Using Advanced Stats for Player Props

What Makes Advanced Stats Better Than Box Score Averages?

The core problem with box score averages is that they average across every game situation, good matchups and terrible ones, favorable game scripts and negative ones, high-pace games and defensive slogs. The result is a single number that obscures the contextual variation that determines whether tonight is an Over or Under situation.

Advanced stats are designed to separate out the signal from that noise. They show you:

• How efficient a player is per opportunity rather than just how much they accumulate
• How many real chances they're generating versus how many they're converting based on luck
• Whether a recent statistical change reflects a genuine role shift or just short-term variance

That distinction, between a player who is genuinely better and a player who is running hot, is exactly what makes the difference between a good prop projection and a misleading one.

Read More: Best Strategies for Betting Player Props

Want to see which players are trending before you bet? Visit our Player Props page to track prop trends, streaks, and key stats all in one place.

Which Advanced Stats Matter Most for NFL Props?

NFL advanced metrics are the most developed and most publicly accessible of any major sport. The ones that directly improve prop projections:

For quarterback passing props:

• EPA per play: expected points added per play measures how efficiently an offence generates scoring value beyond just yardage. A quarterback with high EPA per play is producing value that raw yards don't fully capture, and their efficiency projection against specific defences is more reliable from EPA than from yards per attempt alone
• Pressure rate and time to throw: these affect yards per attempt and completion percentage projections when the matchup includes a strong pass rush or a leaky offensive line

For wide receiver and tight end receiving props:

• Target share: what percentage of the offence's total targets does this player absorb? This is the primary volume driver for receiving props, more stable than raw targets because it accounts for changes in overall team passing volume
• Air yards and average depth of target: these tell you whether a receiver's yards come from deep routes, which have higher variance, or short routes with high catch rates and YAC. A receiver with a high aDOT has more volatile single-game receiving yards than a slot receiver with a low aDOT and high catch rate
• Route participation rate: how often is the player actually running routes? A receiver who is on the field for 90% of routes is a fundamentally different prop target from one who participates in 60%

For running back rushing props:

• Rush share: the back's percentage of total backfield carries. In committees, this is the critical number for projecting volume
• Success rate: how often do the back's carries produce a positive play? This is more predictive of sustained volume than yards per carry, which has higher variance

Read More: Rushing and Receiving Props Explained

Which Advanced Stats Matter Most for NBA Props?

NBA advanced metrics translate directly into the projection formula for counting stats because basketball is the most possession-countable of the major team sports.

For scoring props:

• Usage rate: the percentage of team possessions a player ends with a shot, free throw trip, or turnover. This is the single most important variable for points props because it measures how much of the offence runs through this player
• True shooting percentage: measures scoring efficiency across all three types of attempts, field goals, three-pointers, and free throws, in one number. More stable than raw field goal percentage for projecting scoring output
• Touch time and frequency: how many seconds of ball possession per game and how often they receive the ball. These directly feed scoring and assist projections beyond what usage rate alone captures

For rebounds props:

• Rebound chances per minute: how many available rebounds fall near this player per minute of play? This is more predictive than raw rebounds per game because it accounts for the fact that some players rebound in higher-opportunity environments
• Contested rebound rate: how often does the player compete for and win contested boards? This measures rebounding skill independent of how many uncontested rebounds they happen to be near

For assists and PRA props:

• Assist rate: percentage of teammate field goals a player assists while on the floor. Adjusts for playing time and pace, making it more stable than raw assists per game
• Potential assists: passes that lead to shot attempts regardless of whether the shot goes in. This is a leading indicator for actual assists that smooths single-game variance

Read More: Points and Rebounds Props Strategy

Before placing a prop, check the bigger picture. Our Player Props page shows player trends and streak data so you can spot patterns that matter.

Which Advanced Stats Matter Most for Soccer Props?

Soccer analytics have developed rapidly and the publicly available metrics for goalscorer and shots props are genuinely powerful for separating finishing luck from underlying chance quality.

For anytime goalscorer and goals props:

• xG per 90: expected goals per 90 minutes, measuring the quality and frequency of chances a player generates regardless of whether they've converted recently. A player with 0.40 xG per 90 is creating high-quality scoring chances consistently even if they've gone five games without a goal. Their finishing rate will normalise
• xG overperformance and underperformance: a player finishing at 0.55 goals per game on 0.30 xG per 90 is overperforming their chance quality by a large margin. Their conversion rate will likely regress toward the xG baseline

For shots and shots on target props:

• xShots per 90: the volume and positioning quality of a player's shot attempts. This drives shots on target props more directly than goals
• Shot-creating actions: passes, dribbles, and drawn fouls that directly lead to shots. For assist props, this is more predictive than raw assists

For match-level context:

• PPDA: passes allowed per defensive action, which measures how intensively a team presses. High-press matches produce more chaotic, higher-shot-volume environments that inflate shots props for attackers on both sides

Read More: Soccer Player Props and Specialty Markets Explained

Looking for an edge in the prop market? Head to our Player Props page to view player prop trends and streaks across multiple sportsbooks in one easy hub.

How Do You Actually Use Advanced Stats in a Prop Projection?

The process isn't complicated. A basic advanced-stat prop model pulls three types of input together and compares the result to the posted line:

Step one: usage and opportunity. How many chances will this player have? For NBA props that's minutes and usage rate. For NFL props it's target share and snap participation. For soccer it's xShots per 90 adjusted for minutes projection.

Step two: efficiency and context. At what rate does the player convert those chances? Apply the relevant efficiency metric, true shooting percentage, EPA per play, or xG per 90, rather than simple averages.

Step three: context adjustment. Apply the pace, matchup, and game script layer to the baseline projection. A high-usage NBA scorer in a fast-paced, high-total game projects higher than the same player in a defensive grind regardless of efficiency.

The output is a projection grounded in genuine performance indicators rather than surface averages. Compare that to the book's line and evaluate the gap.

FAQ

Do you need to build your own advanced stat model to use these metrics?

No. Publicly available databases across all major sports report the metrics described above for free. For NBA: Basketball Reference and Cleaning the Glass. For NFL: Pro Football Reference, Next Gen Stats, and PlayerProfiler. For soccer: FBref and Understat. You don't need to calculate anything from scratch to use these in your prop analysis.

Which advanced stat is most underused by casual prop bettors?

Target share in NFL receiving props and xG in soccer goalscorer props are both significantly underused relative to how predictive they are. Most casual bettors look at recent stats and reputation. Bettors who anchor projections in target share and xG have more stable projections with less reactive adjustment to short-term variance.

Can advanced stats predict a breakout game?

Not reliably, but they can identify when underlying performance quality suggests a statistical rebound is coming. A player with strong advanced metrics but recent results below their baseline is a better Over candidate than their recent stats suggest. That's not predicting a breakout, it's identifying that the results will likely normalise toward what the metrics say they should be producing.

How often should you update the advanced stats you're using for projections?

Weekly for in-season betting is appropriate for most metrics. Role and usage changes show up quickly in per-game metrics but take a few games to stabilise. A player whose target share has increased in three straight games since a roster change has enough evidence to adjust the projection. A single-game spike isn't sufficient to update a season-long baseline.