All posts tagged Opta

MCFC Analytics blogposts Summary #9

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

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.
@MarkTaylor0 analyzed Blocked shots to find if blocking shots is a talent.
@hpstats visualized points difference “with/without” a player in the starting lineup. Also from the same blog is profiling players based on their shooting
@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

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.
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.
The man in the yellow shirt – an analysis of the refs by @PedroAfonso85
An interactive visualization of the direction of a player’s passes by @alekseynp . Some of the outliers are very interesting.
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!

1 Comment

by Ravi Ramineni on October 23, 2012 • Permalink

Posted in Barclays Premier League, Bolton Wanderers, Data Visualization, English Premier League, Football Analytics, Manchester City, MCFC Analytics, Opta, Premier League, Soccer Analytics, Tableau

Tagged #MCFCAnalytics, Barclays Premier League, EPL, football analytics, Manchester City, MCFC Analytics, Opta, Soccer analytics, soccer data visualization

Posted by Ravi Ramineni on October 23, 2012

https://analysefootball.com/2012/10/23/mcfcanalytics-blogposts-summary-8/

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.

An interactive viz of Bolton – Manchester City match data by @JBurnMurdoch on @GuardianData blog
@HPStats attempts at defining metrics to be able to cluster players based on their style. Here is a good first step on Passing
@shots_on_target made a summary of vital stats regarding goals, shooting accuracy, penalties etc..
Scouting report on Tim Howard by @footballfactman
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!
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!

2 Comments

by Ravi Ramineni on October 15, 2012 • Permalink

Posted in Bolton Wanderers, English Premier League, Football Analytics, Manchester City, MCFC Analytics, Opta, Soccer Analytics

Tagged #MCFCAnalytics, Barclays Premier League, EPL 2011-12, MCFC Analytics, Opta, OptaPro, Soccer analytics, soccer data visualization

Posted by Ravi Ramineni on October 15, 2012

https://analysefootball.com/2012/10/15/mcfc-analytics-blogposts-summary-7/

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

@PedroAfonso85 has a couple posts using the advanced data set
- This on how to interpret the data and put it into a context
- This post attempts to read the mind of Joe Hart
@ChrisJLilley continues with his positional analysis series with Strikers and Central attacking midfielders
@FootballFactman ‘s piece talks about what to look for in goalkeepers of the premier league
@shots_on_target talks about the correlation between points in fantasy football and attacking stats
In my weekly opposition analysis series I analyzed at Sunderland using last season’s data.

Visualization posts

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.
@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

by Ravi Ramineni on October 9, 2012 • Permalink

Posted in Barclays Premier League, English Premier League, Football Analytics, MCFC Analytics, Opposition Analysis, Opta, Premier League, Soccer Analytics

Tagged #MCFCAnalytics, English Premier League, EPL 2011-12, MCFC Analytics, Opta, OptaPro, soccer data visualization

Posted by Ravi Ramineni on October 9, 2012

https://analysefootball.com/2012/10/09/mcfc-analytics-summary-of-blogposts-6/

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 – 12^th (excluding own goals)
Strong on direct free kicks	5 goals – 1^st
% of Open play goals	76% – 4^th
% of goals from inside the box	69.7% – 19th
% of goals from outside the box	30% – 2nd
Shots on Target	3.71 per game – 18^th
Efficiency: Goals/shots On + off Target	7^th
Efficiency Inside the box	16^th
Efficiency Outside the box	3^rd
Assists per Goals scored	18^th
Poor from inside the box	19^th in proportion of goals from inside & 16^th in efficiency
Strong from outside the box	5^th most goals and 3^rd most efficient from outside
Final 3^rdcompletions / comp %	16^th / 14^th
Poor in the final third and opposition box	18^thin final 3^rd touches & 19^th in touches in opp. box
Poor in short passingcompletions / comp %	15^th /15^th
Good in long balls: # of successful / success %	9^th / 8^th
Good in Open play crosses	7^th in # of crosses & crossing accuracy
Very few Through balls	18^th – 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 3^rd Completions	Sessegnon – 401, Larsson – 281, Bardsley – 253
Final 3^rd 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 (19^th 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, 18^th in the EPL) or assists (18^th in assists per goal scored). Sunderland was poor in short passing (15^th 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 – 5^th fewest
Final 3^rd passes completions allowed	100/game 6^th most
Short passes allowed	343/game 2^nd most
Shots on Target Conceded	8^th fewest
Lots of headed clearances	7^th most
Fouls conceded	10.8/game – 8^th fewest
Tackling machines!	1^stin tackles won76% tackle success rate – 5^th highest 5^th in last man tackles
Weak in aerial duels, strong in ground duels	19^thin % of aerial duels won5^th in % of ground duels won
Corners	7^th most corners conceded but conceded just 1 goal from corners, fewest in the league
Make it easy for opponent GKs	3^rd highest GK distribution success for opponent GKs
Opponents get a lot of clean sheets	3^rd 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%, 3^rd 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 – 6^th fewest
Goals from outside	0.31/game – 4^th most in the league
Saves made	3.2/game – 7^th most
GK distribution efficiency(Successful GK distribution/Total GK distribution)	17^th of 18 GKs with 29 or more starts
Long passes completion	34% – 16^th of 18
Short passes completion rate	77.4% – 17^th of 18(53 attempts 2^nd 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.

