Analyzing NHL Data from 2007-2008 to 2018-2019

Author
Affiliation

Coby Darer

Appalachian State University

Abstract

This project will explore 5 v 5 data on NHL teams and players from the 2007-2008 season to the 2018-2019 season.

Loading the Necessary Packages

library(nhldata)
library(dplyr)
library(lubridate)
library(tidyr)
library(ggplot2)

Introduction

This project will explore some of the 5v5 data on NHL teams from the 2007-2008 season to the 2018-2019 season. Specifically 5v5 scoring over these years. It will look at data on skaters, goalies and teams as a whole. The division alignment has changed within this time frame so the teams will be split up into their current divisions (This is for readability more than comparing separate divisions).

Code 
West_conf <- teams %>%
  filter(team %in% c("ANA", "ARI", "CGY", "CHI", "COL", "DAL", "EDM", "LA", "MIN", "NSH", "SJ", "STL", "VAN", "ATL", "WPG", "VGK"))

East_conf <- teams %>%
  filter(team %in% c("BOS", "BUF", "CAR", "CBJ", "DET", "FLA", "MTL", "NJ", "NYI", "NYR", "OTT", "PHI", "PIT", "TB", "TOR", "WSH"))

Atlantic_div <- East_conf %>%
  filter(team %in% c("BOS", "BUF", "DET", "FLA", "MTL", "OTT", "TB", "TOR"))

Metro_div <- East_conf %>%
  filter(team %in% c("CAR", "CBJ", "NJ", "NYI", "NYR", "PHI", "PIT", "WSH"))

Central_div <- West_conf %>%
  filter(team %in% c("ARI", "CHI", "COL", "DAL", "MIN", "NSH", "STL", "ATL", "WPG"))

Pacific_div <- West_conf %>%
  filter(team %in% c("ANA", "CGY", "EDM", "LA", "SJ", "VAN", "VGK"))

Exploring some data teams

glimpse(teams)
Rows: 362
Columns: 28
$ team      <chr> "ANA", "ANA", "ANA", "ANA", "ANA", "ANA", "ANA", "ANA", "ANA…
$ season    <chr> "2007-2008", "2008-2009", "2009-2010", "2010-2011", "2011-20…
$ gp        <dbl> 81, 82, 82, 82, 82, 48, 82, 82, 82, 82, 82, 81, 81, 81, 82, …
$ toi       <dbl> 3396.38, 3645.19, 3746.91, 3852.90, 3890.68, 2319.80, 3822.9…
$ cf        <dbl> 2596, 3060, 3141, 3024, 3326, 1970, 3500, 3610, 3646, 3495, …
$ ca        <dbl> 2523, 2945, 3493, 3794, 3526, 2140, 3528, 3474, 3310, 3542, …
$ c_plumin  <dbl> 73, 115, -352, -770, -200, -170, -28, 136, 336, -47, -207, -…
$ cf_pct    <dbl> 50.71, 50.96, 47.35, 44.35, 48.54, 47.93, 49.80, 50.96, 52.4…
$ cf_60     <dbl> 45.86, 50.37, 50.30, 47.09, 51.29, 50.95, 54.93, 55.51, 56.3…
$ ca_60     <dbl> 44.57, 48.47, 55.93, 59.08, 54.38, 55.35, 55.37, 53.42, 51.1…
$ gf        <dbl> 107, 145, 146, 134, 138, 89, 187, 158, 128, 145, 158, 136, 1…
$ ga        <dbl> 95, 130, 144, 154, 154, 72, 133, 149, 131, 128, 135, 152, 13…
$ g_plumin  <dbl> 12, 15, 2, -20, -16, 17, 54, 9, -3, 17, 23, -16, -12, -32, 2…
$ gf_pct    <dbl> 52.97, 52.73, 50.34, 46.53, 47.26, 55.28, 58.44, 51.47, 49.4…
$ gf_60     <dbl> 1.89, 2.39, 2.34, 2.09, 2.13, 2.30, 2.93, 2.43, 1.98, 2.25, …
$ ga_60     <dbl> 1.68, 2.14, 2.31, 2.40, 2.37, 1.86, 2.09, 2.29, 2.02, 1.98, …
$ xgf       <dbl> 108.93, 134.91, 132.19, 123.01, 133.14, 81.87, 153.17, 149.0…
$ xga       <dbl> 114.61, 129.11, 156.27, 156.05, 149.34, 81.65, 144.53, 136.8…
$ xg_plumin <dbl> -5.68, 5.80, -24.08, -33.04, -16.20, 0.22, 8.64, 12.21, 16.5…
$ xgf_pct   <dbl> 48.73, 51.10, 45.83, 44.08, 47.13, 50.07, 51.45, 52.13, 53.0…
$ xgf_60    <dbl> 1.92, 2.22, 2.12, 1.92, 2.05, 2.12, 2.40, 2.29, 2.26, 2.37, …
$ xga_60    <dbl> 2.02, 2.13, 2.50, 2.43, 2.30, 2.11, 2.27, 2.10, 2.00, 2.19, …
$ pent      <dbl> 406, 407, 378, 336, 274, 162, 292, 263, 329, 304, 271, 242, …
$ pend      <dbl> 372, 337, 351, 309, 260, 137, 293, 251, 272, 275, 223, 204, …
$ p_plumin  <dbl> -34, -70, -27, -27, -14, -25, 1, -12, -57, -29, -48, -38, -5…
$ sh_pct    <dbl> 7.30, 8.40, 8.23, 7.87, 7.99, 8.59, 9.83, 8.48, 6.69, 7.77, …
$ sv_pct    <dbl> 93.62, 92.42, 92.75, 92.31, 91.66, 93.01, 92.62, 91.82, 92.3…
$ pdo       <dbl> 100.93, 100.82, 100.98, 100.18, 99.65, 101.60, 102.45, 100.3…

