5  Researching Spanish soccer data

NSC-R Tidy Tuesday March 2022

Author

Wim Bernasco

Published

March 22, 2022

5.1 Introduction

Sports in general and football (soccer) in particular provides a sheer endless source of open data that can be explored at various levels (player, team, match, competition) and from various perspectives. In this workshop Wim Bernasco uses open data about football borrowed from a GitHub repository maintained by James Curley (James P. Curley (2016). More information here (Bernasco, 2022)

[1^]Wim Bernasco, Franziska Yasrebi-de Kom provided fertile suggestions on code and comments

5.2 Packages and data

Install packages, the admin required to get going

#install.packages('tidyverse')
    #install.packages('lubridate')
    #install.packages('devtools')
    #library(devtools)
    #install_github("jalapic/engsoccerdata")

Load libraries

library(tidyverse)
library(lubridate)
library(engsoccerdata)

engsoccerdata is a package that includes data. Let us fist see what data are included (list the data in this package).

data(package="engsoccerdata")  

If you like sports data, checkout for free datasets

5.3 Exploration

What type of dataset is spain? I hope a data.frame.

spain |> class()
[1] "data.frame"

Great, it is a data.frame

What are the names of the variables. I hope they are self-explanatory.

spain |> names()
 [1] "Date"    "Season"  "home"    "visitor" "HT"      "FT"      "hgoal"  
 [8] "vgoal"   "tier"    "round"   "group"   "notes"  

Let us peek into the data.

spain |> glimpse()
Rows: 23,915
Columns: 12
$ Date    <chr> "1929-02-10", "1929-02-10", "1929-02-10", "1929-02-10", "1929-~
$ Season  <dbl> 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 19~
$ home    <chr> "Arenas de Getxo", "Espanyol Barcelona", "Real Madrid", "Real ~
$ visitor <chr> "Atletico Madrid", "Real Union", "CE Europa", "Athletic Bilbao~
$ HT      <chr> "0-2", "1-0", "0-0", "1-1", "0-0", "0-1", "3-0", "0-3", "0-1",~
$ FT      <chr> "2-3", "3-2", "5-0", "1-1", "0-2", "1-2", "9-0", "0-3", "3-1",~
$ hgoal   <dbl> 2, 3, 5, 1, 0, 1, 9, 0, 3, 5, 3, 3, 1, 0, 2, 1, 2, 3, 2, 2, 3,~
$ vgoal   <dbl> 3, 2, 0, 1, 2, 2, 0, 3, 1, 2, 0, 1, 1, 4, 1, 2, 1, 0, 2, 0, 3,~
$ tier    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ round   <chr> "league", "league", "league", "league", "league", "league", "l~
$ group   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
$ notes   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~

You should see something like this:

# Rows: 25,435
# Columns: 12
# $ Date    <date> 1929-02-10, 1929-02-10, 1929-02-10, 1929-02-10, 1929-02-12, 1929-02-~
# $ Season  <dbl> 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 1928, 192~
# $ home    <chr> "Arenas de Getxo"

This is much more intuitive, but it only works interactively/ Try it out yourself, but do not include the View function in your script

spain |> View()

Alternatively, a quick look at the first 10 rows.

spain |> 
  select(Date, home, visitor, FT) |> 
  head(n = 10)
         Date               home            visitor  FT
1  1929-02-10    Arenas de Getxo    Atletico Madrid 2-3
2  1929-02-10 Espanyol Barcelona         Real Union 3-2
3  1929-02-10        Real Madrid          CE Europa 5-0
4  1929-02-10      Real Sociedad    Athletic Bilbao 1-1
5  1929-02-12   Racing Santander       FC Barcelona 0-2
6  1929-02-17       FC Barcelona        Real Madrid 1-2
7  1929-02-17    Athletic Bilbao Espanyol Barcelona 9-0
8  1929-02-17    Atletico Madrid      Real Sociedad 0-3
9  1929-02-17         Real Union   Racing Santander 3-1
10 1929-02-17          CE Europa    Arenas de Getxo 5-2

Let us look at frequencies of some variables.

