NFL Props and Advanced Analytics: Using EPA, DVOA and Next Gen Stats
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For the first five years of my NFL prop betting career, I used box score stats – passing yards, rushing attempts, touchdowns – and wondered why my results were mediocre. Then I discovered EPA, DVOA, and the NFL’s Next Gen Stats tracking data, and everything changed. Not because these metrics are magic, but because they measure what actually matters: efficiency, context, and opportunity. Box scores tell you what happened. Advanced analytics tell you why it happened and whether it’s likely to happen again.
This isn’t a statistics lecture. It’s a practical guide to the metrics I use every week to build my prop card, with specific examples of how each one translates into actionable betting decisions.
Expected Points Added and DVOA for Props
I nearly gave up on advanced stats the first time I read an EPA explainer. The academic language made it sound impenetrable. Then a sharp bettor I follow online summarised it in one sentence: “EPA tells you how much each play is worth in terms of points.” That clicked immediately.
Expected Points Added measures the value of every individual play relative to what an average play would produce in the same situation. A 5-yard run on 3rd-and-4 that picks up the first down has a much higher EPA than a 5-yard run on 1st-and-10 that gains nothing strategically. For prop betting, EPA matters because it reveals which players are generating value beyond their raw yardage. A quarterback who throws for 250 yards with an EPA of +8.5 is vastly more efficient – and more likely to sustain his output – than one who throws for 280 yards with an EPA of +1.2. The second quarterback might have padded his stats in garbage time, which inflates his box score but doesn’t reflect consistent performance.
DVOA – Defence-adjusted Value Over Average – takes a similar approach but adjusts for opponent quality. A running back who gains 100 yards against the league’s worst run defence is performing differently than one who gains 85 yards against the best. DVOA accounts for this, giving you a percentage that tells you how much better or worse a player or unit performed compared to league average against the same quality of opposition. For props, DVOA is most useful when evaluating matchups. If a wide receiver’s team has a passing DVOA of +15% and they’re facing a secondary with a pass defence DVOA of -20%, the matchup grades out as highly favourable – the kind of spot where you might back the over on receiving yards or receptions.
Dimers’ prop model, which runs 10,000 simulations per game, has demonstrated that NFL receptions bets with identified edges have returned 6.6% profit across 93 comparable wagers. That kind of edge doesn’t come from eyeballing box scores. It comes from systematically applying efficiency metrics like EPA and DVOA to identify where the market has mispriced a player’s expected output.
Next Gen Stats: Speed, Separation and Air Yards
The NFL’s Next Gen Stats platform tracks every player’s movement using chips embedded in their shoulder pads. It’s the kind of data that didn’t exist a decade ago, and most casual bettors still don’t use it. That gap between data availability and data adoption is where edges hide.
Three Next Gen metrics matter most for prop betting. First, average separation – the distance in yards between a receiver and the nearest defender at the point of the catch or the targeted pass. Receivers who consistently create 2.5+ yards of separation are more likely to sustain high catch rates, which directly affects their receptions and receiving yards props. If you see a receiver whose separation numbers have dropped from 3.0 to 1.8 over the past three weeks, his prop lines might not have adjusted yet. Back the under.
Second, air yards – the distance the ball travels in the air on a targeted pass. A receiver with a high average depth of target (aDOT) sees fewer but longer targets, which creates more variance in his yardage output. A slot receiver averaging 5.2 air yards per target will have more consistent yardage games than an outside receiver averaging 13.8. For prop betting, consistency matters because the over/under line is set around the median expected output. High-aDOT receivers have wider distributions, which means they’re more likely to bust their line in either direction. That’s useful information if you’re deciding between an over and an under.
Third, rushing yards over expected – a metric that compares a running back’s actual yards to what an average back would have gained on the same carry, based on the blocking, defender positions, and hole created. A back who averages +1.5 yards over expected is genuinely elite – he’s creating yardage that the offensive line isn’t giving him for free. These backs are more likely to sustain high rushing totals even when the blocking regresses, making their over props more reliable than backs who are benefiting from a strong offensive line but showing +0.2 yards over expected.
The Dimers team put it directly: their top tip for using prop analysis is to focus on finding the edges, noting that most days the biggest betting edges appear in player prop bets rather than traditional moneylines. Next Gen Stats are one of the sharpest tools for finding those edges.
Applying Analytics to Real Prop Lines
Theory is useless without application. Here’s the workflow I follow each week, condensed into a repeatable process.
Step one: pull the current prop lines for the games I’m interested in. I focus on 3-4 games per week rather than trying to cover the full slate. Step two: look up the relevant players’ EPA, DVOA, and Next Gen Stats for the past four weeks – not the full season. Recent performance is a better predictor of the following week’s output than season-long averages, because of injuries, role changes, and scheme adjustments. Step three: cross-reference with the opposing defence’s metrics. A prop analysis calculator or odds comparison tool can speed up this step significantly. Step four: compare my estimated output to the implied probability in the bookmaker’s line. If the gap is wide enough to overcome the overround, I have a bet.
The key discipline is not forcing bets. Some weeks, the analytics don’t surface any meaningful edges, and the right play is to sit on your hands. In my experience, about 40% of NFL weeks produce genuinely actionable prop opportunities based on advanced metrics. The rest of the time, the lines are fair and the expected value is negative. Knowing the difference – and having the patience to wait – is what separates this approach from guessing with extra steps.
One final point: advanced analytics are only as good as the data’s recency. A player’s EPA from three weeks ago might not reflect a coaching change, a new offensive coordinator’s scheme, or a shift in role. I treat any stat older than four weeks as context rather than evidence. The NFL moves fast, and your data needs to keep pace.
Where can I access free NFL advanced stats for prop research?
The NFL’s official Next Gen Stats portal provides speed, separation, and air yards data at no cost. Pro Football Reference offers EPA, DVOA approximations, and detailed splits. Football Outsiders publishes DVOA rankings weekly during the season. All three sources are free and updated regularly – between them, they cover the core metrics needed for data-driven prop analysis.
What does a player’s air yards total tell me about their receiving props?
Air yards – the distance the ball travels before reaching the receiver – indicate the type of targets a player sees. High air yards suggest deep targets with more variance in yardage outcomes, making overs and unders less predictable. Low air yards indicate short, consistent targets with tighter distributions. For prop betting, receivers with low average depth of target tend to have more predictable outputs, while deep-threat receivers offer more volatile, higher-risk lines.
Prepared by the top nfl Prop Bets editorial staff.
