It’s springtime within the U.S., which implies one thing as American as apple pie is again: baseball. And since there’s every kind of nice information round one of many nation’s nice pastimes, we determined for this week’s put up to have a look at Main League Baseball (MLB) attendance statistics from the final 20 years, which is printed on many web sites together with the one we used to get the information you’ll discover within the charts beneath: ESPN.com.
To gather the attendance information from ESPN, we used Jupyter Workspaces (at present in beta in Domo) and the Python package deal Lovely Soup to parse the HTML. And since Domo can now schedule code in Jupyter Workspaces to run on a daily schedule, you may make certain that this web page will proceed to replace with the 2022 information.
The very first thing you’ll most likely discover when trying on the information is that 2020 is lacking. That’s as a result of, because of the pandemic, baseball was performed with out followers that yr. There was a little bit of a return to normalcy in 2021, nevertheless it wasn’t till this season that each one spectating restrictions had been lifted, so it is going to be fascinating to observe how attendance rebounds (although, in full transparency, we solely have the information for full years proper now, so we aren’t capturing any information associated to seasonality, corresponding to how climate or a group’s place within the playoff race impacts ticket gross sales).
One good strategy to evaluate this information is with an previous favourite of many information scientists: a field and whisker plot. The chart exhibits the minimal and most common attendance for every group within the whiskers (the highest and backside strains). I’ve sorted this to indicate the group with the best peak attendance yr on the left, and the bottom on the correct:
The place the visualization will get extra fascinating for me is with the field components. Every field exhibits the house between twenty fifth and seventy fifth percentiles, which is supposed to replicate how a lot a group’s attendance has swung through the years. The larger packing containers inform me these groups (corresponding to Philadelphia and Detroit) have had some nice years for attendance and a few not so nice years. Smaller packing containers (corresponding to Boston) say {that a} group has been very constant in its attendance numbers. We now have additionally filtered the chart for pre-pandemic years solely since 2021 (and to a lesser extent partial 2022 information) skews the information.
An alternate strategy to understanding how groups rank in attendance is to create indexes of the place a group’s attendance stands relative to the entire MLB common—which is what we’ve performed straight beneath. Darkish blue packing containers imply {that a} group is nicely above the common, whereas darkish orange packing containers imply {that a} group is nicely beneath the common. You should utilize the filters to have a look at no matter league, division, group(s), or yr(s) you’re keen on:
Lengthy-time Domo customers could also be these indexes and considering that I did some pre-calculation in a Magic ETL or a Dataset View. It’s true that doing calculations on such whole ranges usually require pre-calculation. But when I did that, it could be arduous to permit for the yr filter. So, the key is out: With Domo’s new FIXED beast modes (at present in beta), you are able to do FIXED degree of element features proper in a beast mode. For the above “Index to League Avg”, that is the calculation:
You possibly can see there are two issues taking place right here. First, when I’ve the SUM FIXED by League, then it’s summing throughout all values with the identical league because the row I’m on. That enables me to get that league whole we’d like for the denominator of the index. Second, it’s utilizing FILTER ALLOW to inform Domo that filters on Yr can influence the FIXED features. There are alternatives for FILTER ALLOW, FILTER DENY, and FILTER NONE.
Right here’s one final instance of how helpful the FIXED with FILTER DENY may be. The bar charts beneath are defaulted to the New York Yankees (my boss’ favourite group). The primary chart is just not utilizing FIXED, so once I filter for the Yankees, the Min, Max, and Median fields change into meaningless since they get filtered to be the identical as the chosen group. The second chart makes use of FIXED and DENY on group identify in order that the Min, Max, and Median stay as references to the principle common, which is for the Yankees.
One of many issues I really like—and likewise at instances discover maddening—about exploring new information is that there’s all the time extra to discover. As I labored on this put up, I noticed that it could be fairly fascinating to herald groups’ win/loss information in addition to data on stadium capability. However then I assumed: Let’s possibly save that for a future put up.
