This is the running document of F1 Fantasy Stats for the 2025 season.
Individual race results are below
DT::datatable(results,
filter = "top",
extensions = "Buttons",
options = list(
buttons = c("csv", "pdf"),
pageLength = 10,
searchCols = list(list(search = ''))
)
)race_prizes <- results |>
filter(Player != "NA") |>
group_by(Grand_Prix, Player) %>%
summarize(Total_Points = sum(Total)) %>%
group_by(Grand_Prix) %>%
mutate(
# rank = rank(-Total_Points),
Prize = case_when(
rank(-Total_Points) == 1 ~ 30,
rank(-Total_Points) == 2 ~ 20,
rank(-Total_Points) == 3 ~ 10,
rank(-Total_Points) == 1.5 ~ 25,
rank(-Total_Points) == 2.5 ~ 15,
rank(-Total_Points) == 3.5 ~ 5,
TRUE ~ 0
)) %>% suppressMessages() #fix ties next
DT::datatable(race_prizes,
filter = "top",
extensions = "Buttons",
options = list(buttons = c("csv", "pdf")))saveRDS(race_prizes, "data/race_prizes.rds")cumulative_score <- results |>
filter(Player != "NA") |>
group_by(Player) %>%
summarize(Total_Score = sum(Total)) %>%
arrange(-Total_Score) %>%
suppressMessages()
DT::datatable(cumulative_score,
filter = "top",
extensions = "Buttons",
options = list(buttons = c("csv", "pdf")))total_prize <- race_prizes %>%
group_by(Player) %>%
summarize(Total_Prize = sum(Prize)) %>%
arrange(-Total_Prize) %>%
suppressMessages()
DT::datatable(total_prize,
filter = "top",
extensions = "Buttons",
options = list(buttons = c("csv", "pdf")))Mean and 95% CI
plot_data_draft <- results |>
mutate(Draft_Pos = paste0(Draft_Round,".",Draft_Pos),
Draft_Pos = ifelse(Draft_Round == "NA", "NA", Draft_Pos)) |>
group_by(Draft_Pos) %>%
summarize(mean = mean(Total),
sd = sd(Total),
min = min(Total),
n = n(),
max = max(Total)) %>%
mutate(#Draft_Pos = factor(Draft_Pos,
# levels = c(as.character(1:10),
# paste0("PB",1:5))),
lb = mean - 1.96*sd/sqrt(n), #update later with error cis
lb = ifelse(lb <= 0, 0, lb),
ub = mean + 1.96*sd/sqrt(n))
p <- ggplot(plot_data_draft,
aes(x = Draft_Pos, y = mean, colour = Draft_Pos)) +
geom_point() +
geom_errorbar(aes(ymin = lb, ymax = ub)) +
ylab("Points")
ggplotly(p)plot_data_driver <- results %>%
rowwise() %>%
mutate(Points = sum(c(Race_Points, Qualifying_Points, Sprint_Points, Sprint_Qualifying),
na.rm = TRUE)) %>%
group_by(Driver) %>%
summarize(mean = mean(Points),
sd = sd(Points),
min = min(Points),
n = n(),
max = max(Points)) %>%
mutate(
sd = ifelse(is.na(sd), 0, sd),
lb = mean - 1.96*sd/sqrt(n), #update later with error cis
lb = ifelse(lb <= 0, 0, lb),
ub = mean + 1.96*sd/sqrt(n))
p <- ggplot(plot_data_driver,
aes(x = Driver, y = mean, colour = Driver)) +
geom_point() +
theme(axis.text.x = element_text(angle = 45)) +
geom_errorbar(aes(ymin = lb, ymax = ub)) +
ylab("Points (No Bonus)")
ggplotly(p)[model description by chatGPT (to update), will run model and associated outputs following race 5]
The hierarchical model used in this analysis is structured as follows:
Let \(y_i\) represent the observed data for the \(i\)-th observation, and \(\mu_i\) represent the underlying true value for that observation.
For each observation \(i\) (ranging from 1 to \(N\)), \(\mu_i\) is assumed to follow a normal distribution with mean \(\mu_{\text{group}}[i]\), where \(\mu_{\text{group}}[i]\) is a group-specific mean corresponding to the group of the \(i\)-th observation. The variance of this normal distribution is \(\tau_{\text{group}}[i]\), representing the group-specific variance.
Additionally, the observed data \(y_i\) is assumed to follow a normal distribution with mean \(\mu_i\) and a common variance parameter \(\tau\).
The group-specific means \(\mu_{\text{group}}[i]\) are assumed to follow a normal distribution with mean \(\mu_{\text{prior}}\) and variance \(\tau_{\mu}\).
The hyperparameters \(\sigma\) and \(\sigma_{\mu}\) represent the standard deviations for the common variance \(\tau\) and the group-specific means’ variance \(\tau_{\mu}\) respectively.
The priors for \(\sigma\) and \(sigma_{\mu}\) are specified as uniform distributions between 0 and 100.
The prior for \(\mu_{\text{prior}}\) is assumed to be a normal distribution with mean 0 and a very small variance to reflect little prior information about the group means.
