NFL 2022 Data

Sports
Author

Ryan Horn

Published

December 11, 2023

Here is some analytics in relation to Data from the 2022 NFL season!

Q2a

In data.frame, NFL2022_stuffs, remove observations for which values of posteam is missing.


Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Answer:

NFL2022_stuffs_cleaned <- NFL2022_stuffs %>% 
  filter(!is.na(posteam))

head(NFL2022_stuffs_cleaned)
  play_id         game_id drive week posteam qtr down half_seconds_remaining
1      43 2022_01_BAL_NYJ     1    1     NYJ   1   NA                   1800
2      68 2022_01_BAL_NYJ     1    1     NYJ   1    1                   1796
3      89 2022_01_BAL_NYJ     1    1     NYJ   1    1                   1769
4     115 2022_01_BAL_NYJ     1    1     NYJ   1    2                   1765
5     136 2022_01_BAL_NYJ     1    1     NYJ   1    3                   1741
6     172 2022_01_BAL_NYJ     1    1     NYJ   1    4                   1733
  pass        wp
1    0 0.5462618
2    0 0.5469690
3    1 0.5725734
4    0 0.5545366
5    1 0.5401673
6    0 0.4880532

Q2b

-Summarize the mean value of pass for each posteam when all the following conditions hold:

  1. wp is greater than 20% and less than 75%;

  2. down is less than or equal to 2; and

  3. half_seconds_remaining is greater than 120

Answer:

filtered_data <- NFL2022_stuffs %>%
  filter(
    !is.na(posteam),       
    wp > 0.2 & wp < 0.75,   
    down <= 2,              
    half_seconds_remaining > 120  
  )

summary_data <- filtered_data %>%
  group_by(posteam) %>%
  summarize(mean_pass = mean(pass, na.rm = TRUE))

print(summary_data)
# A tibble: 32 × 2
   posteam mean_pass
   <chr>       <dbl>
 1 ARI         0.553
 2 ATL         0.4  
 3 BAL         0.520
 4 BUF         0.604
 5 CAR         0.458
 6 CHI         0.420
 7 CIN         0.657
 8 CLE         0.491
 9 DAL         0.474
10 DEN         0.493
# ℹ 22 more rows

Q2c

-Provide both (1) a ggplot code with geom_point() using the resulting data.frame in Q2b and (2) a simple comments to describe the mean value of pass for each posteam. In the ggplot, reorder the posteam categories based on the mean value of pass in ascending or in descending order.

Answer:

library(ggplot2)
result <- NFL2022_stuffs %>%
  filter(
    wp > 0.2 & wp < 0.75,
    down <= 2,
    half_seconds_remaining > 120
  ) %>%
  group_by(posteam) %>%
  summarise(mean_pass = mean(pass, na.rm = TRUE))

result$posteam <- factor(result$posteam, levels = result$posteam[order(result$mean_pass)])

ggplot(result, aes(x = posteam, y = mean_pass)) +
  geom_point() +
  labs(title = "Mean Value of Pass for Each posteam",
       x = "posteam",
       y = "Mean Pass Value") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Q2d

  • Create the data.frame, NFL2022_stuffs_EPA, that includes

  1. All the variables in the data.frame, NFL2022_stuffs;

  2. The variables, passer, receiver, and epa, from the data.frame, NFL2022_epa. by joining the two data.frames.

  • In the resulting data.frame, NFL2022_stuffs_EPA, remove observations with NA in passer.

Answer:

NFL2022_epa <- read.csv('https://bcdanl.github.io/data/NFL2022_epa.csv')

NFL2022_stuffs_EPA <- left_join(NFL2022_stuffs, NFL2022_epa, by = c("play_id", "game_id", "drive", "posteam"))

