Borrowing the concept from my last post on Texas Alcohol Revenue, a friend pointed me towards the Colorado tax receipts on Retail Marijuana Sales:
It's hard to pull much out of this dataset besides "wow that's a lot". Sadly the state does not report sales by individual business as in the Texas Alcohol example. I hoped to see clearer seasonal trends, but it is difficult to do so in the early rapid growth stage. There does seem to be a slight dip in Q4, possibly due to family holidays? I guess this isn't a family activity quite yet...
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New Series!
Thanks to some friends, I found a fun set of data to work on this week. Here in the great state of Texas, we like our drinkin'. Due to certain taxes that must be paid to the state for the privilege of selling alcoholic beverages and the public nature of those filings, we can back into the revenue generated by alcohol sales at any venue in Texas. Newspapers/local blogs like to sift through this and post "top 10" lists. It makes for a nice slideshow post (barf). 'Hey what hot new bar that we should've thought of is killing it???' So I set out to do better...
In order to do that, I've considered the entire state together, and put a considerable amount of legwork into adding context. More importantly, I have an interactive interface to view the data. To start, there are over 15,000 businesses that report. For the time being I filtered out anything below $1.0mm in annual revenue which culls the list down to about 10% of the original. The tax filings data only includes the name of the legal entity, which can be anything. Sometimes they are obvious, sometimes not. So I set out to add actual business names and a "type" category. Type is a broad category, which I hope to subdivide later for even more context.
At this point I've added detail for the top 200 businesses and tackled some franchises all at once. I hope to chip away at the list as I update the data. There also seems to be a lag in reporting, so I've conservatively chosen to start a couple months back.
You can filter by type and by county -> Dallas / Ft. Worth Metro = Dallas, Tarrant, Collin, Denton Counties, Austin = Travis County, Houston = Harris County, San Antonio = Bexar County (pronounced bear to be safe, if not necessarily accurate)
2016/03 Trailing Twelve Months - Texas Alcohol Revenue
In order to do the map plotting in Tableau I had to get latitude and longitude of each business. In order to do this I used a simple python/pandas script and google maps api. I'll try to get that up on GitHub soon. It worked pretty well, but if you notice any glaring location errors please let me know (my first plot put some businesses in Kansas). Also as far as errors go, I can't for the life of me figure out why Topgolf Allen isn't in these reports. I looked at every entry from Allen and nearby cities, it's not there. If you have an idea why that could be, please let me know (wild speculation encouraged).
If your state/country has similar data, I'd love to know. It would be fun to compare across states. If you have any other ideas/suggestions to improve this, please comment!
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Games Qualifiers
For 3 weekends in the month of May, over 300 individual athletes in both the Individual Men’s and Individual Women’s divisions competed in 1 of 8 Regional competitions. Each of these featured 7 events over 3 days, and each came with 5 tickets to the CrossFit Games.
Here are the qualifiers from each Regional:
This is a breakdown of the Regions that produced each qualifier:
What Does A Games Athlete Look Like?
There is a general idea in the CrossFit community that CrossFit has a bias toward shorter athletes, but do the numbers support that? I’ll start with a simple scattergram of height and weight. As you can see there is quite a range of sizes.
The distribution of sizes has a nice normal shape. In addition to size, I looked at age, and the ratio of weight : height.
Just looking at the numbers, it’s clear that there are many more male athletes in the 5’8”-5’11” range, but does that mean anything? The average American male is about 5’10”, with only about 23% of U.S. men taller than 6’. Based on the 2016 sample, the average Regionals athlete (both male and female) is about .5 inches shorter than the American average each with a lower standard deviation (more tightly packed around the mean).
TAKEAWAY: There are more factors to control to tighten up this study. For instance, I’m comparing more than just American athletes to the American mean. I’d like to add results from previous years to expand the sample size and further refine this research. With that said, the data does suggest a small bias to the 5’8”-5’11” range for men and 5’4” for the women.
How Much Better Is A Games Athlete?
CFHQ beat me to the punch on this one (sort of). Here is a look at the average benchmarks of Qualifiers vs Cut athletes. (Note: Benchmarks are self-reported and took quite a bit of scrubbing…)
TAKEAWAY: On the women’s side, there is specific emphasis on the lifts, and the Fran/Grace times reflect that. The better you can move a barbell, the better you are going to fare in the competition. There are actually 2 benchmarks that the Cut athletes did better in – Filthy 50 and Sprint 400. It’s possible that there is some issues with the self-reported data, or this might be a reflection that the Regionals favored stronger athletes over faster ones. For the men, Snatch was the key lift with C&J also showing a meaningful delta. The Qualifiers do a better job of converting the raw strength of deadlift into the skills needed for the Oly lifts (more on this in a future post). This is reflected by the Grace times, but interestingly, the Cut athletes had similar Fran times. Qualified athletes on average finish the Filthy 50 a full minute faster. The ultimate chipper with 500 total reps, this works out to an almost 2 reps / minute faster pace – they add up. CFHQ's piece on this has some good historical data on their post, check it out here.
FINAL WORD: Not much to do about your height, but if you are like me and want to improve your Open performance next year you would do well to hit the barbell on a regular basis. As one of my favorite athlete’s to follow on Instagram says “Lift Heavy Often As In Everyday” (@elijahezmuhammad).
Next in this series we’ll take a shot at figuring out if the best 40 athletes made it to the Games, and how this compares to the past (and future?).
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