Analyze the prop.
The model starts with the player, market, line, book, game context, team environment, and recent usage.
Methodology
How the engine thinks.
Propeller converts player context, market movement, matchup data, and recent form into one directional confidence score, then grades every logged result against the final stat.
Score the direction.
Signals lean the prop toward OVER or UNDER. Confidence measures how far that signal is from neutral, not whether it is an OVER.
Grade the outcome.
After the game, the final stat determines win, loss, or push. The public record updates from those graded results.
Agent system
Separate signals, one research view.
Each agent looks at a different part of the prop. The product experience is intentionally simple, but the model keeps the reasons separate so users can inspect what is driving a pick.
Opponent tendencies, position context, pace, and defensive shape.
Minutes, role, workload, lineup changes, and recent opportunity.
Book lines, movement, implied probability, and available prices.
Recent production, volatility, and trend quality without blindly chasing streaks.
Status updates, replacement usage, and team-level ripple effects.
Total, spread, pace environment, and how the matchup is likely to play.
Why UNDERs matter
High confidence is not the same as high score.
The raw model score is over-perspective. A low raw score can be a very strong UNDER. That is why the public record normalizes confidence before grouping results by range.
Audit path
The record has to reconcile.
Sport totals, confidence ranges, OVER/UNDER splits, and the overall record all come from public API endpoints. If a number cannot reconcile, it does not belong on the page.
Founder 500
Use the method on today's board.
Founder 500 members get free lifetime core access while launch spots remain.