6 PRE vs. Post BYE Movement

6.1 BYE Week Effect on Player Movement

Code

in_week1 <- read.csv("data/train/input_2023_w01.csv")
in_week2 <- read.csv("data/train/input_2023_w02.csv")
in_week3 <- read.csv("data/train/input_2023_w03.csv")
in_week4 <- read.csv("data/train/input_2023_w04.csv")
in_week5 <- read.csv("data/train/input_2023_w05.csv")
in_week6 <- read.csv("data/train/input_2023_w06.csv")
in_week7 <- read.csv("data/train/input_2023_w07.csv")
in_week8 <- read.csv("data/train/input_2023_w08.csv")
in_week9 <- read.csv("data/train/input_2023_w09.csv")
in_week10 <- read.csv("data/train/input_2023_w10.csv")
in_week11 <- read.csv("data/train/input_2023_w11.csv")
in_week12 <- read.csv("data/train/input_2023_w12.csv")
in_week13 <- read.csv("data/train/input_2023_w13.csv")
in_week14 <- read.csv("data/train/input_2023_w14.csv")
in_week15 <- read.csv("data/train/input_2023_w15.csv")
in_week16 <- read.csv("data/train/input_2023_w16.csv")
in_week17 <- read.csv("data/train/input_2023_w17.csv")
in_week18 <- read.csv("data/train/input_2023_w18.csv")

sup_data <- read.csv("data/supplementary_data.csv")

Another aspect we were interested in looking at was how the schedules of teams might affect their performance during the regular season. Each team gets one BYE week at some point during the season where they do not play an opponent, and instead get an extra week to rest. Within these schedules, one question we had was how the bye week might affect players.

One way we decided to look at how this rest was affecting the team was how much players were running on average across a game. While we were not able to get the precise amount of how far each player was running since our data was given to us in frames, we could look at the shift between frames and use that as an approximation for the distance within a play. We could then plot these distances to get a distribution across the game and see if there was a difference between before and after the bye.

Code

library(dplyr)
library(ggplot2)
CLE_W4_PID <- sup_data %>%
  filter(week == 4 & (home_team_abbr == 'CLE' | visitor_team_abbr == 'CLE'))

CLE_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'CLE' | visitor_team_abbr == 'CLE'))

CLE_W4_plays <- in_week4 %>%
  filter(in_week4$play_id %in% CLE_W4_PID$play_id)

total_distanceCLE4 <- CLE_W4_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

CLE_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% CLE_W6_PID$play_id)

total_distanceCLE6 <- CLE_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceCLE4<- total_distanceCLE4 %>% mutate(group = "PRE")
total_distanceCLE6<- total_distanceCLE6 %>% mutate(group = "POST")
CLE_Combined <- bind_rows(total_distanceCLE4, total_distanceCLE6)

ggplot(CLE_Combined, aes(x = total_distance, fill = group)) +
  geom_density(alpha = 0.6, color = NA) +
  labs(
    title = "Distribution of Player Distances by Team",
    x = "Distance (yards)",
    y = "Density"
  ) + theme_minimal()

Looking at the Cleveland Browns, we can see that the distributions are fairly similar before and after the bye with a slight increase in before the bye for the upper end of the tail. However, we can graph all of the teams together and overlay to see if a larger trend emerges.

Code

LAC_W4_PID <- sup_data %>%
  filter(week == 4 & (home_team_abbr == 'LAC' | visitor_team_abbr == 'LAC'))

LAC_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'LAC' | visitor_team_abbr == 'LAC'))

LAC_W4_plays <- in_week4 %>%
  filter(in_week4$play_id %in% LAC_W4_PID$play_id)

total_distanceLAC4 <- LAC_W4_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

LAC_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% LAC_W6_PID$play_id)

total_distanceLAC6 <- LAC_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceLAC4<- total_distanceLAC4 %>% mutate(group = "PRE")
total_distanceLAC6<- total_distanceLAC6 %>% mutate(group = "POST")
LAC_Combined <- bind_rows(total_distanceLAC4, total_distanceLAC6)

Code

SEA_W4_PID <- sup_data %>%
  filter(week == 4 & (home_team_abbr == 'SEA' | visitor_team_abbr == 'SEA'))

SEA_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'SEA' | visitor_team_abbr == 'SEA'))

SEA_W4_plays <- in_week4 %>%
  filter(in_week4$play_id %in% SEA_W4_PID$play_id)

total_distanceSEA4 <- SEA_W4_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

SEA_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% SEA_W6_PID$play_id)

total_distanceSEA6 <- SEA_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceSEA4<- total_distanceSEA4 %>% mutate(group = "PRE")
total_distanceSEA6<- total_distanceSEA6 %>% mutate(group = "POST")
SEA_Combined <- bind_rows(total_distanceSEA4, total_distanceSEA6)

Code

TB_W4_PID <- sup_data %>%
  filter(week == 4 & (home_team_abbr == 'TB' | visitor_team_abbr == 'TB'))

TB_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'TB' | visitor_team_abbr == 'TB'))

TB_W4_plays <- in_week4 %>%
  filter(in_week4$play_id %in% TB_W4_PID$play_id)
total_distanceTB4 <- TB_W4_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

TB_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% TB_W6_PID$play_id)

total_distanceTB6 <- TB_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceTB4<- total_distanceTB4 %>% mutate(group = "PRE")
total_distanceTB6<- total_distanceTB6 %>% mutate(group = "POST")
TB_Combined <- bind_rows(total_distanceTB4, total_distanceTB6)

Code

GB_W5_PID <- sup_data %>%
  filter(week == 5 & (home_team_abbr == 'GB' | visitor_team_abbr == 'GB'))

