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 #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 # 2


I got good response for the first summary post I did last week. Here is a summary of articles done using MCFC Analytics data in the past week.

@MarkTaylor0 did a great post called “How teams win”. Mark calculated a list of various correlations that lead to wins.

Mark also did another interesting post  on  Newcastle’s 2011/12 season and the role of luck in their success.

@JimmyCoverdale Did a post enumerating how “Will he score goals in the Premier League? Is a wrong question to ask “ of newcomers to the league.

Jimmy also has a great post discussing the “Effectiveness of Crossing and the correlation with chances created”

@Zahlenwerkstatt did a post ranking goalkeepers in the 2011-12 season based on minutes played, save % and goals conceded.
I have made a couple of follow-ups based on the feedback of Final 3rd Analysis  Follow up #1 & Follow up #2

I have a couple of new posts lined up for later this week.

@OptaPro & Gavin Fleig‘s update on the Advanced data & T & Cs

Simon and Gavin released the updated T & Cs this past week allaying apprehensions of some of the bloggers regarding some of the language in the original T & Cs.

They have also announced that the first installment of the advanced data set will be released this week! I am excited.

If you find an article that is using MCFC Analytics data and is not posted here, please let me know. I will add it in the next week’s summary.

Passing in the final third and goals – EPL 2011-12 #MCFCAnalytics


Question:

Is there a correlation between passing in the final third and the goals scored?

I used the #MCFCAnalytics data set to find the answer.

Analysis

Plot of  Total # of completed passes in the final vs. Goals scored for all the 20 teams in the 2011-12 season of the Barclays Premier League

 Findings:

  • Linear regression had an R2 of 0.671indicating a strong correlation between passes completed in the final third and goals scored.
    Excluding the outlier of Liverpool from the dataset the R2jumped to 0.827.
  • Liverpool is ranked 3rd in the # of passes completed in the final third. However, they are only ranked 15th in goal scored.
  • 75.73– Liverpool’s expected goals scored based on the above regression. However, they managed to score only 42 goals.
    • What is the reason for the huge negative difference?
  • Swansea’s case is interesting. You may remember the term “Swansealona” was one of the favorites with EPL analysts and reporters last season due to their reputation for passing style and high amounts of possession. However, they are below the league average on passes completed in the final third.
  • Newcastle  is ranked 18th in passes completed in the final third. However, Newcastle is ranked 7th in goal scored.Expected goals scored for Newcastle is 29.6. They managed to score 51!
  • Blackburn is ranked last in passes completed in the final third. However, Blackburn scored a lot more goals (44) than their expected goals scored (24.2)
  • Stoke is at the bottom – Lowest # of goals scored and 2nd lowest # of passes completed in the final third.  Not surprising based on their style of play.

Liverpool

I hypothesized that

  1. Liverpool might be crossing a lot and
  2. Most crosses occur in the final third. (I would love to look at (X,Y) data to establish this fact.)
  3. Poor shot quality (which might or might be related to their propensity to cross)

Findings:

  • 1103 – Liverpool attempted the highest # of crosses +corners of all teams in 2011-12
  • 840 –  Liverpool attempted the highest # of open play crosses in 2011-12
  • 19th in overall crossing efficiency  (#of successful crosses+corners/# of successful  + # of unsuccessful crosses+corners)
  • 14th in open play crossing efficiency (# of successful open play crosses/# of successful + # of unsuccessful open play crosses)
  • 18th in overall shooting efficiency ( shots on target/shots on target + shots off target + blocked shots)
  • 15thin shooting efficiency not including blocked shots (shots on target/shots on target + shots off target)

    A glance at the top 10 open play crossers of Liverpool in 2011-12.

Player

Attempts

Efficiency

Downing

148

0.209

José Enrique

138

0.210

Henderson

72

0.125

Adam

70

0.157

Gerrard

69

0.203

Bellamy

67

0.194

Johnson

65

0.185

Kuyt

57

0.246

Suárez

47

0.149

Kelly

38

0.105

Liverpool Average

0.192

League Average

0.202

  • 2 – According this article on EPLIndex, Liverpool scored just 2 goals from 840 open play crosses all season. That is 1 goal per every 420 open play crosses.
  • 79 – The average # open play crosses per goal scored in the 2011-12 season. Liverpool are almost 10 times worse than Man United (44.5)  and Norwich (45.1) in open play crosses/goals category. If there ever was a stat that would (or should) regress to the mean, this is it.

Liverpool had a very talented team in 2011-12. This manifested itself in their high # of completions in the final third where the defensive pressure is highest. Once they are in possession in the final third, they seem to have relied heavily on “crossing the ball” to enable their center-forward Andy  Carroll to take a shot (or head) OR knock it down for their attacking midfielders and wide forwards to take a shot. One big problem was that delivering  crosses is not a very efficient way of passing the ball.  Another problem was they did not seem to have a plan B. It is quite possible that opponents have figured out Liverpool’s crossing strategy and their lack of plan B. The combination of these three factors has contributed significantly to the poor offensive display of Liverpool last season.

Newcastle United

  • 4th – Newcastle is 4thbest in shooting efficiency (goals scored/(shots on target + shots off target)). They stayed 4th even when I included blocked shots in the denominator.
    • This could be the reason why they are an outlier in the final-third completions vs goal scored plot.
    • Manchester City, Arsenal and Manchester United are the top – 3 in shooting efficiency.

Newcastle had two great strikers in Demba Ba and Papisse Cisse who accounted for 29 goals between them. These two were the focus of Newcastle attack and were very efficient with their shots. They did not need a high # of completed passes in the final third to score their goals as they were able to convert a higher % of their shots into goals.

Blackburn Rovers

  • 7thBlackburn are 7th best in shooting efficiency inside the box (goals scored from inside the box/(shots on target inside the box + shots off target inside the box)).
  • Yakubu scored 17 goals for Blackburn and has the 2ndbest  Goals to Shots ratio among all the forwards who have scored than 10 goals.
    • This could be one of the reasons for their big positive differential between actual goals scored (44) and the expected goals scored (24.2).

Summary

# of successful passes in the final third has a strong correlation to goals scored.

Final third is a “high-value” area for scoring goals. More completions in the final third means a team is spending more time in the high-value area. This translates into more opportunities to take a shot or draw errors from defenders to win set pieces from close range, which further increase scoring opportunities.

A high number of completions in the final third alone might not guarantee goals. Liverpool and Newcastle , two examples from the two extremes of the outlier spectrum are cases in point. However, it is one of the key contributing factors to scoring goals. The fact R2 jumped from 0.671 to 0.827 when Liverpool’s data was excluded from the data set strengthens is a case in point.

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