We share R tutorials from scientists at academic and scientific institutions with a goal to give everyone in the world access to a free knowledge. Our tutorials cover different topics including statistics, data manipulation and visualization!

Programming

I am really enjoying Uefa Euro 2016 Footbal Competition, even because our national team has done pretty well so far. That’s why after browsing for a while statistics section of official EURO 2016 website I decided to do some analysis on the data they share

Just to be clear from the beginning: we are not talking of anything too rigourus, but just about some interesting questions with related answers gathered mainly through data visualisation.

We can divide following analyses into two main parts: a first part were we analyse distribution of fouls and their incidence on matches outcome ( data as at the 21th of June) and a second part where ball possession is analysed, once again looking at relationship between this stat and matches outcome (data as at 28th june). Let’s start with some specs on data import.

As usual we first need to load required packages and our data into the R environment. We perform the task running the following lines of code:

# Load required packages library(rio) library(plyr) library(dplyr) library(choroplethr) library(choroplethrMaps) library(ggplot2) library(dummies) #data from http://www.uefa.com/uefaeuro/season=2016/statistics/index.html players_stat <- read.csv('players_stats.csv',sep = ";") players_stat$Team <- tolower(players_stat$Team) team_stat <- read.csv('team_stats.csv' ,sep = ";") team_stat$Team <- tolower(team_stat$Team) possession_stat <- read.csv('possession.csv', sep = ";") possession_stat$team <- tolower(possession_stat$team)

I am not going to spend too much time on this chunk but I would like to highlight you the use of rio package by Thomas J. Leeper, since it is a really powerfull package that can make your life really easy when talking about data import in R.

You can find referred .csv file within the rstudio project Rstudio project I have uploaded on Github.

Here we are with the first question. And here it is the answer:

We obtained the plot aggregating fouls data by team and leveraging `country_choropleth()`

function from `choroplethr`

package

#sum up fouls data from player view to team view by_team <- group_by(players_stat,Team) team_sums <- summarise(by_team, sum(Yellow.Cards), sum(Red.Cards), sum(Fouls.Committed), sum(Fouls.Suffered)) # subset columns to plot only the number of fouls committed fouls_data <- data.frame("region" = team_sums$Team,"value" = team_sums$`sum(Fouls.Committed)`) # plot fouls_plot <- country_choropleth(fouls_data, title = "number of Fouls Committed by region", legend = "# fouls", num_colors = 1) + xlim(-31.266001, 39.869301) + ylim(27.636311, 81.008797) + coord_map("lambert", lat0 = 27.636311, lat1 = 81.008797) fouls_plot

An important disclaimer is needed here: since *country_choropleth()* is not able to handle *Northern Ireland*, *Wales* and *England* as separate regions I decided not to populate them within map plot, given that joining related team data into one fictious *United Kingdom* team would have resulted in a misrepresentation of analyzed statistics.

Given that black countries are countries not playing the competition it seams Romania committed quite a great number of fouls. You may wonder: were them serious fouls? Let’s have a look to the number of yellow and red cards to answer that legitimate question. Let’s start from yellow cards, plotting `sum(Yellow.Cards)`

against each team with standard barplot:

ggplot(team_sums,aes(x = Team,y = `sum(Yellow.Cards)`, fill = `sum(Yellow.Cards)` )) + geom_bar(stat = 'identity') + coord_flip()

Well, Romania togheter with Albania is standing at the top of yellow cards ranking as well. Getting to red cards we have to underline that we dont’ have a lot of data about them, since just two of them were assigned. Guess to whom? Albania and Austria. All that summed up we can definitely say Albania am Romania played toughest matches overall. but was that worthing? Uhm I guess no, since both of them didn’t get trough staging phase. And this raise another interesting question:

To answer this technically silly question we can try to plot total number of fouls against number of wins by country team. Let’s have a look to the plot code:

# merge team data total_stats <- merge(team_sums,team_stat) #plot fouls committed against number of wins by country ggplot(total_stats,aes(x = total_stats$`sum(Fouls.Committed)`,y = total_stats$Wins, label = Team)) + geom_point() + geom_text(nudge_y = 0.2) + geom_smooth(method = 'lm', formula = y~x)

