GEOG 588 with Marcela Suárez, Penn State
Load necessary packages
library(tidyverse)
library(here)
library(dplyr)
library(janitor)
Load required data
The data was accessed from a paper titled “A public data set of
spatio-temporal match events in soccer competitions” found here: https://www.nature.com/articles/s41597-019-0247-7
The data described in this paper have been collected and provided by
Wyscout, a leading company in the soccer industry which connects soccer
professionals worldwide, supports more than 50 soccer associations and
more than 1,000 professional clubs around the world. The procedure of
data collection is performed by expert video analysts (the operators),
who are trained and focused on data collection for soccer, through a
proprietary software (the tagger).
Tidy names
head(clean_names(wcevents))
## event_id sub_event_name tags player_id
## 1 8 Simple pass [{'id': 1801}] 122671
## 2 8 High pass [{'id': 1801}] 139393
## 3 1 Air duel [{'id': 703}, {'id': 1801}] 103668
## 4 1 Air duel [{'id': 701}, {'id': 1802}] 122940
## 5 8 Simple pass [{'id': 1801}] 122847
## 6 8 Simple pass [{'id': 1801}] 122832
## positions match_id event_name team_id
## 1 [{'y': 50, 'x': 50}, {'y': 53, 'x': 35}] 2057954 Pass 16521
## 2 [{'y': 53, 'x': 35}, {'y': 19, 'x': 75}] 2057954 Pass 16521
## 3 [{'y': 81, 'x': 25}, {'y': 83, 'x': 37}] 2057954 Duel 14358
## 4 [{'y': 19, 'x': 75}, {'y': 17, 'x': 63}] 2057954 Duel 16521
## 5 [{'y': 17, 'x': 63}, {'y': 15, 'x': 71}] 2057954 Pass 16521
## 6 [{'y': 15, 'x': 71}, {'y': 11, 'x': 92}] 2057954 Pass 16521
## match_period event_sec sub_event_id id
## 1 1H 1.656214 85 258612104
## 2 1H 4.487814 83 258612106
## 3 1H 5.937411 10 258612077
## 4 1H 6.406961 10 258612112
## 5 1H 8.562167 85 258612110
## 6 1H 10.991292 85 258612113
cleanwcevents <- clean_names(wcevents)
# rename match_period to half (use format: new name = old name)
cleanwcevents <- rename(cleanwcevents, half = match_period)
names(cleanwcevents) #list the new names
## [1] "event_id" "sub_event_name" "tags" "player_id"
## [5] "positions" "match_id" "event_name" "team_id"
## [9] "half" "event_sec" "sub_event_id" "id"
I changed the match_period name to half, which is more widely
understood
Filter data to World Cup Final match
wcfinal <- cleanwcevents %>%
filter(match_id == 2058017)
head(wcfinal)
## event_id sub_event_name tags player_id
## 1 8 Simple pass [] 14943
## 2 8 Simple pass [{'id': 1801}] 69968
## 3 8 Simple pass [{'id': 1801}] 8287
## 4 8 Simple pass [{'id': 1801}] 69409
## 5 8 Launch [{'id': 1801}] 135747
## 6 1 Ground loose ball duel [{'id': 703}, {'id': 1801}] 14943
## positions match_id event_name team_id half
## 1 [{'y': 51, 'x': 50}] 2058017 Pass 9598 1H
## 2 [{'y': 52, 'x': 39}, {'y': 74, 'x': 34}] 2058017 Pass 9598 1H
## 3 [{'y': 74, 'x': 34}, {'y': 93, 'x': 30}] 2058017 Pass 9598 1H
## 4 [{'y': 93, 'x': 30}, {'y': 65, 'x': 11}] 2058017 Pass 9598 1H
## 5 [{'y': 65, 'x': 11}, {'y': 51, 'x': 64}] 2058017 Pass 9598 1H
## 6 [{'y': 51, 'x': 64}, {'y': 20, 'x': 63}] 2058017 Duel 9598 1H
## event_sec sub_event_id id
## 1 1.892339 85 263883958
## 2 3.889375 85 263883959
## 3 6.140946 85 263883960
## 4 9.226570 85 263883963
## 5 12.658969 84 263883964
## 6 15.685687 13 263885674
Ask questions about the data:
Which players were best passers in the World Cup Final?
