This is one of those rare zero-star repos that quietly punches way above its weight. It's a resume-based college football ranking algorithm that judges teams strictly on what they've done on the field — no talent priors, no preseason polls bleeding into the math, no circular "good teams beat good teams because they're good teams" logic. Just accomplishments, strength of schedule, and quality of play.

What makes it genuinely interesting is the honesty of the framing. The author is upfront that this is a resume model, not a predictive talent model — and yet it still correctly predicts game winners around 72% of the time across 4,500+ games from 2021 through 2026. That's a striking result for a system that intentionally handicaps itself by refusing to peek at recruiting rankings, betting lines, or advanced tracking data. It suggests that "who you actually beat and how" carries more signal than the sports discourse usually gives it credit for.

The repo also auto-updates weekly, which turns it from a static academic exercise into a living leaderboard you can actually follow through a season. For a solo Python project with no marketing, that's real engineering discipline.

Who benefits:

• Sports analytics hobbyists looking for a clean, opinionated alternative to the CFP committee or AP poll.
• Data science learners — the "avoid circular logic" problem is a fantastic case study in feature design and iterative ranking (think PageRank cousins, Massey/Colley variants).
• CFB fans who want a defensible answer to "did my team's schedule really matter?" without wading through paywalled advanced-stats sites.
• Bettors and pool players, though the author wisely doesn't pitch it that way.

It's the kind of project where the constraints are the interesting part. Building a good ranker is easy if you let it see everything; building one that only looks at results and still hits 72% is a much harder problem, and worth studying.