The hierarchical model aims to estimate the group-specific means and variances, as well as the common variance parameter, using the observed data and specified prior distributions.
Model summary statistics by player:
#fit model
results_jags <- results %>%
group_by(Grand_Prix, Player) %>%
summarise(points = sum(Total)) %>%
filter(Player != "NA") |>
suppressMessages()
jags_data <- list(
player = as.integer(factor(results_jags$Player)),
y = results_jags$points,
N = length(results_jags$points),
J = length(unique(results_jags$Player))
)
model_text <- "
model {
for (i in 1:N) {
mu[i] ~ dnorm(mu_group[player[i]], tau_group[player[i]]) # group-specific means
y[i] ~ dnorm(mu[i], tau) #T(0, ) # likelihood
}
# Group priors
for (j in 1:J) {
mu_group[j] ~ dnorm(mu_prior, tau_mu) #T(0, ) # prior for group mean
tau_group[j] ~ dgamma(0.001, 0.001) # prior for group variance (precision)
}
# Priors
tau <- pow(sigma, -2)
sigma ~ dunif(0, 100)
tau_mu <- pow(sigma_mu, -2)
sigma_mu ~ dunif(0, 100)
mu_prior ~ dnorm(0, 0.001)
}
"
model <- jags.model(textConnection(model_text),
data = jags_data,
n.chains = 1,
quiet = T,
inits = list(.RNG.seed = seed,
.RNG.name = "base::Super-Duper"))
update(model, n.iter = 1e5)
mcmc <- coda.samples(model = model, variable.names = c("mu_group"), n.iter = 1e4)
samples <- lapply(mcmc, as_tibble) %>% bind_rows()
#plots
plot_samples <- samples %>%
pivot_longer(cols = everything()) %>%
mutate(player_number = str_extract(name, "\\d+\\.?\\d*"),
player = case_when(
player_number == "1" ~ "Akshata",
player_number == "2" ~ "Eric",
player_number == "3" ~ "Forrest",
player_number == "4" ~ "Kevin",
player_number == "5" ~ "Melissa",
player_number == "6" ~ "Mitch"
))
mcmc_stats <- plot_samples %>% group_by(player) %>%
summarize(
mean = mean(value),
sd = sd(value),
lb = quantile(value, 0.025),
ub = quantile(value, 0.975)
) %>%
rename(Player = player)
obs_stats <- results_jags %>%
group_by(Player) %>%
summarize(mean_obs = mean(points),
sd_obs = sd(points),
n = n())
stats <- full_join(obs_stats, mcmc_stats, by = "Player") %>%
select(
Player,
"Player Mean" = mean_obs,
"Player SD" = sd_obs,
"Model Mean" = mean,
"Model SD" = sd,
"95% CrI LB" = lb,
"95% CrI UB" = ub
)
plot_stats <- full_join(obs_stats, mcmc_stats, by = "Player") %>%
mutate(lb_obs = mean_obs - 1.96*sd_obs/sqrt(n),
ub_obs = mean_obs + 1.96*sd_obs/sqrt(n)) %>%
select(Player, mean_obs, lb_obs, ub_obs, mean, lb, ub)
plot_obs_stats <- plot_stats %>%
select(Player, mean = mean_obs, lb = lb_obs, ub = ub_obs) %>%
mutate(stat = "Observed")
plot_model_stats <- plot_stats %>%
select(Player, mean, lb = lb, ub = ub) %>%
mutate(stat = "Modeled")
plot_stats_full <- bind_rows(plot_obs_stats, plot_model_stats)
forest_plot <- ggplot(plot_stats_full,
aes(x = Player, y = mean, colour = stat)) +
geom_point(position=position_dodge(width=0.5)) +
geom_errorbar(aes(ymin = lb, ymax = ub),
width = 0.1,
position=position_dodge(width=0.5)) +
geom_hline(yintercept = mean(obs_stats$mean_obs), linetype = "dashed") +
coord_flip()
ranks <- samples %>%
rowwise() %>%
mutate(rank1 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[1],
rank2 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[2],
rank3 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[3],
rank4 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[4],
rank5 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[5],
rank6 = rank(-c_across(`mu_group[1]`:`mu_group[6]`), ties.method = "first")[6]) %>%
pivot_longer(rank1:rank6) %>%
mutate(player_number = str_extract(name, "\\d+\\.?\\d*"),
player = case_when(
player_number == "1" ~ "Akshata",
player_number == "2" ~ "Eric",
player_number == "3" ~ "Forrest",
player_number == "4" ~ "Kevin",
player_number == "5" ~ "Melissa",
player_number == "6" ~ "Mitch"
)) %>%
group_by(player) %>%
summarize("1" = mean(value == 1),
"2" = mean(value == 2),
"3" = mean(value == 3),
"4" = mean(value == 4),
"5" = mean(value == 5),
"6" = mean(value == 6)) %>%
pivot_longer("1":"6") %>%
