NBA Prop Bet Win Rates: Category-by-Category Data from the 2025-26 Season

Editorial Team «Basketball Prop Bets» June 19, 2026 Reading time: 18 min

For three years, I treated all player props as roughly equal opportunities. Points, rebounds, assists, blocks – I analysed each market the same way and assumed the edges were distributed evenly. Then I started tracking my results by category, and the data shattered that assumption completely.

Not all props are created equal. Some categories consistently offer better win rates than others, and the differences are not small. The 2025-26 season data I am about to share shows spreads of nearly 15 percentage points between the most and least profitable prop types. If you have been betting player props without considering these category-level differences, you have been leaving money on the table.

This analysis examines win rate data across six major prop categories: blocks, three-pointers, steals, assists, rebounds, and points. The numbers come from over 10,500 graded picks during the 2025-26 season – a sample size large enough to reveal genuine patterns rather than statistical noise. What emerges is a clear hierarchy of opportunity that should reshape how you allocate your betting attention.

Data Sources and Methodology

The numbers in this analysis come from tracking services that grade prop picks against closing lines – the final odds available before tip-off. This matters because closing lines represent the market’s most informed assessment of probability. Beating closing lines consistently indicates genuine edge rather than lucky timing.

The dataset covers 10,580 graded picks from the 2025-26 NBA regular season, distributed across categories with varying sample sizes. Points props have the largest sample at 2,402 picks because they are the most heavily traded market. Blocks and steals have smaller samples – 379 and 294 picks respectively – because fewer bettors target these categories and fewer quality opportunities arise.

Sample size shapes confidence levels differently across categories. A 69.9% win rate on 379 picks is statistically meaningful but carries wider confidence intervals than a 55.7% win rate on 2,402 picks. Both numbers reflect real patterns, but the larger sample provides more precision about where the true win rate actually falls.

I have cross-referenced these results against my own betting records and found the category-level patterns hold remarkably well. My personal blocks win rate over the past two seasons sits at 67%, slightly below the broader dataset but confirming the same directional advantage. When multiple independent sources point the same direction, confidence increases.

One important caveat: these win rates reflect picks made using systematic analysis, not random selection. A bettor choosing blocks props arbitrarily would not achieve 69.9% wins. The edge comes from combining category selection with sound game-by-game analysis. The category merely identifies where the most opportunity exists – you still need process to capture it.

Blocks Props: 69.9% Win Rate Explained

The first time I saw blocks leading the win rate tables, I assumed it was a data anomaly. Nearly 70% seemed impossibly high for any betting market. After digging into the mechanics, the number started making sense – and it changed how I allocate my prop betting attention.

Blocks props achieved a 69.9% win rate across 379 graded picks during the 2025-26 season. That figure demands explanation because it sits so far above breakeven. The answer lies in how sportsbooks price low-volume statistical categories.

The Propeller Picks analytics team captured it well: high-variance stat categories that sportsbooks struggle to price precisely – blocks, threes, steals – are where genuine edge concentrates. The heavily traded markets like points and PRA are more efficiently priced because they attract more betting volume and more sophisticated analysis from oddsmakers.

Blocks represent the most volatile major stat in basketball. A rim protector might record 4 blocks one night and zero the next, depending entirely on whether opponents attack the paint. This volatility creates pricing difficulty. Oddsmakers must set lines based on limited predictive information, and the inherent randomness in block totals makes precise pricing nearly impossible.

The small sample size of blocks per game amplifies this effect. A player averaging 1.8 blocks faces a line of 1.5, and the difference between hitting the over or under often comes down to a single play in the final minutes. That margin-of-error environment favours sharp bettors who identify situations where oddsmakers have misjudged the probability distribution.

Matchup analysis matters enormously for blocks. A shot-blocking centre facing a team that attacks the rim aggressively will see more block opportunities than one facing a perimeter-oriented offence. These matchup-specific factors are harder for oddsmakers to incorporate into automated line-setting processes, creating systematic advantages for bettors willing to do positional defensive analysis.

The practical application is straightforward: when you identify a rim protector facing a team that ranks top-10 in paint attempts per game, the blocks over becomes a serious consideration. Conversely, when that same player faces a three-point heavy team, the under gains attraction. These matchup dynamics rarely move lines by enough to eliminate the edge because oddsmakers simply cannot weight every opponent tendency into their models.

Three-Pointers Props: 63.2% and Shooting Variance

Three-point props posted a 63.2% win rate on 723 picks – the second-best category in the dataset and nearly double the sample size of blocks. This larger sample provides stronger confidence that the edge is real and repeatable.