NFL2022_stuffs_EPA <- NFL2022_stuffs_EPA %>%
  filter(!is.na(passer))

head(NFL2022_stuffs_EPA)
  play_id         game_id drive week posteam qtr down half_seconds_remaining
1      89 2022_01_BAL_NYJ     1    1     NYJ   1    1                   1769
2     136 2022_01_BAL_NYJ     1    1     NYJ   1    3                   1741
3     202 2022_01_BAL_NYJ     2    1     BAL   1    1                   1722
4     230 2022_01_BAL_NYJ     2    1     BAL   1    2                   1701
5     301 2022_01_BAL_NYJ     2    1     BAL   1    2                   1579
6     412 2022_01_BAL_NYJ     3    1     NYJ   1    2                   1451
  pass        wp   receiver    passer         epa
1    1 0.5725734  Mi.Carter  J.Flacco -0.49219242
2    1 0.5401673       <NA>  J.Flacco -2.40220026
3    1 0.4958201  R.Bateman L.Jackson  0.07512748
4    1 0.4965942 D.Duvernay L.Jackson -0.10512029
5    1 0.5067707 D.Duvernay L.Jackson  0.41113183
6    1 0.5001284    Br.Hall  J.Flacco -0.17972556

Q2e

  • Provide both (1) a single ggplot and (2) a simple comment to describe the NFL weekly trend of weekly mean value of epa for each of the following two passers,
  1. “J.Allen”
  2. “P.Mahomes”

Answer:

ggplot(NFL2022_stuffs_EPA, aes(x = week, y = epa, color = passer, linetype = passer)) +
  geom_line(linewidth = 1.2) +
  labs(title = "NFL Weekly Trend of Mean EPA for Passers",
       x = "Week",
       y = "Mean EPA") +
  scale_color_manual(values = c("J.Allen" = "blue", "P.Mahomes" = "red")) +
  scale_linetype_manual(values = c("J.Allen" = "solid", "P.Mahomes" = "dashed")) +
  theme_minimal()

This line plot illustrates the weekly trend of the mean value of Expected Points Added (EPA) for two excellent passers.

Q2f

  • Calculate the difference between the mean value of epa for “J.Allen” the mean value of epa for “P.Mahomes” for each value of week.

Answer:

epa_difference <- NFL2022_stuffs_EPA %>%
  group_by(week) %>%
  summarise(mean_epa_difference = mean(epa[passer == "J.Allen"]) - mean(epa[passer == "P.Mahomes"]))

print(epa_difference)
# A tibble: 22 × 2
    week mean_epa_difference
   <int>               <dbl>
 1     1             -0.169 
 2     2              0.339 
 3     3             -0.0763
 4     4             -0.0803
 5     5              0.325 
 6     6              0.173 
 7     7            NaN     
 8     8            NaN     
 9     9             -0.304 
10    10             -0.429 
# ℹ 12 more rows

Q2g

  • Summarize the resulting data.frame in Q2d, with the following four variables:

    • posteam: String abbreviation for the team with possession.
    • passer: Name of the player who passed a ball to a receiver by initially taking a three-step drop, and backpedaling into the pocket to make a pass. (Mostly, they are quarterbacks.)
    • mean_epa: Mean value of epa in 2022 for each passer
    • n_pass: Number of observations for each passer

  • Then find the top 10 NFL passers in 2022 in terms of the mean value of epa, conditioning that n_pass must be greater than or equal to the third quantile level of n_pass.

Answer:

passer_summary <- NFL2022_stuffs_EPA %>%
  group_by(posteam, passer) %>%
  summarise(
    mean_epa = mean(epa, na.rm = TRUE),
    n_pass = n()
  )
`summarise()` has grouped output by 'posteam'. You can override using the
`.groups` argument.
quantile_threshold <- quantile(passer_summary$n_pass, 0.75)

top_passers <- passer_summary %>%
  filter(n_pass >= quantile_threshold) %>%
  arrange(desc(mean_epa)) %>%
  slice_head(n = 10)

print(top_passers)
# A tibble: 29 × 4
# Groups:   posteam [29]
   posteam passer     mean_epa n_pass
   <chr>   <chr>         <dbl>  <int>
 1 ARI     K.Murray     0.0163    465
 2 ATL     M.Mariota    0.0251    370
 3 BAL     L.Jackson    0.0549    398
 4 BUF     J.Allen      0.172     785
 5 CHI     J.Fields    -0.0455    469
 6 CIN     J.Burrow     0.153     854
 7 CLE     J.Brissett   0.0912    445
 8 DAL     D.Prescott   0.147     529
 9 DEN     R.Wilson    -0.0163    609
10 DET     J.Goff       0.171     661
# ℹ 19 more rows