NHL 2023-2024 Regular Season Point Leader Analysis

Author

Brooke Aho

Introduction

Growing up in a hockey oriented family, I do not remember a day that went by without one of my family members screaming at the tv because of a so called “terrible call” or “bad pass”. Moments like these allowed me to fall in love with not only the sport of hockey, but everything that goes on behind the scenes.

I have always admired sports analysts and individuals who get to work careers that involve analyzing sports teams and players. What are the best teams doing that the worst teams are not? How are some individuals excelling and others not? Questions like these are so prevalent in today’s world, and it is ultimately up to analysts and sports critics to solve them.

Throughout this project, my goal was to be a so called “sports analysts” for the time being. I aimed to take on this kind of mindset - to the best of my abilities - while analyzing data regarding the top 100 NHL players with the most points in the 2023-2024 regular season. In doing so, I attempted to answer 2 main questions:

  1. What factors/variables had a significant contribution to NHL players being top point leaders in the league during this season?
  2. Is there any correlation between the number of wins a player’s team had and the total amount of points they accumulated?

Selecting Data

After searching long and hard for a dataset that I thought was interesting, I landed on the website: https://www.quanthockey.com/nhl/seasons/nhl-players-stats.html, which had pretty much every statistic you could find on each of the players. From there, I took the top 100 players, and converted it to a CSV file to start my initial analysis.

Data Dictionary

Below is a list of all of the variables included within the dataset, as well as any clarification that might be needed.

  • Rank: Ranking number of the player out of the top 100 players

  • Name

  • Team

  • Age

  • Position: F for forward, D for defense

  • Games.Played: Total number of games played this season

  • Goals: Total number of goals scored this season

  • Assists: Total number of assists this season

  • Points: Sum of goals and assists

  • Time.on.Ice: Total minutes played this season

  • Even.Strength.Time: Number of minutes played when both teams had 5 players on the ice

  • Power.Play.Time: Number of minutes played when opponent had less players on the ice

  • Short.Handed.Minutes: Number of minutes played when own team had less players on the ice

  • Even.Strength.Goals: Number of goals scored when both teams had 5 players on the ice

  • Power.Play.Goals: Number of goals scored when opponent had less players on the ice

  • Short.Handed.Goals: Number of goals scored when own team had less players on the ice

  • Game.Winning.Goals: Number of goals scored that lead to a win

  • Overtime.Goals: Number of goals scored in overtime

  • Even.Strength.Assists: Number of assists completed when both teams had 5 players on the ice

  • Power.Play.Assists: Number of assists completed when opponent had less players on the ice

  • Short.Handed.Assists: Number of assists completed when own team had less players on the ice

  • Game.Winning.Assists: Number of assists completed that lead to a win

  • Overtime.Assists: Number of assists completed in overtime

  • Even.Strength.Points: Number of points accumulated when both teams had 5 players on the ice (Even.Strength.Goals + Even.Strength.Assists)

  • Power.Play.Points: Number of points accumulated when opponent had less players on the ice (Power.Play.Goals + Power.Play.Assists)

  • Short.Handed.Points: Number of points accumulated when own team had less players on the ice (Short.Handed.Goals + Short.Handed.Assists)

  • Game.Winning.Points: Number of points accumulated that lead to a win (Game.Winning.Goals + Game.Winning.Assists)

  • Overtime.Points: Number of points accumulated in overtime (Overtime.Goals + Overtime.Assists)

  • Goals.Games.Played: Percentage of goals per number of games played

  • Assists.Games.Played: Percentage of assists per number of games played

  • Points.Games.Played: Percentage of points per number of games played

  • Shots: Total number of shots on goal this season

  • Hits: Total number of hits this season

  • Blocked.Shots: Total number of blocked shots this season

  • Face.Off.Wins: Number of face offs won this season

  • Face.Off.Losses: Number of face offs lost this season

Summary Statistics

Since our main focus is on points (which is comprised of goals and assists), I included a few distributions of summary statistics below relating to the variables: goals, assists, and points.

Analysis - Question 1

To recall, the first question I attempt to answer is: What factors/variables had a significant contribution to NHL players being top point leaders in the league during this season?

In order to answer this question, I first want to look at the correlation between points and goals vs. that of points and assists. Obviously, I do anticipate pretty high correlations here, as points are comprised of both goals and assists.

There is a moderately strong, positive correlation of .57 between points and goals. There is a positive, strong correlation of .79 between points and assists. Thus, more of the variability in points is accounted for by assists than goals. Overall, this makes sense since players have a higher number of assists than goals, so the points a player accumulates is more closely related to assists.

Next, I looked to see if two other variables (that do not directly go into point totals) had any factor into the number of points earned by a player: shots and age. Prior to running this analysis, I wanted to see if these higher ranked players had more shots on goal. Additionally, I was curious if there was any evidence that suggested whether or not younger or older aged players had more or less points. Both of these visuals are shown below

As far as the correlation between points and shots on goal, there does seem to be a moderately strong, positive relationship, which suggests that more shots on goal generally correlates to players with more points.

