Alright, so here’s the lowdown on how I wrestled with those Twins pitching stats – a real grind, but kinda satisfying in the end.
First off, I had this itch to dig deeper into how our Twins pitchers were actually performing, not just the usual ERA and win-loss stuff. I wanted the real nitty-gritty.
So, I started by hitting up the usual spots for baseball stats. You know, those sites everyone uses. I scraped the basic data – innings pitched, strikeouts, walks, hits allowed, all that jazz – into a big ol’ spreadsheet. It was messy, I ain’t gonna lie. Different formats, weird abbreviations, the whole shebang.
Cleaning up the data was the first real battle. I’m talkin’ hours of find and replace, fixing typos, making sure everything lined up right. I even had to write a few little scripts in Python to automate some of the repetitive stuff. Nothing fancy, just enough to get the job done.
Next, I started calculating some more advanced stats. Stuff like WHIP (walks plus hits per inning pitched), K/9 (strikeouts per nine innings), and BB/9 (walks per nine innings). These give you a better picture of a pitcher’s control and ability to prevent runs.
- WHIP: (Walks + Hits) / Innings Pitched
- K/9: (Strikeouts / Innings Pitched) 9
- BB/9: (Walks / Innings Pitched) 9
Then, I wanted to see how these stats compared to league averages. Are our guys above average in strikeouts? Below average in walks? This helps put their performance in context. I grabbed league-wide data and calculated the same stats for the whole league.
Visualization time! All those numbers were starting to blur together, so I fired up a charting library and started making some graphs. Scatter plots of K/9 vs. BB/9, bar charts of WHIP, that kind of thing. This really helped me see the trends and identify which pitchers were standouts (for good or bad).
I even tried to build a simple model to predict future performance based on these stats. It was a super basic linear regression, nothing too fancy, but it was fun to play around with. I’m not saying it’s gonna win any awards, but it gave me some interesting insights.
Finally, I put everything together in a little report. I summarized the key findings, highlighted the top performers, and pointed out areas where the team could improve. It wasn’t perfect, but it was a heck of a lot more informative than just looking at the box scores.
The biggest takeaway? Digging into the data always reveals more than you expect. It’s a grind, for sure, but you end up with a much deeper understanding of what’s really going on. Plus, it’s just kinda cool to see the numbers tell a story.
So yeah, that’s how I tackled those Twins pitching stats. It was a bit of a journey, but totally worth it in the end.