GB_W7_PID <- sup_data %>%
  filter(week == 7 & (home_team_abbr == 'GB' | visitor_team_abbr == 'GB'))

GB_W5_plays <- in_week5 %>%
  filter(in_week5$play_id %in% GB_W5_PID$play_id)

total_distanceGB5 <- GB_W5_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

GB_W7_plays <- in_week7 %>%
  filter(in_week7$play_id %in% GB_W7_PID$play_id)

total_distanceGB7 <- GB_W7_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceGB5<- total_distanceGB5 %>% mutate(group = "PRE")
total_distanceGB7<- total_distanceGB7 %>% mutate(group = "POST")
GB_Combined <- bind_rows(total_distanceGB5, total_distanceGB7)

Code

PIT_W5_PID <- sup_data %>%
  filter(week == 5 & (home_team_abbr == 'PIT' | visitor_team_abbr == 'PIT'))

PIT_W7_PID <- sup_data %>%
  filter(week == 7 & (home_team_abbr == 'PIT' | visitor_team_abbr == 'PIT'))

PIT_W5_plays <- in_week5 %>%
  filter(in_week5$play_id %in% PIT_W5_PID$play_id)

total_distancePIT5 <- PIT_W5_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

PIT_W7_plays <- in_week7 %>%
  filter(in_week7$play_id %in% PIT_W7_PID$play_id)

total_distancePIT7 <- PIT_W7_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distancePIT5<- total_distancePIT5 %>% mutate(group = "PRE")
total_distancePIT7<- total_distancePIT7 %>% mutate(group = "POST")
PIT_Combined <- bind_rows(total_distancePIT5, total_distancePIT7)

Code

CAR_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'CAR' | visitor_team_abbr == 'CAR'))

CAR_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'CAR' | visitor_team_abbr == 'CAR'))

CAR_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% CAR_W6_PID$play_id)

total_distanceCAR6 <- CAR_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

CAR_W8_plays <- in_week8 %>%
  filter(in_week8$play_id %in% CAR_W8_PID$play_id)

total_distanceCAR8 <- CAR_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceCAR6<- total_distanceCAR6 %>% mutate(group = "PRE")
total_distanceCAR8<- total_distanceCAR8 %>% mutate(group = "POST")
CAR_Combined <- bind_rows(total_distanceCAR6, total_distanceCAR8)

Code

CIN_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'CIN' | visitor_team_abbr == 'CIN'))

CIN_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'CIN' | visitor_team_abbr == 'CIN'))

CIN_W6_plays <- in_week6 %>%
  filter(in_week6$play_id %in% CIN_W6_PID$play_id)

total_distanceCIN6 <- CIN_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

CIN_W8_plays <- in_week8 %>%
  filter(in_week8$play_id %in% CIN_W8_PID$play_id)

total_distanceCIN8 <- CIN_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceCIN6<- total_distanceCIN6 %>% mutate(group = "PRE")
total_distanceCIN8<- total_distanceCIN8 %>% mutate(group = "POST")
CIN_Combined <- bind_rows(total_distanceCIN6, total_distanceCIN8)

Code

DAL_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'DAL' | visitor_team_abbr == 'DAL'))

DAL_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'DAL' | visitor_team_abbr == 'DAL'))

DAL_W6_plays <- in_week6 %>%
  filter(play_id %in% DAL_W6_PID$play_id)

total_distanceDAL6 <- DAL_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

DAL_W8_plays <- in_week8 %>%
  filter(play_id %in% DAL_W8_PID$play_id)

total_distanceDAL8 <- DAL_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceDAL6<- total_distanceDAL6 %>% mutate(group = "PRE")
total_distanceDAL8<- total_distanceDAL8 %>% mutate(group = "POST")
DAL_Combined <- bind_rows(total_distanceDAL6, total_distanceDAL8)

Code

HOU_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'HOU' | visitor_team_abbr == 'HOU'))

HOU_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'HOU' | visitor_team_abbr == 'HOU'))

HOU_W6_plays <- in_week6 %>%
  filter(play_id %in% HOU_W6_PID$play_id)

total_distanceHOU6 <- HOU_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

HOU_W8_plays <- in_week8 %>%
  filter(play_id %in% HOU_W8_PID$play_id)

total_distanceHOU8 <- HOU_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceHOU6<- total_distanceHOU6 %>% mutate(group = "PRE")
total_distanceHOU8<- total_distanceHOU8 %>% mutate(group = "POST")
HOU_Combined <- bind_rows(total_distanceHOU6, total_distanceHOU8)

Code

NYJ_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'NYJ' | visitor_team_abbr == 'NYJ'))

NYJ_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'NYJ' | visitor_team_abbr == 'NYJ'))

NYJ_W6_plays <- in_week6 %>%
  filter(play_id %in% NYJ_W6_PID$play_id)

total_distanceNYJ6 <- NYJ_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

NYJ_W8_plays <- in_week8 %>%
  filter(play_id %in% NYJ_W8_PID$play_id)

total_distanceNYJ8 <- NYJ_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceNYJ6<- total_distanceNYJ6 %>% mutate(group = "PRE")
total_distanceNYJ8<- total_distanceNYJ8 %>% mutate(group = "POST")
NYJ_Combined <- bind_rows(total_distanceNYJ6, total_distanceNYJ8)

Code

TEN_W6_PID <- sup_data %>%
  filter(week == 6 & (home_team_abbr == 'TEN' | visitor_team_abbr == 'TEN'))

TEN_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'TEN' | visitor_team_abbr == 'TEN'))

TEN_W6_plays <- in_week6 %>%
  filter(play_id %in% TEN_W6_PID$play_id)

total_distanceTEN6 <- TEN_W6_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