How many teams?

spain |> count(home)
                  home    n
1           AD Almeria   34
2             Albacete  135
3      Arenas de Getxo   65
4      Athletic Bilbao 1362
5      Atletico Madrid 1288
6      Atletico Tetuan   15
7            Burgos CF  102
8           CA Osasuna  639
9            CD Alaves  171
10         CD Alcoyano   54
11        CD Castellon  167
12           CD Condal   15
13          CD Leonesa   15
14         CD Logrones  173
15           CD Malaga  323
16         CD Numancia   76
17         CD Tenerife  247
18           CE Europa   27
19         CE Sabadell  213
20      CF Extremadura   40
21           CP Merida   40
22            Cadiz CF  223
23          Celta Vigo  830
24          Cordoba CF  141
25 Deportivo La Coruna  746
26            Elche CF  339
27  Espanyol Barcelona 1294
28        FC Barcelona 1362
29           Getafe CF  228
30           Gimnastic   58
31          Granada CF  352
32         Hercules CF  314
33          Levante UD  201
34           Malaga CF  285
35       Pontevedra CF   90
36        RCD Mallorca  494
37    Racing Santander  713
38      Rayo Vallecano  321
39          Real Betis  845
40         Real Burgos   57
41           Real Jaen   45
42         Real Madrid 1362
43         Real Murcia  293
44         Real Oviedo  596
45       Real Sociedad 1132
46          Real Union   36
47     Real Valladolid  733
48       Real Zaragoza  993
49   Recreativo Huelva   93
50       SD Compostela   80
51            SD Eibar   38
52          Sevilla FC 1185
53      Sporting Gijon  710
54          UD Almeria  114
55       UD Las Palmas  529
56        UD Salamanca  212
57           UE Lleida   34
58         Valencia CF 1313
59       Villarreal CF  304
60            Xerez CD   19

Frequencies of variable-combinations

How many times did team A host team B? (Results not shown here, output=false, because of length)

spain |> count(home, visitor)

Frequencies of match outcomes

spain |> count(hgoal, vgoal)
   hgoal vgoal    n
1      0     0 1811
2      0     1 1320
3      0     2  742
4      0     3  299
5      0     4  117
6      0     5   30
7      0     6   12
8      0     7    4
9      0     8    4
10     1     0 2815
11     1     1 2570
12     1     2 1168
13     1     3  431
14     1     4  155
15     1     5   58
16     1     6   11
17     1     7    2
18     1     8    1
19     2     0 2039
20     2     1 2228
21     2     2 1035
22     2     3  357
23     2     4  135
24     2     5   35
25     2     6    6
26     2     7    2
27     2     8    2
28     3     0 1196
29     3     1 1262
30     3     2  652
31     3     3  218
32     3     4   47
33     3     5   14
34     3     6    6
35     3     8    1
36     4     0  643
37     4     1  669
38     4     2  325
39     4     3  127
40     4     4   28
41     4     5    8
42     4     6    2
43     4     7    1
44     5     0  268
45     5     1  288
46     5     2  149
47     5     3   47
48     5     4   22
49     5     5    2
50     5     6    1
51     6     0  110
52     6     1  116
53     6     2   67
54     6     3   19
55     6     4    7
56     6     6    1
57     7     0   54
58     7     1   48
59     7     2   23
60     7     3   11
61     7     4    1
62     7     5    1
63     8     0   21
64     8     1   20
65     8     2   10
66     8     3    6
67     9     0   12
68     9     1    5
69     9     2    2
70     9     3    1
71     9     4    1
72     9     5    1
73    10     0    3
74    10     1    5
75    10     2    1
76    10     3    1
77    11     1    1
78    11     2    1
79    12     1    1

Same, but using the combined FT variable.

