USL in the 538 Global Club Soccer Rankings

Conor Tompkins
Last updated on Sep 26, 2020 3 min read R, USL, Riverhounds

This post was originally run with data from August 2018. 538 does not provide historical rankings, so I had to rerun the code with September 2020 data when I migrated my blog.

538 recently added the United Soccer League to their Soccer Power Index ratings. I’m a Riverhounds fan, so I wanted to see how the team compared to teams from leagues around the world.

library(tidyverse)
library(ggrepel)

theme_set(theme_bw())
df <- read_csv("https://projects.fivethirtyeight.com/soccer-api/club/spi_global_rankings.csv", progress = FALSE) %>% 
  group_by(league) %>% 
  mutate(league_spi = median(spi)) %>% 
  ungroup() %>% 
  mutate(league = fct_reorder(league, league_spi))

df
## # A tibble: 633 x 8
##     rank prev_rank name           league              off   def   spi league_spi
##    <dbl>     <dbl> <chr>          <fct>             <dbl> <dbl> <dbl>      <dbl>
##  1     1         1 Bayern Munich  German Bundesliga  3.42 0.27   95.3       66.4
##  2     2         2 Manchester Ci… Barclays Premier…  3.09 0.24   94.2       71.2
##  3     3         3 Liverpool      Barclays Premier…  2.84 0.33   91.4       71.2
##  4     4         4 Barcelona      Spanish Primera …  2.88 0.43   90.1       70.2
##  5     5         5 Real Madrid    Spanish Primera …  2.52 0.32   89.2       70.2
##  6     6         6 Paris Saint-G… French Ligue 1     2.89 0.51   89.0       58.8
##  7     7         8 Atletico Madr… Spanish Primera …  2.28 0.35   86.3       70.2
##  8     8        10 Internazionale Italy Serie A      2.51 0.570  84.3       60.8
##  9     9         9 Chelsea        Barclays Premier…  2.44 0.54   84.2       71.2
## 10    10        12 RB Leipzig     German Bundesliga  2.45 0.55   84.1       66.4
## # … with 623 more rows
df %>% 
  ggplot(aes(spi, league)) +
  geom_jitter(aes(color = league), show.legend = FALSE,
              height = .2,
              alpha = .7) +
  geom_jitter(data = df %>% filter(name == "Pittsburgh Riverhounds"),
              show.legend = FALSE,
              height = .2,
              alpha = .7) +
  geom_label_repel(data = df %>% filter(name == "Pittsburgh Riverhounds"), 
                   aes(label = name), 
                   size = 3,
                   show.legend = FALSE,
                   force = 6) +
  labs(title = "538 Soccer Power Index",
       subtitle = "One dot = one team",
       y = NULL,
       x = "Soccer Power Index",
       caption = "538 data, @conor_tompkins")

df %>% 
  ggplot(aes(spi, league)) +
  geom_jitter(aes(color = league), show.legend = FALSE,
              height = .2,
              alpha = .7) +
  labs(title = "538 Soccer Power Index",
       subtitle = "One dot = one team",
       y = NULL,
       x = "Soccer Power Index",
       caption = "538 data, @conor_tompkins")

This shows the offensive and defensive ratings of each USL team. The Riverhounds are squarely in the #LilleyBall quadrant.

df %>% 
  filter(league == "United Soccer League") %>% 
  ggplot(aes(off, def, label = name)) +
  geom_point() +
  geom_label_repel(size = 4,
                   force = 4) +
  scale_y_reverse() +
  labs(title = "538 Soccer Power Index",
       y = "Defensive rating (scale reversed)",
       x = "Offensive rating",
       caption = "538 data, @conor_tompkins")

R USL Riverhounds

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