Alright, let’s dive into this little experiment I did with predicting the outcome of a basketball game: fdu vs stonehill. It wasn’t some super serious data science project, more like a “can I kinda-sorta guess what’s gonna happen?” kind of thing.
Phase 1: Gathering the Goods
- Scraped Some Data: First, I needed some stats. I wasn’t trying to build a full-blown model, so I kept it simple. Got basic team stats like points scored per game, points allowed, maybe some rebounding numbers, from ESPN. Just copied and pasted into a spreadsheet – real old school.
- Historic Matchups: Dug around for past games between these two. Not a ton out there, but grabbed what I could. Again, just looking for basic win/loss and point differentials.
Phase 2: The “Model” (and I use that term loosely)
- Simple Averages: I’m talking REALLY simple. Averaged FDU’s points scored and points allowed. Did the same for Stonehill.
- Point Differential: Subtracted Stonehill’s points allowed from FDU’s points scored. That gave me a rough idea of how much FDU might outscore them. Did the reverse too, just to see both sides.
- Home Court Advantage: Gave FDU a small bump since they were playing at home. Like, added 3 points to their potential score – completely arbitrary, I admit.
Phase 3: Making the Call
I basically looked at my super-basic point differential and said, “Okay, based on this napkin math, FDU should win by about [number] points.” I took into account my gut feeling, because, let’s face it, stats don’t tell the whole story.
Phase 4: Reality Check
The game happened, and… well, my prediction was off. Not horribly, but definitely not right. I think I overestimated FDU’s offense and underestimated Stonehill’s defense. This is where the fun is, right? Seeing what you messed up.
Lessons Learned
- More Data Needed: If I were to actually TRY to do this right, I’d need way more data. Individual player stats, shooting percentages, maybe even things like pace of play.
- Context is King: Stats alone are never enough. Injuries, team morale, recent performance – all that stuff matters big time.
- Simplicity Has Limits: My “model” was laughably simple. It was more of an educated guess than anything. More sophisticated techniques (regression, machine learning, etc.) would be needed for any real accuracy.
So, yeah, that was my fdu vs stonehill prediction adventure. Didn’t nail it, but I learned a thing or two. And honestly, that’s what it’s all about!
