How to Use Advanced Stats in Betting Predictions

Why Do Advanced Stats Beat Traditional Stats for Predictions?

Teams and players regress toward their underlying efficiency levels over time, not toward their raw results. Traditional stats capture the results. Advanced stats capture the underlying efficiency.

The practical case in the NFL: teams with strong efficiency differentials in the first half of a season, measured by the metrics below, win at approximately 62 to 65% rates in the second half when that differential diverges from their actual win-loss record. The underlying performance was there. The results hadn't caught up yet. Surface-level bettors anchored on the win-loss record miss this. Bettors working from efficiency metrics don't.

That predictive gap between what advanced stats show and what the market prices in is where the betting edge lives.

Read More: How Data Models Generate Sports Predictions

If you want data behind the picks, visit our Predictions page to see today's Shurzy AI prediction model and how it's performing right now.

Which Advanced NFL Metrics Matter Most?

EPA per play (Expected Points Added): The single most predictive NFL metric for future performance. It measures how much each play shifts the expected scoring outcome, adjusted for down, distance, and field position. A pass gaining 8 yards on third and 7 is worth far more EPA than an 8-yard gain on first and 10. Teams with strong EPA differentials at season midpoint outperform their market-implied win probability in the second half at a measurable rate.

DVOA (Defense-adjusted Value Over Average): Football Outsiders' efficiency metric adjusts for opponent quality and game situation across every play in a season. It generates separate ratings for offence, defence, and special teams. When the DVOA-implied margin for a matchup diverges from the posted spread by 2 or more points, the prediction becomes a candidate value bet worth acting on.

Success Rate: The percentage of plays that gain expected value, defined as 50% or more of needed yards on first down, 70% or more on second, and 100% or more on third and fourth. Success rate is more stable than EPA over small samples because it removes the outlier effect of explosive plays. Teams with high success rates but average EPA tend to outperform their market-implied win probability in close, low-tempo games where big plays don't materialise.

EPA plus CPOE composite for quarterbacks: Combines EPA per dropback with Completion Percentage Over Expected, which measures how often a quarterback completes passes relative to the difficulty of each throw. A quarterback with plus-3.5 CPOE is performing significantly above expectation on difficult throws. One with minus-2.1 CPOE is likely benefiting from easy routes and scheme that inflates raw completion percentage beyond what their actual performance warrants.

Read More: NFL Betting Predictions Guide

Which Advanced Soccer Metrics Should You Use?

Expected Goals (xG): The foundation of modern soccer prediction. xG measures the quality of scoring chances independently of whether shots went in. Teams that consistently outscore their xG, scoring more than their chance quality warrants, will regress toward their underlying chance-creation level. A team outperforming xG by 0.4 goals per game over eight matches has roughly a 70% probability of regressing over the following five matches. Most market prices don't fully account for this because they anchor on recent results rather than underlying chance quality.

PPDA (Passes Allowed per Defensive Action): Measures pressing intensity. A low PPDA score means aggressive high pressing. A high PPDA means a passive, deep-block style. Prediction models use PPDA to identify pressing mismatches: a high-press team facing a build-from-the-back opponent with technical defenders creates a completely different game dynamic than a deep-block team meeting a high-possession style. That dynamic has direct implications for pace, total goals, and which team's attackers reach dangerous positions.

xPoints: Calculates how many points a team should have earned based on xG outcomes across all matches. The gap between actual points and xPoints identifies teams that are over- or under-performing relative to underlying quality. A team sitting 14th in the table but with xPoints placing them 7th is a systematic undervalue in both futures markets and match odds, because underlying quality predicts future results more reliably than current standings position.

Read More: Football Betting Predictions Explained

Looking for a second opinion before you bet? Check out our Predictions page to review today's Shurzy AI model and its impressive success rate.

Which Advanced NBA and MLB Metrics Are Most Useful?

Net Rating (NBA): Points scored per 100 possessions minus points allowed per 100 possessions, adjusted for pace differences between teams. Net rating is the most accurate single-number team quality indicator in basketball, more predictive than win percentage at comparable sample sizes. A team's home versus away net rating split, combined with pace matchup against a specific opponent, produces the most accurate single-game totals baseline available for NBA predictions.

FIP and xFIP (MLB): Fielding Independent Pitching strips out defensive variance to measure what a pitcher's ERA should be based only on outcomes fully within their control: strikeouts, walks, hit batters, and home runs. xFIP goes further by normalising home run rate to league average, removing short-term park effects and luck. When a pitcher's ERA is significantly better than their FIP or xFIP, they're outperforming their true skill level. That's a regression candidate whose upcoming starts are potential under bets or fade opportunities in team totals markets where an artificially inflated reputation is inflating the line.

Don't rely on gut feel alone. Head over to our Predictions page to see today's Shurzy AI projections and how they stack up across the board.

FAQ

Where do you find advanced stats for free?

Pro Football Reference, Football Outsiders, and Next Gen Stats for NFL. Basketball Reference and Cleaning the Glass for NBA. FanGraphs and Baseball Savant for MLB. FBRef and StatsBomb for soccer. Most of these are free or have substantial free tiers.

How many games of advanced stat data do you need before it's reliable?

More than you might think. EPA and DVOA stabilise after around 8 to 10 games in the NFL. xG in soccer requires around 10 to 15 matches for meaningful signal. Net rating in the NBA stabilises after roughly 20 to 25 games. Below those thresholds, small-sample variance can make any efficiency metric misleading.

Can you use advanced stats without building a formal model?

Yes. Even a basic approach of comparing two teams' EPA differentials or xG records against the posted spread puts you ahead of bettors working purely from box scores and narratives. A formal model maximises the value of the data, but the underlying metrics are useful at any level of analytical sophistication.

What's the biggest mistake bettors make when using advanced stats?

Treating current-season metrics as equally reliable at week two and week twelve. Early-season advanced stats carry far more variance than mid-to-late season data. Weighting recent performance appropriately and using multi-season historical context where sample sizes are thin prevents drawing false conclusions from early-season noise.