There are 28 variables in the teams data set:

Note: Corsi stat in hockey is Shot attempts

  1. team: Team name

  2. season: Season

  3. gp: Games Played

  4. toi : Time on the ice

  5. cf: Corsi for

  6. ca: Corsi against

  7. c_plumin : Corsi plus/minus (cf-ca)

  8. cf_60: Corsi for per 60 minutes on the ice

  9. cf_pct: Corsi for as a percentage of total corsi

  10. ca_60: Corsi against per 60 minutes on the ice

  11. gf : Goals scored for a player’s team

  12. ga: Goals scored against player’s team

  13. g_plumin: Goals for - goals against

  14. gf_pct: Percentage of all goals scored by team

  15. gf_60: Goals for per 60 minutes on the ice

  16. ga_60: Goals against per 60 minutes on the ice

  17. xgf: Expected goals for

  18. xga: Expected goals against

  19. xg_plumin: Expected goals for - expected goals against

  20. xgf_pct: Expected goals for as a percentage of total expected goals for

  21. xgf_60: Expected goals for per 60 minutes on the ice

  22. xga_60: Expected goals against per 60 minutes on the ice

  23. pent: Penalties taken

  24. pend: Penalties drawn

  25. p_plumin: Penalties taken - individual penalties drawn

  26. sh_pct: Shooting percentage

  27. sv_pct: Save percentage

  28. pdo: Just Win Baby

Since we are exploring 5v5, let’s see how important it is by looking at each teams goals for.

Code 
Central_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = gf, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Goals For")

Pacific_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = gf, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Goals For")

Atlantic_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = gf, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Goals For")

Metro_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = gf, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Goals For")
Figure 1: Number of 5v5 goals for each team each season
Figure 2: Number of 5v5 goals for each team each season
Figure 3: Number of 5v5 goals for each team each season
Figure 4: Number of 5v5 goals for each team each season

Now let’s look at shot attempts over the years.

