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ikashnitsky/euro2020-predictions.R

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Win predictions of #EURO2020 pooled together from 18 sources -- https://twitter.com/ikashnitsky/status/1403470245407477764
Raw
euro2020-predictions.R
#===============================================================================
# 2021-06-11 -- twitter
# UEFA European Cup predictions
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com, @ikashnitsky
#===============================================================================
library(tidyverse)
library(magrittr)
library(ggdark)
library(paletteer)
library(hrbrthemes)
library(cowplot)
# Visualizing the data from
# https://twitter.com/JanVanHaaren/status/1403374087163023360
win_prob <- tibble::tribble(
~Country, ~Average.win, ~Median.win, ~Maximum.win, ~Minimum.win, ~AZ, ~CR, ~DTAI, ~FF, ~FS, ~GI, ~GS, ~HC, ~KL, ~LB, ~LG, ~MJ, ~MS, ~OLE, ~OP, ~SG, ~SJ, ~SP,
"Belgium", 15.41, 14.09, 28.93, 2.98, 8.32, 8.69, 28.93, 2.98, 22.83, 7.69, 17.1, 13.92, 24.55, 13.71, 24.32, 14.1, 8.32, 23.79, 12.09, 14.08, 16.26, 15.67,
"France", 14.38, 13.51, 21.27, 6.9, 14.79, 12.79, 13.55, 13.19, 19.85, 7.69, 10.2, 15.53, 6.9, 13.47, 19.82, 12.77, 11.69, 21.27, 15.39, 11.26, 18.22, 20.47,
"Spain", 11.65, 11.45, 14.11, 9.29, 12.35, 9.89, 13.89, 11.62, 13.9, 9.29, 13.1, 10.33, 13.55, 9.73, 13.01, 13.55, 9.7, 14.11, 9.61, 10.77, 9.98, 11.28,
"England", 10.2, 10.01, 18.56, 4.3, 13.46, 10.49, 6.06, 14.49, 5.66, 7.09, 12.9, 9.53, 16.65, 6.64, 4.3, 11.76, 8.85, 4.36, 13.97, 13.6, 18.56, 5.19,
"Portugal", 8.53, 8.15, 18.95, 3.9, 10.08, 6.39, 8.71, 18.95, 9.83, 3.9, 9.5, 7.97, 6.9, 5.92, 8.01, 6.62, 8.29, 8.83, 9.15, 7.59, 7.34, 9.58,
"Italy", 8.39, 8.19, 13.5, 2.6, 7.92, 7.29, 8.82, 8.14, 9.53, 4.3, 10.4, 9.04, 13.5, 11.69, 2.6, 9.57, 8.23, 7.36, 8.06, 9.12, 7.84, 7.59,
"Germany", 6.91, 6.61, 16.5, 1.4, 10.07, 7.69, 2.52, 16.5, 3.28, 10.19, 3.1, 6.38, 1.4, 10.78, 7.11, 5.47, 6.83, 3.54, 10.03, 5.65, 4.1, 9.78,
"Netherlands", 5.86, 6.01, 8.57, 2.82, 6.13, 5.59, 3.13, 2.82, 3.97, 8.29, 6.8, 5.82, 4.3, 8.57, 6.41, 7.67, 6.62, 8.29, 6.39, 5.07, 3.64, 5.89,
"Denmark", 4.81, 5.14, 7.41, 0.99, 4.56, 5, 7.41, 0.99, 5.96, 5.29, 5.8, 5.43, 7.15, 6.45, 1.5, 4.62, 4.7, 3.16, 3.08, 6.63, 3.42, 5.39,
"Switzerland", 2.14, 2.12, 3.9, 0.87, 2.18, 3.2, 1.37, 0.87, 1.09, 3.9, 2.5, 2.76, 1.75, 1.71, 2.8, 2.63, 2.06, 1.11, 1.16, 3.44, 1.74, 2.3,
"Croatia", 2.04, 1.94, 4.3, 0.35, 3.05, 2.8, 0.56, 2.62, 0.35, 3.4, 1.1, 1.63, 0.5, 2.4, 4.3, 1.91, 4.16, 1.2, 2.37, 1.42, 1.98, 1,
"Sweden", 1.43, 1.34, 2.5, 0.24, 1, 2.5, 0.97, 0.24, 1.19, 2.5, 1.5, 2.04, 0.5, 2.11, 0.8, 1.75, 2.47, 0.64, 0.88, 2.1, 1.12, 1.5,
"Poland", 1.33, 1.24, 2.7, 0.4, 1.24, 2.1, 0.58, 2.27, 0.79, 2.7, 0.6, 1.56, 0.8, 2.25, 0.4, 1.4, 2.37, 0.45, 1.08, 1.31, 1.24, 0.8,
"Ukraine", 1.28, 1.06, 3.9, 0.3, 0.3, 2.8, 0.91, 0.5, 0.3, 3.9, 1.6, 1.82, 0.3, 0.81, 1.4, 1.25, 2.53, 0.66, 1.21, 1.28, 0.66, 0.8,
"Austria", 1.04, 0.62, 5.89, 0.05, 1.46, 1.9, 0.21, 1.28, 0.05, 5.89, 0.4, 0.81, 0.2, 0.46, 0.2, 0.36, 2.55, 0.27, 0.86, 0.81, 0.78, 0.2,
"Wales", 0.99, 0.87, 2.1, 0.02, 0.59, 2.1, 1.27, 0.02, 0.74, 1.3, 1, 1.83, 0.6, 1.79, 0.5, 0.57, 1.19, 0.52, 0.43, 1.29, 1.54, 0.6,
"Turkey", 0.88, 0.77, 2.4, 0.07, 0.74, 1.6, 0.55, 0.07, 0.25, 1.5, 0.9, 0.82, 0.1, 0.28, 0.8, 2.4, 2.35, 0.11, 1.58, 0.9, 0.4, 0.4,