Alternatively, with a correlation coefficient of -.03, there does not appear to be any correlation between age and points, illustrating that age is not a factor that influences points.

Lastly, I did a simply column chart to gauge how many offensive vs. defensive players were in the top 100 list. My results were not surprising showcasing that the list is made up of 11 defensive players and 89 offensive players. It would be interesting to see how these numbers have changed overtime and which years defensive players were closer in number to offensive.

Analysis - Question 2

Moving on, the second question I answered was: Is there any correlation between the number of wins a player’s team has and the total amount of points they accumulated?

First, I looked at what teams have the most players in the top 100, which is shown below.

From this, we notice that the Stars have the most players out of the top 100 sitting at 6, followed by the Lightning, Maple Leafs, Rangers, Oilers all with 5. On the other hand, the Blackhawks, Blue Jackets, Ducks, Flyers, and Sharks have only 1 player on this list.

So, does this mean that the Stars had the most wins this season, while the Blackhawks, Blue Jackets, Ducks, etc. had the least? In order to further this analysis, I had to add a wins column to the data that simply totaled the number of wins each player’s team had this season.

Team Wins
Rangers 55
Hurricanes 52
Jets 52
Panthers 52
Stars 52
Avalanche 50
Canucks 50
Oilers 49
Bruins 47
Predators 47
Maple Leafs 46
Knights 45
Lightning 45
Kings 44
Blues 43
Red Wings 41
Capitals 40
Islanders 39
Sabres 39
Wild 39
Devils 38
Flames 38
Flyers 38
Penguins 38
Senators 37
Coyotes 36
Kraken 34
Canadiens 30
Blue Jackets 27
Ducks 27
Blackhawks 23
Sharks 19

From here, we can takeaway that the Stars did have the second highest number of wins at 52, but they were second to the Rangers at 55. Moreover, the Blackhawks, Blue Jackets, Ducks, Flyers, and Sharks are all near the bottom of the list but there are various other teams who joined them with few wins that have more players on the top list, so this relationship is still not clear.

Let’s go even further! It would be best to see this relationship on a scatterplot and attempt to get a correlation coefficient, so that we can tell how strong this relationship truly is.

Alas, we find that the correlation coefficient between points and wins is .34 indicating a weak/moderate but still positive correlation between players who are ranked higher and those that have more wins.

What Do Critics Have to Say?

Over the past few seasons, CBS Sports experts and analysts have developed what they call “NHL Power Rankings”. Each season, they give their two cents on what teams are looking good and those that are not doing so hot. Their reviews are compiled in the middle of the season, prior to playoffs, and include a short analysis of how the team looks, as well as how they predict the team will do weighing in their current circumstances.

I decided to use their reviews to get a better understanding of how their sentiment differs between the eastern vs. western NHL conferences. Furthermore, I want to see if these scores have any relationship with which conference has more top ranked players.

Analysis - What Do Critics Have to Say?

First, let’s look at the conference breakdown of players with the most points this season.

We find that overall, the conferences were very close in terms of how many ranked players they had this season. The western conference fell slightly below the eastern conference ending with 48 and 52 players, respectively.

Taking this into account, let’s look into this question: How did the emotional sentiment counts for each conference differ? In other words, what kinds of emotions did CBS sports analysts use when talking about both the eastern and western conferences? In order to answer this, I filtered the data to only include reviews from the 2022-2023 season and 2023-2024 (current) regular seasons.

Although the western conference has a higher number of emotional sentiment words in every category except for trust, there are a few key takeaways we might be able to relate to our findings above. The most noticeable difference in scores is found in the comparison of negative words. The western conference has a substantially larger number of negative words used in their reviews than the eastern conference does. Additionally, they almost have double the amount of words that are classified as eliciting fear and sadness.

Let’s consider at one last comparison: Were CBS analysts more optimistic about the eastern or western conference during the 2023-2024 regular season?

Although there weren’t too many things said for just this 1 season, CBS experts did have more positive things to say about the eastern conference vs. the western. Interestingly enough, the eastern conference had 4 more players on our original list.

Summary

There was a lot discovered by completing this NHL analysis. A few significant findings include that total number of assists have a stronger correlation with points than total number of goals do. Moreover, shots on goal is moderately correlated with points, but age has no found correlation at all. Moving on, we found that the number of wins a player’s team had did not have as strong of a relationship with total points accumulated as I anticipated, but still a positive one.

Finally, we noted that the eastern conference had a slightly greater amount of top point players. Comparatively, what CBS experts had to say about each conference (talking more highly of the eastern) did somewhat lineup with this measure.

If I were to go even further in my analysis, the first thing I might look into is testing to see if there are any notable differences using the even strength, power play, and short handed classifications of goals and assists found within the data.