u/Head_Vermicelli_6032

Do we teach here too?

I've been teaching myself sports analytics and machine learning by building an MLB home run prediction model from scratch in Python and MySQL.

Current version:

• ~260,000 historical batter-game records
• XGBoost classifier
• Daily automated pipeline
• Predicts probability of a player hitting a home run in today's games

Current features include:

Hitter Features

• HR last 3, 5, 10, 15, and 30 games
• Hits last 3, 5, 10, 15, and 30 games
• AVG, OBP, SLG, OPS rolling windows
• HR rates over multiple windows

Pitcher Features

• HR allowed
• HR/9
• ERA
• WHIP
• K/9

Using rolling windows:

• Last 3
• Last 5
• Last 10
• Last 15
• Last 30

Matchup Features

• Batter vs Pitcher history (BvP)
• Plate appearances
• Hits
• Home runs
• Strikeouts
• Walks

Context Features

• Home/Away
• Batting order
• Probable starting pitcher
• Confirmed daily lineups

One challenge I've run into is balancing recent performance against small-sample-size BvP data. Early versions of the model heavily overvalued BvP, so I've been reducing its influence and letting recent HR trends drive more of the prediction.

A few questions for anyone who has worked on similar baseball models:

1. What features gave you the biggest improvement when predicting home runs?
2. Did park factors or weather meaningfully improve results?
3. Have you found Statcast metrics (barrel %, hard-hit %, launch angle, xSLG, etc.) to outperform traditional rolling stats?
4. Would you treat HR prediction as a pure classification problem, or try to predict expected HR probability another way?

This project started as a learning exercise, but it's turning into a pretty fun sports analytics project. Any feedback is appreciated.