How it works

It is a second opinion on the draft. We taught a model to study every college prospect since 2009: how they scored, passed, defended, and rebounded, how young they were, how big they were, and who they did it against. Then we watched what those players actually became in the NBA. Stars, starters, role players, or guys who washed out.

When a new prospect comes along, the model finds what history says about players with his profile. It never gives one answer. It gives chances: maybe 60% chance he becomes a starter, 12% chance he becomes a star, 5% chance he never sticks. That is honest. Nobody knows for sure, so the model does not pretend to.

Because we tested it the hard way. For every draft from 2009 to 2021, we hid that class from the model, asked it to grade those players using only what was knowable before draft night, and then checked its answers against real careers.

Here is the honest result: on the average pick, NBA teams beat the model. They should. They have workouts, medicals, interviews, and intel we do not. But when the model disagreed loudly with where a player was drafted, the model was right far more often than chance. Its favorite overlooked players outplayed their draft slots badly, and its biggest fades underplayed theirs. The lesson is simple: do not use this to re-rank the whole board. Use it to find the players worth a second look.

The same things a good scout checks on the stat sheet, weighted by what has actually predicted careers. Age matters a lot: a 19-year-old and a 23-year-old putting up the same numbers are not the same prospect. Free-throw shooting says more about a future NBA jumper than college three-point percentage does. Steals tell you who sticks in the league. Blocks tell you which big men become special. And production against real competition beats production against nobody.

Just as important is what it ignores: where the player was drafted, mock drafts, and big boards. The model forms its opinion blind to the market. That is the whole point. You cannot grade the room's opinion if you copied off the room.

Film. Medicals. Character. Work ethic. How a guy handles coaching. The model knows none of that, which is why the lottery is where teams beat it worst, and why every player page shows a range instead of a single number. When it says a player has a 50% star chance with a range of 26 to 80, it is telling you it has real uncertainty. Believe the range.

It also only covers players from Division 1 college basketball. International prospects show market prices only, with a badge saying so.

This is the part built for people who watch the games. Write what you saw: "the jumper is real but he floats on defense." The system reads your note, scores it against a fixed checklist of skills, and nudges the player's chances accordingly. Good news nudges them up. Concerns nudge them down.

One rule keeps everyone honest: a note is evidence, never a veto. No single note, however glowing, can turn a 3% star chance into a 30% one. Your eyes and the numbers each get a vote. Neither gets to overrule the other completely.

Pick a draft slot and see who was realistically still on the board. We ran the draft ten thousand times, letting players rise and slide the way they actually do on draft night, including the occasional big fall. Standing at pick 9, you can see which of the model's favorite players had a real chance of being there. That is the conversation a front office wants to have before draft week, not during it.

The full technical write-up (the model, the tests, the exact definitions, and every design decision with its reasoning) lives in the project repository, along with everything needed to reproduce every number on this site.