spain |> count(FT)
     FT    n
1   0-0 1811
2   0-1 1320
3   0-2  742
4   0-3  299
5   0-4  117
6   0-5   30
7   0-6   12
8   0-7    4
9   0-8    4
10  1-0 2815
11  1-1 2570
12  1-2 1168
13  1-3  431
14  1-4  155
15  1-5   58
16  1-6   11
17  1-7    2
18  1-8    1
19 10-0    3
20 10-1    5
21 10-2    1
22 10-3    1
23 11-1    1
24 11-2    1
25 12-1    1
26  2-0 2039
27  2-1 2228
28  2-2 1035
29  2-3  357
30  2-4  135
31  2-5   35
32  2-6    6
33  2-7    2
34  2-8    2
35  3-0 1196
36  3-1 1262
37  3-2  652
38  3-3  218
39  3-4   47
40  3-5   14
41  3-6    6
42  3-8    1
43  4-0  643
44  4-1  669
45  4-2  325
46  4-3  127
47  4-4   28
48  4-5    8
49  4-6    2
50  4-7    1
51  5-0  268
52  5-1  288
53  5-2  149
54  5-3   47
55  5-4   22
56  5-5    2
57  5-6    1
58  6-0  110
59  6-1  116
60  6-2   67
61  6-3   19
62  6-4    7
63  6-6    1
64  7-0   54
65  7-1   48
66  7-2   23
67  7-3   11
68  7-4    1
69  7-5    1
70  8-0   21
71  8-1   20
72  8-2   10
73  8-3    6
74  9-0   12
75  9-1    5
76  9-2    2
77  9-3    1
78  9-4    1
79  9-5    1

Frequencies of a sum (total goals in match)

spain |> count(hgoal + vgoal)
   hgoal + vgoal    n
1              0 1811
2              1 4135
3              2 5351
4              3 4891
5              4 3488
6              5 2131
7              6 1146
8              7  543
9              8  237
10             9  113
11            10   42
12            11   17
13            12    5
14            13    4
15            14    1

Frequencies of function work as well (year of match date).

spain |> count(year(Date))
   year(Date)   n
1        1929 115
2        1930  84
3        1931  91
4        1932  95
5        1933 110
6        1934  75
7        1935 150
8        1936  84
9        1939  30
10       1940 186
11       1941 139
12       1942 182
13       1943 175
14       1944 189
15       1945 182
16       1946 189
17       1947 175
18       1948 189
19       1949 189
20       1950 205
21       1951 240
22       1952 216
23       1953 256
24       1954 248
25       1955 216
26       1956 264
27       1957 232
28       1958 240
29       1959 240
30       1960 232
31       1961 264
32       1962 216
33       1963 239
34       1964 249
35       1965 240
36       1966 233
37       1967 239
38       1968 248
39       1969 248
40       1970 232
41       1971 246
42       1972 324
43       1973 306
44       1974 288
45       1975 315
46       1976 306
47       1977 297
48       1978 306
49       1979 306
50       1980 332
51       1981 306
52       1982 297
53       1983 310
54       1984 320
55       1985 306
56       1986 324
57       1987 366
58       1988 379
59       1989 401
60       1990 368
61       1991 371
62       1992 380
63       1993 391
64       1994 370
65       1995 428
66       1996 451
67       1997 454
68       1998 350
69       1999 401
70       2000 369
71       2001 400
72       2002 350
73       2003 401
74       2004 379
75       2005 381
76       2006 369
77       2007 391
78       2008 370
79       2009 370
80       2010 390
81       2011 380
82       2012 390
83       2013 380
84       2014 369
85       2015 390
86       2016 211

Note: this works because R knows that ‘Date’ is a date

How can you know that R knows? Either: (1) type ‘spain %>% glimpse’ and observe that the class of ‘Date’ is a
(2) type ‘spain %>% pull(Date) %>% class()’ to obtain that information (pull returns a single variable from a dataframe)

spain |> pull(Date) |> class()
[1] "character"

What is this ‘round’ variable?

spain |> count(round)
   round     n
1 league 23825
2 phase2    90

Apparently 90 matches (‘phase2’) are not regular La Liga matches. In subsequent analyses, we will not use these 90 matches. So we create a new dataframe excluding these 90 matches.

spain_league <-
  spain |>
  filter(round=="league")

We will try to answer a couple of simple questions:
1. Is it true that there are less goals today than in earlier days?
2. Is the number of goals related to the season of the year?
3. Is playing home really an advantage?
4. If so, has this advantage changed over time?

