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

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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!

MCFC Analytics-Summary of blogposts #6


This week I saw a few more new bloggers getting into the act with the data.

First up, there was this article by @RWhittall of TheScore.com where Richard talked about “soccer data abuse by some bloggers using the MCFC data”. The gist of the article is that some of the bloggers are extrapolating too much with their conclusions based on one year’s worth of data from one league. The other point made in the article is that the output of the majority of  the work in soccer analytics isn’t groundbreaking and it is just adding a data context to what we already knew.

While I see where Richard is coming from, I don’t quite agree either with his assessment of the state of soccer analytics or the “data abuse” bit.

Unquestionably, we haven’t even scratched the surface of what we can do with data in soccer. The majority of the research work in the soccer analytics is carried out in the private domain.  That is because soccer data is not a commodity like it is in other sports like Baseball. The MCFC & Opta project could be a significant step in the direction of making soccer data more accessible to a wider audience,  if it can get enough passionate people interested in the project. However, like in any type of writing in the public domain, there is the good and the not so good. One of the things we discussed with Gavin Fleig, Head of Performance Analysis at Manchester City, Simon Farrant, Marketing coordinator at Opta et al is to build a community that fosters communication, collaboration and open feedback among the members and the readers. This should help everyone get better in some time.

Without further ado, here are links to some interesting work I found in this past week.

@MarkTaylor0 has a comprehensive piece on the state of soccer analytics and where it stands vis-à-vis other sports like NFL and Baseball. – The case for data analysis in football. This is a must read.

Analytics posts

  1. @PedroAfonso85 has a couple posts using the advanced data set
  2. @ChrisJLilley continues with his positional analysis series with Strikers and Central attacking midfielders
  3. @FootballFactman ‘s piece talks about what to look for in goalkeepers of the premier league
  4. @shots_on_target talks about the correlation between points in fantasy football and attacking stats
  5. In my weekly opposition analysis series I analyzed at Sunderland using last season’s data.

Visualization posts

  1. Earlier today I saw Voetstat, a neat blog by @Voetstat_craig which has some visualizations of pass completion + heatmaps. There are multiple posts. I haven’t had a chance to read all of them yet.
  2. @TomBerthon has this visualization of how goals were scored in the Bolton – City game from last season

If I missed any links, post them in the comments section and tweet them with the hashtag #MCFCAnalytics. I will retweet them.

Previous Summaries

Summary #5

Summary #4

Summary #3

Summary #2

Summary #1

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).

MCFC Analytics – Summary of blog posts #4


It has been about a month since the basic MCFC data set has been released and it is great to see lots of people churning out stuff using both the basic and advanced data sets.

Based on the tweets with #MCFCAnalytics tag, there are quite a few peoples’ projects are in progress. Good luck to all of you. Make sure you share your project/blog links with the hashtag.

Some people are looking for partners and contributors to the projects they are working on. If you are interested, please keep a tab on the #MCFCAnalytics tab and get in touch with folks directly.

Analysis posts

  1. @MarkTaylor0Analyzing the passes by comparing them to their expected pass completion rates using passes of James Milner in Bolton Vs. Manchester City from 2011-12 season.
  2. Mark also has post on how Man City and Bolton passed the ball
  3. @JdewittHow goals are scored in EPL
  4.  @ChrisJLilleyAnalyzing center-backs of the premier league
  5. @analysefooty (this blog!)Opposition analysis of Arsenal

Visualization posts

  1. @DanJHarrington – a very interesting visualizations of passes using Vector diagrams in Tableau Public
  2. @MarchiMax – a visualization of where the ball is a few seconds before a shot is taken
  3. @OngoalsscoredVisualization of the goalscorer’s body parts. Very neat!

If I missed any please post your links in the comments section.

Links to previous summaries

Summary #1

Summary #2

Summary #3

Feel free to tweet me or email me if you want to chat with me on something specific!

MCFC Analytics – Summary of blog posts # 3


Thanks for the amazing response to Summary of blog posts #1 & Summary of blog posts #2

I also want to thank people who have reached out to me via twitter with links to their blogs & posts.

Goalscorer ‘footedness’ by @DavidAHopkins measures the footedness or the foot favoured by Premier League goalscorers.