rename(Position = name)
density_plot <- ggplot(plot_samples,
aes(x = value, colour = player, fill = player)) +
geom_density(alpha = 0.5, adjust = 4)
rank_plot <- ggplot(ranks,
aes(x = Position, y = value, colour = Position, fill = Position)) +
geom_bar(stat = "identity", alpha = 0.75) +
facet_wrap(~player) +
geom_text(aes(label = round(value, 2)), colour = "black", size = 4)
DT::datatable(stats,
filter = "top",
extensions = "Buttons",
options = list(buttons = c("csv", "pdf"))) %>%
formatRound(2:7, 2)Summary Plots
overall_mean <- mean(race_prizes$Total_Points)
ggplot(data = race_prizes,
aes(x = Total_Points, colour = Player, fill = Player)) +
geom_histogram() +
facet_wrap(~ Player) +
geom_vline(xintercept = overall_mean, linetype = "dashed")`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
forest_plot
density_plot
rank_plot
# best_bottom <- results |>
# filter(Draft_Round == "BB") |>
# group_by(Player) |>
# summarize(bottom_total = sum(Total))
#
# final_prize <- total_prize %>%
# mutate(Most_Extreme = ifelse(Player == "Mitch", 20, 0),
# Most_Average = ifelse(Player == "Melissa", 20, 0),
# Best_Bottom = ifelse(Player == "Eric", 20, 0),
# Third_Place = ifelse(Player == "Eric", 50, 0),
# Second_Place = ifelse(Player == "Melissa", 100, 0),
# First_Place = ifelse(Player == "Akshata", 200, 0),
# Weekly_Prize = Total_Prize
# ) |>
# select(-Total_Prize) %>%
# mutate(
# Total_Prize = rowSums(select(., -Player))
# )
#
# entry_cost <- sum(final_prize$Total_Prize)/nrow(final_prize)
#
# final_prize <- final_prize %>%
# mutate(Entry_Cost = entry_cost + 13 + 2/3,
# Cumulative_Total = Total_Prize - Entry_Cost)
#
# DT::datatable(final_prize,
# filter = "top",
# extensions = "Buttons",
# options = list(buttons = c("csv", "pdf")))# DT::datatable(final_prize %>% select(c(Player, Total_Prize, Entry_Cost, Cumulative_Total)),
# filter = "top",
# extensions = "Buttons",
# options = list(buttons = c("csv", "pdf")))sample <- samples[sample(1:nrow(samples), 3),]
draft_order <- bind_rows(rank(sample[1,]),
rank(sample[2,]),
rank(sample[3,])) %>%
mutate(rnd = 1:3) %>%
pivot_longer(cols = starts_with("mu")) %>%
arrange(rnd, value) %>%
mutate(player_number = str_extract(name, "\\d+\\.?\\d*"),
player = case_when(
player_number == "1" ~ "Akshata",
player_number == "2" ~ "Eric",
player_number == "3" ~ "Forrest",
player_number == "4" ~ "Kevin",
player_number == "5" ~ "Melissa",
player_number == "6" ~ "Mitch"
)) %>%
select(rnd, pick = value, player)
draft_order$draft_score <- 1:nrow(draft_order)
## temporary code for first five races
# players <- c("Akshata", "Eric", "Forrest", "Kevin", "Melissa", "Mitch")
# rnd1 <- sample(players, length(players), replace = FALSE)
# rnd2 <- sample(players, length(players), replace = FALSE)
# rnd3 <- sample(players, length(players), replace = FALSE)
# draft_order <- tibble(
# rnd = rep(c(1, 2, 3), each = length(players)),
# pick = rep(1:length(players), 3),
# player = c(rnd1, rnd2, rnd3)
# )
#draft_order$draft_score <- 1:nrow(draft_order)
# draft_pb <- draft_order %>%
# group_by(player) %>%
# summarize(draft_sum = sum(pick),
# draft_rnd1 = min(draft_score)) %>%
# rowwise() %>%
# arrange(-draft_sum, -draft_rnd1) %>%
# select(player) %>%
# ungroup() %>%
# mutate(rnd = 3,
# pick = row_number())
#
# draft_order <- draft_order |> select(-draft_score)
#
# draft_order_full <- bind_rows(
# draft_order, draft_pb
# )
#uncomment this to display next year
knitr::kable(draft_order)| rnd | pick | player | draft_score |
|---|---|---|---|
| 1 | 1 | Akshata | 1 |
| 1 | 2 | Mitch | 2 |
| 1 | 3 | Forrest | 3 |
| 1 | 4 | Melissa | 4 |
| 1 | 5 | Kevin | 5 |
| 1 | 6 | Eric | 6 |
| 2 | 1 | Melissa | 7 |
| 2 | 2 | Kevin | 8 |
| 2 | 3 | Forrest | 9 |
| 2 | 4 | Mitch | 10 |
| 2 | 5 | Eric | 11 |
| 2 | 6 | Akshata | 12 |
| 3 | 1 | Forrest | 13 |
| 3 | 2 | Kevin | 14 |
| 3 | 3 | Mitch | 15 |
| 3 | 4 | Melissa | 16 |
| 3 | 5 | Akshata | 17 |
| 3 | 6 | Eric | 18 |
saveRDS(draft_order, "draft_order.rds")