Champions League + Europa League Elo ratings per team/group

elo_europe_cups_plot.RMD

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```

# Packages

```{r}
pacman::p_load(tidyverse, scales, lubridate, 
               ggrepel, glue, extrafont,
               polite, rvest)
loadfonts(quiet = TRUE)
```


# Elo Ratings 8.31.19

```{r}
## Elo Ratings from 8.31.19
elo_ratings_raw <- read.csv("http://api.clubelo.com/2019-08-31")
```


# Europa League

## webscrape

```{r}
url <- "https://en.wikipedia.org/wiki/2019%E2%80%9320_UEFA_Europa_League_group_stage"

session <- bow(url)

EL_teams <- scrape(session) %>% 
  html_nodes(".wikitable th+ td") %>% 
  html_text() %>% 
  as_tibble() %>% 
  rename(team_name = value) %>% 
  mutate(team_name = team_name %>% trimws())

country_league <- scrape(session) %>% 
  html_nodes("th+ td .thumbborder") %>% 
  html_attr("alt") %>% 
  as_tibble() %>% 
  rename(country_league = value)

EL_teams_clean <- EL_teams %>% 
  bind_cols(country_league)
```

## clean

```{r}
EL_teams_df <- EL_teams_clean %>% 
  mutate(team_name = team_name %>% 
           iconv(from = "UTF-8", to = "ASCII//TRANSLIT"),
         elo_name = case_when(
           team_name == "Qarabag" ~ "Karabakh Agdam",
           team_name == "F91 Dudelange" ~ "Dudelange",
           team_name == "Copenhagen" ~ "FC Kobenhavn",
           team_name == "Malmo FF" ~ "Malmoe",
           team_name == "Krasnodar" ~ "FC Krasnodar",
           team_name == "Sporting CP" ~ "Sporting",
           team_name == "PSV Eindhoven" ~ "PSV",
           team_name == "Eintracht Frankfurt" ~ "Frankfurt",
           team_name == "Standard Liege" ~ "Standard",
           team_name == "Vitoria de Guimaraes" ~ "Guimaraes",
           team_name == "CSKA Moscow" ~ "CSKA Moskva",
           team_name == "Ludogorets Razgrad" ~ "Razgrad",
           team_name == "Ferencvaros" ~ "Ferencvaros",
           team_name == "VfL Wolfsburg" ~ "Wolfsburg",
           team_name == "KAA Gent" ~ "Gent",
           team_name == "Saint-Etienne" ~ "Saint-Etienne",
           team_name == "Oleksandriya" ~ "Olexandriya",
           team_name == "Borussia Monchengladbach" ~ "Gladbach",
           team_name == "Istanbul Basaksehir" ~ "Bueyueksehir",
           team_name == "Wolfsberger AC" ~ "Wolfsberg",
           team_name == "Beşiktas" ~ "Besiktas",
           team_name == "Wolverhampton Wanderers" ~ "Wolves",
           team_name == "Manchester United" ~ "Man United",
           team_name == "Astana" ~ "FK Astana",
           team_name == "AZ" ~ "Alkmaar",
           TRUE ~ team_name
         )) %>% 
  ## join Club info to Elo data
  left_join(elo_ratings_raw, by = c("elo_name" = "Club")) %>% 
  janitor::clean_names() %>% 
  select(-level, -from, -to, -elo_name) %>% 
  ## fill out group names per 4 rows
  mutate(group = rep(LETTERS[1:12], each = 4, length.out = 48) %>% 
           as.factor()) %>% 
  ## per group stats
  group_by(group) %>% 
  mutate(avg_elo = mean(elo),
         med_elo = median(elo),
         max_elo = max(elo),
         min_elo = min(elo)) %>% 
  ungroup() %>% 
  ## manually change names
  ## manually spread out the labels
  mutate(
    team_name = case_when(
      team_name == "Wolverhampton Wanderers" ~ "Wolves",
      team_name == "PSV Eindhoven" ~ "PSV",
      team_name == "Borussia Monchengladbach" ~ "Gladbach",
      team_name == "Sporting CP" ~ "Sporting",
      team_name == "Vitoria de Guimaraes" ~ "Vitoria S.C.",
      TRUE ~ team_name),
    elo_2 = case_when(
   ## slightly nudge the Elo points so labels won't overlap
   ## this means the below 7 teams will appear on slightly different points total than reality...
      team_name == "CFR Cluj" ~ elo - 10,
      team_name == "Sporting" ~ elo + 10,
      team_name == "PSV" ~ elo - 10,
      team_name == "Braga" ~ elo + 10,
      team_name == "Besiktas" ~ elo - 10,
      team_name == "AZ" ~ elo + 10,
      team_name == "Partizan" ~ elo - 5,
      TRUE ~ elo),
    group = forcats::fct_reorder(group, med_elo))
```


