Created in 2019, Hoop Explorer is a free and open college basketball analytics platform. Explore the game through interactive stats and visualizations you can't find elsewhere.
Used by college teams, NBA teams, Draft Twitter, sports bettors, analytics gurus, basketball journalists and bloggers, and fans like me who enjoy digging into stats when there are no games to watch.
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Every qualifying player ranked by the stats that matter -- RAPM, efficiency, usage, and more. Sort, filter, visualize, and export.
Includes advanced sorting options with conference and positional filters.
Conference-adjusted efficiency rankings for every D1 team. Compare offensive and defensive efficiency, play style, shooting splits, and turnover rates side by side.
Search any player from 2018 onward. View career stats, shot charts, play style breakdowns, and cross-season comparisons all in one place.
Deep-dive into every lineup combination your team has used. See how frontcourt, backcourt, and custom groupings perform together.
Combine and compare lineup stats across flexible positional groupings.
Categorize any team or player's offense and defense into intuitive styles -- Post-Up, Transition, Perimeter Sniper, Pick & Roll, and more.
Available across multiple pages for both team-level and player-level analysis.
The comprehensive team page: adjusted efficiency, four-factors, player ratings, shooting stats, with deep filtering and split options.
Includes shot charts, style breakdowns, and CSV export for every view.
Hex-map visualizations of shot frequency and efficiency vs D1 averages for any team or player. Apply filters and splits to find patterns.
Available across multiple pages for both team-level and player-level analysis.
Advanced single-game analytics with unique insights you won't find elsewhere.
Head-to-head matchup analytics between any two D1 teams, complete with predictive charts.
Measure how your team's stats shift with each player on and off the court. Includes a detailed RAPM breakdown into four-factor components.
Build custom visualizations showing how player stats relate and evolve. Each dataset includes current and prior seasons for year-over-year comparison.
Ask questions like "how did they improve?" and see the answer instantly.
Create a fully custom team ranking by weighting the factors you care about: wins, efficiency, dominance, recency -- your call.
Every player is classified into a positional role based on box score stats and height, powering many of Hoop Explorer's advanced features.
Continuously refined -- report any misclassifications and they'll be corrected.
Transparent preseason predictions built from public data. See exactly how they're constructed and adjust the inputs to match your own expectations. Check out Hoop Explorer's very simple off-season predictions
(please don't be mad at me.)