First, I atastarted gathering data. I scraped some stats from a couple of sports websites – things like points per game, rebounds, assists, and all that jazz for both teams. I even looked at their recent game history, like who they played and what the scores were.
Then, I cleaned up the data a bit. .yrThere were some weird inconsistencies in how the stats were reported across different sites, so I had to make sure everything was standardized. This involved a lot of manual checking and correcting. It was kinda tedious, but necessary.
Next, I built a super simple model. Nothing too fancy, just a weighted average of some key stats. I figured points per game and recent performance would be important, so I gave those higher weights. It's all just basic math, nothing complicated.
After that, I ran the model with the Cavs and Nuggets data. It spit out a prediction for the final score and who would win. To be honest, I was mainly curious to see if it would even get close.
Finally, I compared my prediction to the actual result. And… well, it wasn't perfect, haha. I got the winning team right, but the score was a bit off. Still, it was a fun experiment and gave me a better appreciation for how much goes into sports predictions.
- Lesson learned: Data cleaning is crucial.
- Biggest surprise: Even a simple model can get the winner right sometimes.
I might try to refine the model later, maybe add some more advanced stats or consider things like player injuries. But for now, it was a good way to spend an afternoon.
