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library(tidyverse)
library(gganimate)
library(mvtnorm)
team_ <- "DET"
qb_ <- "M.Stafford"
min_offense_play_result <- 25
stafford_play <- df_plays %>%
dplyr::filter(possessionTeam == team_,
stringr::str_detect(playDescription, qb_),
offensePlayResult > min_offense_play_result) %>%
dplyr::arrange(-offensePlayResult) %>%
dplyr::select(gameId, playId, possessionTeam, playDescription, absoluteYardlineNumber, yardsToGo) %>%
dplyr::slice(7)
stafford_game <- df_games %>%
dplyr::filter(gameId == stafford_play$gameId)
stafford_track <- df_tracking %>%
dplyr::filter(gameId == stafford_game$gameId, playId == stafford_play$playId)
stafford_direction <- stafford_track %>% head(1) %>% dplyr::pull(playDirection)
stafford_track <- stafford_track %>%
dplyr::select(x, y, s, dir, event, displayName, jerseyNumber, frameId, team)
stafford_game <- as_factor(stafford_game)
typeof(stafford_game)
stafford_track <- stafford_track %>%
dplyr::mutate(
dir_rad = dir * pi / 180,
v_x = sin(dir_rad) * s,
v_y = cos(dir_rad) * s,
v_theta = atan(v_y / v_x),
v_theta = ifelse(is.nan(v_theta), 0, v_theta),
team_name = case_when(
team == "home" ~ "DET",
team == "away" ~ "GB",
TRUE ~ team,
)
) %>%
dplyr::select(frameId, event, team = team_name, jerseyNumber, displayName, x, y, s, v_theta, v_x, v_y)
plot_field <- function(field_color="#ffffff", line_color = "#212529", number_color = "#adb5bd") {
field_height <- 160/3
field_width <- 120
field <- ggplot() +
theme_minimal() +
theme(
plot.title = element_text(size = 13, hjust = 0.5),
plot.subtitle = element_text(hjust = 1),
legend.position = "bottom",
legend.title.align = 1,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank(),
panel.background = element_rect(fill = field_color, color = "white"),
panel.border = element_blank(),
aspect.ratio = field_height/field_width
) +
# major lines
annotate(
"segment",
x = c(0, 0, 0,field_width, seq(10, 110, by=5)),
xend = c(field_width,field_width, 0, field_width, seq(10, 110, by=5)),
y = c(0, field_height, 0, 0, rep(0, 21)),
yend = c(0, field_height, field_height, field_height, rep(field_height, 21)),
colour = line_color
) +
# hashmarks
annotate(
"segment",
x = rep(seq(10, 110, by=1), 4),
xend = rep(seq(10, 110, by=1), 4),
y = c(rep(0, 101), rep(field_height-1, 101), rep(160/6 + 18.5/6, 101), rep(160/6 - 18.5/6, 101)),
yend = c(rep(1, 101), rep(field_height, 101), rep(160/6 + 18.5/6 + 1, 101), rep(160/6 - 18.5/6 - 1, 101)),
colour = line_color
) +
# yard numbers
annotate(
"text",
x = seq(20, 100, by = 10),
y = rep(12, 9),
label = c(seq(10, 50, by = 10), rev(seq(10, 40, by = 10))),
size = 10,
colour = number_color,
) +
# yard numbers upside down
annotate(
"text",
x = seq(20, 100, by = 10),
y = rep(field_height-12, 9),
label = c(seq(10, 50, by = 10), rev(seq(10, 40, by = 10))),
angle = 180,
size = 10,
colour = number_color,
)
return(field)
}
fetch_team_colors <- function(team_colors_=NULL, h_team_, a_team_, diverge_=FALSE) {
colors_url <- "https://raw.githubusercontent.com/asonty/ngs_highlights/master/utils/data/nfl_team_colors.tsv"
if (is.null(team_colors_)) {
team_colors_ <- suppressMessages(readr::read_tsv(colors_url))
}
h_team_color1 <- team_colors_ %>% filter(teams == h_team_) %>% pull(color1)
h_team_color2 <- team_colors_ %>% filter(teams == h_team_) %>% pull(color2)
a_team_color1 <- team_colors_ %>% filter(teams == a_team_) %>% pull(color1)
a_team_color2 <- team_colors_ %>% filter(teams == a_team_) %>% pull(color2)
if (diverge_ == TRUE) {
h_team_color1_family <- team_colors_ %>% filter(teams == h_team_) %>% select(color1_family) %>% pull()
a_team_color1_family <- team_colors_ %>% filter(teams == a_team_) %>% select(color1_family) %>% pull()
if (h_team_color1_family == a_team_color1_family) {