TEN_W8_plays <- in_week8 %>%
  filter(play_id %in% TEN_W8_PID$play_id)

total_distanceTEN8 <- TEN_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceTEN6<- total_distanceTEN6 %>% mutate(group = "PRE")
total_distanceTEN8<- total_distanceTEN8 %>% mutate(group = "POST")
TEN_Combined <- bind_rows(total_distanceTEN6, total_distanceTEN8)

Code

DB_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'DEN' | visitor_team_abbr == 'DEN'))

DB_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'DEN' | visitor_team_abbr == 'DEN'))

DB_W8_plays <- in_week8 %>%
  filter(play_id %in% DB_W8_PID$play_id)

total_distanceDB8 <- DB_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

DB_W10_plays <- in_week10 %>%
  filter(play_id %in% DB_W10_PID$play_id)

total_distanceDB10 <- DB_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceDB8<- total_distanceDB8 %>% mutate(group = "PRE")
total_distanceDB10<- total_distanceDB10 %>% mutate(group = "POST")
DB_Combined <- bind_rows(total_distanceDB8, total_distanceDB10)

Code

#Detroit Lions
DL_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'DET' | visitor_team_abbr == 'DET'))

DL_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'DET' | visitor_team_abbr == 'DET'))

DL_W8_plays <- in_week8 %>%
  filter(play_id %in% DL_W8_PID$play_id)

total_distanceDL8 <- DL_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

DL_W10_plays <- in_week10 %>%
  filter(play_id %in% DL_W10_PID$play_id)

total_distanceDL10 <- DL_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceDL8<- total_distanceDL8 %>% mutate(group = "PRE")
total_distanceDL10<- total_distanceDL10 %>% mutate(group = "POST")
DL_Combined <- bind_rows(total_distanceDL8, total_distanceDL10)

Code

JAX_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'JAX' | visitor_team_abbr == 'JAX'))

JAX_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'JAX' | visitor_team_abbr == 'JAX'))

JAX_W8_plays <- in_week8 %>%
  filter(play_id %in% JAX_W8_PID$play_id)

total_distanceJAX8 <- JAX_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

JAX_W10_plays <- in_week10 %>%
  filter(play_id %in% JAX_W10_PID$play_id)

total_distanceJAX10 <- JAX_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceJAX8<- total_distanceJAX8 %>% mutate(group = "PRE")
total_distanceJAX10<- total_distanceJAX10 %>% mutate(group = "POST")
JAX_Combined <- bind_rows(total_distanceJAX8, total_distanceJAX10)

Code

SF_W8_PID <- sup_data %>%
  filter(week == 8 & (home_team_abbr == 'SF' | visitor_team_abbr == 'SF'))

SF_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'SF' | visitor_team_abbr == 'SF'))

SF_W8_plays <- in_week8 %>%
  filter(play_id %in% SF_W8_PID$play_id)

total_distanceSF8 <- SF_W8_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

SF_W10_plays <- in_week10 %>%
  filter(play_id %in% SF_W10_PID$play_id)

total_distanceSF10 <- SF_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceSF8<- total_distanceSF8 %>% mutate(group = "PRE")
total_distanceSF10<- total_distanceSF10 %>% mutate(group = "POST")
SF_Combined <- bind_rows(total_distanceSF8, total_distanceSF10)

Code

#Kansas City Chiefs
KC_W9_PID <- sup_data %>%
  filter(week == 9 & (home_team_abbr == 'KC' | visitor_team_abbr == 'KC'))

KC_W11_PID <- sup_data %>%
  filter(week == 11 & (home_team_abbr == 'KC' | visitor_team_abbr == 'KC'))

KC_W9_plays <- in_week9 %>%
  filter(play_id %in% KC_W9_PID$play_id)

total_distanceKC9 <- KC_W9_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

KC_W11_plays <- in_week11 %>%
  filter(play_id %in% KC_W11_PID$play_id)

total_distanceKC11 <- KC_W11_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceKC9<- total_distanceKC9 %>% mutate(group = "PRE")
total_distanceKC11<- total_distanceKC11 %>% mutate(group = "POST")
KC_Combined <- bind_rows(total_distanceKC9, total_distanceKC11)

Code

#Los Angelos Rams
LAR_W9_PID <- sup_data %>%
  filter(week == 9 & (home_team_abbr == 'LA' | visitor_team_abbr == 'LA'))

LAR_W11_PID <- sup_data %>%
  filter(week == 11 & (home_team_abbr == 'LA' | visitor_team_abbr == 'LA'))

LAR_W9_plays <- in_week9 %>%
  filter(play_id %in% LAR_W9_PID$play_id)

total_distanceLAR9 <- LAR_W9_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

LAR_W11_plays <- in_week11 %>%
  filter(play_id %in% LAR_W11_PID$play_id)

total_distanceLAR11 <- LAR_W11_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceLAR9<- total_distanceLAR9 %>% mutate(group = "PRE")
total_distanceLAR11<- total_distanceLAR11 %>% mutate(group = "POST")
LAR_Combined <- bind_rows(total_distanceLAR9, total_distanceLAR11)

Code

#Miami Dolphins
MIA_W9_PID <- sup_data %>%
  filter(week == 9 & (home_team_abbr == 'MIA' | visitor_team_abbr == 'MIA'))

MIA_W11_PID <- sup_data %>%
  filter(week == 11 & (home_team_abbr == 'MIA' | visitor_team_abbr == 'MIA'))

MIA_W9_plays <- in_week9 %>%
  filter(play_id %in% MIA_W9_PID$play_id)

total_distanceMIA9 <- MIA_W9_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

