Alright, so yesterday I was messing around, trying to see if I could get a decent read on the Dodgers Braves game coming up. Thought I’d share how it went down, maybe someone else can pick up a thing or two.
First Things First: Gathering the Intel
Okay, so first off, I started by grabbing all the obvious stuff. You know, team stats, recent performance, injuries – the usual drill. ESPN, *, a couple of those sports news sites. Just dumped all that data into a spreadsheet. Looked like a jumbled mess at first.
- Team Stats: Batting averages, ERAs, all that jazz.
- Recent Games: Wins, losses, and how they played.
- Injury Reports: Key players out? Gotta know.
Diving into the Pitching Matchup
Next up, the pitching matchup. This is where things get interesting. Who’s on the mound for each team? Their stats? What kind of pitches do they throw? I dug into Baseball Savant for that stuff. That site’s a goldmine, honestly. Then I looked at how each team usually hits against those kinds of pitchers. It’s like a puzzle, trying to fit it all together.
I focused on:
- Pitcher A (Dodgers): Velocity, movement, control.
- Pitcher B (Braves): Same drill.
- Batter vs. Pitcher History: Any history between specific batters and pitchers? Sometimes you find little gems there.
Running Some Basic Simulations (Kind Of)
Alright, so I’m no data scientist, but I figured I’d try to run some basic simulations. I created a super simple model in Python, just using the stats I’d gathered. Nothing fancy, just spitting out win probabilities based on the inputs. It was crude, I know, but it gave me a ballpark idea.
Here’s the gist of what I did:
- Feed the stats into my “model”.
- Run a bunch of “simulations” (like, a thousand or so).
- See how often each team wins.
The Unexpected Curveball: The Gerrit Cole Factor
Then, BAM! I saw the news about Gerrit Cole’s Tommy John surgery. Totally threw a wrench in my plans. How does this news affect the Yankees and the whole league dynamic? The news said, “After news of Gerrit Cole’s looming Tommy John surgery came out, the Yankees dropped to +850 to win the World Series.” I’m like, “Okay, gotta factor that in somehow.” I had to adjust my mental model.
I didn’t really change anything in my “model” because it didn’t directly affect this particular game but made a note to be way more cautious about future Yankees games. Also remembered that even the best data can be useless if you don’t account for new information.
The Final “Prediction” (More Like a Gut Feeling)
So, after all that, what’s the prediction? Honestly, it’s still a toss-up. My super basic model gave the Dodgers a slight edge, but it was within the margin of error (or, you know, my own incompetence). In the end, I’m leaning slightly towards the Dodgers, but it’s more of a gut feeling based on everything I looked at. Don’t bet the farm on it!
That was my little adventure. Was it perfect? Nope. Did I learn something? Absolutely. Always fun to try and figure these things out.
