Adding libraries.

options(warn = -1)
library(tidyverse)
library(elo)
library(ggthemes) # Themes for ggplot2.
library(ggimage) # Supports images in ggplot2.
library(gt) # Create tables.

Import dataset.

ff_scores <- read.csv(file = "C:/Users/chris/Documents/Datasets/FFL/ff_scores.csv")
ff_scores <- ff_scores %>%
  select(date, team_1, team_2, win) %>%
  arrange(date)

Generate teams table with initial elo value = 1500.

teams <- data.frame(team = unique(ff_scores$team_1)) %>%
  mutate(elo = 1500)

Calculate changes in elo rating based on weekly results.

team1_elo_pre_vector <- c()
team2_elo_pre_vector <- c()
team1_elo_post_vector <- c()
team2_elo_post_vector <- c()

for (i in seq_len(nrow(ff_scores))) {
  match <- ff_scores[i, ]
  
  # Pre-match ratings.
  team_1_elo <- subset(teams, team == match$team_1)$elo
  team_2_elo <- subset(teams, team == match$team_2)$elo
  
  # Elo calculation.
  new_elo <- elo.calc(wins.A = match$win,
                      elo.A = team_1_elo,
                      elo.B = team_2_elo,
                      k = 30)
    
  # Post-match ratings  .
  team_1_new_elo <- new_elo[1, 1]
  team_2_new_elo <- new_elo[1, 2]

  team1_elo_pre_vector <- append(team1_elo_pre_vector, team_1_elo)
  team2_elo_pre_vector <- append(team2_elo_pre_vector, team_2_elo)
  team1_elo_post_vector <- append(team1_elo_post_vector, team_1_new_elo)
  team2_elo_post_vector <- append(team2_elo_post_vector, team_2_new_elo)

  # Update team elo ratings table.
  teams <- teams %>%
    mutate(elo = case_when(
        (team == match$team_1) ~ team_1_new_elo,
        (team == match$team_2) ~ team_2_new_elo,
        ((team != match$team_1) & (team != match$team_2)) ~ elo
    ))
}

Create columns to save pre/post-week elo ratings.

ff_scores$team1_elo_pre <- team1_elo_pre_vector
ff_scores$team2_elo_pre <- team2_elo_pre_vector
ff_scores$team1_elo_post <- team1_elo_post_vector
ff_scores$team2_elo_post <- team2_elo_post_vector

Elo rating history table.

head(ff_scores)

A data.frame: 6 × 8

	date	team_1	team_2	win	team1_elo_pre	team2_elo_pre	team1_elo_post	team2_elo_post
	<chr>	<chr>	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>
1	2014-01	CHRIS	CARLEN	0	1500	1500	1485.000	1515.000
2	2014-01	ANDREW	KYLE	0	1500	1500	1485.000	1515.000
3	2014-01	STEVE	DREW	1	1500	1500	1515.000	1485.000
4	2014-01	MATT	NATHAN	1	1500	1500	1515.000	1485.000
5	2014-02	MATT	CHRIS	1	1515	1485	1528.708	1471.292
6	2014-02	STEVE	ANDREW	0	1515	1485	1498.708	1501.292

Current elo rating table.

teams <- teams %>%
  mutate(elo = as.integer(round(elo,0))) %>%
  filter(!(team %in% c("DREW", "NATHAN", "CARLEN"))) %>%
  arrange(-elo)
teams

A data.frame: 10 × 2

team	elo
<chr>	<int>
CHRIS	1653
ANDREW	1581
STEVE	1530
SAM	1520
NICK	1514
KYLE	1500
NOAH	1472
JASON	1470
DYLAN	1427
MATT	1388

Create current elo ratings graphic.

teams_gt <- teams %>%
  gt() %>%
  cols_label(team = "",
             elo = "Current Elo") %>%
  cols_align(align = "center",
             columns = c(team, elo)) %>%
  gtExtras::gt_theme_espn() %>%
  tab_header(title = "Extravaganza FFL, 2021", subtitle = "Current Elo Ratings") %>%
  tab_options(heading.title.font.size = 24,
              heading.border.bottom.color = "#808080") %>%
  tab_style(style = cell_borders(sides = "right",
                                 color = "#808080",
                                 weight = px(5)),
            locations = cells_body(columns = c(team, elo),
                                   rows = everything())) %>%
  tab_style(style = cell_borders(sides = "bottom",
                                 color = "#808080",
                                 weight = px(2)),
            locations = cells_body(columns = everything(),
                                   rows = c(10))) %>%
  tab_style(style = cell_borders(sides = "top",
                                 color = "#808080",
                                 weight = px(2)),
            locations = cells_body(columns = everything(),
                                   rows = 1)) %>%
  data_color(columns = elo,
             colors = scales::col_numeric(palette = c("#FEFE69", "#DDF969", "#A9F36A", "#78EC6C", "#57E86B"),
                                          domain = c(1350, 1700)))

gtsave(teams_gt, "current-elo-ratings.png")

Export elo rating history table to CSV.

write.csv(ff_scores,"C:\\Users\\chris\\Desktop\\elo_history.csv", row.names = FALSE)