MIA_W11_plays <- in_week11 %>%
  filter(play_id %in% MIA_W11_PID$play_id)

total_distanceMIA11 <- MIA_W11_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceMIA9<- total_distanceMIA9 %>% mutate(group = "PRE")
total_distanceMIA11<- total_distanceMIA11 %>% mutate(group = "POST")
MIA_Combined <- bind_rows(total_distanceMIA9, total_distanceMIA11)

Code

#Philadelphia Eagles
PHI_W9_PID <- sup_data %>%
  filter(week == 9 & (home_team_abbr == 'PHI' | visitor_team_abbr == 'PHI'))

PHI_W11_PID <- sup_data %>%
  filter(week == 11 & (home_team_abbr == 'PHI' | visitor_team_abbr == 'PHI'))

PHI_W9_plays <- in_week9 %>%
  filter(play_id %in% PHI_W9_PID$play_id)

total_distancePHI9 <- PHI_W9_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

PHI_W11_plays <- in_week11 %>%
  filter(play_id %in% PHI_W11_PID$play_id)

total_distancePHI11 <- PHI_W11_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distancePHI9<- total_distancePHI9 %>% mutate(group = "PRE")
total_distancePHI11<- total_distancePHI11 %>% mutate(group = "POST")
PHI_Combined <- bind_rows(total_distancePHI9, total_distancePHI11)

Code

#Atlanta Falcons
ATL_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'ATL' | visitor_team_abbr == 'ATL'))

ATL_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'ATL' | visitor_team_abbr == 'ATL'))

ATL_W10_plays <- in_week10 %>%
  filter(play_id %in% ATL_W10_PID$play_id)

total_distanceATL10 <- ATL_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

ATL_W12_plays <- in_week12 %>%
  filter(play_id %in% ATL_W12_PID$play_id)

total_distanceATL12 <- ATL_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceATL10<- total_distanceATL10 %>% mutate(group = "PRE")
total_distanceATL12<- total_distanceATL12 %>% mutate(group = "POST")
ATL_Combined <- bind_rows(total_distanceATL10, total_distanceATL12)

Code

#Indiana Colts
IND_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'IND' | visitor_team_abbr == 'IND'))

IND_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'IND' | visitor_team_abbr == 'IND'))

IND_W10_plays <- in_week10 %>%
  filter(play_id %in% IND_W10_PID$play_id)

total_distanceIND10 <- IND_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

IND_W12_plays <- in_week12 %>%
  filter(play_id %in% IND_W12_PID$play_id)

total_distanceIND12 <- IND_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceIND10<- total_distanceIND10 %>% mutate(group = "PRE")
total_distanceIND12<- total_distanceIND12 %>% mutate(group = "POST")
IND_Combined <- bind_rows(total_distanceIND10, total_distanceIND12)

Code

#New England Patriots
NE_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'NE' | visitor_team_abbr == 'NE'))

NE_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'NE' | visitor_team_abbr == 'NE'))

NE_W10_plays <- in_week10 %>%
  filter(play_id %in% NE_W10_PID$play_id)

total_distanceNE10 <- NE_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

NE_W12_plays <- in_week12 %>%
  filter(play_id %in% NE_W12_PID$play_id)

total_distanceNE12 <- NE_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceNE10<- total_distanceNE10 %>% mutate(group = "PRE")
total_distanceNE12<- total_distanceNE12 %>% mutate(group = "POST")
NE_Combined <- bind_rows(total_distanceNE10, total_distanceNE12)

Code

#New Orleans Saints
NO_W10_PID <- sup_data %>%
  filter(week == 10 & (home_team_abbr == 'NO' | visitor_team_abbr == 'NO'))

NO_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'NO' | visitor_team_abbr == 'NO'))

NO_W10_plays <- in_week10 %>%
  filter(play_id %in% NO_W10_PID$play_id)

total_distanceNO10 <- NO_W10_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

NO_W12_plays <- in_week12 %>%
  filter(play_id %in% NO_W12_PID$play_id)

total_distanceNO12 <- NO_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceNO10<- total_distanceNO10 %>% mutate(group = "PRE")
total_distanceNO12<- total_distanceNO12 %>% mutate(group = "POST")
NO_Combined <- bind_rows(total_distanceNO10, total_distanceNO12)

Code

#Baltimore Ravens
BAL_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'BAL' | visitor_team_abbr == 'BAL'))

BAL_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'BAL' | visitor_team_abbr == 'BAL'))

BAL_W12_plays <- in_week12 %>%
  filter(play_id %in% BAL_W12_PID$play_id)

total_distanceBAL12 <- BAL_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

BAL_W14_plays <- in_week14 %>%
  filter(play_id %in% BAL_W14_PID$play_id)

total_distanceBAL14 <- BAL_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceBAL12<- total_distanceBAL12 %>% mutate(group = "PRE")
total_distanceBAL14<- total_distanceBAL14 %>% mutate(group = "POST")
BAL_Combined <- bind_rows(total_distanceBAL12, total_distanceBAL14)

Code

#Buffalo Bills
BUF_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'BUF' | visitor_team_abbr == 'BUF'))

BUF_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'BUF' | visitor_team_abbr == 'BUF'))

BUF_W12_plays <- in_week12 %>%
  filter(play_id %in% BUF_W12_PID$play_id)

total_distanceBUF12 <- BUF_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

BUF_W14_plays <- in_week14 %>%
  filter(play_id %in% BUF_W14_PID$play_id)

total_distanceBUF14 <- BUF_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceBUF12<- total_distanceBUF12 %>% mutate(group = "PRE")
total_distanceBUF14<- total_distanceBUF14 %>% mutate(group = "POST")
BUF_Combined <- bind_rows(total_distanceBUF12, total_distanceBUF14)