Code 
Central_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = cf/gp, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Shot Attempts per Game")

Pacific_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = cf/gp, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Shot Attempts per Game")

Atlantic_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = cf/gp, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Shot Attempts per Game")

Metro_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = cf/gp, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Shot Attempts per Game")
Figure 5: Number of 5v5 shot attempts for each team each season
Figure 6: Number of 5v5 shot attempts for each team each season
Figure 7: Number of 5v5 shot attempts for each team each season
Figure 8: Number of 5v5 shot attempts for each team each season

So it looks like there was some fluctuation, but on average teams are scoring slightly more on 5v5 now than they used to and they are attempting more shots per game.

Exploring some data on skaters

glimpse(skaters)
Rows: 9,402
Columns: 48
$ player      <chr> "SEBASTIAN AHO", "AARON DOWNEY", "AARON EKBLAD", "AARON EK…
$ season      <chr> "2017-2018", "2007-2008", "2014-2015", "2015-2016", "2016-…
$ team        <chr> "NYI", "DET", "FLA", "FLA", "FLA", "FLA", "FLA", "DAL", "W…
$ team2       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "EDM", NA,…
$ team3       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ position    <chr> "D", "R", "D", "D", "D", "D", "D", "C", "C", "C", "D", "D"…
$ position2   <chr> "D", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ position3   <chr> "R", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gp          <dbl> 22, 56, 81, 78, 68, 82, 82, 19, 7, 10, 30, 38, 41, 56, 10,…
$ toi         <dbl> 324.64, 253.35, 1400.56, 1319.22, 1132.67, 1440.06, 1498.7…
$ g           <dbl> 0, 0, 6, 9, 4, 9, 7, 0, 0, 3, 0, 2, 2, 2, 0, 0, 1, 0, 1, 0…
$ a           <dbl> 3, 2, 16, 13, 7, 11, 12, 2, 0, 0, 1, 4, 4, 12, 0, 0, 6, 0,…
$ p           <dbl> 3, 2, 22, 22, 11, 20, 19, 2, 0, 3, 1, 6, 6, 14, 0, 0, 7, 0…
$ p1          <dbl> 1, 2, 14, 15, 7, 11, 13, 0, 0, 3, 0, 3, 5, 7, 0, 0, 2, 0, …
$ p_60        <dbl> 0.55, 0.47, 0.94, 1.00, 0.58, 0.83, 0.76, 0.84, 0.00, 2.46…
$ p1_60       <dbl> 0.18, 0.47, 0.60, 0.68, 0.37, 0.46, 0.52, 0.00, 0.00, 2.46…
$ gs          <dbl> 1.99, 0.92, 38.10, 28.15, 21.36, 19.78, 23.60, 1.64, 1.17,…
$ gs_60       <dbl> 0.37, 0.22, 1.63, 1.28, 1.13, 0.82, 0.94, 0.69, 1.07, 2.83…
$ cf          <dbl> 289, 193, 1299, 1059, 1084, 1392, 1402, 127, 72, 64, 287, …
$ ca          <dbl> 325, 183, 1112, 1034, 1003, 1520, 1432, 121, 58, 63, 304, …
$ c_plumin    <dbl> -36, 10, 187, 25, 81, -128, -30, 6, 14, 1, -17, 28, -83, -…