a_team_color1 <- team_colors_ %>% filter(teams == a_team_) %>% select(color2) %>% pull()
a_team_color2 <- team_colors_ %>% filter(teams == a_team_) %>% select(color1) %>% pull()
}
}
df_colors <- tibble(
home_1 = h_team_color1, home_2 = h_team_color2, away_1 = a_team_color1, away_2 = a_team_color2
)
return(df_colors)
}
if (stafford_direction == "left") {
line_of_scrimmage = stafford_play$absoluteYardlineNumber
to_go_line = line_of_scrimmage - stafford_play$yardsToGo
} else {
line_of_scrimmage = 100 - stafford_play$absoluteYardlineNumber
to_go_line = line_of_scrimmage + stafford_play$yardsToGo
}
df_colors <- fetch_team_colors(h_team_ = "DET", a_team_ = "GB", diverge_ = T)
play_frames <- plot_field() +
# line of scrimmage
annotate(
"segment",
x = line_of_scrimmage, xend = line_of_scrimmage, y = 0, yend = 160/3,
colour = "#0d41e1", size = 1.5
) +
# 1st down marker
annotate(
"segment",
x = to_go_line, xend = to_go_line, y = 0, yend = 160/3,
colour = "#f9c80e", size = 1.5
) +
# away team velocities
geom_segment(
data = stafford_track %>% dplyr::filter(team == "GB"),
mapping = aes(x = x, y = y, xend = x + v_x, yend = y + v_y),
colour = df_colors$away_1, size = 1, arrow = arrow(length = unit(0.01, "npc"))
) +
# home team velocities
geom_segment(
data = stafford_track %>% dplyr::filter(team == "DET"),
mapping = aes(x = x, y = y, xend = x + v_x, yend = y + v_y),
colour = df_colors$home_2, size = 1, arrow = arrow(length = unit(0.01, "npc"))
) +
# away team locs and jersey numbers
geom_point(
data = stafford_track %>% dplyr::filter(team == "GB"),
mapping = aes(x = x, y = y),
fill = "#f8f9fa", colour = df_colors$away_2,
shape = 21, alpha = 1, size = 8, stroke = 1.5
) +
geom_text(
data = stafford_track %>% dplyr::filter(team == "GB"),
mapping = aes(x = x, y = y, label = jerseyNumber),
colour = df_colors$away_1, size = 4.5
) +
# home team locs and jersey numbers
geom_point(
data = stafford_track %>% dplyr::filter(team == "DET"),
mapping = aes(x = x, y = y),
fill = df_colors$home_1, colour = df_colors$home_2,
shape = 21, alpha = 1, size = 8, stroke = 1.5
) +
geom_text(
data = stafford_track %>% dplyr::filter(team == "DET"),
mapping = aes(x = x, y = y, label = jerseyNumber),
colour = df_colors$home_2, size = 4.5,
) +
# ball
geom_point(
data = stafford_track %>% dplyr::filter(team == "football"),
mapping = aes(x = x, y = y),
fill = "#935e38", colour = "#d9d9d9",
shape = 21, alpha = 1, size = 4, stroke = 1
) +
# title
labs(title = stafford_play$playDescription) +
# animation stuff
transition_time(frameId) +
ease_aes('linear') +
NULL
play_length <- length(unique(stafford_track$frameId))
play_anim <- animate(
play_frames,
fps = 10,
nframe = play_length,
width = 800,
height = 400,
end_pause = 0
)
play_anim
############## implementing a field control model ##############
# 1. compute player's distance from ball
compute_distance_from_ball <- function(tracking_data) {
tracking_data <- tracking_data %>%
dplyr::inner_join(
tracking_data %>%
dplyr::filter(team == "football") %>%
dplyr::select(frameId, ball_x = x, ball_y = y),
by = "frameId"
) %>%
dplyr::mutate(
distance_from_ball = sqrt((x-ball_x)^2 + (y-ball_y)^2)
) %>%
dplyr::select(-ball_x, -ball_y)
return(tracking_data)
}
stafford_track <- stafford_track %>% compute_distance_from_ball()
# 2. compute each player's speed ratio
# here we're using a max speed of 13 yds/s,
# which about lines up with the max speeds seen in
# the Next Gen Stats Fastest Ballcarrier tables
compute_speed_ratio <- function(tracking_data, s_max = 13.00) {
tracking_data <- tracking_data %>%
dplyr::mutate(
s_ratio = s / s_max
)
return(tracking_data)
}
stafford_track <- stafford_track %>% compute_speed_ratio()
