It’s finally here! I guess it’s a lot like marmite, you either love it or hate it. This will be a 5 part series going through my experiences each day of Dashboard Week. I’ll mainly be going through my thought process and how I tackled the challenge every day.
What is Dashboard Week?
Dashboard week is pretty much Makeover Monday but on steroids. Each day, we (DS20) get set a challenge from Andy in which by 3:30pm we have to have created a dashboard that meets certain requirements with a data set given to us. When 3:30 comes, we present back to Andy and our fellow cohortians.
What data was given?
Day 1 saw us handling baseball data. Lots of it. I know absolutely nothing about baseball (and after a morning of frantically researching what on Earth the barrel zone is, I think I’ve increased my knowledge on the game from 0% to 0.5%). Although I know nothing about baseball, there was plenty for us to get our teeth sunk into. The requirements for the dashboard were a KPI style dashboard using this baseball data (contextual numbers, overviews, that kind of thing). Lucky for us, the data was available to download as a .csv which saved us a whole lot of web scraping.
When I initially saw the data I was extremely confused on what to do, so after some research and looking at the data, I decided to have a look at the hitters in the 2020 season. Once I knew what table I was using, I could then digest the metrics needed and go and write a plan. Note: if you’re a future cohort reading this who’s about to go into dashboard week, do yourself a favour and PLAN. It will help you out massively in giving you a clear direction, and if you lose yourself in what you want to achieve it’s always good to go back and remind yourself what it is you’re trying to build.
I chose to go down the route of a ‘Player Overview’. I wanted to look at how many balls they’ve hit and average speed, distance and angle and how these compared to their 2019 stats. Also included was a couple of the baseball jargon extras such as % of ‘hard hit’ balls and % of ‘barrelled’ balls. From there I took more of a League view and show the top 10 for a certain metric and I wanted to see if there was any correlation between the ‘Sweet Spot’ percentage and a given metric, like average distance for example. Here’s what I ended up with:
It’s by no means perfect, there are definitely things that can be improved. The white text is a little hard to read on the League AVG bar charts below the numbers for example. Overall I was fairly pleased with what I had produced. Click here to view the viz on Tableau Public 😊.
Let’s see what day 2 has to offer.