$ cf_pct      <dbl> 47.07, 51.33, 53.88, 50.60, 51.94, 47.80, 49.47, 51.21, 55…
$ rel_cf_pct  <dbl> 2.26, -7.30, 3.83, 2.92, 1.97, -2.16, 0.09, 3.14, 8.63, 0.…
$ gf          <dbl> 15, 6, 59, 56, 30, 69, 80, 3, 0, 5, 15, 23, 18, 29, 4, 1, …
$ ga          <dbl> 19, 6, 43, 43, 47, 67, 82, 6, 1, 4, 11, 10, 25, 37, 4, 0, …
$ g_plumin    <dbl> -4, 0, 16, 13, -17, 2, -2, -3, -1, 1, 4, 13, -7, -8, 0, 1,…
$ gf_pct      <dbl> 44.12, 50.00, 57.84, 56.57, 38.96, 50.74, 49.38, 33.33, 0.…
$ rel_gf_pct  <dbl> -0.48, -12.50, 11.78, -3.07, -7.58, 1.50, 5.93, -11.98, -4…
$ xgf         <dbl> 10.49, 7.16, 50.88, 42.44, 38.23, 57.36, 63.91, 5.83, 2.89…
$ xga         <dbl> 15.73, 7.76, 42.32, 44.38, 42.23, 65.83, 69.28, 4.84, 2.15…
$ xg_plumin   <dbl> -5.24, -0.60, 8.56, -1.94, -4.00, -8.47, -5.37, 0.99, 0.74…
$ xgf_pct     <dbl> 40.01, 47.99, 54.59, 48.88, 47.51, 46.56, 47.98, 54.64, 57…
$ rel_xgf_pct <dbl> -3.96, -10.26, 4.30, -0.22, -0.17, -3.54, -0.04, 7.76, 13.…
$ ipent       <dbl> 2, 31, 10, 15, 21, 20, 12, 1, 0, 1, 13, 11, 11, 5, 4, 0, 8…
$ ipend       <dbl> 4, 15, 10, 6, 13, 9, 9, 0, 0, 1, 4, 5, 5, 7, 1, 1, 4, 0, 2…
$ ip_plumin   <dbl> 2, -16, 0, -9, -8, -11, -3, -1, 0, 0, -9, -6, -6, 2, -3, 1…
$ icf         <dbl> 50, 22, 229, 208, 248, 271, 227, 25, 12, 17, 33, 53, 70, 9…
$ icf_60      <dbl> 9.24, 5.21, 9.81, 9.46, 13.14, 11.29, 9.09, 10.55, 11.02, …
$ ixgf        <dbl> 0.77, 0.94, 3.98, 5.41, 5.59, 6.83, 5.35, 0.84, 0.54, 1.17…
$ ixgf_60     <dbl> 0.14, 0.22, 0.17, 0.25, 0.30, 0.28, 0.21, 0.35, 0.50, 0.96…
$ ish_pct     <dbl> 0.00, 0.00, 5.26, 7.50, 2.74, 6.98, 5.56, 0.00, 0.00, 27.2…
$ pdo         <dbl> 99.82, 99.80, 100.86, 101.59, 96.77, 100.94, 99.22, 97.67,…
$ zsr         <dbl> 43.37, 81.97, 60.73, 55.80, 58.93, 41.48, 45.76, 48.05, 58…
$ toi_pct     <dbl> 30.30, 10.37, 36.09, 35.98, 35.20, 36.61, 37.72, 16.25, 18…
$ toi_pct_qot <dbl> 27.25, 26.06, 28.14, 28.15, 27.69, 29.52, 29.32, 26.44, 29…
$ cf_pct_qot  <dbl> 47.37, 57.52, 49.53, 48.73, 50.29, 51.68, 49.32, 44.92, 50…
$ toi_pct_qoc <dbl> 28.78, 27.06, 28.56, 28.77, 28.91, 29.52, 29.89, 28.06, 28…
$ cf_pct_qoc  <dbl> 50.10, 48.87, 49.31, 49.94, 50.02, 50.12, 50.19, 50.72, 48…

There are 48 variables in the skaters data set:

Note: Corsi stat in hockey is Shot attempts

  1. player: Player name

  2. season: Season

  3. team: First team player played for in a given season

  4. team2 : Second team player played for in a given season

  5. team3: Third team player played for in a given season

  6. position: Player’s first position

  7. position2 : Player’s second position

  8. position3: Player’s third position

  9. gp: Games Played

  10. toi: Time on the ice

  11. g : Goals scored

  12. a: Assists

  13. p: Points

  14. p1: Primary points (goals + primary assists)