Code

#Chicago Bears
CHI_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'CHI' | visitor_team_abbr == 'CHI'))

CHI_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'CHI' | visitor_team_abbr == 'CHI'))

CHI_W12_plays <- in_week12 %>%
  filter(play_id %in% CHI_W12_PID$play_id)

total_distanceCHI12 <- CHI_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

CHI_W14_plays <- in_week14 %>%
  filter(play_id %in% CHI_W14_PID$play_id)

total_distanceCHI14 <- CHI_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceCHI12<- total_distanceCHI12 %>% mutate(group = "PRE")
total_distanceCHI14<- total_distanceCHI14 %>% mutate(group = "POST")
CHI_Combined <- bind_rows(total_distanceCHI12, total_distanceCHI14)

Code

#Las Vegas Raiders

LAV_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'LV' | visitor_team_abbr == 'LV'))

LAV_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'LV' | visitor_team_abbr == 'LV'))

LAV_W12_plays <- in_week12 %>%
  filter(play_id %in% LAV_W12_PID$play_id)

total_distanceLAV12 <- LAV_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

LAV_W14_plays <- in_week14 %>%
  filter(play_id %in% LAV_W14_PID$play_id)

total_distanceLAV14 <- LAV_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceLAV12<- total_distanceLAV12 %>% mutate(group = "PRE")
total_distanceLAV14<- total_distanceLAV14 %>% mutate(group = "POST")
LAV_Combined <- bind_rows(total_distanceLAV12, total_distanceLAV14)

Code

#Minnesota Vikings

MIN_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'MIN' | visitor_team_abbr == 'MIN'))

MIN_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'MIN' | visitor_team_abbr == 'MIN'))

MIN_W12_plays <- in_week12 %>%
  filter(play_id %in% MIN_W12_PID$play_id)

total_distanceMIN12 <- MIN_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

MIN_W14_plays <- in_week14 %>%
  filter(play_id %in% MIN_W14_PID$play_id)

total_distanceMIN14 <- MIN_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceMIN12<- total_distanceMIN12 %>% mutate(group = "PRE")
total_distanceMIN14<- total_distanceMIN14 %>% mutate(group = "POST")
MIN_Combined <- bind_rows(total_distanceMIN12, total_distanceMIN14)

Code

#New York Giants

NYG_W12_PID <- sup_data %>%
  filter(week == 12 & (home_team_abbr == 'NYG' | visitor_team_abbr == 'NYG'))

NYG_W14_PID <- sup_data %>%
  filter(week == 14 & (home_team_abbr == 'NYG' | visitor_team_abbr == 'NYG'))

NYG_W12_plays <- in_week12 %>%
  filter(play_id %in% NYG_W12_PID$play_id)

total_distanceNYG12 <- NYG_W12_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

NYG_W14_plays <- in_week14 %>%
  filter(play_id %in% NYG_W14_PID$play_id)

total_distanceNYG14 <- NYG_W14_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceNYG12<- total_distanceNYG12 %>% mutate(group = "PRE")
total_distanceNYG14<- total_distanceNYG14 %>% mutate(group = "POST")
NYG_Combined <- bind_rows(total_distanceNYG12, total_distanceNYG14)

Code

#Arizona Cardinals

ARI_W13_PID <- sup_data %>%
  filter(week == 13 & (home_team_abbr == 'ARI' | visitor_team_abbr == 'ARI'))

ARI_W15_PID <- sup_data %>%
  filter(week == 15 & (home_team_abbr == 'ARI' | visitor_team_abbr == 'ARI'))

ARI_W13_plays <- in_week13 %>%
  filter(play_id %in% ARI_W13_PID$play_id)

total_distanceARI13 <- ARI_W13_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

ARI_W15_plays <- in_week15 %>%
  filter(play_id %in% ARI_W15_PID$play_id)

total_distanceARI15 <- ARI_W15_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceARI13<- total_distanceARI13 %>% mutate(group = "PRE")
total_distanceARI15<- total_distanceARI15 %>% mutate(group = "POST")
ARI_Combined <- bind_rows(total_distanceARI13, total_distanceARI15)

Code

#Washington Commanders

WAS_W13_PID <- sup_data %>%
  filter(week == 13 & (home_team_abbr == 'WAS' | visitor_team_abbr == 'WAS'))

WAS_W15_PID <- sup_data %>%
  filter(week == 15 & (home_team_abbr == 'WAS' | visitor_team_abbr == 'WAS'))

WAS_W13_plays <- in_week13 %>%
  filter(play_id %in% WAS_W13_PID$play_id)

total_distanceWAS13 <- WAS_W13_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

WAS_W15_plays <- in_week15 %>%
  filter(play_id %in% WAS_W15_PID$play_id)

total_distanceWAS15 <- WAS_W15_plays %>%
  arrange(play_id, nfl_id, frame_id) %>% 
  group_by(play_id, nfl_id) %>%
  mutate(
    dx = x - lag(x),
    dy = y - lag(y),
    step_dist = sqrt(dx^2 + dy^2)
  ) %>%
  summarize(total_distance = sum(step_dist, na.rm = TRUE)
)%>%
  ungroup()

total_distanceWAS13<- total_distanceWAS13 %>% mutate(group = "PRE")
total_distanceWAS15<- total_distanceWAS15 %>% mutate(group = "POST")
WAS_Combined <- bind_rows(total_distanceWAS13, total_distanceWAS15)

