Alright, let’s talk about Jakob Meyers’ ADP. I’ve been messing around with this for a bit, and I gotta say, it’s been a rollercoaster.
So, where did I even start? Well, first things first, I had to download the data. Found it on some fantasy football stat site, CSV file and all. Nothing too fancy there. Then, I fired up my Python environment. Yep, good ol’ Python. You could use R, but I’m more comfy with Python for data stuff.
Next step, importing all the necessary libraries. Pandas, obviously. Gotta wrangle that data, ya know? And NumPy, because… math. Some visualization libraries too like Matplotlib to see some basic charts. Nothing too fancy, just trying to get a feel for the numbers.
Then came the fun part: cleaning the data. This took ages! There were missing values all over the place. I decided to fill them with the mean ADP for that position. Might not be perfect, but it’s a start. Also, had to convert some strings to numbers. Those always trip me up.
Okay, data’s (mostly) clean. Time to analyze. I wanted to see how Jakob Meyers’ ADP compared to other wide receivers. I filtered the data to only show WRs and then calculated some basic stats: mean, median, standard deviation. You know, the usual stuff. Then I plotted a histogram of WR ADPs with Meyers’ ADP as a vertical line, just to get some visual context.
After that, I started looking at trends. Was Meyers’ ADP higher or lower compared to the previous year? Did it change drastically during the season? For this, I had to find some historical ADP data (more downloading and cleaning… ugh). I tried to plot these trends too. Sometimes the plots were useful, sometimes not so much. I experimented with different chart types a bit to see what worked best.
One thing I wanted to figure out was if there were any correlations. Did Meyers’ ADP correlate with his projected fantasy points? Did it correlate with his team’s implied point total? I used Pandas to calculate correlation coefficients. I didn’t find anything earth-shattering, but there were some interesting (weak) positive correlations between projected points and ADP as you might expect.
Finally, I put all my findings together in a little notebook. Added some markdown explanations, some charts, and some tables. Nothing too fancy, but it helps me keep track of what I’ve done and what I’ve learned.
It wasn’t perfect, and there’s a lot more I could do, but it was a fun project. I’m not a data scientist or anything, just a guy messing around with fantasy football stats.
