Alright, let’s dive into my little project on Orioles 2023 stats. It was a fun one!
First things first, I got hooked on the O’s last season. Seriously, what a turnaround! So, naturally, I wanted to dig into the numbers and see what really made them tick.
I started by just grabbing data from some of the usual suspects – *, Baseball-Reference, FanGraphs. You know, the standard spots. I wanted to pull in everything I could get my hands on: batting stats, pitching stats, fielding stats, the whole shebang.
Next up, data cleaning. Ugh, this always takes longer than you think, right? I had to wrangle different formats, deal with missing values, and make sure everything lined up correctly. I used Python with Pandas for this part. Pandas is a lifesaver, I swear. I wrote a bunch of scripts to clean and transform the data.
- I filled missing values with appropriate placeholders (like 0 for at-bats when a player didn’t have any).
- I converted data types to make sure everything was numerical when it should be.
- I handled weird characters and inconsistencies in player names.
Now, the fun part – analysis! I wanted to see who the standout players were, what their strengths and weaknesses were, and how they contributed to the team’s success. I started with some basic stuff, like looking at batting averages, home runs, RBIs, ERA, and WHIP.
Then I got a little fancier. I calculated some advanced stats like wRC+ (weighted Runs Created Plus) and WAR (Wins Above Replacement) to get a better sense of overall player value. FanGraphs is amazing for these stats.
Visualization time! Numbers are cool and all, but pictures are even better. I used Matplotlib and Seaborn to create some charts and graphs. I made:
- Histograms of batting averages and ERA to see the distribution of player performance.
- Scatter plots of home runs vs. RBIs to see who the big run producers were.
- Bar charts of WAR for each player to easily compare their overall contribution.
I even tried to visualize some of the team’s trends over the season. I looked at their run differential game by game to see how they performed at different points in the year. That was pretty cool.
Key Findings
Here’s what I learned:
- Adley Rutschman is a beast. Seriously, what a player! His offensive and defensive contributions were huge.
- Gunnar Henderson lived up to the hype. That dude can rake!
- The bullpen was surprisingly good. A few guys really stepped up and became reliable arms.
Challenges? Oh yeah, plenty. Dealing with different data sources was a pain. And figuring out the best way to visualize certain stats took some trial and error. Plus, my Python skills are still a work in progress, so I had to Google a lot of stuff.
Overall, it was a really fun project. It helped me understand the Orioles’ success last season, and it gave me some good practice with data analysis and visualization. I’m already looking forward to digging into the 2024 stats!
