Code Examples

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2023)

# Fourth down decisions
fourth_downs <- pbp %>%
  filter(down == 4, !is.na(play_type)) %>%
  mutate(
    decision = case_when(
      play_type %in% c("pass", "run") ~ "Go for it",
      play_type == "field_goal" ~ "Field Goal",
      play_type == "punt" ~ "Punt",
      TRUE ~ "Other"
    )
  ) %>%
  filter(decision != "Other")

# Decision by field position
decision_analysis <- fourth_downs %>%
  mutate(
    zone = case_when(
      yardline_100 <= 3 ~ "Goal line (1-3)",
      yardline_100 <= 10 ~ "Red zone (4-10)",
      yardline_100 <= 40 ~ "Opp territory",
      yardline_100 <= 60 ~ "Midfield",
      TRUE ~ "Own territory"
    )
  ) %>%
  group_by(zone, decision) %>%
  summarize(plays = n(), .groups = "drop") %>%
  pivot_wider(names_from = decision, values_from = plays, values_fill = 0)

print("Fourth Down Decisions by Field Position:")
print(decision_analysis)

# Go-for-it success rate
go_for_it <- fourth_downs %>%
  filter(decision == "Go for it") %>%
  summarize(
    attempts = n(),
    conversions = sum(first_down == 1 | touchdown == 1, na.rm = TRUE),
    success_rate = mean(first_down == 1 | touchdown == 1, na.rm = TRUE) * 100
  )

print("\nGo-for-it Success Rate:")
print(go_for_it)

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2019:2023)

# Two-point conversion analysis
two_point <- pbp %>%
  filter(two_point_attempt == 1)

# Overall success rate
overall <- two_point %>%
  summarize(
    attempts = n(),
    successes = sum(two_point_conv_result == "success"),
    success_rate = mean(two_point_conv_result == "success") * 100
  )

print("Overall Two-Point Conversion Success:")
print(overall)

# By play type
by_type <- two_point %>%
  filter(play_type %in% c("pass", "run")) %>%
  group_by(play_type) %>%
  summarize(
    attempts = n(),
    successes = sum(two_point_conv_result == "success", na.rm = TRUE),
    success_rate = mean(two_point_conv_result == "success", na.rm = TRUE) * 100
  )

print("\nBy Play Type:")
print(by_type)

# Team leaders
team_2pt <- two_point %>%
  group_by(posteam) %>%
  summarize(
    attempts = n(),
    successes = sum(two_point_conv_result == "success", na.rm = TRUE),
    success_rate = mean(two_point_conv_result == "success", na.rm = TRUE) * 100
  ) %>%
  filter(attempts >= 5) %>%
  arrange(desc(success_rate))

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2023)

# Late game clock management (4th quarter, close game)
late_game <- pbp %>%
  filter(
    qtr == 4,
    game_seconds_remaining <= 300,  # Last 5 minutes
    abs(score_differential) <= 8,
    play_type %in% c("pass", "run")
  )

# Play selection by score differential
late_plays <- late_game %>%
  mutate(
    situation = case_when(
      score_differential > 0 ~ "Leading",
      score_differential < 0 ~ "Trailing",
      TRUE ~ "Tied"
    )
  ) %>%
  group_by(situation) %>%
  summarize(
    plays = n(),
    pass_rate = mean(play_type == "pass") * 100,
    avg_play_clock = mean(play_clock, na.rm = TRUE),
    .groups = "drop"
  )

print("Late Game Play Selection:")
print(late_plays)

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2023)

# Timeout usage by situation
timeouts <- pbp %>%
  filter(timeout == 1) %>%
  mutate(
    half = if_else(qtr <= 2, "First Half", "Second Half"),
    close_game = abs(score_differential) <= 8
  )

# Timeout distribution
timeout_dist <- timeouts %>%
  group_by(half, qtr) %>%
  summarize(
    timeouts = n(),
    .groups = "drop"
  )

print("Timeout Distribution by Quarter:")
print(timeout_dist)

# Team timeout usage patterns
team_timeouts <- timeouts %>%
  group_by(timeout_team) %>%
  summarize(
    total_timeouts = n(),
    first_half = sum(half == "First Half"),
    second_half = sum(half == "Second Half"),
    close_games = sum(close_game),
    avg_time_remaining = mean(game_seconds_remaining, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(total_timeouts))

print("\nTeam Timeout Usage:")
print(team_timeouts)

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2019:2023)

# Replay review analysis
replays <- pbp %>%
  filter(!is.na(replay_or_challenge_result))

# Overall success rate
success_rate <- replays %>%
  mutate(overturned = replay_or_challenge_result == "overturned") %>%
  summarize(
    total_reviews = n(),
    overturned = sum(overturned),
    success_rate = mean(overturned) * 100
  )

print("Overall Challenge Results:")
print(success_rate)

# By year
by_year <- replays %>%
  mutate(
    overturned = replay_or_challenge_result == "overturned",
    year = season
  ) %>%
  group_by(year) %>%
  summarize(
    reviews = n(),
    overturned = sum(overturned),
    rate = mean(overturned) * 100,
    .groups = "drop"
  )

print("\nChallenge Success by Year:")
print(by_year)

# By play type challenged
by_type <- replays %>%
  mutate(overturned = replay_or_challenge_result == "overturned") %>%
  group_by(play_type) %>%
  summarize(
    reviews = n(),
    rate = mean(overturned) * 100,
    .groups = "drop"
  ) %>%
  filter(reviews >= 10) %>%
  arrange(desc(rate))

print("\nSuccess Rate by Play Type:")
print(by_type)

library(nflfastR)
library(tidyverse)

pbp <- load_pbp(2023)

# Penalty analysis
penalties <- pbp %>%
  filter(penalty == 1)

# Most common penalties
common_penalties <- penalties %>%
  filter(!is.na(penalty_type)) %>%
  count(penalty_type) %>%
  arrange(desc(n)) %>%
  head(15)

print("Most Common Penalties:")
print(common_penalties)

# Team penalty rates
team_penalties <- pbp %>%
  filter(play_type %in% c("pass", "run", "no_play")) %>%
  group_by(posteam) %>%
  summarize(
    plays = n(),
    penalties = sum(penalty == 1 & penalty_team == posteam, na.rm = TRUE),
    penalty_rate = mean(penalty == 1 & penalty_team == posteam, na.rm = TRUE) * 100,
    penalty_yards = sum(penalty_yards[penalty_team == posteam], na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(penalty_rate))

print("\nTeam Penalty Rates:")
print(team_penalties)

# Penalty impact on EPA
penalty_impact <- pbp %>%
  filter(play_type %in% c("pass", "run")) %>%
  mutate(had_penalty = penalty == 1) %>%
  group_by(had_penalty) %>%
  summarize(
    plays = n(),
    avg_epa = mean(epa, na.rm = TRUE),
    .groups = "drop"
  )

print("\nEPA Impact of Penalties:")
print(penalty_impact)