## plot

```{r, fig.height = 7, fig.width=15}
EL_teams_df %>% 
  ggplot(aes(x = group, y = elo, group = group)) +
  geom_segment(aes(x = group, xend = group,
                   y = max_elo, yend = min_elo,
                   group = group),
               size = 2.25, color = "lightgrey") +
  geom_point(aes(y = med_elo, group = group),
             size = 4.5, color = "red") +
  geom_text(aes(y = elo_2, label = team_name), 
            vjust = 0.5,
            size = 4.5, family = "Roboto Condensed",
            show.legend = FALSE) +
  geom_curve(aes(x = 12.8, xend = 12.1, 
                 y = 1675, yend = 1675),
             arrow = arrow(length = unit(0.07, "inch")), 
             size = 0.5, color = "black", curvature = 0.35) +
  geom_text(x = 12.85, y = 1675,
            hjust = 0, color = "red",
            family = "Roboto Condensed",
            size = 4,
            label = glue::glue("
                               Median ELO 
                               (per group)")) +
  scale_x_discrete(expand = expand_scale(add = c(0.5, 1.75))) +
  labs(title = "Europa League (2019/2020) Elo Ratings",
       subtitle = "Ratings as of August 31st, 2019",
       x = "Group (Ordered by Median Elo Rating per Group)", y = "Elo Rating",
       caption = "@R_by_Ryo                                                                                                                                                                                                                                                                            Source: ClubElo.com") +
  theme_minimal() +
  theme(text = element_text(family = "Roboto Condensed"),
        plot.title = element_text(size = 22),
        plot.subtitle = element_text(size = 18),
        plot.caption = element_text(size = 14, hjust = 0),
        axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))
```


```{r}
ggsave(filename = here::here(
  "Champions League & Europa League 2019-2020/outputs/euroleague_eloPlot.png"),
  width = 15, height = 7)
```



# Champions League

## webscrape

```{r}
url2 <- "https://en.wikipedia.org/wiki/2019%E2%80%9320_UEFA_Champions_League_group_stage"

session2 <- bow(url2)

CL_teams <- scrape(session2) %>% 
  html_nodes("h3+ .wikitable .flagicon+ a") %>% 
  html_text() %>% 
  as_tibble() %>% 
  rename(team_name = value) %>% 
  mutate(team_name = team_name %>% trimws())

country_leagueCL <- scrape(session2) %>% 
  html_nodes("h3+ .wikitable .thumbborder") %>% 
  html_attr("alt") %>% 
  as_tibble() %>% 
  rename(country_league = value)

CL_teams_clean <- CL_teams %>% 
  bind_cols(country_leagueCL)
```