  15. p_60: Points per 60 minutes on the ice

  16. p1_60: Primary points per 60 minutes on the ice

  17. gs: Game score

  18. gs_60: Game score per 60 minutes on the ice

  19. cf: Corsi for (shot attempts by player’s team while player is on the ice)

  20. ca: Corsi against (shot attempts by opposing team will player is on the ice)

  21. c_plumin: Corsi plus/minus (cf-ca)

  22. cf_pct: Corsi percentage

  23. rel_cf_pct: Relative corsi percentage

  24. gf: Goals scored for a player’s team

  25. ga: Goals scored against player’s team

  26. g_plumin: Shooting Goals for - goals against

  27. gf_pct: Save Percentage of all goals scored by player’s team

  28. rel_gf_pct: Relative goals for percentage

  29. xgf: Expected goals for

  30. xga: Expected goals against

  31. xg_plumin: Expected goals for - expected goals against

  32. xgf_pct: Expected goals for as a percentage of a team’s total expected goals for

  33. rel_xgf_pct: Relative expected goals for as a percentage of a team’s total expected goals for

  34. ipent: Individual penalties taken

  35. ipend: Individual penalties drawn

  36. ip_plumin: Individual penalties taken - individual penalties drawn

  37. icf: Individual corsi for

  38. icf_60: Individual corsi for per 60 minutes on the ice

  39. ixgf: Individual expected goals for

  40. ixgf_60: Individual expected goals for per 60 minutes on the ice

  41. ish_pct: Individual shooting percentage

  42. pdo: Just Win Baby

  43. zsr: Zone start ratio

  44. toi_pct: Percentage of team’s total time on ice played by player

  45. toi_pct_qot: Percentage of team’s total time on ice played by player, weighted by quality of teammates

  46. cf_pct_qot: Corsi for percentage weighted by quality of player’s teammates

  47. toi_pct_qoc: Percentage of team’s total time on ice played by player, weighted by quality of opponents

  48. cf_pct_qoc: Corsi for percentage weighted by quality of player’s opponents

It would be nice to see if the individual player stats reflect this slight increase in production.

Code 
skaters %>%
  filter(season != "2012-2013") %>%
  filter(p >= 50) %>%
  ggplot(aes(x = season)) +
    geom_bar(color = "black", fill = "lightblue") +
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Count")
Figure 9: Number of players with 50 or more 5v5 points
Code 
skaters %>%
  filter(season != "2012-2013") %>%
  filter(ish_pct >= 20) %>%
  ggplot(aes(x = season)) +
  geom_bar(color = "black", fill = "lightblue") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Count")
Figure 10: Number of players with at least 20% shooting percentage
Code 
skaters %>%
  filter(season != "2012-2013") %>%
  filter(ish_pct >= 10) %>%
  ggplot(aes(x = season)) +
  geom_bar(color = "black", fill = "lightblue") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Count")
Figure 11: Number of players with at least 10% shooting percentage

Interesting, the amount of players with at least 50 points seems to be trending up in the last couple of years but is not more than some of the years in the past. Also shooting percentage has stayed relatively consistent, but at 20% there were some spikes.

