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@joelnitta
Last active January 21, 2024 07:11
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Calculate 17lands win-rate by rank across sets
# To install magicr
# pak::pkg_install("joelnitta/magicr")
library(tidyverse)
library(magicr)
library(ggrepel)
# Win-rate by rank and set ----
# Define function to calculate win-rate by rank for a particular set
get_wr_by_rank <- function(set_get) {
cli::cli_inform("Working on {set_get}")
mr_get_17lands_data(
set = set_get, "game", "premier", select = c("rank", "won")) %>%
filter(rank != "") %>%
filter(rank != "none") %>%
group_by(rank) %>%
summarize(
n_won = sum(won),
n_total = n()
) %>%
mutate(win_rate = n_won / n_total) %>%
arrange(desc(win_rate)) %>%
mutate(rank = as.factor(rank) %>%
fct_relevel(
c("bronze", "silver", "gold", "platinum", "diamond", "mythic"))
) %>%
mutate(set = set_get)
}
# Define vector of sets to analyze
sets_to_get <- c(
"KTK", "LCI", "WOE", "LTR", "MOM",
"SIR", "ONE", "BRO", "DMU", "HBG",
"SNC", "NEO"
)
# Calculate win-rank by set
wr_by_rank <- map_df(sets_to_get, get_wr_by_rank)
# Define where to plot set labels
set_labs <- wr_by_rank %>%
filter(rank == "mythic")
# Make plot
ggplot(wr_by_rank, aes(x = rank, y = win_rate, color = set, group = set)) +
geom_line() +
geom_point(aes(size = n_total)) +
geom_label_repel(
data = set_labs,
aes(label = set),
nudge_x = .5, direction = "y", hjust = "right"
) +
guides(color = "none") +
scale_size_continuous(label = scales::label_number(big.mark = ",")) +
scale_y_continuous(label = scales::label_percent()) +
labs(x = "Rank", y = "Win Rate", size = "Num. games") +
theme_grey(base_size = 24)
# Win rate by rank and day ----
# Define function to calculate win-rate by rank and add day of the month
get_wr_by_rank_day <- function(set_get) {
cli::cli_inform("Working on {set_get}")
game_data <- mr_get_17lands_data(
set = set_get, "game", "premier", select = c("rank", "won", "draft_time"))
if (!"draft_time" %in% colnames(game_data)) return(tibble())
game_data %>%
filter(rank != "") %>%
filter(rank != "none") %>%
mutate(day = lubridate::day(draft_time)) %>%
group_by(rank, day) %>%
summarize(
n_won = sum(won),
n_total = n(),
.groups = "drop"
) %>%
mutate(
win_rate = n_won / n_total,
set = set_get)
}
# Calculate win-rank by rank and day
wr_by_day <- map_df(sets_to_get, get_wr_by_rank_day)
# Aggregate across sets
wr_by_day_totals <- wr_by_day %>%
group_by(rank, day) %>%
summarize(
n_won = sum(n_won),
n_total = sum(n_total),
.groups = "drop"
) %>%
mutate(win_rate = n_won/n_total) %>%
mutate(rank = as.factor(rank) %>%
fct_relevel(c("bronze", "silver", "gold", "platinum", "diamond", "mythic")) %>%
fct_rev()
)
# Set labels for plot
rank_labs <- wr_by_day_totals %>%
filter(day == max(day))
# Make plot
ggplot(wr_by_day_totals, aes(x = day, y = win_rate, color = rank, group = rank)) +
geom_point(aes(size = n_total)) +
geom_line() +
geom_label_repel(
data = rank_labs,
aes(label = rank),
nudge_x = 5, direction = "y", hjust = "right", size = 6
) +
theme_grey(base_size = 24) +
scale_size_continuous(label = scales::label_number(big.mark = ",")) +
scale_y_continuous(label = scales::label_percent()) +
guides(color = "none") +
labs(x = "Day of the Month", y = "Win Rate", size = "Num. games") +
theme(legend.position = "bottom")
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