Uphill Battle
For those who read last week’s post, my fledgling efforts to identify an NBA or NCAA team that has been successful while fielding a shorter-than-average roster did not yield much result. This past week’s efforts may yet produce a “diamond in the rough” but I have to admit that this week was a difficult ride for me.
I hit a wall with my data analysis which is a result of my inexperience and an inevitable part of any new endeavor. It seems that whenever I try something new I come in with “beginner’s optimism”. I tend to think that I can pick up a new skill and the process won’t be frustrating or messy.
I also had some personal challenges that were compounded by the struggles my jump training is presenting. If you are reading this right now, I want to say thanks for coming on this journey that is surely to present hills and valleys (or maybe even mountaintops and deep craters).
Back in the NCAA Trenches
This week’s challenge was centered on gathering data on NCAA rosters so that I can compare average roster height to wins. The problem is there are 362 D1 men’s basketball teams and I am only one man with a full-time job, a small family, and a desire to dunk a basketball. In the small amount of time I am spending to gather data I can only gather a few rosters of data each day. At my current rate, it would take another couple months to have a complete picture of the results.
As a result, I turned to technology to see if I could automatically scrape the data from the NCAA website. I have done some web scraping for projects in the past but it has been awhile. In the past I have either used third-party software, or written VBA code in Excel. The first option can cost money (I am about as cheap as they come) and the second option can be incredibly laborious.
I tried to turn to my available resources and was able to scrape a lot of the data I needed using Microsoft BI (a Microsoft program for aggregating and displaying business data), but there is a LOT of cleaning/modifying still pending, and it took what felt like an NBA season’s worth of referee call-reviews to get the data. (For those who haven’t watched NBA basketball lately sometimes it feels like it takes a lifetime for referees to review controversial calls. I admit it probably feels worse than it is, but you get the point.)
I also learned in the process that I am probably going to have to “bite the bullet” and learn how to code in R or Python in the near future to simplify this processes going forward.
Look Ma!
All messiness aside here is a look at the mess I am now trying to sort through:
It may not be a picture my mom is going to post on her refrigerator, but that was the best artwork I came up with this week. Actionable insights are still pending, but I am hopeful I can start gathering data that can guide some fun analyses going forward.
Training Update
This week was certainly a down week for my training. I briefly mentioned in my previous post that two weeks ago on March 23rd I injured my shoulders (particularly my left shoulder). I had a great jump session that day and was feeling nice and bouncy. I wanted to take the opportunity to jump until my legs felt like mashed potatoes, so I jumped at max effort for probably an hour and a half. I think it was great for my legs and jumping. My shoulders however, did not take so kindly to the abuse.
Later that day, while folding laundry, I felt like a ninja entered into my left shoulder, removed his katana and gave my shoulder a swift “stabby stabby”. My pain hit nearly a 7/10 for a moment. I don’t know if wasn’t cognizant of pain during the jump session, but in that moment I knew that my shoulders were in a bad spot.
It turns out the area I aggravated is called the AC joint. You can see it here:
I had known my AC joint was worn out- it had felt fatigued for some time after doing lots of pull-ups, handstand push-ups, and freestyle swimming. This was the first time it crossed the threshold into what seems to be a more serious injury. Shoulder rehab will be an additional wrinkle in my learning process as I train with different parts of my body at sub-optimal levels.
My shin splints felt good to start this week so I tried to put some load on them this past Wednesday. The experiment didn’t come back with great results. I will need to continue my cautious approach with my shin splints because after performing a hard toe-off while doing a hurdle-jump I could feel the pain flare-up.
By the time my jump day (Saturday) came around this week I felt exhausted and it showed in my jumps. The spring just wasn’t all there. I was definitely jumping lower on a one-step approach, my full approach seemed maybe a little higher, but my measured height was definitely lower- probably in the 26-27” range.
Here are my jumps:
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