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 – 12^th (excluding own goals)
% of Open play goals	74.4% – 6^th
% of goals from inside the box	86% – 6^th
% of goals from outside the box	14% – 15^th
Shots on Target	5.13 pg – 7^th
Efficiency: Goals/shots On + off Target	11% – 17^th
Assists per Goals scored	0.74 – 7^th
Strong from inside the box	6^st in % of goals from inside the box
Weak from outside the box	15^th in % of goals from outside the box
Final 3^rdcompletions / comp %	9^th / 8^th
Short passescompletions / comp %	8^th / 7^th
Poor at winning corners and scoring from corners	14^th– corners won – 4.92 corners/game18^thgoals from corners – 416^th 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 3^rd Completions	Dembélé – 472, Murphy – 461, Dempsey – 416, Duff – 244
Final 3^rd 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 9^th 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 (17^th), converting corners (18^th) and headed goals (16^th). 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 (18^th) 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 – 8^th lowest
Final 3^rd passes completions allowed	101.1/game 5^th highest
Opponents Final 3^rd pass completion %	68.94% – 2^nd highest
Successful open play crosses allowed	4.08/game – 4^th highest
Shots on Target Conceded	3^rd highest2^nd highest – From outside the box
Tackles	Win 77% of their tackles – 3^rd most18^st in last man tackles
Fouls committed	9.9/game – 4^th fewest
Other aspects	Allowed the opposition to score first in 22 games (5^th 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 (5^th 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 – 10^th fewest
Saves made	11^th most
GK distribution efficiency(Successful GK distribution/Total GK distribution)	3^nd best – 72%
Long passes completion	34% – 3^rd lowest
Short passes completion rate	95.2% – 5^th 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 3^rd 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).

1 Comment

by Ravi Ramineni on September 26, 2012 • Permalink

Posted in Barclays Premier League, English Premier League, Football Analytics, Fulham, Manchester City, MCFC Analytics, Opposition Analysis, Opta

Tagged #CityOpposition, #MCFCAnalytics, EPL, FFC, Fulham, Fulham vs. City, MCFC, Opposition Analysis, Opta, Soccer analytics

Posted by Ravi Ramineni on September 26, 2012

https://analysefootball.com/2012/09/26/fulham-opposition-analysis-ffc-vs-mcfc/

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.

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.

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.

6 Comments

by Ravi Ramineni on September 17, 2012 • Permalink

Posted in Barclays Premier League, Data Visualization, English Premier League, Football Analytics, Manchester City, MCFC Analytics, Soccer Analytics

Tagged #MCFCAnalytics, Bolton Wanderers, EPL 2011-12, Manchester City, Opta, Soccer analytics, soccer data visualization

Posted by Ravi Ramineni on September 17, 2012

https://analysefootball.com/2012/09/17/visualizing-momentum-shifts-in-bolton-vs-man-city-mcfcanalytics/

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.

