Adjusted Efficiency Ratings
Offensive and defensive efficiency per 100 possessions, adjusted for schedule strength. See which teams are actually good versus who just played bad schedules.
Adjusted efficiency ratings for all 365 Division I teams, a spread model, player impact ratings, and 11 seasons of history. Built independently, updated every morning, and free with an account while the site is in beta.
I'm Will Langfeldt, a business economics graduate from the University of Cincinnati. I got into basketball analytics during March Madness, trying to work out the why and the who behind every upset. Why does a 12-seed knock off a 5? Which teams are quietly better than their record says, and which ones are frauds waiting to get found out? Chasing those answers pulled me out of the box score and into efficiency ratings, and before long I was reading the analytics sites more closely than the standings. Eventually I wanted to run the numbers myself instead of trusting someone else's, so I spent a year learning the math and building this.
Everything on the site runs on a model I wrote in R. It re-rates all 365 teams each morning after the night's games, projects every spread, and grades every rotation player in the country. It's a one-person operation, which means when something breaks I fix it, and when you email an idea, I'm the one reading it.
Points per 100 possessions above average, adjusted for schedule strength. Final 2025-26 season ratings; 2026-27 projections live in the app.
Not a feature checklist. These are the pages I built because I kept wanting them during real games.
Offensive and defensive efficiency per 100 possessions, adjusted for schedule strength. See which teams are actually good versus who just played bad schedules.
My projected spread for every game, built from the efficiency delta, pace, home court, and rest days. Shown alongside win probability so you can see both the margin and the confidence.
Every rotation player in the country gets a profile: impact rating, percentile bars, game log, and a projection for next season. Great for finding guys who don't show up in the scoring column.
Roster changes with projected production impact. I find this useful in the offseason when trying to figure out which teams actually got better or worse.
A projected 68-team bracket with round-by-round odds, plus conference race simulations. Built for the way-too-early arguments as much as for March.
Efficiency ratings going back 11 seasons for every program. Useful for putting a team's current run in context. Is this a rebuilding year or the new baseline?
I use all of these. They're good. Here's my honest read on where each one shines, and where I'm trying to be different.
The reference point for two decades, and the numbers everyone quotes on broadcasts. It's team-first by design, and most of the site sits behind a subscription. If you want the industry standard, that's the one.
Free, deep, and full of clever tools once you know where to look. The interface is intentionally bare-bones, which regulars love and newcomers tend to bounce off of.
The most interesting player-impact ratings out there, built on lineup data. The full experience is a paid subscription, and the focus leans player-side more than betting or team tools.
A free, long-running predictive-ratings and daily-projections site. The numbers are solid and it's genuinely free, though the interface is dated and it stays focused on team ratings and game predictions.
Team ratings, full player profiles, a spread model with its performance tracked in public, bracket projections, NIL value estimates, and 11 seasons of history in one modern interface. I'm the new guy and I know it, so I ship improvements weekly and answer every email.
All of those sites are independent projects I respect, and none of them are affiliated with LangIndex. If you're serious about college basketball, honestly, use more than one model. Different lenses catch different things.
The core is a ridge-regression model fit on every game result from the season. It jointly estimates offensive and defensive efficiency for all 365 teams at once, rather than calculating each team independently.
The "ridge" part adds a shrinkage penalty that pulls extreme estimates back toward average, which matters a lot early in November when you've seen six games. As the season goes on and samples grow, the model trusts the data more and shrinks less.
Spreads are built on top of the efficiency gap between teams, adjusted for pace, home court, and rest. It's not magic, and Vegas still has the edge, but it works as a reasonable second opinion.
Every tool on the site is free while the app is in beta. Down the road, likely next season, a few of the heavier betting tools will move behind a low-cost membership. The core ratings will always stay free.
Free account, no card, no catch. Signing up takes about 30 seconds and unlocks all of it.
A low monthly plan that helps cover data and hosting. Targeted for next season. Nothing is locked today.
Everything is free right now. Create a free account and explore the ratings, spreads, and player numbers for every team in the country.
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