We added a linear regression model to the scatter plot to investigate the hypothesis of linear relationship but nothing happened, so no, committing more fouls is not a winning strategy. Let us ask one last reasonable question about fouls:

Once again let’s the data speak out, plotting number of goal against togheter with number of fouls committed:

ggplot(total_stats,aes(x = total_stats$`sum(Fouls.Committed)`, y = total_stats$Total.goals.against, label = Team)) + geom_point() + geom_text(nudge_y = 0.2) + geom_smooth(method = 'lm', formula = y~x)

Ok, it could seems we can answer a sound yes, but I get a bit disturbed by that 52 romanian fouls so I am going to try to remove them and plot again:

total_stats_no_romania <- total_stats[-16,] ggplot(total_stats_no_romania,aes(x = total_stats_no_romania$`sum(Fouls.Committed)`, y = total_stats_no_romania$Total.goals.against, label = Team)) + geom_point() + geom_text(nudge_y = 0.2) + geom_smooth(method = 'lm',formula = y~x)

Our first guess seems to be confirmed: number of fouls committed is negatively correlated to the number of goals against suffered, which appears to be reasonable since we may assume a great number of fouls are committed to stop rivals from dangerous game actions.

Ball possession is a key start when analysing a football match, so let’s start answering this introductory question: who’s scoring the highest possession stat within this competition? Once again we can visualise it on a europe map:

Before actually producing this plot we had to treat quite heavily raw data since they where produced in untidy format, meaning each row stored more than one observation of the attribute we are investigating: ball possession. For isntance first record was related to Northern Ireland vs Germany match played on 21/06/2016, showing a possession possession stat related to Northern Ireland on a column, and another possession stat related to Germany in a second column. We then needed to split our dataset into two separate data frames, each one storing one of two possession stat columns, and then binding those data frames togheter merging possession stats columns:

possession_a <- possession_stat[,-c(4,8)] possession_a$win possession_a$score_ possession_b <- possession_stat[,-c(3,7)] possession_b$win <- possession_b$score < possession_b$score_ possession_a <- possession_a[,-5] possession_b <- possession_b[,-4] colnames(possession_b) <- colnames(possession_a) possession_tidy <- rbind(possession_a,possession_b)

Once our data are ready, we can easily produce above showed mpa leveraging once again `country_choropleth()`

function:

by_team_pos <- group_by(possession_tidy,team) team_means <- summarise(by_team_pos, mean(possession)) colnames(team_means) <- c("region","value") possession_plot <- country_choropleth(team_means, title = "mean ball possession", legend="% possession", num_colors=1)+ xlim(-31.266001, 39.869301)+ ylim(27.636311, 81.008797) + coord_map("lambert", lat0=27.636311, lat1=81.008797) possession_plot

Table: Giving a closer look to first five teams by ball possession we find a prevalence of north western europe countries:

Country |
Mean Possession |

Germany | 71% |

ukraine | 65% |

portugal | 62% |

spain | 62% |

so here’s come the question that has always stimulated my curiosity: ball possession is always considered a relevant topic, but is it correlated with matches victory? in other words:

we are going to tackle this visually, plotting percentage of ball possession against a `win_dummy`

variable, scoring 1 for win and 0 for defeat (ties were considered as defeats) , which can be easily done leveraging *dummies* and *ggplot* packages :

dummy_win <- as.data.frame(dummy(possession_tidy$win)) possession_tidy$win_dummy <- dummy_win[,2] ggplot(data = possession_tidy,aes(x = possession)) + geom_point(aes(y = win_dummy)) + stat_smooth(aes(y = win_dummy),method = "glm")

As you can see we have also added an estimated logistic regression which visually enforces the truth already visible from raw data: there is no positive correlation, or at least not a strong one, between ball possession and matches outcome. This seems to me a non obvious result, since a great number successful of football coaches have founded their success on possession ball. It is actually quite a debated topic as you can see simply googling “correlation between ball possession win”.

Within this post we tried to answer some questions about matches occured so far during UEFA EURO 2016 and we definitely found that being tough is not a good tactic to win your matches but at least can help you avoid suffering a great number of goals. Moreover we understood that ball possession is only weakly related to matches outcome. If you are interested in reperforming showed analyses or even further explore related raw data you can have a look to the Rstudio project I have uploaded on Github.

That said: may the best win!

Let me know if you have any comment or question.