We would believe that as shots increase, so do number of goals. As the great Wayne Gretzky/Michael Scott once said, “you miss 100% of the shots you don’t take”. We will use a hexplot to analyze how number of goals scored is related to number of shot attempts.Īgain, this graph can be somewhat inherent. The observation frequency bar graphs can be seen along the spines as an additional reference for information. A darker color hexbin means that there are more observations, or more density, within that region. A hexplot splits the plotting window into several hexbins and then the number of observations which fall into each bin corresponds with a color to indicate density. Hexplot - sns.jointplot(kind = ‘hex’)Īnother way of visualizing a bivariate relationship, in particular when we have a large amount of data, is the hexplot. Therefore we can deduce that there is a slight positive relationship between the two. By looking at those, we can see that as number of penalties increase, there are less players populating those regions. But, the jointplot gives us the benefit of showing the distributions along the top and right spines.
However, the scatter plot itself does not show a strong relationship in either direction. It is inherent to think that a small number of penalties would mean more time spent on the ice, which means more opportunities for scoring. If we look at the main scatter plot, we can‘t really make out much of a distinction. The boxplot for points by team can be seen below:
Specifically, boxplots help us identify where the medians, ranges, and variabilities of data lie.
Boxplot - sns.catplot(kind = ‘box’)Īnother type of plot which helps give us an idea of what our data looks like is the Boxplot.
SNS DISTPLOT RENAME X AXIS CODE
You can eliminate both the KDE and the rug from the histogram by setting the code arguments to False. The rug simply shows us where the individual data observations are located on the graph. The tick marks which we see at the bottom of the graph are known as the rug. In simpler terms, if new player data was introduced to the set, there is the highest likelihood that it would fall under the tallest peaks of the smoothed line. The smoothed line which we see is the kernel density estimation (KDE) - a technique which estimates unknown probability distributions of the variable based on the samples we already have. The histogram above shows us that overwhelmingly, the majority of the league scores between 0 and 20 points. The DataFrame we will be left working with looks like this: Louis Blues, Colorado Avalanche, Minnesota Wild, Winnipeg Jets, & the Dallas Stars. Next, because there are 31 NHL teams and this is a lot to deal with for these instructional purposes we will limit the data to that only from teams in the Central Division: Chicago Blackhawks, Nashville Predators, St. We will select data from skaters in all situations (5v5, man advantages, shorthanded, etc.). We begin by cleaning the information we have a little bit. The GitHub repository for this notebook can be found here. Seaborn is one of Python’s most powerful and essential visualization packages, and there are endless possibilities for telling visual stories through your data. So, to make up for the lack of life which sports brings to so many of us, I decided to put together an overview of something which brings life to a select few of us, NHL statistics data. This unfortunate pandemic also means that we are missing my personal favorite time of the year, the NHL playoffs. However, enter 2020 and the time of COVID-19, and here we are, watching replays of the 2003 NCAA Tournament’s second round pretending that we are just as invested as if it were the 2020 tournament (which should be happening as I type this). If you’re like me, a world without sports is basically no world at all.
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