Best College Basketball Prediction Sites 2026: A Calibration-First Buyer's Guide
If you're building an app, a bot, or a betting workflow that needs college basketball predictions, you have a confusing set of choices. KenPom is the gold standard for analysts but is a paywalled HTML table. Bart Torvik is free but not really an API. FiveThirtyEight has historical gravitas but paused their basketball coverage in 2023. ESPN BPI is widely cited but never publishes calibration metrics. Half the "expert picks" sites are marketing wrappers for affiliate sportsbook links.
This guide evaluates 6 real options for college basketball prediction sites in 2026, ranked by three criteria that matter for programmatic use: calibration (ECE), transparency, and measured accuracy. No affiliate fluff. No "top picks of the day" content. Just a developer's framework for choosing what to actually integrate with.
The Evaluation Framework
Four dimensions to judge any prediction source:
- Calibration (ECE): When the source says 70%, does the team actually win 70% of the time? Measured via Expected Calibration Error.
- Transparency: Can you see their methodology? Can you download historical predictions? Do they publish their misses?
- Measured accuracy: Historical accuracy on a known holdout set, not self-reported.
- Programmatic access: Is there an API? Rate limits? Authentication? Or just HTML you have to scrape?
1. KenPom (kenpom.com)
The gold standard for college basketball analytics since 2003. Ken Pomeroy's adjusted efficiency ratings (AdjO/AdjD/AdjEM) are cited in every major media broadcast and used by virtually every NCAA coaching staff.
| Calibration (ECE) | Estimated 2-3% (never published, but inferred from his ~72% accuracy on a well-calibrated distribution) |
|---|---|
| Transparency | Partial. Methodology is documented in FAQ but no reliability tables or per-game historical predictions |
| Measured accuracy | ~72% on regular season, ~70-72% on tournament |
| Programmatic access | None. HTML only. $20/year paid subscription required for full data |
| Best for | Analysts who want the most trusted single number, are comfortable scraping, and don't need API-scale volume |
Verdict: If you're building a model you're going to publish or bet on, KenPom is what you benchmark against. If you're building a product that needs to serve predictions at scale, you can't use KenPom — the licensing and delivery just aren't there.
2. Bart Torvik (barttorvik.com)
The free alternative to KenPom. Bart Torvik publishes adjusted efficiency rankings, matchup predictions, and game-by-game win probabilities.
| Calibration (ECE) | Published in detail — historically around 3-4% on the full season |
|---|---|
| Transparency | Excellent. Model methodology is public, reliability tables are posted every March, misses are quantified |
| Measured accuracy | ~70-72% regular season, in line with KenPom |
| Programmatic access | Unofficial JSON endpoints exist but change. No stable public API |
| Best for | Serious analysts who want transparency at zero cost |
Verdict: If KenPom is the benchmark, Torvik is the most respected free alternative. Methodology transparency is a genuine selling point over KenPom. Still not a production-ready data feed.
3. FiveThirtyEight (archived GitHub)
FiveThirtyEight published college basketball predictions from 2015-2023 using their Elo-based model. Production ceased when ABC shut down the team in 2023. The archive is still publicly available.
| Calibration (ECE) | Historically ~4-5% on NCAAMB, competitive but not best-in-class |
|---|---|
| Transparency | Outstanding in its time. Every model's methodology was open-sourced on GitHub. Historical predictions downloadable. |
| Measured accuracy | ~67-70% regular season historically |
| Programmatic access | Archived CSV files, no live endpoint |
| Best for | Researchers doing historical analysis, people who want to replicate an established methodology |
Verdict: Valuable as a historical reference, not an active prediction source. If you're modeling college basketball yourself, the 538 GitHub archives are a great feature-engineering reference.
4. ESPN BPI (espn.com/bpi)
ESPN's Basketball Power Index combines team strength with strength-of-schedule adjustments. Referenced widely in ESPN's bracketology and game previews.
| Calibration (ECE) | Not published |
|---|---|
| Transparency | Limited. No published methodology, no reliability tables, no historical miss tracking |
| Measured accuracy | Roughly 68-70% on a full season — competitive but unverified |
| Programmatic access | No public API. Data is embedded in HTML game previews |
| Best for | Casual viewers who want a second opinion alongside KenPom |
Verdict: Too opaque for serious use. If you can't see the methodology and can't see the historical misses, you can't integrate it into a workflow that requires trust.
