Alright, let’s dive into my little project: Dirk Nowitzki versus Kevin Garnett. This wasn’t some hardcore statistical analysis; more like a “let’s see what shakes out” kind of thing.
First off, I got the idea when I was arguing with my buddy about who was the better power forward from that era. You know, the usual bar talk. So, I thought, why not actually do something about it instead of just yelling?
Step 1: Data Gathering. I started by scraping their stats. I used a simple Python script with Beautiful Soup to pull data from Basketball-Reference. It took a while, cleaning the HTML was a pain, but I managed to get season-by-season stats for both of them. Fields like points, rebounds, assists, you name it. The usual suspects.
Step 2: Data Cleaning. This was honestly the grimiest part. Dates were formatted differently across seasons, some stats were missing, and there were weird characters everywhere. I used Pandas in Python to clean everything up. Replaced missing values with 0 (probably not the best choice, but hey, it’s my project). Standardized date formats. Basically, made the data usable.
Step 3: Basic Stats Comparison. Once the data was clean, I calculated some basic stuff: career averages, total points, peak seasons, that kind of jazz. This was easy, just Pandas operations. I wrote functions to find the best seasons based on scoring, rebounding, etc. Nothing too fancy, mostly using `.mean()` and `.max()` functions.
Step 4: Visualization. Numbers are boring. I needed something visual. So, I used Matplotlib to plot their career stats. Simple line graphs showing points per game, rebounds per game, and assists per game over their careers. Added labels, legends, and a title. Made it look presentable.
Step 5: Deeper Dive (Attempted). I tried to do some more advanced stuff like calculating win shares and value over replacement player (VORP). This is where I hit a wall. The formulas are complicated, and I didn’t have all the necessary data. I ended up just using the VORP values that were already available on Basketball-Reference.
Step 6: Write-up. Finally, I wrote up my “findings” in a simple markdown file. Compared their stats, talked about their strengths and weaknesses, and basically just summarized what I had found. No groundbreaking revelations, just a decent comparison based on stats.
What I Learned:
- Data cleaning is a HUGE part of any data project. Seriously, like 80% of the work.
- Pandas is your friend. It makes data manipulation so much easier.
- Matplotlib is great for simple visualizations, but it can be a pain to customize.
- Sometimes, you have to accept that you can’t do everything you want to do. VORP calculation, I’m looking at you.
In the end, did I definitively decide who was better? Nope. It’s still subjective. But at least now I have some data to back up my arguments at the bar. And hey, I learned a few things along the way.