"Russia", 0.75, 0.63, 2.43, 0.05, 0.35, 2, 0.13, 1.14, 0.15, 1.7, 0.3, 0.77, 0.05, 0.48, 0.8, 0.42, 2.43, 0.07, 0.89, 0.78, 0.12, 1,
"Czech Republic", 0.63, 0.46, 2.2, 0.12, 0.33, 1.2, 0.15, 0.5, 0.3, 2.2, 0.6, 0.77, 0.15, 0.25, 0.5, 0.43, 1.83, 0.12, 0.58, 0.98, 0.32, 0.2,
"Scotland", 0.41, 0.14, 2.2, 0, 0.58, 1.2, 0.02, 0.68, 0, 2.2, 0, 0.3, 0, 0.09, 0.1, 0.12, 0.91, 0.01, 0.33, 0.54, 0.16, 0.1,
"Slovakia", 0.32, 0.16, 2.2, 0, 0.3, 0.8, 0.02, 0.01, 0, 2.2, 0, 0.06, 0, 0.35, 0, 0.27, 0.82, 0.01, 0.27, 0.28, 0.28, 0.04,
"Hungary", 0.29, 0.17, 1, 0, 0.04, 1, 0.12, 0.1, 0, 0.9, 0.4, 0.56, 0, 0.02, 0.2, 0.19, 0.66, 0.07, 0.21, 0.53, 0.16, 0.1,
"Finland", 0.22, 0.13, 1.3, 0, 0.12, 0.6, 0.15, 0, 0, 1.3, 0.1, 0.26, 0.15, 0.02, 0.1, 0.15, 0.28, 0.05, 0.18, 0.33, 0.08, 0.1,
"North Macedonia", 0.11, 0.05, 0.7, 0, 0.06, 0.4, 0.01, 0, 0, 0.7, 0.1, 0.06, 0, 0.02, 0, 0.04, 0.13, 0, 0.17, 0.24, 0.06, 0.02
) %>%
janitor::clean_names() %>%
mutate(
country = country %>% as_factor %>% fct_reorder(median_win)
)
# boxplots ----------------------------------------------------------------
win_prob %>%
pivot_longer(names_to = "source", values_to = "win_prob", cols = az:sp) %>%
ggplot(aes(country, win_prob, color = country))+
geom_jitter(width = .25, alpha = .25)+
geom_boxplot(fill = NA, notch = T, notchwidth = .2, outlier.color = NA)+
coord_flip()+
scale_y_continuous(position = "right")+
scale_color_paletteer_d("Polychrome::light", guide = NULL)+
dark_theme_minimal(base_family = font_rc)+
labs(
title = "Who is going to win EURO 2020? Chances in %",
caption = "Data: @JanVanHaaren, pooled from 18 sources | Design: @ikashnitsky",
x = NULL, y = NULL
)+
theme(
plot.title = element_text(face = 2, family = "Roboto Slab", size = 18),
axis.text.y = element_text(face = 2, size = 12)
)
p_box <- last_plot()
# treemap -----------------------------------------------------------------
library(treemapify)
win_prob %>%
ggplot(
aes(
area = average_win, fill = country,
label = paste0(country, "\n", average_win %>% round(1))
)
)+
geom_treemap()+
scale_fill_paletteer_d("Polychrome::light", guide = NULL)+
geom_treemap_text(family = font_rc, fontface = 2, place = "centre", reflow = T, grow = F)+
theme_void()
p_tree <- last_plot()
# inset -------------------------------------------------------------------
joined <- ggdraw(p_box)+
draw_plot(p_tree, x = .53, width = .45, y = 0.05, height = .55)+
draw_text(
text = "Average of the 18 predictions",
x = .75, y = .62, vjust = 0,
family = "Roboto Slab", fontface = 2, size = 18
)
ggsave(
"~/Downloads/18-predictions.png", joined,
width = 7, height = 5, type = "cairo"
)
# geometric averaging -----------------------------------------------------
geom_avg <- win_prob %>%
pivot_longer(names_to = "source", values_to = "win_prob", cols = az:sp) %>%
filter(!win_prob==0) %>%
group_by(country) %>%
summarise(geom_avg = win_prob %>% prod %>% raise_to_power(1/18)) %>%
ungroup() %>%
mutate(geom_avg = geom_avg %>% prop.table %>% multiply_by(100))
geom_avg %>%
ggplot(
aes(
area = geom_avg, fill = country,
label = paste0(country, "\n", geom_avg %>% round(1))
)
)+
geom_treemap()+
scale_fill_paletteer_d("Polychrome::light", guide = NULL)+
geom_treemap_text(family = font_rc, fontface = 2, place = "centre", reflow = T, grow = F)+
theme_void()
p_tree_geom <- last_plot()
ggsave(
"~/Downloads/geom_centered.png", p_tree_geom,
width = 5, height = 5, type = "cairo"
)
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