Our AI Sports Prediction Accuracy: A Fully Transparent Breakdown

Transparency is the single biggest problem in the sports prediction industry. Services claim 70%, 80%, even 90% accuracy, but when you ask for the receipts — the full, unfiltered record of every prediction including the misses — they either dodge the question or show you a cherry-picked sample from their best month.

We're doing the opposite. This article breaks down our actual prediction accuracy across all five sports. Every number here is verifiable on our live dashboard, which logs every prediction we make, every result, and every miss. Nothing is hidden.

How We Measure Accuracy

Before diving into numbers, let's define terms. "Accuracy" in sports prediction can mean different things depending on how you measure it:

• Straight-up accuracy (moneyline): The percentage of games where our predicted winner actually won. This is the simplest measure.
• Against-the-spread (ATS) accuracy: For sports like NFL and NCAAB where spread betting is dominant, this measures how often our prediction correctly identifies the team that covers the spread.
• Win probability calibration: When we say a team has a 70% chance of winning, do they actually win 70% of the time? This is the most rigorous measure of model quality.

We track all three on the dashboard. For this breakdown, we'll focus primarily on straight-up accuracy because it's the most intuitive, with notes on calibration where relevant.

NFL Accuracy

The NFL model predicts the winner of every regular season and playoff game. The NFL is one of the most heavily analyzed and efficiently priced sports, so edges are smaller here than in other sports.

What we see: Our NFL model operates in the 58-64% straight-up accuracy range across full seasons. For context, the average public bettor picks winners at roughly 50-52%. NFL moneyline favorites win about 66% of the time, so a model needs to do more than just pick favorites — it needs to correctly identify underdog wins to add value.

Where the model excels: Games with clear rest advantages, weather impacts, and late-season motivational spots. The model is strongest in Weeks 10-17 when season-long data has stabilized and scheduling fatigue becomes a factor.

Where the model struggles: Week 1 (minimal current-season data), playoff games (small sample size, coaches adjust between games), and games with last-minute QB changes announced after our prediction is published.

NHL Accuracy

The NHL model predicts every regular season game. Hockey has more randomness than most sports due to the outsized impact of goaltending and the low-scoring nature of the game.

What we see: Straight-up accuracy in the 57-62% range. This may sound modest, but in a sport where the home team only wins about 54% of the time and where a hot goalie can beat any team on any night, consistent 60%+ accuracy is meaningful.

Where the model excels: Back-to-back scheduling situations, goaltender fatigue, and early-season games where the market is still anchored to prior-season ratings. The biggest edges come when the model identifies a fatigue mismatch that the market underprices.

Where the model struggles: Overtime games (essentially 50/50 coin flips once regulation ends), games where the confirmed starting goalie is pulled early, and late-season games where playoff positioning creates unusual motivational dynamics.

NBA Accuracy

The NBA model covers every regular season game. The NBA is generally more predictable than the NHL due to higher-scoring games (reducing randomness) and the consistent dominance of the league's best teams.

What we see: Straight-up accuracy in the 62-67% range. The NBA has the most stratified talent distribution of any major sport — the top teams are significantly better than the bottom teams — which naturally boosts moneyline accuracy. The challenge is beating the market's pricing, not just picking winners.

Where the model excels: Rest differentials (particularly during the condensed mid-season schedule), road trip fatigue for teams on extended West Coast swings, and games where key players are listed as questionable until close to tipoff.

Where the model struggles: Load management games where star players rest unexpectedly, the final week of the regular season when playoff seeding is locked, and nationally televised games where teams sometimes outperform expectations.

NCAAB Accuracy

The NCAAB model covers Division I games. College basketball has the widest talent gap of any major sport, with 360+ teams ranging from powerhouse programs to tiny conferences playing essentially different levels of basketball.

What we see: Straight-up accuracy in the 63-68% range for all tracked games. Against the spread, the model performs in the 53-57% range, which is the more relevant metric for NCAAB bettors since ATS betting dominates college basketball.

Where the model excels: Conference tournament games, early-round NCAA tournament matchups (especially identifying upset-prone favorites), and mid-major matchups where the market has less data and pricing is softer.

Where the model struggles: Non-conference early-season games where teams haven't played enough to establish current-season patterns, and late-tournament games where single-elimination variance dominates.

Tennis Accuracy

The Tennis model covers ATP and WTA tour matches. Tennis is a unique prediction challenge because it's individual (not team), surface-dependent, and heavily influenced by physical fatigue across multi-week tournament swings.

What we see: Match winner accuracy in the 60-65% range for ATP main draw matches. WTA accuracy is lower (57-61%) due to higher variance in women's tennis. Qualifying round accuracy is lower for both tours because data on lower-ranked players is sparser.

Where the model excels: Surface transition periods (when the tour moves from hard to clay or clay to grass), fatigue-driven upsets where players are coming off deep tournament runs, and early-round matches in Grand Slams where the model correctly identifies overvalued low seeds.

Where the model struggles: Retirement matches (impossible to predict), players returning from long injury layoffs (limited recent data), and first-round matches at 250-level events where motivation is hard to quantify.

The Learning Curve: How Models Improve Over Time

One thing the dashboard shows that static accuracy numbers don't capture is the model's improvement trajectory. AI models learn from every game they predict. Early in a season, accuracy is lower because the model has limited current-season data. As the season progresses and the model ingests more results, accuracy tends to improve.

This is visible on the dashboard's time-series charts. If you compare the model's performance in Week 1-4 versus Week 10-17 of any NFL season, the later weeks are consistently more accurate. The model is literally learning from its mistakes and adjusting.

What Accuracy Means for Profitability

High accuracy does not automatically equal profitability. A model that picks 65% winners but only picks heavy favorites would lose money because the moneyline pricing on those favorites already accounts for their likelihood of winning.

Profitability comes from the gap between the model's predicted probability and the market's implied probability. A model that picks a 55% winner when the market prices them at 45% is making a more valuable prediction than one that picks a 90% winner when the market already prices them at 88%.

We publish both accuracy and probability calibration on the dashboard. This lets you evaluate not just whether we pick winners, but whether our confidence levels are well-calibrated — which is what actually drives betting value.

Why We Publish Our Misses

Every prediction service has bad days. We had weeks where the NFL model went 5-11. We've had stretches where the tennis model missed on multiple "high confidence" picks in a row. These results are on the dashboard for anyone to see.

We publish them because hiding losses is the first step toward becoming the kind of scam service that plagues this industry. If you can't see our worst weeks, you can't evaluate whether our best weeks are skill or luck. Transparency requires showing the full picture.

We'd rather have a prospective user look at our worst month and decide we're not good enough than have them sign up based on a misleading highlight reel. That's a better foundation for trust.

How to Verify Our Claims

Everything in this article is verifiable:

• Visit the accuracy dashboard to see real-time accuracy for all five sports
• Check sport-by-sport breakdowns including recent streaks and historical performance
• Review individual predictions with timestamps and results
• Compare our stated accuracy ranges against the live data

If the numbers on the dashboard don't match what we've written here, call us out. That accountability is the entire point.

The Bottom Line

Our models are good. They're not perfect. They beat the baseline in every sport, they improve over time, and they operate in accuracy ranges that are competitive with far more expensive services. But they have bad weeks, they miss upsets they should have caught, and they're better in some situations than others.

That honesty is rare in this industry. Most services would rather show you a curated highlight reel than a complete record. We think the complete record — wins, losses, hot streaks, cold streaks, all of it — is what you deserve before spending a single cent.

The dashboard is public. The data is real. Check it yourself.