
Final 3rd Completions Sessegnon – 401, Larsson – 281, Bardsley – 253
Final 3rd Completion% Sessegnon – 77.4%, Larsson – 66.27%, Bendtner – 60.6%
Touches in opposition box Sessegnon – 120, Bendtner – 98

Sunderland – Offensive summary

Major personnel changes for 2012-13

IN – Steven Fletcher; OUT – Nicklas Bendtner

Bendtner was highest goal-scorer for Sunderland last season with eight. He also had five assists. Steven Fletcher is doing more than enough to replace him. Fletcher has scored all the five goals of Sunderland so far. There have not been any major changes apart from this.

What the numbers say

A mixture of long balls, great long-range shooting, some great free kicks and accurate crossing were the mainstay of Sunderland’s offense last season. Their attack ran through Stephané Sessegnon, Sebastian Larsson and Nicklas Bendtner.

Sunderland was poor in the final third and even worse from inside the box (19th in touches inside the opposition box). They scored 30% of their goals (13) from outside the box. They do not have a lot of through balls (less than 1 per game, 18th in the EPL) or assists (18th in assists per goal scored). Sunderland was poor in short passing (15th in # of completions and completion %). These stats indicate that Sunderland were very direct in attack. The low # of assists per goal is likely due to Sunderland playing a counterattacking style football. (= a lesser emphasis on interplay between multiple players in the final third to create a chance). They used long balls to good effect to get close to the opponents goal and take shots from outside the box. Their shooting and shooting efficiency from inside the box is poor.

So far this season

Steven Fletcher has accounted for all the five goals Sunderland scored this season. They have a hard time keeping the possession of the ball (like last season). They are unbeaten this season with four draws and a win. Their inability to hold on to leads (or scoring an extra goal) has cost them dearly. They had a lead into the second half in four of the five games but have won only once. Their problems with keeping the ball imply that opponents find it easier to breakthrough, especially in the second half when Sunderland is most likely trying to protect a lead or the point.

Steven Fletcher – One man army, so far. Picture courtesy – dailyrecord.co.uk

Sunderland – Defence

Goals conceded 1.21/game – 5th fewest
Final 3rd passes completions allowed 100/game 6th most
Short passes allowed 343/game 2nd most
Shots on Target Conceded 8th fewest
Lots of headed clearances 7th most
Fouls conceded 10.8/game – 8th fewest
Tackling machines! 1stin tackles won76% tackle success rate – 5th highest

5th in last man tackles

Weak in aerial duels, strong in ground duels 19thin % of aerial duels won5th in % of ground duels won
Corners 7th most corners conceded but conceded just 1 goal from corners, fewest in the league
Make it easy for opponent GKs 3rd highest GK distribution success for opponent GKs
Opponents get a lot of clean sheets 3rd highest # of clean sheets for opponents

Sunderland – Defensive summary

Based on the numbers, Sunderland is a clean tackling defence who do not concede many shots on target. However, they allow opponents a lot of short passes & pass completions in the final third. This indicates that they are likely not pressing and defend deep. Opponent GK’s have great success (over 70%, 3rd in the league) distributing the ball against Sunderland, another indicator that they do not press much and defend off the player. Their relatively low foul count is probably indicative of this. They concede a high number of corners but have just conceded one goal off of corners last season. They are strong in ground duels and are one of the worst teams in aerial duels. They also employ a high number of head clearances.

City had a lot of success against Sunderland in the final third with 181 & 167 completions away and home respectively (average: 135). However, this advantage did not translate into shots on target for City. This could be a side effect of their clean tackling and high # of headed clearances.

Sunderland – Goalkeeping – Simon Mignolet

Goals conceded overall 1.13/game – 6th fewest
Goals from outside 0.31/game – 4th most in the league
Saves made 3.2/game – 7th most
GK distribution efficiency(Successful GK distribution/Total GK distribution) 17th of 18 GKs with 29 or more starts
Long passes completion 34% – 16th of 18
Short passes completion rate 77.4% – 17th of 18(53 attempts 2nd fewest)
Ratio of Long to short passes 90-10

Sunderland – Goalkeeping Summary

Mignolet is good with saves and does not allow many goals (which, is probably a reflection of the overall defensive scheme, not just the goalkeeper). However, he seems to have trouble distributing and passing the ball. The proportion of long passes of the total passes is highly skewed in favor of the long passes. These numbers indicate that Mignolet hoofs the ball as far as possible and most of the time his passes end in loss of possession.

The low number of short passes and pass completion rate of short passes could be indicative of an overall scheme and/or that Mignolet & the Sunderland central defenders are not very good at passing short from their goal.

This means pressing the ball high in the defensive third of Sunderland could be a very productive strategy for opponents. City forwards might enjoy a lot of success prolonging their possessions in the final third by keeping the pressure on the Sunderland GK and defence.

