I’m not an expert in sports modeling — more of a builder who got curious and went down the rabbit hole.

This season I built something called BracketIQ because I wanted a single place that combined a bunch of models I was already looking at (KenPom, Torvik, etc.) instead of bouncing between sites and trying to mentally aggregate everything.

Honestly, I built it for myself — using it throughout the season helped me think about games more clearly, so I figured I’d share it here and get feedback from people who know this space way better than I do.

At a high level.... it combines 8 different approaches into one probability per game, including:

• Efficiency models (KenPom-style, Torvik)
• Simple baselines (NET, BPI, logistic)
• Possession-level Monte Carlo (~10k sims)
• And a weird experiment: an LLM layer that adjusts probabilities slightly based on roster / recent form context

I stacked everything with a logistic meta-model to get one consensus number/bet.

**For the full season....**I stacked and figured I'd see how it would shape out:

• Stacked model log-loss: 0.685 (best overall)
• Torvik alone was surprisingly strong:
  • Best Brier score
  • Comparable accuracy to everything else

So the ensemble approach did help on log-loss, but not by a huge margin — I’m still trying to understand why.

The biggest issue....IMO is overconfidence on favorites.

0.80–0.85 predicted → ~0.68 actual
0.90+ predicted → ~0.85 actual

My (very non-expert) hypothesis:

Feedback?

1. Calibration: If you’ve worked with stacked models ... how did it work for you? The high-end gap makes me lean towards cutting models, but I’m not sure.
2. LLM as a model input: Is this a bad idea altogether? It felt useful for context (injuries, roster), but the calibration data is making me question whether it should just be separate commentary instead of touching probabilities.
3. Stacking in general: For tournament-style models — have you actually seen stacking materially outperform your best base model? Or does it usually just converge toward it?

I published the full breakdown for every game (each model + reasoning) at bracketiq.us

There’s also a “How it works” page and model descriptions — all free, no paywall. I mostly just want feedback and to learn.

Built this solo, so I’m sure there are blind spots. Appreciate any pushback — that’s why I’m posting here.