# 3. compute each player's next location
compute_next_loc <- function(tracking_data, delta_t = 0.50) {
tracking_data <- tracking_data %>%
dplyr::mutate(
x_next = x + v_x * delta_t,
y_next = y + v_y * delta_t
)
return(tracking_data)
}
stafford_track <- stafford_track %>% compute_next_loc()
# 4. compute each player's radius of influence for a given frame
# here we're using a model that approximates the plot shown in
# the appendix of Wide Open Spaces. this original function was
# found by Will Thomson. the modification that I'll make is that
# I'll add a few parameters to the equation, so we can alter the
# min/max radius of influence a player can have, as well as the
# rate at which that radius changes (based on their proximity
# to the ball)
compute_radius_of_influence <- function(tracking_data,
min_radius = 4.00,
max_radius = 10.00,
max_distance_from_ball = 20.00) {
tracking_data <- tracking_data %>%
dplyr::mutate(
radius_of_influence = min_radius + distance_from_ball^3 * (max_radius-min_radius) / max_distance_from_ball,
radius_of_influence = dplyr::case_when(
radius_of_influence > max_radius ~ max_radius,
TRUE ~ radius_of_influence
)
)
return(tracking_data)
}
stafford_track <- stafford_track %>% compute_radius_of_influence()
compute_rotation_matrix <- function(v_theta) {
R <- matrix(
c(cos(v_theta), -sin(v_theta),
sin(v_theta), cos(v_theta)),
nrow = 2,
byrow = TRUE
)
return(R)
}
compute_scaling_matrix <- function(radius_of_influence, s_ratio) {
S <- matrix(
c(radius_of_influence * (1 + s_ratio), 0,
0, radius_of_influence * (1 - s_ratio)),
nrow = 2,
byrow = TRUE
)
return(S)
}
compute_covariance_matrix <- function(v_theta, radius_of_influence, s_ratio) {
R <- compute_rotation_matrix(v_theta)
S <- compute_scaling_matrix(radius_of_influence, s_ratio)
Sigma <- R %*% S %*% S %*% solve(R)
return(Sigma)
}
# note that this is meant operate on just 1 row of the tracking dataset
compute_player_zoi <- function(player_frame_tracking_data, field_grid = NULL) {
if(is.null(field_grid)) {
field_grid <- expand_grid(
x = seq(0, 120, length.out = 120),
y = seq(0, 160/3, length.out = 160/3)
)
}
frameId_ <- player_frame_tracking_data %>% pull(frameId)
displayName_ <- player_frame_tracking_data %>% pull(displayName)
jerseyNumber_ <- player_frame_tracking_data %>% pull(jerseyNumber)
team_ <- player_frame_tracking_data %>% pull(team)
zoi_center_x_ <- player_frame_tracking_data %>% pull(x_next)
zoi_center_y_ <- player_frame_tracking_data %>% pull(y_next)
v_theta_ <- player_frame_tracking_data %>% pull(v_theta)
radius_of_influence_ <- player_frame_tracking_data %>% pull(radius_of_influence)
s_ratio_ <- player_frame_tracking_data %>% pull(s_ratio)
mu <- c(zoi_center_x_, zoi_center_y_)
Sigma <- compute_covariance_matrix(v_theta_, radius_of_influence_, s_ratio_)
player_zoi <- field_grid %>%
dplyr::mutate(
influence = mvtnorm::dmvnorm(x = field_grid, mean = mu, sigma = Sigma),
influence = influence / max(influence),
frameId = frameId_,
displayName = displayName_,
jerseyNumber = jerseyNumber_,
team = team_
)
return(player_zoi)
}
compute_team_frame_control <- function(frame_tracking_data, home_team) {
team_frame_control <- frame_tracking_data %>%
dplyr::filter(team != "football") %>%
dplyr::group_split(displayName) %>%
purrr::map_dfr(., compute_player_zoi) %>%
dplyr::mutate(
influence = dplyr::case_when(
team == home_team ~ -1 * influence,
TRUE ~ influence
)
) %>%
dplyr::group_by(frameId, x, y) %>%
dplyr::summarise(control = sum(influence), .groups = "keep") %>%
dplyr::mutate(control = 1 / (1 + exp(control)))
return(team_frame_control)
}
df_control <- stafford_track %>%
dplyr::filter(team != "football") %>%
dplyr::group_split(frameId) %>%
purrr::map_dfr(., compute_team_frame_control, "DET")
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