  1. Less goals today?

Key variables, just to check we have the variables we need and they look OK. Just list the first 10 cases

spain_league |>
  select(Date, home, visitor, hgoal, vgoal) |>
  head(n=10)
         Date               home            visitor hgoal vgoal
1  1929-02-10    Arenas de Getxo    Atletico Madrid     2     3
2  1929-02-10 Espanyol Barcelona         Real Union     3     2
3  1929-02-10        Real Madrid          CE Europa     5     0
4  1929-02-10      Real Sociedad    Athletic Bilbao     1     1
5  1929-02-12   Racing Santander       FC Barcelona     0     2
6  1929-02-17       FC Barcelona        Real Madrid     1     2
7  1929-02-17    Athletic Bilbao Espanyol Barcelona     9     0
8  1929-02-17    Atletico Madrid      Real Sociedad     0     3
9  1929-02-17         Real Union   Racing Santander     3     1
10 1929-02-17          CE Europa    Arenas de Getxo     5     2

Same, but this time a random sample of rows.

spain_league |>
  select(Date, home, visitor, hgoal, vgoal) |>
  slice_sample(n=10)
         Date            home            visitor hgoal vgoal
1  1955-09-25     Real Murcia       FC Barcelona     0     1
2  1962-12-23      Real Betis       RCD Mallorca     2     1
3  1948-03-07 Atletico Madrid       FC Barcelona     2     2
4  1956-11-25    FC Barcelona         Celta Vigo     4     1
5  2001-10-28       CD Alaves      UD Las Palmas     1     0
6  1967-12-31 Athletic Bilbao         Real Betis     8     0
7  1985-10-27   UD Las Palmas Espanyol Barcelona     3     1
8  1993-01-24        Cadiz CF        CD Logrones     2     2
9  1992-09-19     CD Logrones         Celta Vigo     0     1
10 1976-12-19     Valencia CF          Burgos CF     3     1

Once more, number of goals in match

spain_league |> count(hgoal + vgoal)
   hgoal + vgoal    n
1              0 1807
2              1 4122
3              2 5330
4              3 4870
5              4 3472
6              5 2120
7              6 1142
8              7  543
9              8  237
10             9  113
11            10   42
12            11   17
13            12    5
14            13    4
15            14    1

How did the number of goals per match develop over time?

spain_league |>
  # number of goals per match, and year of the match
  mutate(goals = hgoal+vgoal,
         year  = year(Date)
  ) |>
  ggplot() + 
  geom_point(aes(x=year, y=goals))

Year and goals

Oeps, this was not what I had in mind. I need to aggregate first! The ‘group_by’ function does group the data per year, so that we can then use the summarize function to obtain the mean goals scored per match (mean_goals_pm) per year.

spain_league |>
  mutate(goals = hgoal + vgoal,
         year  = year(Date)
  ) |>
  group_by(year) |>
  summarize(mean_goals_pm = mean(goals)) |>
  ggplot() + 
  geom_line(aes(x=year, y=mean_goals_pm))

Goals grouped by year

Yes, there were more goals back in the old days (before 1950).

