I treated MLB games like a factor model for 3 years. 7 season backtest, 10.5% avg ROI. Here's what I actually found.

On a $10k bankroll this model has averaged around $20k profit per season over a 7 year backtest. That's not a fluke season or a cherry-picked strategy that's the average across 4 independent strategies run systematically over 2017–2023.

I came at this from a finance background and the reframe that unlocked everything was this: stop trying to predict baseball, start trying to predict where the market is wrong. Those are completely different problems. The first one is nearly impossible. The second one is just a pricing inefficiency problem, and sports markets have way more of those than equity markets because the participant base is so much less sophisticated.

The 4 strategies each capture alpha from a different structural gap. They're uncorrelated by design so when one is bleeding the others aren't moving the same direction. Over 7 seasons the whole thing was profitable in 6 of them. The one down year was 2020,shortened season, weird sample, -2.1%. Every other year was green.

Best single strategy in a season hit +24.3%. The most consistent ones averaged 12–14% annually with low variance. Signal persistence held across the full backtest window without meaningful decay which tells me the edge is structural not lucky.

I'm not going to post the methodology here for obvious reasons. But if anyone is actually running systematic approaches to sports markets or wants to talk factor construction, signal decay, or out-of-sample validation — drop a comment or DM me. Happy to talk shop.