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exploratory_data_analysis_3.Rmd
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exploratory_data_analysis_3.Rmd
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---
title: "Exploring Predictors of World Cup Wins"
output:
html_document:
toc: TRUE
toc_float: TRUE
---
<style type="text/css">
h1.title {
text-align: center;
}
</style>
```{r setup, include=FALSE}
# Load packages
library(tidyverse)
library(viridis)
library(plotly)
# Set ggplot theme
knitr::opts_chunk$set(
echo = TRUE,
warning = FALSE,
fig.width = 6,
fig.asp = .6,
out.width = "90%"
)
```
```{r import df, include=FALSE, warning=FALSE, message=FALSE}
# Read in csv datafile
wc_df <- read_csv("./data/worldcup_final.csv") %>%
janitor::clean_names() %>%
mutate(
gd = gsub("\\+","",gd),
gd = gsub("\\−","-",gd),
gd = as.numeric(gd)
) %>%
mutate(
prop_w = w / pld,
prop_d = d / pld,
prop_l = l / pld,
gf_per_game = gf / pld,
ga_per_game = ga / pld) %>%
mutate_if(is.numeric, round, digits = 2)
```
<br>
This page contains bivariable explorations between the proportion of games won and various predictors for each country that has participated in the FIFA World Cup.
<br>
### Codebook
To ensure comparability between countries in this exploratory analysis, the following additional variables were created:
* `prop_w`. Proportion of games a country has won in the World Cup. Calculated as the number of games a country has won in the World Cup divided by the number of games a country has played in the World Cup (`w` / `pld`) <br>
* `gf_per_game`. Average number of goals a country has scored against opponents (i.e. "goals for") per game. Calculated as the total number of goals a country has scored against opponents in the World Cup divided by the number of games a country has played in the World Cup (`gf` / `pld`) <br>
* `ga_per_game`. Average number of goals scored against a country per game. Calculated as the total number of goals scored against a country in the World Cup divided by the number of games a country has played in the World Cup (`ga` / `pld`) <br>
<br>
### 2022 FIFA Ranking
<br>
```{r echo=FALSE, warning=FALSE, message=FALSE}
wc_df %>%
mutate(text_label = str_c("Country: ", country, "\nConfederation: ", confederation, "\nProportion of Games Won: ", prop_w, "\nFIFA Rank: ", rank)) %>%
plot_ly(
x = ~rank,
y = ~prop_w,
type = "scatter",
mode = "markers",
color = ~confederation,
alpha = 0.7,
text = ~text_label,
legendgroup = ~confederation,
colors = viridis_pal(option = "D")(3)) %>%
layout(
title = "2022 FIFA Ranking vs. Proportion of Games Won",
xaxis = list(title = "FIFA Ranking (2022)"),
yaxis = list(title = "Proportion of Games Won"))
```
There is a negative association between FIFA Ranking and the proportion of games won in the World Cup. As a team's FIFA Ranking increases (i.e. moves further away from the #1 slot), the proportion of games won in the World Cup decreases. This is expected due to the FIFA ranking scheme that assigns the #1 slot to the top performing team.
<br>
### World Cup Participations
<br>
```{r echo=FALSE, warning=FALSE, message=FALSE}
wc_df %>%
mutate(text_label = str_c("Country: ", country, "\nConfederation: ", confederation, "\nProportion of Games Won: ", prop_w, "\nWorld Cup Participations: ", part)) %>%
plot_ly(
x = ~part,
y = ~prop_w,
type = "scatter",
mode = "markers",
color = ~confederation,
text = ~text_label,
alpha = 0.7,
legendgroup = ~confederation,
colors = viridis_pal(option = "D")(3)) %>%
layout(
title = "Number of World Cup Participations vs. Proportion of Games Won",
xaxis = list(title = "World Cup Participations"),
yaxis = list(title = "Proportion of Games Won"))
```
There is a positive association between number of times a country has participated in the World Cup and the proportion of games won. This is expected, as teams that that win a high proportion of the games are likely some of the best teams and make it to more World Cup tournaments as a result.
<br>
### Games Played
<br>
```{r echo=FALSE, warning=FALSE, message=FALSE}
wc_df %>%
mutate(text_label = str_c("Country: ", country, "\nConfederation: ", confederation, "\nProportion of Games Won: ", prop_w, "\nNumber of Games Played: ", pld)) %>%
plot_ly(
x = ~pld,
y = ~prop_w,
type = "scatter",
mode = "markers",
color = ~confederation,
text = ~text_label,
alpha = 0.7,
legendgroup = ~confederation,
colors = viridis_pal(option = "D")(3)) %>%
layout(
title = "Number of Games Played vs. Proportion of Games Won",
xaxis = list(title = "Number of Games Played"),
yaxis = list(title = "Proportion of Games Won"))
```
There is a positive association between the number of games played and the proportion of games won in the World Cup. This is expected, as teams that that are doing well and winning a high proportion of games have the opportunity to advance further in the tournament and play a higher number of games.
<br>
### Average Goals For Per Game
<br>
```{r echo=FALSE, warning=FALSE, message=FALSE}
wc_df %>%
mutate(text_label = str_c("Country: ", country, "\nConfederation: ", confederation, "\nProportion of Games Won: ", prop_w, "\nAvg Goals For Per Game: ", gf_per_game)) %>%
plot_ly(
x = ~gf_per_game,
y = ~prop_w,
type = "scatter",
mode = "markers",
color = ~confederation,
text = ~text_label,
alpha = 0.7,
legendgroup = ~confederation,
colors = viridis_pal(option = "D")(3)) %>%
layout(
title = "Average Goals Scored Against Opponent vs. Proportion of Games Won",
xaxis = list(title = "Avg Goals For (per game)"),
yaxis = list(title = "Proportion of Games Won"))
```
There is a positive association between the average number of goals a country has scored against opponents (i.e. "goals for") per game and the proportion of games a country has won in the World Cup. This is expected, as teams that that are doing well and winning a high proportion of the games are scoring more goals per game than teams that are not doing as well.
<br>
### Average Goals Against Per Game
<br>
```{r echo=FALSE, warning=FALSE, message=FALSE}
wc_df %>%
mutate(text_label = str_c("Country: ", country, "\nConfederation: ", confederation, "\nProportion of Games Won: ", prop_w, "\nAvg Goals Against Per Game: ", ga_per_game)) %>%
plot_ly(
x = ~ga_per_game,
y = ~prop_w,
type = "scatter",
mode = "markers",
color = ~confederation,
text = ~text_label,
alpha = 0.7,
legendgroup = ~confederation,
colors = viridis_pal(option = "D")(3)) %>%
layout(
title = "Average Goals Scored Against vs. Proportion of Games Won",
xaxis = list(title = "Avg Goals Against (per game)"),
yaxis = list(title = "Proportion of Games Won"))
```
There is a negative association between the average number of goals scored against a country (i.e. "goals against") per game and the proportion of games a country has won in the World Cup. This is expected, as teams that that are doing well and winning a high proportion of the games are receiving less goals scored against them than teams that are not doing as well.