Original research · Bankroll Guardian
How predictable is each sport? The same model called 65% of NBA winners — and 53% of NHL winners
Published · Methodology and limitations below · No picks, no affiliate links
Key findings
- An identical Elo-style rating model, run walk-forward over a full season of each league, predicted ~65% of NBA winners but only ~53% of NHL winners — the same method, a 12-point predictability gap.
- The NHL's ~53% is barely above a coin flip: at the single-game level, hockey outcomes are dominated by variance (low scoring, goaltending, parity by design).
- The NBA's structural features — many possessions, dominant stars, real home-court effect — make single games substantially more signal than noise.
- Implication for bettors: variance budgets are sport-specific. An NHL bettor needs materially larger samples and steadier staking than an NBA bettor before any win-rate or ROI conclusion means anything.
Not all sports are equally predictable, and the difference is measurable. We ran the same prediction model, with the same walk-forward discipline, across a full season of the NBA and the NHL and simply compared how often it called the winner. The narrative version lives in the original write-up; this is the structured record.
Methodology
Model. The same Elo-style team rating system used in our NBA market-efficiency test: ratings from game results only, a win probability per matchup, strictly walk-forward (each prediction uses only games already played). Sample: one full regular season per league. Metric: straight winner-prediction accuracy — deliberately simple and identical across sports, so the gap reflects the sports, not the method.
The gap, in one table
| League | Winner accuracy (same model) | Reading |
|---|---|---|
| NBA | ~65% | Strong single-game signal |
| NHL | ~53% | Near coin-flip; variance dominates |
What explains the 12-point gap
Basketball generates on the order of a hundred possessions a side — enough repetitions for skill differences to express themselves in nearly every game — and its best players are on the floor for most of it. Hockey is a handful of goals decided at the margin: a goaltender running hot, a puck off the post. Add a league structure that deliberately compresses team quality, and the best team in hockey still loses constantly — which is exactly what a ~53% model accuracy is telling you.
What a 12-point gap costs you in sample size
“Judge an NHL edge on a bigger sample” is easy to say and easy to ignore, so it is worth pricing out. A win rate estimated from n bets carries a standard error of √(p(1−p)/n), and to claim an edge you need the bottom of that interval to sit above the break-even rate — 52.4% at standard −110 pricing.
Rearranged, that gives the number of settled bets required before a win rate is distinguishable from break-even at 95% confidence:
| True win rate | Edge over break-even | Settled bets needed |
|---|---|---|
| 53% | 0.6 pts | 26,582 |
| 54% | 1.6 pts | 3,728 |
| 55% | 2.6 pts | 1,407 |
| 56% | 3.6 pts | 731 |
| 57% | 4.6 pts | 445 |
| 58% | 5.6 pts | 299 |
| 60% | 7.6 pts | 160 |
The curve is brutal near the bottom because the required sample scales with the inverse square of the edge. A genuinely good 55% bettor needs on the order of 1,400 settled bets before the record itself proves anything — several seasons for most people. At 53%, the honest answer is that a lifetime of betting will not settle it.
Run the same arithmetic on a 100-bet sample and the interval spans 45% to 65%. That range contains “comfortably losing” and “professional” simultaneously. It is the single best argument for judging yourself on closing line value instead: CLV is measured per bet against a known reference, so it stabilises in a fraction of the sample a win rate needs.
What it means for bettors
Neither number is a betting edge — markets price both sports sharply. The usable lesson is about variance: in the NHL, streaks are longer, results lie harder, and a month of outcomes proves almost nothing. Judge an NHL edge on a materially bigger sample than an NBA one, size stakes more conservatively, and in every sport measure yourself on Closing Line Value rather than short-run win rate.
Limitations
One season per league and one model family. A hockey-specialized model (shot quality, goaltending form) would score higher than generic ratings — but the comparison here is deliberately like-for-like: the same simple method, so the difference isolates the sports themselves. Accuracy is measured against winners, not against closing prices; see the companion study for why accuracy alone is not an edge.
One caveat we would want from anyone else’s study and should state about our own: these figures are not reported against a naive baseline. The first question to ask of any accuracy number is how it compares to simply picking the home team every night — a rule that requires no model at all. A model that beats that baseline comfortably in one sport and barely in another would tell a sharper story than raw accuracy does, and the gap reported here should be read with that missing comparison in mind.
Common questions
- Which sport is hardest to predict, NBA or NHL?
- The NHL, by a wide margin in our test. An identical rating model called about 65% of NBA winners but only about 53% of NHL winners — barely above a coin flip. Hockey's low scoring, goaltending swings, and engineered parity make single games far more random than basketball.
- Why is the NBA more predictable than the NHL?
- Basketball has many scoring possessions per game, so luck averages out and the better team wins more often; star players reliably tilt outcomes; and home court matters. Hockey is low-scoring, a hot goalie or a bounce decides many nights, and the league is built for parity.
- Does a more predictable sport mean easier profits?
- No. Predictability describes the game, not the market — NBA lines are sharper precisely because the sport is more modelable. The practical difference is variance: NHL bettors need bigger sample sizes, longer evaluation windows, and more conservative staking before trusting any conclusion about their edge.
Related: the NBA market-efficiency test · bankroll management 101 · Kelly staking calculator
Cite this study
Bankroll Guardian (2026). How predictable is each sport? The same model called 65% of NBA winners — and 53% of NHL winners. https://www.bankrollguardian.com/research/nba-vs-nhl-predictability
Free to cite with attribution and a link. Questions about the data or method: support@bankrollguardian.com.
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