Alright, let’s dive into this “evan carter projections” thing. It was a bit of a rabbit hole, but I think I’ve got something solid to share.
So, I started by Googling “evan carter projections.” Pretty straightforward, right? I needed to figure out what kind of data was out there, and honestly, how reliable any of it might be. I saw a bunch of different sites with their own takes, mostly focused on baseball stats.
Next up, I grabbed a bunch of the data. I found some CSV files floating around, and a few sites let me copy tables into a spreadsheet. It was messy, to be honest. Different sites tracked different stats, different date ranges… ugh. Data cleaning was definitely step one.
Then, the fun part (sort of): wrangling the data. I used Python with Pandas – total lifesaver. I started by standardizing the column names (lowercase, underscores – the usual). Then I had to deal with missing values. Some I filled with zeros, some with the average of the column, depending on the stat.
I calculated some basic stats – mean, median, standard deviation – for the main metrics like hits, runs, RBIs, etc. This gave me a baseline to work with. Nothing fancy, just getting a feel for the data’s distribution.
Okay, here’s where I tried to get a little smarter. I started plotting the data – histograms, scatter plots, you name it. I wanted to see if there were any obvious trends or correlations. Did his batting average trend up or down over time? Did his home run rate change with certain pitchers? Stuff like that. Saw some minor upward trends in a couple of areas, but nothing groundbreaking.
After that, I built a basic projection model. I decided to go with a weighted average of his past performance, giving more weight to recent data. The idea was that what he did lately is more indicative of what he’ll do in the near future. I played around with the weights a bit, trying to find something that seemed reasonable based on his historical performance.
I didn’t stop there though, i took my previous models and ran some simulations with it. Monte Carlo simulations, to be exact. I wanted to see how the projections might vary given some randomness. What’s the best-case scenario? Worst-case? Most likely? This gave me a range of possible outcomes instead of just a single number. It’s way more realistic.
To visualize the results, I created a simple dashboard. It showed the main projections (hits, runs, RBIs, etc.) along with the range of possible outcomes from the simulations. I used some basic charting libraries in Python to make it look decent. This made it easy to see the projections at a glance and get a sense of the uncertainty involved.
Lessons Learned:
- Data cleaning is ALWAYS the biggest time sink. Seriously, budget extra time for it.
- Simple models can be surprisingly effective. Don’t overcomplicate things unless you have to.
- Visualizing the results is crucial. If you can’t easily understand what the model is telling you, it’s not very useful.
It wasn’t perfect, by any means, but it was a fun exercise. And now I have my own “evan carter projections,” which, let’s be honest, are probably just as good (or bad) as the ones from the “experts.”