## clean

```{r}
CL_teams_df <- CL_teams_clean %>% 
  mutate(
    team_name = team_name %>% 
      iconv(from = "UTF-8", to = "ASCII//TRANSLIT"),
    elo_name = case_when(
      team_name == "Paris Saint-Germain" ~ "Paris SG",
      team_name == "Club Brugge" ~ "Brugge",
      team_name == "Bayern Munich" ~ "Bayern",
      team_name == "Tottenham Hotspur" ~ "Tottenham",
      team_name == "Olympiacos" ~ "Olympiakos",
      team_name == "Red Star Belgrade" ~ "Crvena Zvezda",
      team_name == "Manchester City" ~ "Man City",
      team_name == "Shakhtar Donetsk" ~ "Shakhtar",
      team_name == "Atletico Madrid" ~ "Atletico",
      team_name == "Bayer Leverkusen" ~ "Leverkusen",
      team_name == "Lokomotiv Moscow" ~ "Lok Moskva",
      team_name == "Red Bull Salzburg" ~ "Salzburg",
      team_name == "Borussia Dortmund" ~ "Dortmund",
      team_name == "Inter Milan" ~ "Inter",
      team_name == "Slavia Prague" ~ "Slavia Praha",
      team_name == "Zenit Saint Petersburg" ~ "Zenit",
      TRUE ~ team_name)) %>% 
  ## join Club info to Elo data
  left_join(elo_ratings_raw, by = c("elo_name" = "Club")) %>% 
  janitor::clean_names() %>% 
  select(-level, -from, -to, -elo_name) %>% 
  ## fill out group names per 4 rows
  mutate(group = rep(LETTERS[1:12], each = 4, length.out = 32) %>% 
           as.factor()) %>% 
  ## per group stats
  group_by(group) %>% 
  mutate(avg_elo = mean(elo),
         med_elo = median(elo),
         max_elo = max(elo),
         min_elo = min(elo)) %>% 
  ungroup() %>% 
  mutate(group = forcats::fct_reorder(group, med_elo),
  ## slightly nudge the Elo points so labels won't overlap
  ## this means the below four teams will appear on slightly different points total than reality...
         elo_2 = case_when(
           team_name == "RB Leipzig" ~ elo + 10,
           team_name == "Benfica" ~ elo - 10,
           team_name == "Shakhtar" ~ elo + 10,
           team_name == "Atalanta" ~ elo - 10,
           TRUE ~ elo))
```


## plot

```{r, fig.height = 7, fig.width=15}
CL_teams_df %>% 
  ggplot(aes(x = group, y = elo, group = group)) +
  geom_segment(aes(x = group, xend = group,
                   y = max_elo, yend = min_elo,
                   group = group),
               size = 2.25, color = "lightgrey") +
  geom_point(aes(y = med_elo, group = group),
             size = 4.5, color = "red") +
  geom_text(aes(y = elo_2, label = team_name), 
            vjust = 0.5,
            size = 4.5, family = "Roboto Condensed",
            show.legend = FALSE) +
  geom_curve(aes(x = 8.8, xend = 8.1, 
                 y = 1825, yend = 1825),
             arrow = arrow(length = unit(0.07, "inch")), 
             size = 0.5, color = "black", curvature = 0.35) +
  geom_text(x = 8.85, y = 1825,
            hjust = 0, color = "red",
            family = "Roboto Condensed",
            size = 4,
            label = glue::glue("
                               Median ELO 
                               (per group)")) +
  scale_x_discrete(expand = expand_scale(add = c(0.5, 1.35))) +
  labs(title = "Champions League (2019/2020) Elo Ratings",
       subtitle = "Ratings as of August 31st, 2019",
       x = "Group (Ordered by Median Elo Rating per Group)", 
       y = "Elo Rating",
       caption = "@R_by_Ryo                                                                                                                                                                                                                                                                            Source: ClubElo.com") +
  theme_minimal() +
  theme(text = element_text(family = "Roboto Condensed"),
        plot.title = element_text(size = 22),
        plot.subtitle = element_text(size = 18),
        plot.caption = element_text(size = 14, hjust = 0),
        axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))
```



```{r}
ggsave(filename = here::here(
  "Champions League & Europa League 2019-2020/outputs/champleague_eloPlot.png"),
  width = 15, height = 7)
```