Code

total_distance_PRE <- bind_rows(total_distanceCLE4,total_distanceGB5,total_distanceLAC4,total_distanceSEA4, total_distanceTB4, total_distancePIT5, total_distanceCAR6, total_distanceCIN6, total_distanceDAL6, total_distanceHOU6, total_distanceNYJ6, total_distanceTEN6, total_distanceDB8, total_distanceLAR9, total_distanceMIA9, total_distancePHI9, total_distanceATL10, total_distanceIND10, total_distanceNE10,total_distanceNO10, total_distanceBAL12, total_distanceBUF12, total_distanceCHI12, total_distanceLAV12, total_distanceMIN12, total_distanceNYG12, total_distanceARI13, total_distanceWAS13, total_distanceDL8, total_distanceJAX8, total_distanceSF8, total_distanceKC9)

total_distance_POST <- bind_rows(total_distanceCLE6,total_distanceGB7,total_distanceLAC6,total_distanceSEA6, total_distanceTB6, total_distancePIT7, total_distanceCAR8, total_distanceCIN8, total_distanceDAL8, total_distanceHOU8, total_distanceNYJ8, total_distanceTEN8, total_distanceDB10, total_distanceLAR11, total_distanceMIA11, total_distancePHI11, total_distanceATL12, total_distanceIND12, total_distanceNE12,total_distanceNO12, total_distanceBAL14, total_distanceBUF14, total_distanceCHI14, total_distanceLAV14, total_distanceMIN14, total_distanceNYG14, total_distanceARI15, total_distanceWAS15, total_distanceDL10, total_distanceJAX10, total_distanceSF10, total_distanceKC11)

Total_combined <- bind_rows(total_distance_PRE, total_distance_POST)
print(ggplot(Total_combined, aes(x = total_distance, fill = group)) +
  geom_density(alpha = 0.5, color = NA) +
  labs(
    title = "Distribution of Player Distances by Team",
    x = " Distance Ran By Player Per Play (yards)",
    y = "Density"
  ) + scale_fill_brewer(palette = "Set1")+ theme_minimal())

When we look at the total distribution of teams across the league, we see that any differences we noticed in individual cases from before are no longer apparent, and the two distributions look nearly identical. This shows that though for individual teams, we might see some minor differences between the energy levels before and after a bye week, across the entire league there is essentially no difference.

Code

total_distanceCLE4<- total_distanceCLE4 %>% mutate(BYE_WEEK = 5)
total_distanceCLE6<- total_distanceCLE6 %>% mutate(BYE_WEEK = 5)
total_distanceLAC4<- total_distanceLAC4 %>% mutate(BYE_WEEK = 5)
total_distanceLAC6<- total_distanceLAC6 %>% mutate(BYE_WEEK = 5)
total_distanceSEA4<- total_distanceSEA4 %>% mutate(BYE_WEEK = 5)
total_distanceSEA6<- total_distanceSEA6 %>% mutate(BYE_WEEK = 5)
total_distanceTB4<- total_distanceTB4 %>% mutate(BYE_WEEK = 5)
total_distanceTB6<- total_distanceTB6 %>% mutate(BYE_WEEK = 5)
total_distanceGB5<- total_distanceGB5 %>% mutate(BYE_WEEK = 6)
total_distanceGB7<- total_distanceGB7 %>% mutate(BYE_WEEK = 6)
total_distancePIT5<- total_distancePIT5 %>% mutate(BYE_WEEK = 6)
total_distancePIT7<- total_distancePIT7 %>% mutate(BYE_WEEK = 6)
total_distanceCAR6<- total_distanceCAR6 %>% mutate(BYE_WEEK = 7)
total_distanceCAR8<- total_distanceCAR8 %>% mutate(BYE_WEEK = 7)
total_distanceCIN6<- total_distanceCIN6 %>% mutate(BYE_WEEK = 7)
total_distanceCIN8<- total_distanceCIN8 %>% mutate(BYE_WEEK = 7)
total_distanceDAL6<- total_distanceDAL6 %>% mutate(BYE_WEEK = 7)
total_distanceDAL8<- total_distanceDAL8 %>% mutate(BYE_WEEK = 7)
total_distanceHOU6<- total_distanceHOU6 %>% mutate(BYE_WEEK = 7)
total_distanceHOU8<- total_distanceHOU8 %>% mutate(BYE_WEEK = 7)
total_distanceNYJ6<- total_distanceNYJ6 %>% mutate(BYE_WEEK = 7)
total_distanceNYJ8<- total_distanceNYJ8 %>% mutate(BYE_WEEK = 7)
total_distanceTEN6<- total_distanceTEN6 %>% mutate(BYE_WEEK = 7)
total_distanceTEN8<- total_distanceTEN8 %>% mutate(BYE_WEEK = 7)
total_distanceDB8<- total_distanceDB8 %>% mutate(BYE_WEEK = 9)
total_distanceDB10<- total_distanceDB10 %>% mutate(BYE_WEEK = 9)
total_distanceDL8<- total_distanceDL8 %>% mutate(BYE_WEEK = 9)
total_distanceDL10<- total_distanceDL10 %>% mutate(BYE_WEEK = 9)
total_distanceJAX8<- total_distanceJAX8 %>% mutate(BYE_WEEK = 9)
total_distanceJAX10<- total_distanceJAX10 %>% mutate(BYE_WEEK = 9)
total_distanceSF8<- total_distanceSF8 %>% mutate(BYE_WEEK = 9)
total_distanceSF10<- total_distanceSF10 %>% mutate(BYE_WEEK = 9)
total_distanceKC9<- total_distanceKC9 %>% mutate(BYE_WEEK = 10)
total_distanceKC11<- total_distanceKC11 %>% mutate(BYE_WEEK = 10)
total_distanceLAR9<- total_distanceLAR9 %>% mutate(BYE_WEEK = 10)
total_distanceLAR11<- total_distanceLAR11 %>% mutate(BYE_WEEK = 10)
total_distanceMIA9<- total_distanceMIA9 %>% mutate(BYE_WEEK = 10)
total_distanceMIA11<- total_distanceMIA11 %>% mutate(BYE_WEEK = 10)
total_distancePHI9<- total_distancePHI9 %>% mutate(BYE_WEEK = 10)
total_distancePHI11<- total_distancePHI11 %>% mutate(BYE_WEEK = 10)
total_distanceATL10<- total_distanceATL10 %>% mutate(BYE_WEEK = 11)
total_distanceATL12<- total_distanceATL12 %>% mutate(BYE_WEEK = 11)
total_distanceIND10<- total_distanceIND10 %>% mutate(BYE_WEEK = 11)
total_distanceIND12<- total_distanceIND12 %>% mutate(BYE_WEEK = 11)
total_distanceNE10<- total_distanceNE10 %>% mutate(BYE_WEEK = 11)
total_distanceNE12<- total_distanceNE12 %>% mutate(BYE_WEEK = 11)
total_distanceNO10<- total_distanceNO10 %>% mutate(BYE_WEEK = 11)
total_distanceNO12<- total_distanceNO12 %>% mutate(BYE_WEEK = 11)
total_distanceBAL12<- total_distanceBAL12 %>% mutate(BYE_WEEK = 13)
total_distanceBAL14<- total_distanceBAL14 %>% mutate(BYE_WEEK = 13)
total_distanceBUF12<- total_distanceBUF12 %>% mutate(BYE_WEEK = 13)
total_distanceBUF14<- total_distanceBUF14 %>% mutate(BYE_WEEK = 13)
total_distanceCHI12<- total_distanceCHI12 %>% mutate(BYE_WEEK = 13)
total_distanceCHI14<- total_distanceCHI14 %>% mutate(BYE_WEEK = 13)
total_distanceLAV12<- total_distanceLAV12 %>% mutate(BYE_WEEK = 13)
total_distanceLAV14<- total_distanceLAV14 %>% mutate(BYE_WEEK = 13)
total_distanceMIN12<- total_distanceMIN12 %>% mutate(BYE_WEEK = 13)
total_distanceMIN14<- total_distanceMIN14 %>% mutate(BYE_WEEK = 13)
total_distanceNYG12<- total_distanceNYG12 %>% mutate(BYE_WEEK = 13)
total_distanceNYG14<- total_distanceNYG14 %>% mutate(BYE_WEEK = 13)
total_distanceARI13<- total_distanceARI13 %>% mutate(BYE_WEEK = 14)
total_distanceARI15<- total_distanceARI15 %>% mutate(BYE_WEEK = 14)
total_distanceWAS13<- total_distanceWAS13 %>% mutate(BYE_WEEK = 14)
total_distanceWAS15<- total_distanceWAS15 %>% mutate(BYE_WEEK = 14)