City vs. Sunderland Head – to – head 2011-12

  • Sunderland had great success against City last season. They took four points from the Champions
  • At the Etihad, City needed a big comeback from 1-3 down to salvage a point.
  • Sebastian Larsson x 2 and Nicklas Bendtner were the scorers for Sunderland. Mario Balotelli x 2 and Alexsandr Kolarov scored for City
  • At the Stadium of light, Sunderland upset City 1-0 with a late goal from Di Jong Won.
  • City had 181 (away) and 167 (home) completions in the final third, both higher than their average of 135/game. Shots were close to their game averages.

Final word

City is very likely to have a lot of success pressing Sunderland in their defensive third. They might not find it very difficult to pass short and have lengthy possession spells in the Sunderland final third. However, they need to stay patient as Sunderland defend very well as a team. Sessegnon, Fletcher and Larsson are the three players to watch out for at the other end of the pitch.

MCFC Analytics – Summary of blog posts #5


We had a great meeting this weekend to discuss how to move our community forward. We discussed some great ideas. As @MCFCGavinFleig pointed out on twitter, the next big announcements and steps forward will be public in late November/early December when the “CityAnalyticsCommunity” will be launched. Until then, keep blogging away with the data.

Here is a summary of the blog posts based on #MCFCAnalytics data.

Analytics posts

  1. @MarkTaylor0How passing sequences create chances – the title is self-explanatory. Great post
  2. @JDewittLong passing in the Premiership – John looks at the long passing and its correlation to finishing position in the league table. Interesting post. A question that came up when I read this post is, how is correlation to points or goals scored instead of position in the league table?
  3. @TheWestStandO digs deeper into Fernando Torres’ struggles in front of goal last season
  4. @ChrisJLilley defines metrics and rates the attacking midfielders, central midfielders and the defensive midfielders of last season.
  5. @We_R_PLComparison of Top scorers in EPL
  6. @Hpstats  – A better passing statistic this was posted in the comments of summary #4
  7. Fulham – opposition analysis by me

Visualization posts

  1. @AlexThamks – a neat viz of Assists, chances created & key passes per formation + the best 11 for each formation using Tableau Public
  2. @Tomberthon  – a visualization of the advanced data set and how to make sense out of it

Other

  1. @MarchiMax has a refined version #Rstats code for parsing the F-24 XML
  2. @DannyPage has implemented a Ruby on rails code for importing the F-24 XML

Past summaries

Summary #4

Summary #3

Summary #2

Summary #1

Fulham – Opposition Analysis #FFC vs. #MCFC


This is an “Opposition analysis” of Fulham, City’s opponent on Saturday 29 September at Craven Cottage. I used the #MCFCAnalytics Lite data set to do this analysis.

Goals 1.12 per game – 12th (excluding own goals)
% of Open play goals 74.4% – 6th
% of goals from inside the box 86% – 6th
% of goals from outside the box 14% – 15th
Shots on Target 5.13 pg – 7th
Efficiency: Goals/shots On + off Target 11% – 17th
Assists per Goals scored 0.74 – 7th
Strong from inside the box 6st in % of goals from inside the box
Weak from outside the box 15th in % of goals from outside the box
Final 3rd completions / comp % 9th / 8th
Short passescompletions / comp % 8th / 7th
Poor at winning corners and scoring from corners 14th– corners won – 4.92 corners/game18thgoals from corners – 416th Headed goals – 7
Inability to score first Scored the first goal 13 times. (3rd worst in the league).

Fulham – Key attacking players

Goals Clint Dempsey – 17
Pogrebnyak – 6
Zamora – 5
Shots On Target Clint Dempsey – 58, Dembélé – 22,Johnson – 16
Efficiency Pogrebynak – 46.2%Zamora – 21.7%Dempsey – 15.6%
Assists Dempsey – 6
Zamora &Murphy –5 each
Final 3rd Completions Dembélé – 472, Murphy – 461, Dempsey – 416, Duff – 244
Final 3rd Completion% Dembélé– 84.1%, Duff – 80%, Dempsey – 75.4% Ruiz – 71.2%
Touches in opposition box Dempsey – 150, Zamora – 73, Johnson – 64, Duff – 62
Other interesting aspects Duff crosses a lot with a very low % of success

Fulham – Offensive summary

Personnel changes

Fulham has a huge player turnover on the offensive side. The top three players in goals, assists, final third passing and shots in the last season are not with the team anymore.

Clint Dempsey and Moussa Dembélé played a huge part in Fulham’s comfortable 9th place finish. Dempsey scored 17 goals and provided six assists. That accounts for about 50% of all Fulham’s goals. Apart from the goals and assists, he completed 416 passes in the final third (third most in Fulham) and had a team, high 150 touches in the opposition’s 18-yard box.

Dembélé had a big impact in the midfield last season and often was the origin of the attacking moves that ended in a Dempsey shot. He was Fulham’s best passer in the final third with 472 completions at an 84.1% completion rate.