How do the more successful clubs keep the ball in EPL by @JDewitt talks about how the top teams in EPL keep possession. Also by John is Successful Passing and Winning

A sneak peek of a very interesting carto by @Kennethfield  Charlie Adam’s “passing wheel”

Football Philosophy – Long passes by @Poolq1984 explores the importance of long ball in football.

@We_R_PL has a nice post on how to use the MCFC dataset more efficiently. He also has spreadsheet which has the own goals calculated per team.

@footballfactman has a post on Darron Gibson using a mix of data from MCFC dataset, whoscored and statszone

The always excellent MarkTaylor0 has detailed post Analysing the quality of shots in Bolton – Manchester City game using the advanced dataset.

@ChrisJLilley has 3 posts on his blog using MCFC data

GK positional analysis

Premier league game changers Part I & Part II

@DanJHarrington has cranked up a lot of things using the advanced dataset

1.  an interactive tableau viz to see touches of each player in Bolton -City on the pitch.

2. Passing visualization using D3.js

3. Dan also has some interesting visualization work in progress. There is a cool video in the link showing ball movement.

Network passing diagrams by @DevinPleuler

Bolton – http://t.co/mcRQ0oHU

Man City – http://t.co/6mtGgJQS

Extracting data from XML

There have been some questions regarding this and some folks have come up with solutions

1. If you have MS Excel 2007 or a later version you can open the file in XML. The only issue with is that XML’s are nested and Excel converts this into a very flat format. So you will see multiple rows for the same events. For example: A successful pass has multiple rows indicating the direction, the x,y coordinates of where it is passed to. Read the data spec thoroughly to understand how the data is formatted in the XML. It will help understand the data much better.

2. Code for R users to extract the F-24 XML by @MarchiMax

3. Code snippets from @JBrisson to extract events from the F-24 XML

4. If you are into programming, most languages have XML parsers. A simple search will get you code snippets to start with.
If I missed any links, please let me know via Twitter or comment on the blog post. Always use #MCFCAnalytics tag in twitter so I can pick them up easily!

Visualizing momentum shifts in Bolton vs. Man City


This is an attempt to visualize the momentum shifts in Bolton vs. City with goals scored and substitutions using the #MCFC analytics advanced data tier – I.

I used possession as a proxy for momentum. The game is divided into 5 minute buckets. If a goal is scored in a bucket, the bucket will end at the minute of the goal scored.
E.g.: 0-4 is bucket #1. If a goal is scored in minute 7, then 5-6 is bucket #2. 7-11 is bucket #3 and so on.

Plots

Figure 1 – Overall cumulative possession difference vs. game time in minutes.

CumulativeAll

2. Figure 2 – Cumulative possession difference up until a goal is scored.
E.g.: Say 1-0 is scored in minute 27 and 2-0 is scored in minute 38. The cumulative possession difference is calculated from minute #1 through 27. After 1-0, cumulative possession difference is calculated from minute 28 onwards (the date of minute 1 through 27 is excluded).
This helps to see if there is any noticeable shift in the momentum of the game after a goal.

CumulativeMomentumShifts

Findings

  • Overall City has dominated possession. The cumulative possession delta was always negative (= in favor of City) in Figure 1.
  • When the cumulative possession difference was reset after each goal scored (Figure 2), we see that Bolton tried to take the initiative after City took the lead in Min 26. They really pushed hard after City scored the 0-2, pulling one back within 2 minutes of City’s 2nd goal.
  • Bolton continued to push for an equalizer until City scored the 1-3 right after the half-time.
  • Bolton enjoyed more possession as they searched for a goal and did a substitution at min 60 (an attacking midfielder for a holding midfielder) – it seems like the move paid off as they scored 2-3 in min 62.
  • Bolton continued to push for an equalizer. City subbed out Aguero for Tevez, both attacking players but Tevez is better at playing a deeper role and hold up the ball.
  • As the game progressed Bolton switched D.Pratley for Chris Eagles (probably a shift in attacking style) and City responded with a defensive move by subbing out Dzeko for Adam Johnson
  • Those two moves by City helped them restore control. Towards the end, City subbed out attacking midfielder David Silva for fullback Zabaleta, a defender to secure the result in the dying minutes.

This is a very quick and simple interpretation & visualization of the moment shifts. All feedback is welcome.

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