total_distance_PRE_BYE <- bind_rows(total_distanceCLE4,total_distanceGB5,total_distanceLAC4,total_distanceSEA4, total_distanceTB4, total_distancePIT5, total_distanceCAR6, total_distanceCIN6, total_distanceDAL6, total_distanceHOU6, total_distanceNYJ6, total_distanceTEN6, total_distanceDB8, total_distanceLAR9, total_distanceMIA9, total_distancePHI9, total_distanceATL10, total_distanceIND10, total_distanceNE10,total_distanceNO10, total_distanceBAL12, total_distanceBUF12, total_distanceCHI12, total_distanceLAV12, total_distanceMIN12, total_distanceNYG12, total_distanceARI13, total_distanceWAS13, total_distanceDL8, total_distanceJAX8, total_distanceSF8, total_distanceKC9)

total_distance_POST_BYE <- bind_rows(total_distanceCLE6,total_distanceGB7,total_distanceLAC6,total_distanceSEA6, total_distanceTB6, total_distancePIT7, total_distanceCAR8, total_distanceCIN8, total_distanceDAL8, total_distanceHOU8, total_distanceNYJ8, total_distanceTEN8, total_distanceDB10, total_distanceLAR11, total_distanceMIA11, total_distancePHI11, total_distanceATL12, total_distanceIND12, total_distanceNE12,total_distanceNO12, total_distanceBAL14, total_distanceBUF14, total_distanceCHI14, total_distanceLAV14, total_distanceMIN14, total_distanceNYG14, total_distanceARI15, total_distanceWAS15, total_distanceDL10, total_distanceJAX10, total_distanceSF10, total_distanceKC11)

Code

BYE_WEEK_TOTAL_DIST <- bind_rows(total_distance_PRE_BYE, total_distance_POST_BYE)
print(ggplot(BYE_WEEK_TOTAL_DIST, aes(x = total_distance, fill = group )) +
  geom_density(alpha = 0.6, color = NA) +
  labs(
    title = "Distribution of Player Movement Per Play Over a Game Faceted by Bye Week",
    x = "Distance of  (yards)",
    y = "Density of "
  ) + coord_cartesian(xlim = c(0, 20)) +
  theme_minimal() + facet_wrap(~factor(BYE_WEEK)))

Faceting this distribution by when the bye week occurs, we see that the global observation holds mostly true for each week. Between all weeks, week 6 seems to have the largest difference between pre and post, with the pre-bye week having a larger percentage of the distribution on the right.