Shooting variance drives the opportunity here. A player might attempt 8 three-pointers per game with a 38% success rate, suggesting roughly 3 makes per night. But three-point shooting is streaky by nature. The same player might go 6-for-8 one game and 1-for-9 the next, both outcomes falling within normal variance. Oddsmakers must set a single line that accounts for this entire distribution, and that compression creates exploitable gaps.

The key insight is that three-point attempts are more stable than three-point makes. A high-volume shooter will get his looks most nights – his role in the offence ensures that. Whether those shots fall depends partly on skill and partly on randomness. By focusing on players with consistent attempt rates facing defences that allow open looks, you can identify situations where the probability of hitting a makes threshold is higher than the line implies.

Defensive scheme matters significantly for three-point props. Teams that aggressively close out on shooters reduce both attempt quality and volume. Teams that pack the paint and dare opponents to shoot create the opposite environment – more open looks and higher conversion rates. When a volume three-point shooter faces a paint-focused defence, the over becomes more attractive regardless of recent shooting performance.

I have found that three-point props on players coming off cold shooting nights often offer the best value. The market overreacts to recent results, pushing lines down after a 1-for-7 performance even when the attempts were good looks that simply did not fall. Regression to mean is powerful in shooting stats, and betting on bounceback games after cold stretches has been consistently profitable.

Steals and Assists: Mid-Tier Edge Categories

Steals and assists occupy the middle tier of prop category profitability, with win rates of 61.9% and 57.6% respectively. Both sit comfortably above breakeven but below the elite edges available in blocks and three-pointers.

Steals props posted 61.9% on 294 picks, benefiting from similar dynamics as blocks – low volume per game and high variance in outcomes. A player averaging 1.5 steals might record 3 one night and zero the next depending on whether passing lanes present themselves. The oddsmaker challenge mirrors blocks: setting precise lines when the underlying stat distribution is inherently noisy.

Steals also correlate with defensive aggression and gambling tendencies. Certain players consistently jump passing lanes and reach for deflections, creating more steal opportunities but also more foul trouble. Understanding which players have sustainable steal rates versus which players’ numbers are inflated by risky defensive habits helps identify whether a line properly reflects expected output.

Opponent tendencies matter significantly for steals. Teams with turnover-prone point guards or offences that rely heavily on cross-court passes create more steal opportunities. The best steals prop situations combine an aggressive defender with a careless opposing ball-handler. These matchup-specific edges rarely get priced into lines because they require granular analysis that automated systems miss.

Assists present a different analytical challenge. The 57.6% win rate on 965 picks reflects a category where the bettor competes against more information. Assists depend heavily on teammate shooting, which introduces a second layer of variance beyond the player’s own performance. A point guard can create 12 quality looks and record anywhere from 4 to 10 assists depending on whether teammates convert.

The mid-tier edge in assists comes from identifying situations where teammate conversion is likely to run hot. Playing against poor perimeter defences increases conversion rates on assisted shots. Having multiple shooters healthy and available improves the odds that quality passes result in quality makes. These factors sit outside the assist-maker’s control but systematically affect his assist totals.

Both steals and assists warrant attention in your prop betting rotation, but neither should displace blocks or threes as primary focus areas given the win rate hierarchy.

Points Props: The Most Efficiently Priced Market

Points props tell the other side of the story. With a 55.7% win rate on 2,402 picks, scoring markets remain profitable but offer significantly less edge than lower-volume categories. The reason is straightforward: everybody bets points.

Points scoring attracts the most betting volume of any prop category. Casual bettors gravitate toward familiar stats, and nothing is more familiar than how many points a player scores. This volume forces oddsmakers to dedicate more resources to pricing points accurately. Sophisticated bettors, sharp syndicates, and algorithmic models all hammer points lines, driving them toward efficiency faster than any other category.

The 55.7% win rate still represents value – any win rate above 52.4% typically indicates profit after accounting for standard juice. But the margin is thin. A bettor hitting 55.7% on points props makes money more slowly and faces higher variance than one hitting 69.9% on blocks. The compound effect over hundreds of bets is substantial.

I still bet points props, but I am more selective about which opportunities I pursue. The edge in points comes from extreme situations: injury-driven role changes, pace mismatches, or defensive vulnerabilities that the market has not fully processed. Routine points props on star players against average defences rarely offer enough edge to justify the bet.

Points props also face the closest scrutiny from sportsbooks. Limits on points bets tend to be lower than limits on blocks or steals bets because books know the points market attracts sharp action. If you develop a reputation for winning points props, you will likely face account restrictions sooner than if you specialised in less popular categories.

The takeaway is not to avoid points props entirely but to recognise their place in the hierarchy. When a genuine edge exists in a points market, take it. But do not force points bets when better opportunities exist in blocks, threes, or steals simply because points feel more comfortable to analyse.