Exploring some data on goalies

glimpse(goalies)
Rows: 856
Columns: 16
$ player   <chr> "AARON DELL", "AARON DELL", "AARON DELL", "ADIN HILL", "ADIN …
$ season   <chr> "2016-2017", "2017-2018", "2018-2019", "2017-2018", "2018-201…
$ team     <chr> "SJ", "SJ", "SJ", "ARI", "ARI", "NYR", "NYR", "ARI", "OTT", "…
$ team2    <chr> NA, NA, NA, NA, NA, NA, NA, "BOS", NA, "NYR", NA, NA, NA, NA,…
$ team3    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gp       <dbl> 20, 29, 25, 4, 13, 10, 33, 32, 43, 24, 16, 14, 11, 24, 22, 13…
$ toi      <dbl> 951.31, 1235.49, 1057.78, 205.05, 545.18, 430.98, 1486.20, 12…
$ sa       <dbl> 449, 606, 473, 113, 255, 274, 822, 534, 823, 472, 278, 215, 1…
$ ga       <dbl> 23, 49, 45, 12, 25, 19, 69, 41, 64, 46, 24, 20, 18, 31, 41, 2…
$ sv_pct   <dbl> 94.88, 91.91, 90.49, 89.38, 90.20, 93.07, 91.61, 92.32, 92.22…
$ xsv_pct  <dbl> 92.44, 91.74, 91.24, 90.27, 91.73, 91.86, 91.76, 92.30, 92.14…
$ dsv_pct  <dbl> 2.43, 0.17, -0.75, -0.88, -1.54, 1.21, -0.15, 0.02, 0.08, -2.…
$ ldsv_pct <dbl> 98.64, 97.60, 98.03, 91.67, 95.76, 99.24, 98.06, 97.22, 98.67…
$ mdsv_pct <dbl> 96.03, 92.97, 89.02, 93.75, 94.94, 89.77, 91.17, 92.24, 90.23…
$ hdsv_pct <dbl> 82.05, 77.52, 78.30, 81.82, 72.41, 83.64, 79.33, 80.23, 83.06…
$ gsaa     <dbl> 10.93, 1.05, -3.57, -1.00, -3.92, 3.31, -1.23, 0.12, 0.65, -1…

There are 16 variables in the teams data set:

  1. player: Player name

  2. season: Season

  3. team: First team player played for in a given season

  4. team2 : Second team player played for in a given season

  5. team3: Third team player played for in a given season

  6. gp: Games Played

  7. toi : Time on the ice

  8. sa: Shots against

  9. ga: Goals against

  10. sv_pct: Save percentage

  11. xsv_pct : Expected save percentage

  12. dsv_pct: Dangerous save percentage

  13. ldsv_pct: Low danger save percentage

  14. mdsv_pc: Medium danger save percentage

  15. hdsv_pct: High danger save percentage

  16. gsaa: Goals saved above average

How have the goalies been affected over the years?

Code 
goalies %>%
  filter(season != "2012-2013") %>%
  filter(sv_pct >= 91.00) %>%
  filter(gp >= 45) %>%
  ggplot(aes(x = season)) + 
  geom_bar(color = "black", fill = "pink") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Count")
Figure 12: Number of goalies with at least 45 games played and a 91% save percentage
Code 
goalies <- goalies %>%
  mutate(avg_sa_gp = sa/gp)

filtered_goalies <- goalies %>%
  filter(season != "2012-2013") %>%
  filter(gp > 5) %>%
  filter(avg_sa_gp >= 20)

ggplot(filtered_goalies, aes(x = season)) + 
  geom_bar(color = "black", fill = "pink") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Count")
Figure 13: Number of who goalies faced an average of at least 20 shots a game

Looking at penalties

Code 
Central_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = pent, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalties Taken")

Pacific_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = pent, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalties Taken")

Atlantic_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = pent, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalties Taken")

Metro_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = pent, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalties Taken")
Figure 14: Number of penalties taken by each team
Figure 15: Number of penalties taken by each team
Figure 16: Number of penalties taken by each team
Figure 17: Number of penalties taken by each team
Code 
Central_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = p_plumin, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalty +-")

Pacific_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = p_plumin, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalty +-")

Atlantic_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = p_plumin, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalty +-")

Metro_div %>%
  filter(season != "2012-2013") %>%
  ggplot(aes(x = season, y = p_plumin, color = team)) + 
    geom_point() +
    geom_line(aes(group = team)) +
    geom_smooth(aes(group = 1), se = FALSE) + 
    theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
    labs(x = "Season",
         y = "Penalty +-")
Figure 18: Net penalties by each team
Figure 19: Net penalties by each team
Figure 20: Net penalties by each team
Figure 21: Net penalties by each team