Title
Overview
How does average time on ice impact player points for different teams?
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The scatter plots generally show a positive correlation between a player’s average time on ice and their number of points. As average time on ice increases, there tends to be an increase in the number of points. This suggests that players who spend more time on the ice often gain more points, indicating a positive relationship between playing time and performance in terms of points.
However, while there seems to be a relatively positive correlation throughout the scatter plots, there are some data points that deviate from this trend. This suggests that an increase in average time on ice does not always lead to an increase in points. A possible explanation for this could be a player’s position on the ice. For example, players in forward positions like center or wing might have more point opportunities than defensemen due to their positioning closer to the net.
How do the number of shots taken at low, medium, and high danger levels influence the number of goals scored from those respective shot types in the NHL?
The results, presented in the bar chart, revealed a compelling pattern: as the danger level of shots increased, so did the conversion rates. The low danger shots had the lowest conversion rate, while the high danger shots had the highest, supporting the hypothesis that shots taken in riskier, often more aggressive play contexts are more likely to result in goals.
This graphical representation of the data clearly illustrates the relationship between shot danger level and scoring efficiency. The significant disparity between the conversion rates of different danger levels underscores the strategic value of creating high-quality scoring opportunities.
Data Source: MoneyPuck
What are the odds of one team winning against another based on seasonal statistics?
The data that I have provided right here shows the calculation I have made for win percentage per team and shows a graphical interpretation of the calculations that I have made. From that I was able to derive the odds of one team winning against another using user input, so if you provide the code with two names such as “New York Rangers”, and “Boston Bruins” the code will return you the odds of each team winning. From this, you can take that information to the bookies and understand any inconsistencies in the odds, for example, if a bookie has the odds of something being very high and the odds on your code say that it should be low, it is a good idea to place that bet because the odds will most likely change throughout the game towards the codes favor as the game continues.
How does goal differential between the last 4 seasons compare between each team?
This data shows how different teams compare to each other over the past 4 seasons when it comes to goal differential. To use this plot, you can click the team names on the side to remove the team from the data set, or if they are already removed, they are added back, You can also isolate teams by double clicking a team name, causing only the team’s stats to show up. You can select the data and compare teams over time. Goals win games, so high goal differential likely means that they have a good win percentage. That being said, if a team has a bad goal differential, they will likely have a bad win percentage. Goal differential can be impacted by both ends of the ice, meaning other factors like save percentage and goals per game can be significantly related.
Conclusion
Thank You
