The Long Game: Exploring NHL Career Longevity

Author

claire morse

The Long Game: Exploring NHL Career Longevity

The Question

1,064 games. That is how many consecutive NHL games Phil Kessel played on his way to holding the all time NHL iron man streak. According to hockey lore, when confronted with the possibility of not being allowed to drink soda in the locker room, Kessel threatened to forgo the rest of his $56 million extension. Despite not being known as the stereotypical conditioning obsessed athlete, Kessel put together one of the most enduring careers in hockey history. Between November 3, 2009, and April 13, 2023, he did not miss a single game, on top of a 17 season NHL career.

This raises the question: what leads to longevitiy in the NHL?

Beloved for its blue collar spirit, board shattering hits, and MMA worthy fights, hockey is one of the most physically taxing sports. To stay in the league players must put up with the physical demands and aging, while also continuing to produce. Some players play for more than two decades and others’ careers end much sooner.

Teams invest millions in player development and players dedicate their lives to the sport often forgoing other opportunities to pursue their dreams.

Therefor it is important to investigate:

What factors are associated with longer career?

Within that we will explore:

Do certain positions tend to produce longer careers than others?

Does player size, such as height and weight have an association with longer careers?

Does play type, ie “high-skilled” or “physical”, have an affect on longevity?

Does ice time affect the length of careers?

The Data

The data was obtained from hockey-reference.com and nhl.com. Statistical variables were obtained from hockey reference while bio graphical data was obtained from the nhl.

Data Scraping and Cleaning

The Data includes three main categories of important variables

  1. Player Identifiers and season information
  • playerId
    • unique ID per player
  • player
    • player name
  • Season
    • NHL season year
  • Age
    • player age during that season
  • Pos
    • player position
  1. Season level performance statistics
  • GP
    • games played in the season
  • G
    • goals scored in the season
  • A
    • assists recorded in the season
  • PTS
    • total points in the season
  • GPG
    • goals per game in the season
  • APG
    • assists per game in the season
  • TOI_total
    • total time on ice for the season
  • ATOI_total
    • average time on ice per game for the season
  1. Career level and physical attributes
  • career_GP
    • total career games played
  • career_G
    • total career goals scored
  • career_PTS
    • total career points
  • career_PPG
    • total career points per game
  • career_TOI
    • total career time on ice
  • seasons_played
    • number of NHL seasons played
  • height
    • player height (inches)
  • weight
    • player weight (pounds)
  • draftOverall
    • overall draft position
  • draftRound
    • draft round selected
player_level %>%
  summarise(
    avg_seasons = mean(seasons_played, na.rm = TRUE),
    avg_career_games = mean(career_GP, na.rm = TRUE),
    avg_goals = mean(career_G, na.rm = TRUE),
    avg_assists = mean(career_A, na.rm = TRUE),
    avg_points = mean(career_PTS, na.rm = TRUE),
    sd_points = sd(career_PTS, na.rm = TRUE)
  )
# A tibble: 1 × 6
  avg_seasons avg_career_games avg_goals avg_assists avg_points sd_points
        <dbl>            <dbl>     <dbl>       <dbl>      <dbl>     <dbl>
1        1.67             82.3      12.7        22.2       34.9      43.4
final_data %>%
  distinct(Player, career_GP) %>%
  ggplot(aes(x = career_GP)) +
  geom_histogram(binwidth = 1, fill = "grey70") +
  geom_vline(aes(xintercept = mean(career_GP)), color = "red", linetype = "dashed") +
  labs(
    title = "How Long Do NHL Players Last?",
    x = "Career Games Played",
    y = "Number of Players"
  ) +
  theme_minimal()

final_data %>%
  distinct(Player, career_GP) %>%
  ggplot(aes(x = career_GP)) +
  geom_histogram(bins = 50, fill = "darkgreen", color = "white") +
  labs(
    title = "Distribution of Career Games Played",
    x = "Career Games Played",
    y = "Number of Players"
  ) +
  theme_minimal()

final_data %>%
  distinct(Player, seasons_played, career_GP) %>%
  ggplot(aes(x = seasons_played, y = career_GP)) +
  geom_point(alpha = 0.03) +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Seasons Played vs Career Games Played",
    x = "Seasons Played",
    y = "Career Games Played"
  ) +
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Data Analysis

# A tibble: 5 × 4
  Pos   avg_career median_career n_players
  <chr>      <dbl>         <dbl>     <int>
1 C          100.           122.       476
2 LW          96.6          116.       298
3 RW          96.4          113        350
4 D           96.3          113        599
5 G           59.0           56        175
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lm(career_GP ~ draftOverall, data = player_level)

Call:
lm(formula = career_GP ~ draftOverall, data = player_level)

Coefficients:
 (Intercept)  draftOverall  
    101.9759       -0.1598  
ggplot(player_level, aes(draftOverall, (career_GP))) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm") +
  scale_x_reverse() +
  theme_minimal()

model_data <- final_data %>%
  select(career_GP, height, weight, draftOverall, ATOI_total, Pos) %>%
  drop_na() %>%
  mutate(across(c(height, weight, draftOverall, ATOI_total), scale))

model_std <- lm(
  career_GP ~ height + weight + draftOverall + ATOI_total + Pos,
  data = model_data
)

tidy(model_std) %>%
  filter(term != "(Intercept)") %>%
  ggplot(aes(x = reorder(term, estimate), y = estimate)) +
  geom_col(fill = "darkgreen") +
  coord_flip() +
  labs(
    title = "Standardized Predictors of NHL Career Longevity",
    x = "",
    y = "Standardized Effect"
  ) +
  theme_minimal()

model <- lm(
  career_GP ~ height + weight +
    draftOverall +
    ATOI_total +
    Pos,
  data = final_data
)

summary(model)

Call:
lm(formula = career_GP ~ height + weight + draftOverall + ATOI_total + 
    Pos, data = final_data)

Residuals:
     Min       1Q   Median       3Q      Max 
-131.436  -27.552    2.725   29.457  122.787 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  164.38139   45.89497   3.582 0.000352 ***
height        -3.44402    0.77923  -4.420 1.06e-05 ***
weight         0.43195    0.10056   4.296 1.85e-05 ***
draftOverall  -0.02748    0.01461  -1.880 0.060286 .  
ATOI_total     7.78881    0.22893  34.023  < 2e-16 ***
PosD         -36.74637    2.97119 -12.368  < 2e-16 ***
PosLW          0.38835    3.23868   0.120 0.904571    
PosRW         -2.30699    3.06210  -0.753 0.451327    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 40.35 on 1495 degrees of freedom
  (395 observations deleted due to missingness)
Multiple R-squared:  0.4716,    Adjusted R-squared:  0.4691 
F-statistic: 190.6 on 7 and 1495 DF,  p-value: < 2.2e-16
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 @ complete: logi TRUE
 @ validate: logi TRUE

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  [list output truncated]
 @ complete: logi TRUE
 @ validate: logi TRUE
`geom_smooth()` using formula = 'y ~ x'

player_level <- player_level %>%
  mutate(
    play_style = case_when(
      GPG > median(GPG, na.rm = TRUE) ~ "Skilled",
      TRUE ~ "Physical"
    )
  )

ggplot(player_level, aes(play_style, career_GP)) +
  geom_boxplot(fill = "tomato") +
  theme_minimal()

Discussion