10 Comments

by Ravi Ramineni on September 10, 2012 • Permalink

Posted in Barclays Premier League, English Premier League, Football Analytics, MCFC Analytics, Opta, Soccer Analytics

Tagged #MCFCAnalytics, MCFC Analytics, Opta, OptaPro, Premier League, Soccer analytics

Posted by Ravi Ramineni on September 10, 2012

https://analysefootball.com/2012/09/10/mcfc-analytics-summary-of-blog-posts-2/

Final 3rd analysis – more follow ups

Thanks a lot for all the feedback and discussion regarding the final third analysis. Here are a few follow-ups on the feedback.

Feedback: The correlation between goals scored and passes in the final third is driven by the top 5 goal scoring clubs. If they are removed from the data set, the correlation might be weak.
This was brought up by @WillTGM & @Chumolo

Follow-up:

The correlation is not nearly as strong if the top 5 (goals scored) are removed. However, 5 teams constitute 25% of the sample space. If we cherry pick the top 5, it is not surprising that the correlation becomes much weaker.
I did an experiment choosing 15 clubs randomly from the 20. In several such experiments, the correlation was strong and significant. R² varied between 0.56 and 0.87. The regression was significant. (F-test)
On a similar note, if outliers like Liverpool and Newcastle are excluded, the correlation becomes much stronger.

Feedback: Significance of the regression

@rui_xu brought up a great point about the importance of the significance of the regression and how just R²might not tell the whole story.

Follow-up:
I did the F-test for all the regressions with the following results

For the overall final third completions vs. total goals scored over a season analysis – the regression was significant

For the game level final third completions vs. goals scored by Manchester City analysis, the regression is not very significant. This is most likely due to the small sample size (38 games).

However, when I did the same analysis using data from all the 380 games of last season (760 samples), the correlation was weak (as observed for the 38 games of Man City) and the regression was significant for the larger sample space.

Please keep the feedback coming!

1 Comment

by Ravi Ramineni on September 7, 2012 • Permalink

Posted in Arsenal, Barclays Premier League, English Premier League, Football Analytics, Manchester City, MCFC Analytics, Opta, QPR, Queens Park Rangers

Tagged #MCFCAnalytics, Arsenal, EPL, Liverpool, MCFC Analytics, Opta, Premier League, QPR, Soccer analytics

Posted by Ravi Ramineni on September 7, 2012

https://analysefootball.com/2012/09/07/final-3rd-analysis-more-follow-ups-mcfcanalytics/

Follow-up analysis: Final third passing and Goals scored per game

This is a follow up to my post regarding the strong correlation between completed in the final third and goals scored.

Question

Is there a correlation between the final third completions & goals scored at the game level?

Analysis

I investigated to see if this correlation exists at the game level using the #MCFCAnalytics data set. I plotted the completions in the final third vs. goals scored for Manchester City in all their 38 games of English Premier League.
Blue = Away ; Orange = Home

Manchester City Goals vs. Pass completions in the final 3rd on a per game basis

Findings:

Linear regression had an R² of 0.04 implying that there is no correlation between passes completed in the final third and goals scored at the game level.
I did the plot for a few other teams and got similar results.
Arsenal – Away and Liverpool – Home. In both cases, Manchester City had very little success completing passes in the final 3^rd. However, they lost 1-0 at the Emirates and won 3-0 at home vs. Liverpool.
Against Liverpool, City had 6 shots on target and 2 off target.
Against Arsenal, City had 0 shots on target and 3 off target.
QPR – Home and QPR – Away. City scored 3 goals each against QPR home and away. However, they had a season high 326 completed passes in the final 3^rd at home vs. just 74 in the away fixture.
Shots vs. QPR Away – 5 on target & 10 off target.
Shots vs. QPR Home – 15 on target and 10 off target.

The City – QPR fixture was that crazy season finale. City fell behind and they threw everyone forward to go for the win and the Premier league title. QPR was a man down from 55^th minute and they defended at the edge of their 18-yard box for most of 2^nd half. This explains the unusually high number of completed passes in the final third.

The above examples underline the rarity of the “goal” event. In any given game, there could be factors like bad shooting, luck, the opponent’s goalkeeper having a great game etc., which could influence the # of goals scored. However, over a season those things seem to even out.

In the next step of analysis I will add a 2^nd variable to the model and analyze.