I’ve had a couple of people ask about how to use the new interactive visualizations we offer at Hockey Graphs, so I thought I’d take the time to provide a tutorial with some visual demonstrations.
The first thing you’ll see is a pre-selected graph (of my choosing) on any one of the individual graphs. I simply picked a particular season that I was intrigued by and put it up when I embedded the graph, as a demonstration of one of the ways you might depict a team.
On the left margin on all the graphs, you should see a grey bar at the top of the legend. I typically title it “Report,” though a couple are titled “Team.” Regardless, to choose a different team and/or season to visualize, click on the grey bar:
This should open up a menu with a number of different sorting options; most important to you will be the “Filter” selection. Ignoring the check mark next to it, click on “Filter.”
For some browsers, this might cause the entire graph to scroll down-screen a bit, but returning to the graph will show that you’ve opened a checklist of all the visualization options for that particular graph. As you browse through the potential selections, be sure to first double-click on “(Select All)” to remove the pre-selected teams.
A quick explanation of the available selections: in addition to all the teams available, I have also added league averages (the exception being the possession charts, where league average is 50%), as well as plus- and minus-one standard deviation from the league average. As I mentioned in the intros for most of the charts, I actually prefer to use the plus- and minus-one standard deviation lines instead of league average. The standard deviation, in general, tells us the typical difference between two teams within the league in the season selected. By taking that measure, adding or subtracting it from the league average, and setting a line there, we can indicate teams that truly transcended the typical range of performances in that metric, that season. I consider it a sort of enhanced league average. One warning: these standard deviation lines get a bit wonkier pre-1967 expansion, as there were only six teams to help establish the lines. In that era, it’s best to regard any team near the top or bottom lines as exceptional, in so far as a team can be exceptional when there are only 5 other teams to compare them to.
The metrics used are explained on the graphs themselves as well as the introductions above the graphs. I chose to use 20-game moving averages (average of games 1 through 20, average of games 2 through 21, and so on) and cumulative averages (average of game 1, average of games 1 and 2, average of games 1 through 3, and so on) to portray the seasons, with the latter demonstrating how the data built to the team’s final measure in that metric, and the former demonstrating the in-season peaks and valleys as they moved towards that final measure.
Using the above example, and following my personal preference, let’s say that I wanted to compare how the Montreal Canadiens’ possession progressed through the 1952-53 season to the Toronto Maple Leafs’ possession that same year. I would select those two teams, as well as the league’s plus- and minus-one standard deviations, and click the “OK” button like so:
The resulting graphs looks like this:
Looking at the graphs, I could say the Canadiens were clearly a strong team in possession all year. The Leafs, on the other hand, had some strong points, but were closer to the middle of the pack. With charts like these, you might feel you want to investigate what was happening in the middle of the season that caused such a peak in the Habs’ measure (who knows, maybe it was they got to play Toronto a lot!).
That was just one one kind of comparison you can make with these graphs. Others include team-to-team comparisons of team histories in possession and shooting; a smorgasbord of single-season team performances to compare to one another (and from one season to another) in offense, defense, possession, shooting, and goaltending; and season-to-season comparisons across a franchise’s entire history in these same metrics. All of these different graphs provide ways to explore what happens when players leave, are injured, are traded or added; how teams compare by their underlying metrics, within season and from one season to another; and how things like momentum, consistency, and regression operate at the team level over the course of a season and even a team’s history.
Finally, all of these graphs are embeds from Excel Online documents, which means that if you leave the page open for awhile you might find this prompt when you try to utilize the graph:
Simply click the “Reload” button and everything should be fine. A plus to these being linked to Excel Online documents is the data and documents are available to you via the icons at the bottom of the graph. For instance, if you have Excel Online, you’ll be able to view the document in that application by clicking on the “View the full-size workbook” icon on the very bottom right corner:
If you don’t have Excel Online, or even if you do and you want to download this document, click the left-most of the icons in the bottom right:
We’re totally okay with you downloading the data and using it for your own work and research. The only thing we ask: if you use it for published work, please accredit us, preferably by our URL “www.Hockey-Graphs.com”, or even just “Hockey-Graphs.com”. We’re also very interested in seeing what you do with the data, even in the event that you critique or improve on it, so feel free to let us know when and where you use it!
For any questions, troubleshooting, accreditation, etc., I have provided a contact form below. Also, since I realize that some visualizations aren’t possible with the current options available, I am open to you using the form for requesting specific reports for use by individuals, media, and organizations; I’m even open to suggestions for additions to the site!