The departure of Danny Murphy along with Dempsey and Dembélé means Fulham has lost all three of its top three passers in the final third. They have added Dimitar Berbatov who could probably make up for the goals of Dempsey. Costa Rican international Bryan Ruiz is likely to feature a lot more in the construction of the attacks. They are off to a fast start so far with three wins in five games tied with Arsenal & City on 9 points. They have also picked up Giorgos Karagounis, who if healthy can be of great value to the team. Hugo Rodallega from Wigan is also a very good pick up. However, questions on who will fill the void left by Murphy and Dembélé remain unanswered.

What the numbers say

Fulham’s attack ran through Dembélé, Murphy and Dempsey. Short passing and shooting from inside the box is the salient feature of Fulham attack. They are poor in shooting efficiency (17th), converting corners (18th) and headed goals (16th). This points to a predominantly ground based attack through the middle. They do not cross a whole lot and Damien Duff who has attempted most open play crosses in Fulham has very poor accuracy. It is likely that opposing teams know that Duff has a propensity to cross the ball and come well prepared to defend them.

Fulham were also very poor at a scoring the first goal (18th) only better than the relegated Bolton & Wolves. This means they are likely a team that is passive in attack and let the opposition take the initiative.

So far this season

Berbatov, Duff, Ruiz have had fast starts to their season. Fulham have scored the most goals (12) and have the fifth best pass completion rate in the league so far this season. Better numbers than City in both categories. Apart from Manchester United away, they have not played any big teams yet. They are unbeaten at home and this could be a big test for both Fulham and City.

Berbatov – key man for the Cottagers. Photo courtesy : UK Eurosport

Fulham – Defence

Goals conceded 1.29/game – 8th lowest
Final 3rd passes completions allowed 101.1/game 5th highest
Opponents Final 3rd pass completion % 68.94% – 2nd highest
Successful open play crosses allowed 4.08/game – 4th highest
Shots on Target Conceded 3rd highest2nd highest – From outside the box
Tackles Win 77% of their tackles – 3rd most18st in last man tackles
Fouls committed 9.9/game – 4th fewest
Other aspects Allowed the opposition to score first in 22 games (5th most)

Fulham – Defensive summary

The numbers from last year indicate that Fulham tends to defend deep and allows opponents a lot completions and possession in their defensive third. They also concede a high number of shots conceded from outside the box. This is a sign of passive defending. They do not seem to close down quickly enough to avoid good shots from outside the box. When they tackle, they tend to tackle cleanly and win a high percentage of them. They also allow a high number of successful open play crosses. Since crossing is inherently has a low percentage of success, this is another sign that Fulham defenders do not close down quickly. They commit very few fouls and do not concede many corners. All in all a defence, which is not aggressive, “lets you play” and tackle clean cleanly.

Fulham allowed their opponents score the first goal 22 times (5th most) as opposed to scoring only 13 themselves (third lowest total). Another indicator of the recurring theme of passiveness in their play.

Fulham – Goalkeeping – Mark Schwarzer

Goals conceded overall 1.23/game – 10th fewest
Saves made 11th most
GK distribution efficiency(Successful GK distribution/Total GK distribution) 3nd best – 72%
Long passes completion 34% – 3rd lowest
Short passes completion rate 95.2% – 5th best
Ratio of Long to short passes 70-30

Fulham – Goalkeeping Summary

Mark Schwarzer has very high distribution efficiency. He is also very good at short passes. Schwarzer has a propensity to punch the ball than any other keeper in the Premier League (among keepers who started at least 20 games). He is also the second highest in the league in catches/game & sixth highest in saves per game.

These stats correlate to the high number of shots on target allowed by Fulham’s defence. Schwarzer’s good goalkeeping is one of the reasons why Fulham did not concede many more goals.

City vs. Fulham Head – to – head 2011-12

§ Fulham came back from 0-2 down to draw 2-2 at the craven cottage. Aguero scored twice for City. Zamora and Kompany’s own goal tied it for Fulham.

§ City easily won 3-0 at the Etihad. Aguero, Dzeko & Chris Baird – own goal were the scorers.

§ City had more final 3rd completions (139. Season average 135) away from home than at home.

§ Fulham had a better final third completion percentage away from home than at home.

Final word

Fulham have played well and have scored a league-high 12 goals so far despite so many new faces in their attack. City will have their hands full defending the likes of Berbatov, Ruiz and Duff. City has not had a clean sheet so far this season. Will there be a lot of goals in this one?

To win City needs to:

§ Control Ruiz and Duff’s influence would be key to win this game. Berbatov is probably more talented than Dempsey but Fulham’s midfield is not as strong as it was last season.

§ Take advantage of the passivity of the Fulham defence. They allow a lot of possession to their opponents in their defensive third and allow opponents to take many shots on target.

§ Be aggressive and take the lead. Fulham has let opponents score the first goal 22 times last season. City has a great record when they score first (25 W, 2 D & 1 L last season).

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