HOOP PROPS
NBA prop research. Streamlined lineup creation.
Your next lineup starts here.
Lineup Analyst turns the Hoop Props slate into ranked NBA prop lineups. Choose your lineup size, risk profile, and filters, then compare options with the numbers behind every leg.
Prefer to build your own? Explore today's slate for individual props, probability estimates, and confidence tiers.
A shorter path to your lineup
Lineup Analyst handles the shortlist and lineup combinations. Set your criteria, compare ranked options, and review the reasoning and warnings before choosing your legs.
Open Lineup Analyst →Room for your own research
The slate puts individual player props and model estimates in your hands. Compare lines, explore confidence tiers, and choose each leg yourself.
Explore today's slate →From slate to lineup
Set your preferences and let Lineup Analyst narrow the slate into lineups you can review.
Choose your slate date and build a lineup with two to six legs.
Set your risk profile, prop types, and over or under preferences.
Include or exclude players and set optional confidence and edge filters.
Compare ranked lineups, review the supporting estimates, and check warnings before deciding.
Inside Lineup Analyst
Control how lineups are built and see why each prop made the shortlist.
Choose conservative, balanced, or aggressive settings. Filter by prop type and direction, then include or exclude players.
Review up to five ranked lineups built from eligible props, with a distinct player for every leg. If too few players qualify, the analyst tells you.
Inspect model probabilities, confidence, edge, and warnings. Lineup strength summarizes individual leg estimates and is not the chance of winning the entire lineup.
The models behind your lineup
Lineup Analyst and the slate use the same Hoop Props probability estimates. Multiple statistical methods evaluate posted NBA prop lines to support your research and lineup decisions.
Estimates are a research tool. They are not guarantees, and individual leg probabilities do not account for every relationship between props.
- Historical performance distributions
- Bayesian priors for small samples
- Kernel density estimates (KDE)
- Monte Carlo simulations
Build your next lineup
Start with Lineup Analyst for ranked options, or dig into the slate to make your own selections.