Code

CLE4_avg <- total_distanceCLE4 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )

CLE6_avg <- total_distanceCLE6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAC4_avg <- total_distanceLAC4 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAC6_avg <- total_distanceLAC6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
SEA4_avg <- total_distanceSEA4 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
SEA6_avg <- total_distanceSEA6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
TB4_avg <- total_distanceTB4 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
TB6_avg <- total_distanceTB6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
GB5_avg <- total_distanceGB5 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
GB7_avg <- total_distanceGB7 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
PIT5_avg <- total_distancePIT5 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
PIT7_avg <- total_distancePIT7 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CAR6_avg <- total_distanceCAR6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CAR8_avg <- total_distanceCAR8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CIN6_avg <- total_distanceCIN6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CIN8_avg <- total_distanceCIN8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DAL6_avg <- total_distanceDAL6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DAL8_avg <- total_distanceDAL8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
HOU6_avg <- total_distanceHOU6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
HOU8_avg <- total_distanceHOU8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NYJ6_avg <- total_distanceNYJ6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NYJ8_avg <- total_distanceNYJ8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
TEN6_avg <- total_distanceTEN6 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
TEN8_avg <- total_distanceTEN8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DB8_avg <- total_distanceDB8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DB10_avg <- total_distanceDB10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DL8_avg <- total_distanceDL8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
DL10_avg <- total_distanceDL10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
JAX8_avg <- total_distanceJAX8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
JAX10_avg <- total_distanceJAX10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
SF8_avg <- total_distanceSF8 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
SF10_avg <- total_distanceSF10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
KC9_avg <- total_distanceKC9 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
KC11_avg <- total_distanceKC11 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAR9_avg <- total_distanceLAR9 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAR11_avg <- total_distanceLAR11 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
MIA9_avg <- total_distanceMIA9 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
MIA11_avg <- total_distanceMIA11 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
PHI9_avg <- total_distancePHI9 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
PHI11_avg <- total_distancePHI11 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
ATL10_avg <- total_distanceATL10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
ATL12_avg <- total_distanceATL12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
IND10_avg <- total_distanceIND10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
IND12_avg <- total_distanceIND12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NE10_avg <- total_distanceATL10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NE12_avg <- total_distanceNE12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NO10_avg <- total_distanceNO10 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NO12_avg <- total_distanceNO12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
BAL12_avg <- total_distanceBAL12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
BAL14_avg <- total_distanceBAL14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
BUF12_avg <- total_distanceBUF12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
BUF14_avg <- total_distanceBUF14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CHI12_avg <- total_distanceCHI12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
CHI14_avg <- total_distanceCHI14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAV12_avg <- total_distanceLAV12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
LAV14_avg <- total_distanceLAV14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
MIN12_avg <- total_distanceMIN12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
MIN14_avg <- total_distanceMIN14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NYG12_avg <- total_distanceNYG12 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
NYG14_avg <- total_distanceNYG14 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
ARI13_avg <- total_distanceARI13 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
ARI15_avg <- total_distanceARI15 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
WAS13_avg <- total_distanceWAS13 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )
WAS15_avg <- total_distanceWAS15 %>%
  summarise(
    across(where(is.numeric), ~ mean(.x, na.rm = TRUE)),
    across(where(is.character), ~ first(.x))
  )

PRE_BYE <- bind_rows(CLE4_avg, LAC4_avg, SEA4_avg, TB4_avg, GB5_avg, PIT5_avg, CAR6_avg, CIN6_avg, DAL6_avg, HOU6_avg, NYJ6_avg, TEN6_avg, DB8_avg, DL8_avg, JAX8_avg, SF8_avg, KC9_avg, LAR9_avg, MIA9_avg, PHI9_avg, ATL10_avg, IND10_avg, NE10_avg, NO10_avg, BAL12_avg, BUF12_avg, CHI12_avg, LAV12_avg, MIN12_avg, NYG12_avg, ARI13_avg, WAS13_avg)

POST_BYE <- bind_rows(CLE6_avg, LAC6_avg, SEA6_avg, TB6_avg, GB7_avg, PIT7_avg, CAR8_avg, CIN8_avg, DAL8_avg, HOU8_avg, NYJ8_avg, TEN8_avg, DB10_avg, DL10_avg, JAX10_avg, SF10_avg, KC11_avg, LAR11_avg, MIA11_avg, PHI11_avg, ATL12_avg, IND12_avg, NE12_avg, NO12_avg, BAL14_avg, BUF14_avg, CHI14_avg, LAV14_avg, MIN14_avg, NYG14_avg, ARI15_avg, WAS15_avg)

ALL_BYE <- bind_rows(PRE_BYE, POST_BYE)

Code

TEST <- ALL_BYE %>%
  group_by(BYE_WEEK, group)%>%
  summarise(
    avg_distance = mean(total_distance, na.rm = TRUE),
    .group = "drop"
  )
ggplot(TEST, aes(x = avg_distance, y = factor(BYE_WEEK), color = group)) +
  geom_point(size = 3, position = position_jitter(width = 0, height = 0.2)) +                      # dots for Pre/Post
  geom_line(aes(group = BYE_WEEK), color = "gray", linetype = "dashed") + # optional connecting lines
  theme_minimal() +
  labs(
    x = "Average Distance Ran by Player Per Play",
    y = "BYE Week In Schedule",
    title = "Movement Per Play In Game Pre/Post Bye-Week",
    color = "Bye-Status"
  )

In continuing the analysis on if when the BYE week occurs matters, we decided to look at a dot plot of the average distance of a player per play throughout a game. We first averaged the distance of each player per play throughout the game and then averaged all the teams with the same bye-week and plotted the pre and post movement on a Cleveland Dot-plot. From this graph, it is not clear that there is much differentiation between when the bye week occurs and how much it appeared to increase energy. Teams with a bye week 5,6, 7, and 11 have the largest separation in and out of the bye week, with week 6 having the largest separation. This might be because this early into the season teams are largely still trying to build themselves up and this break might mess up their rhythm. But as the season continues we see that the separation between the two games tightens for 10, 13, and 14. It is interesting to note that only two weeks, week 9 and 10, had a higher average distance returning from the bye week.