Alright, so I decided to mess around with some NFL data today, specifically looking at “drops” by players. I’ve always been curious about which players tend to drop the ball more often, and today I decided to actually try and figure it out.
First, I needed to find some data. This was a bit trickier than I thought. I poked around a few different sports stats sites, and finally, I managed to scrape together some usable data. It wasn’t perfectly formatted, but hey, that’s part of the fun, right?
I used a python tool to help me extract the data, this is my way to work efficiently.
Wrangling the Data
Once I had the data, the real work began. I’m no data scientist, but I know my way around a spreadsheet. I imported the data into a Google Sheet to keep things simple.
The data had a bunch of columns I didn’t really care about – things like “yards after catch” and “average depth of target”. All I really wanted was the player’s name, the number of targets they had, and the number of drops.
So, I deleted all the extra columns. Then, I needed to calculate a “drop rate”. I did this by creating a new column and using a simple formula: drops divided by targets. This gave me a percentage, showing how often each player dropped the ball relative to how many times they were thrown to.
Sorting and Seeing
After I had the drop rate calculated, it was time to sort things out. I sorted the spreadsheet by the drop rate column, from highest to lowest. This way, the players with the highest drop rates would be at the top.
And there it was! A list of players, ranked by how often they dropped the ball. It was pretty interesting to see some of the names that popped up. Some were guys I expected, but there were definitely a few surprises.
Finally I copy those datas to share!