The UNDER Bias: 60.3% vs 51.6% Split

Here is the number that changed my betting approach more than any other: UNDER picks hit at 60.3% compared to 51.6% for OVER picks during the 2025-26 season. That 8.7 percentage point spread is enormous in betting terms – it represents the difference between solid profitability and breakeven mediocrity.

The UNDER bias exists because of how recreational bettors behave. Most casual bettors prefer overs. Cheering for a player to score more points is simply more fun than hoping he falls short. This preference creates systematic demand for over bets, which sportsbooks accommodate by shading lines slightly higher than true probability would suggest.

The psychology runs deeper than entertainment preference. Recency bias makes recent high performances feel more likely to repeat than recent low performances. When a player scores 32 points, bettors pile onto his over the next game. When he scores 18, fewer bettors want the under despite the identical statistical significance of both outcomes. This asymmetric response to recent results pushes over lines upward across the market.

Sportsbooks are not charities. They know recreational bettors prefer overs, and they adjust their lines accordingly. The adjustment creates value on the under side for bettors willing to bet against the public consistently. You are not outsmarting the sportsbook – you are simply taking the side they have deliberately made more attractive to balance their liability.

The UNDER edge does not apply uniformly to all situations. High-variance categories like blocks and threes show the strongest UNDER bias because the distribution of outcomes skews toward lower numbers. A player with a blocks line of 1.5 is more likely to record 0-1 blocks than 3-4 blocks simply because the ceiling on blocks in any given game is limited. This structural asymmetry makes unders inherently more likely when lines are set near the mean.

I now default to looking for UNDER opportunities before considering overs. When my analysis suggests a player will fall short of his line, I bet it without hesitation. When my analysis suggests he will exceed his line, I pause to consider whether the over bias has already been priced in. This asymmetric approach has improved my overall win rate by roughly 3 percentage points since I adopted it.

Applying Win Rate Data to Your Betting Strategy

Knowing the win rate hierarchy is useless without a framework for applying it. Here is how I translate these numbers into actual betting decisions.

Bankroll allocation should favour higher-edge categories. If you have three potential bets on a given night – one in blocks, one in assists, one in points – the blocks bet deserves more of your bankroll assuming equal confidence in the underlying analysis. The win rate advantage compounds over time, making category selection a meaningful driver of long-term results.

The Kelly criterion provides mathematical guidance here, though I recommend fractional Kelly for risk management. Full Kelly betting becomes aggressive quickly, and a few losing streaks can devastate a bankroll even when your edge is real. I typically bet quarter-Kelly at most, adjusting upward only for situations where my confidence substantially exceeds the baseline category edge.

Time allocation matters as much as money allocation. Spending two hours analysing points props when you could spend that time identifying blocks and threes opportunities represents poor resource deployment. The categories with higher win rates deserve more of your research attention because the expected payoff for finding good bets is greater.

Tracking your results by category validates whether your approach matches the broader patterns. If your personal blocks win rate lags far behind 69.9%, something about your analysis process needs refinement. If your points win rate exceeds 55.7% significantly, you may have developed a specific edge that justifies more attention to that category despite its lower baseline advantage.

The UNDER bias should inform your search process. When scanning for opportunities, start with potential UNDER plays and work outward. The 8.7 percentage point advantage to unders means you need less analytical confidence to justify an under bet than an over bet. This asymmetric threshold shifts your attention toward the higher-probability side of the market.

For a deeper look at how to structure your analysis process around these category insights, the player props analysis framework covers the specific variables that drive outcomes in each statistical category.

Where Edge and Effort Intersect

The win rate data tells a clear story: blocks and three-pointers offer the most exploitable edges, followed by steals and assists, with points bringing up the rear. Unders outperform overs across almost every category. These patterns have held throughout the 2025-26 season and align with fundamental market mechanics that are unlikely to change.

Edge alone does not guarantee profit. You still need sound analysis to identify which specific bets within each category offer value. But starting your search in the right categories dramatically increases your odds of finding genuine opportunities. A disciplined bettor focusing on blocks and threes will outperform an equally skilled bettor spreading attention across all categories simply because the available edge is larger where he is looking.

The win rate hierarchy also suggests optimal portfolio construction for serious prop bettors. Rather than treating every category equally, weight your research time and bankroll allocation toward the high-win-rate categories. Build expertise in blocks and three-pointers first. Add steals and assists as secondary focus areas. Reserve points props for exceptional situations where your analysis reveals unusually strong edge.

Player props now account for 25-30% of basketball betting handle – a massive increase from 15% just three years ago. As this market has grown, efficiency has improved in the most-bet categories while leaving the less-popular categories relatively soft. The numbers I have shared represent this moment in time. They will evolve as more bettors discover the category hierarchy and shift their attention accordingly. For now, the edge remains available for those willing to do the work.

Win Rates FAQ

Written by the editors at Basketball Prop Bets.