Okay, here’s my take on a blog post about tackling “madden combine questions,” focusing on the practical, hands-on experience, just like I was sharing my notes and thoughts.
Alright, so yesterday I decided to dive into some Madden combine data. You know, those questions and drills they use to rate players? I always thought it was kinda cool and wanted to see if I could actually do something with it. I mean, beyond just yelling at the TV when my favorite player gets a bad score.
First thing I did was hunt down some data. I wasn’t gonna build some crazy predictive model or anything fancy. I just wanted to play around. I ended up finding some decent CSV files online – mostly from old Madden games, but hey, it’s a start. A lot of it was a mess, honestly. Missing values, inconsistent naming, the whole shebang. I mainly focused on player name, position, 40-yard dash time, bench press reps, vertical jump, and broad jump.
Next up, cleaning the data. Ugh. This part is never fun, right? I used a bit of Python with Pandas – nothing too complicated. I started by dropping rows with too many missing values. Then, I standardized the position names – you wouldn’t believe how many different ways people type “Wide Receiver”! After that I converted data types. The 40-yard dash was stored as a string, so I turned it into a float. I also cleaned up the names, stripping leading/trailing spaces and making sure they were consistent.
Then, the fun began. I started asking questions, simple ones at first. Like, “What’s the average 40-yard dash time for wide receivers?” “Who has the highest bench press for defensive tackles?” I used Pandas to filter and sort the data. I graphed some of the distributions. I found that wide recievers were much faster than Offensive line players as one would expect.
I wanted to see if there were any correlations. Did a higher vertical jump really mean you were a better receiver? Or was it all hype? I used Pandas to calculate the correlation coefficients. Honestly, most of the correlations were pretty weak. There was a slight positive correlation between vertical jump and 40-yard dash time (meaning guys who jump higher tend to be a little faster), but nothing groundbreaking. Maybe you need more data to get something meaningful.
After that, I tried something a bit more interesting. I decided to create a “combine score”. I normalized each of the combine stats to a 0-100 scale, with 100 being the best. Then I added weights to the normalized scores. For example, for wide receivers, I gave the 40-yard dash a higher weight than the bench press. For defensive tackles, bench press and broad jump got more weight. It’s totally arbitrary, of course, but it was fun to play with. I then summed the individual scores to produce an overall combine score.
Finally, I looked at the top players based on my custom score. It was cool to see which players came out on top, and whether they matched my gut feeling about who’s good. It wasn’t perfect, not by a long shot. But it was a fun way to explore the data and understand the combine a little better.
Here are a few lessons learned:
- Data cleaning is crucial. Garbage in, garbage out. Seriously, spend the time to clean your data.
- Simple questions are good starting points. Don’t try to boil the ocean right away.
- Don’t expect miracles. Combine data is just one piece of the puzzle. It doesn’t tell the whole story.
- Have fun! If you’re not enjoying it, you’re doing it wrong.
What’s next? Maybe I’ll try to find some more recent data and build a simple web app to visualize the results. Or perhaps use more advanced tools to do proper statistical analysis. But for now, it was a fun little project!