5. The Odds API (the-odds-api.com)
A developer-first API that aggregates moneyline, spread, and totals from 30+ sportsbooks across major sports including college basketball.
| Calibration (ECE) | N/A — this is market prices, not a model |
|---|---|
| Transparency | Good for an aggregator. Documents which books are included, how markets are normalized |
| Measured accuracy | Closing-line accuracy (implied probability) hits 71-74% historically, best on the market |
| Programmatic access | Full REST API. Free tier: 500 requests/month. Paid tiers scale up |
| Best for | Developers who want market consensus, not a first-party model |
Verdict: If you want calibrated probabilities derived from market consensus, this is the cleanest programmatic source. You're not getting a model — you're getting the market's model. For a sportsbook-matching bot, that's exactly what you want.
6. ZenHodl API (zenhodl.net)
Disclosure: we built this. So the evaluation is self-interested. But the comparison criteria are the same, and the numbers are the same ones we publish publicly.
| Calibration (ECE) | 4.39% on 5,345-game 2025-26 backtest. Full reliability table published |
|---|---|
| Transparency | Every trade, win probability, and miss is published on /results. Model weights, training data lineage, and ECE measured under our own framework |
| Measured accuracy | 68.19% on 5,345 games (regular season); 71.6% on 67 games (2026 tournament) |
| Programmatic access | REST API with JSON. 7-day free trial, no credit card. $49-499/month paid tiers |
| Best for | Developers building a product that needs calibrated NCAAMB probabilities + historical snapshots |
Verdict: Not as sophisticated as KenPom on per-team adjustment. But we publish every number, every miss, and every trade. That matters more than a 2-3pp accuracy gap if you're building a product that needs to justify its probabilities in public.
Which Should You Pick?
A decision matrix based on your use case:
| Your goal | Best pick |
|---|---|
| Filling out a March Madness bracket | KenPom or Bart Torvik |
| Writing a blog post referencing predictions | KenPom (if paid) or Bart Torvik (free) |
| Building a betting bot that matches sportsbook prices | The Odds API (market consensus) |
| Building an app that needs calibrated first-party probabilities via API | ZenHodl |
| Academic research on historical NCAAMB predictions | FiveThirtyEight archive (GitHub) |
| Daily content for a media outlet | ESPN BPI or The Odds API |
The Calibration Quick-Check That Filters Out Garbage
Any college basketball "prediction site" that won't tell you the following three things is not worth integrating:
- Their historical ECE across a known holdout (should be below 5% for a serious model; below 3% for best-in-class)
- Their accuracy on the most recent full season, measured on games they predicted pre-tipoff
- Their biggest 10 misses — transparency about where the model breaks down
If all three are hidden behind "expert picks" jargon or affiliate pages, walk away. The prediction is almost certainly post-hoc curation of sportsbook lines plus aesthetic commentary, not a calibrated model.
How to Build One Yourself
If you don't want to integrate any of these and instead want to build your own college basketball prediction model, the tutorial stack is straightforward:
- How to build an NCAAMB / March Madness prediction model — full ELO + XGBoost + calibration pipeline
- Calibrating XGBoost probabilities with isotonic regression — why raw predicted probabilities are wrong
- Feature engineering for sports win probability — the 15 features that actually matter
Want calibrated NCAAMB predictions in your app?
ZenHodl's API gives you first-party win probabilities with published ECE. 7-day free trial, no credit card.
Get API access →Summary
Five real options for college basketball predictions in 2026, each for a different use case: KenPom for benchmarking, Bart Torvik for free open-methodology alternatives, FiveThirtyEight for historical research, The Odds API for market consensus, and ZenHodl for first-party calibrated feeds with programmatic access. Pick based on whether you need a single number, a published methodology, or a data feed you can ship in a product.
Related reading: how to build a March Madness prediction model · how to build an NBA Finals prediction model