(Comparing each player’s total passes against the average number of
passes)
Here I convert the event text data and convert it into a numerical
format, with 1 indicating a pass was attempted, and 0 indicating there
was not a pass. This is then used to find the mean which can be used to
analyze if a player passed above the mean or not.
player_pass_summary <- wcfinal %>%
mutate(pass_numeric = ifelse(event_name == "Pass", 1, 0)) %>%
group_by(player_id )%>%
summarize(total_passes = sum(pass_numeric))%>%
mutate(avg_passes = mean(total_passes))%>%
mutate(passing_above_mean = total_passes > avg_passes)
knitr::kable(player_pass_summary, "simple")
| 3309 |
22 |
28.11111 |
FALSE |
| 3476 |
62 |
28.11111 |
TRUE |
| 3682 |
22 |
28.11111 |
FALSE |
| 7936 |
29 |
28.11111 |
TRUE |
| 8200 |
14 |
28.11111 |
FALSE |
| 8287 |
66 |
28.11111 |
TRUE |
| 14812 |
35 |
28.11111 |
TRUE |
| 14943 |
28 |
28.11111 |
FALSE |
| 25381 |
16 |
28.11111 |
FALSE |
| 25393 |
65 |
28.11111 |
TRUE |
| 25397 |
20 |
28.11111 |
FALSE |
| 25437 |
22 |
28.11111 |
FALSE |
| 26010 |
21 |
28.11111 |
FALSE |
| 28115 |
2 |
28.11111 |
FALSE |
| 31528 |
13 |
28.11111 |
FALSE |
| 69396 |
46 |
28.11111 |
TRUE |
| 69409 |
62 |
28.11111 |
TRUE |
| 69411 |
10 |
28.11111 |
FALSE |
| 69616 |
14 |
28.11111 |
FALSE |
| 69968 |
90 |
28.11111 |
TRUE |
| 105361 |
25 |
28.11111 |
FALSE |
| 135747 |
13 |
28.11111 |
FALSE |
| 135810 |
2 |
28.11111 |
FALSE |
| 209091 |
7 |
28.11111 |
FALSE |
| 279545 |
20 |
28.11111 |
FALSE |
| 340646 |
20 |
28.11111 |
FALSE |
| 353833 |
13 |
28.11111 |
FALSE |
Clean data to change positions column into four usable columns
The positions column contains four key data metrics (start y, start
x, end y, end x). I used the separate function from tidyr to split the
positions column based on the symbol delimiters “[,:}{}]”. I then
removed the columns I did not want to leave only the four relevant
columns.
wcfinaltable <- wcfinal %>%
separate(positions, into = c("a", "a1", "start_y", "a2", "start_x", "a3", "a4", "a5", "end_y", "a6", "end_x"),
#separate out commas, semicolon, and all brackets
sep = "[,:}{}]") %>%
#remove if it starts with a
select(-starts_with("a"))
head(wcfinaltable)
## event_id sub_event_name tags player_id start_y
## 1 8 Simple pass [] 14943 51
## 2 8 Simple pass [{'id': 1801}] 69968 52
## 3 8 Simple pass [{'id': 1801}] 8287 74
## 4 8 Simple pass [{'id': 1801}] 69409 93
## 5 8 Launch [{'id': 1801}] 135747 65
## 6 1 Ground loose ball duel [{'id': 703}, {'id': 1801}] 14943 51
## start_x end_y end_x match_id event_name team_id half event_sec sub_event_id
## 1 50 <NA> <NA> 2058017 Pass 9598 1H 1.892339 85
## 2 39 74 34 2058017 Pass 9598 1H 3.889375 85
## 3 34 93 30 2058017 Pass 9598 1H 6.140946 85
## 4 30 65 11 2058017 Pass 9598 1H 9.226570 85
## 5 11 51 64 2058017 Pass 9598 1H 12.658969 84
## 6 64 20 63 2058017 Duel 9598 1H 15.685687 13
## id
## 1 263883958
## 2 263883959
## 3 263883960
## 4 263883963
## 5 263883964
## 6 263885674
Write csv’s for both passing summary and clean data table for future
use
write_csv(player_pass_summary, "player_pass_summary_stats.csv")
write_csv(wcfinaltable, "cleanwctable.csv")