Funny pattern. What about England? Do we have the same variables (yes!)

england |> names()
 [1] "Date"     "Season"   "home"     "visitor"  "FT"       "hgoal"   
 [7] "vgoal"    "division" "tier"     "totgoal"  "goaldif"  "result"  
england |>
  # number of goals per match, and year of the match
  mutate(goals = hgoal+vgoal,
         year  = year(Date)
  ) |>
  group_by(year) |>
  summarize(mean_goals_pm = mean(goals)) |>
  ggplot() + 
  geom_line(aes(x=year, y=mean_goals_pm))

Mean goals grouped by year

England is were the game was invented (they say), so they have a longer history going back to 1888. Let us now combine England and Spain.

spain_series <-
  spain_league |>
  mutate(goals  = hgoal+vgoal,
         year   = year(Date)
  ) |>
  group_by(year) |>
  summarize(mean_goals_pm = mean(goals),
            # Define a constant for Spain
            country       = "Spain")

What des this look like? Do it yourself: View(spain_series).

england_series <-
  england |>
  # number of goals per match, and year of the match
  mutate(goals  = hgoal+vgoal,
         year   = year(Date)
  ) |>
  group_by(year) |>
  summarize(mean_goals_pm = mean(goals),
            # Define a constant for England
            country       = "England")

Stack both datasets on top of each other

series <- bind_rows(spain_series, england_series)

Take a look yourself (note that England started 1888, Spain in 1929)

series |> 
  arrange(year, country) |> 
  View()
  

Plot development in Spain and England in the same graph

series |>
  ggplot() +
  geom_line(aes(x=year, y=mean_goals_pm, color=country))

Developments in Spain and England

Back to Spain

  1. Is the number of goals related to the season of the year? We use month of the year as a season indicator.
spain_league |>
  # number of goals per match, and year of the match
  mutate(goals = hgoal+vgoal,
         month  = month(Date)
  ) |>
  group_by(month) |>
  summarize(mean_goals_pm = mean(goals)) |>
  ggplot() + 
  geom_point(aes(x=month, y=mean_goals_pm)) +
  scale_x_continuous(breaks=1:12)

Goals related to the season

Let us check how many we have in summer (June-August, numbers of games)

spain_league |>
  mutate(goals = hgoal+vgoal,
         month  = month(Date)
  ) |>
  group_by(month) |>
  # This is how we count nr of rows (matches) per month
  summarize(number_of_matches = n()) |>
  ggplot() + 
  geom_point(aes(x=month, y=number_of_matches)) +
  # this gives us a scale nicely labeled 1..12
  scale_x_continuous(breaks=1:12)

Number of games in summer

Let us ignore June-August

spain_league |>
  mutate(goals = hgoal+vgoal,
         month  = month(Date)
  ) |>
  # this removes months 6,7 and 8 (June, July, August)
  #   the exclamation mark (!) means NOT , i.e.
  # !x means the same as (x == FALSE)  
  filter(!(month %in% c(6:8))) |>
  group_by(month) |>
  # This is how we count nr of rows (matches) per month
  summarize(number_of_matches = n()) |>
  ggplot() + 
  geom_point(aes(x=month, y=number_of_matches)) +
  scale_x_continuous(breaks=1:12)

Number of matches without summer

However, the differences are pretty small. This becomes clear when we set the scale of the Y axis

spain_league |>
  mutate(goals = hgoal+vgoal,
         month  = month(Date) ) |>
  filter(!(month %in% c(6:8))) |>
  group_by(month) |>
  summarize(mean_goals_pm = mean(goals)) |>
  ggplot() + 
  geom_point(aes(x=month, y=mean_goals_pm)) +
  scale_x_continuous(breaks=1:12) + 
  # Y axis range is between 0 and 3 goals
  ylim(0,3)

Mean goals scaled

Or as a bar chart

spain_league |>
  mutate(goals = hgoal+vgoal,
         month  = month(Date) ) |>
  filter(!(month %in% c(6:8))) |>
  group_by(month) |>
  summarize(mean_goals_pm = mean(goals)) |>
  ggplot() + 
  # geom_col rather than geom_point
  geom_col(aes(x=month, y=mean_goals_pm)) +
  scale_x_continuous(breaks=1:12) + 
  # Y axis range is between 0 and 3 goals
  ylim(0,3)

Bar chart of goals

The differences are negligible!

  1. Is playing home really an advantage?

Let us create a custom version of the dataset for this analysis

spain_extended <- spain_league |> 
  # Adding '-' in front of a variable means 'throw it away'
  #   or, in other words, 'do not select it'. If you use the -
  #   in select, variables not mentioned are retained.
  select( -Season, -HT, -FT, -tier, -round, -group, -notes ) |>
  mutate(goals = hgoal+vgoal,
         year  = year(Date),
         month  = month(Date),
         # Continuous version : difference in goals
         goals_difference = hgoal - vgoal,
         # Discrete version: home wins, visitors win, equal split
         result_discrete = case_when(goals_difference > 0  ~ "Hosts wins",
                                     goals_difference < 0  ~ "Visitors win",
                                     goals_difference == 0 ~ "Equal split") )

Frequency of wins, losses and equal splits

spain_extended |> count(result_discrete)
  result_discrete     n
1     Equal split  5644
2      Hosts wins 13227
3    Visitors win  4954

Intermezzo (added after the workshop meeting)

You may be familiar with the ‘classic R’ table function to create this table (printed horizontally):

table(spain_extended$result_discrete)

 Equal split   Hosts wins Visitors win 
        5644        13227         4954 

If you prefer the classic table, the below will NOT work because there must always be a function after the pipe symbol (|>) as the pipe symbol means “whatever” is returned by the function before |> will become the first argument of the function after the |>.And ‘result_discrete’ is not a function, but an object.

spain_extended |> result_discrete |> table() 

But this will work, because pull is a function that returns the variable result_discrete as a vector):

spain_extended |> pull(result_discrete) |> table()

 Equal split   Hosts wins Visitors win 
        5644        13227         4954 

Frequency table with proportions.

spain_extended |>
  group_by(result_discrete) |>
  summarize(frequency = n()) |>
  mutate(proportion = frequency / sum(frequency))
# A tibble: 3 x 3
  result_discrete frequency proportion
  <chr>               <int>      <dbl>
1 Equal split          5644      0.237
2 Hosts wins          13227      0.555
3 Visitors win         4954      0.208

Same thing, but now as a bar chart.

spain_extended |>
  group_by(result_discrete) |>
  summarize(frequency = n()) |>
  mutate(proportion = frequency / sum(frequency)) |>
  ggplot() +
  geom_col(aes(x=result_discrete, y=proportion), fill="blue")

Proportions in a bar chart

What does goal difference home-visitors look like

Key descriptive variables.

spain_extended |> 
  select(goals_difference) |> 
  summary()
 goals_difference
 Min.   :-8.000  
 1st Qu.: 0.000  
 Median : 1.000  
 Mean   : 0.805  
 3rd Qu.: 2.000  
 Max.   :11.000  

Mean: On average, home teams score .78 goals more per match than visitors.
Median: In half of the matches, the home team scores more than 1 goal more than the visitors

Let us make a box plot

spain_extended |> 
  ggplot() + 
  geom_boxplot(aes(x=goals_difference)) + 
  scale_x_continuous(breaks = seq(-10, 10,1))

Scores in a box plot

Home teams clearly have an advantage!

  1. Has this advantage changed over time?

Proportions of matches won, lost, equal split

spain_extended |>
  group_by(year, result_discrete) |>
  summarize(frequency = n()) |>
  mutate(proportion = frequency / sum(frequency)) |>
  ggplot() +
  geom_line(aes(x=year, y=proportion, color=result_discrete))

Won, lost and equal in lines

Goal count differences between home team and visitors

spain_extended |>
  group_by(year) |>
  summarize(home_visitors_difference = mean(goals_difference)) |>
  ggplot() +
  geom_line(aes(x=year, y=home_visitors_difference))

Differences between home team and visitors

Both graphs strongly suggest that home advantage decreased over the past century.

5.4 References

Bernasco, W. (2022, March). NSC-R Workshops: NSC-R Tidy Tuesday. NSCR. Retrieved from https://nscrweb.netlify.app/posts/2022-03-22-nsc-r-tidy-tuesday/