Question 1

What is EPA in football analytics?

Accepted Answer

EPA (Expected Points Added) measures the value of each play in terms of expected points. It calculates the difference between the expected points before and after a play. A positive EPA means the offense gained value, while negative EPA means the defense won. EPA is the most important metric in modern NFL analytics and is widely used for sports betting and fantasy football analysis.

Question 2

How can I use NFL analytics for sports betting?

Accepted Answer

NFL analytics can improve sports betting by identifying value in betting lines. Key strategies include: using EPA and success rate to evaluate team efficiency beyond win-loss records, analyzing situational data like fourth-down tendencies, comparing implied probabilities from odds to analytical win probability models, and identifying mismatches using advanced metrics. The most successful bettors combine multiple analytical approaches with market analysis.

Question 3

What is the best metric for evaluating NFL quarterbacks?

Accepted Answer

The best quarterback metrics combine multiple factors: EPA per play measures overall efficiency, CPOE (Completion Percentage Over Expected) isolates accuracy from scheme effects, and air yards metrics show aggressiveness. For fantasy football, look at EPA + rushing value. For betting, focus on EPA against different coverages and pressure rates. No single metric tells the whole story - the best analysis combines EPA, CPOE, and situational splits.

Question 4

How do I get started with football analytics?

Accepted Answer

Start with free tools: nflfastR provides play-by-play data in R, and nflverse offers a complete ecosystem. Learn basic R or Python for data analysis. Begin by understanding EPA and success rate - these form the foundation of modern analytics. This textbook provides a complete 45-chapter guide from basics to advanced machine learning techniques, perfect for fantasy football players, sports bettors, and aspiring analysts.

Question 5

What is win probability in NFL analytics?

Accepted Answer

Win probability (WP) estimates a team's chance of winning based on game situation: score, time remaining, field position, down, and distance. Live win probability updates after each play. WPA (Win Probability Added) measures how much each play changed the win probability. These metrics are essential for live betting, understanding clutch performance, and evaluating coaching decisions like fourth-down attempts.

Question 6

How can analytics help my fantasy football team?

Accepted Answer

Analytics improves fantasy football through: target share and air yards for predicting receiver production, snap counts and opportunity metrics for identifying breakouts, matchup analysis using defensive EPA allowed by position, red zone usage rates for touchdown regression, and yards per route run (YPRR) for finding undervalued receivers. Combine volume metrics with efficiency stats for the best projections.

Question 7

What is DVOA and how does it differ from EPA?

Accepted Answer

DVOA (Defense-adjusted Value Over Average) is Football Outsiders' proprietary metric that compares each play to a league baseline and adjusts for opponent. EPA measures raw expected points value without opponent adjustment. Both are valuable: EPA is freely available and better for player evaluation, while DVOA provides better team rankings by accounting for strength of schedule. Many analysts use both.

Question 8

Should teams go for it on fourth down more often?

Accepted Answer

Analytics strongly suggests NFL teams should be more aggressive on fourth down. Expected value calculations show going for it is often +EV even at midfield with 2-3 yards to go. The break-even distance varies by field position, but most teams punt or kick too often. Fourth-down decision models are among the most robust findings in football analytics, though coaches remain conservative due to career risk aversion.

Question 9

What data sources are best for NFL analytics?

Accepted Answer

The best free source is nflverse/nflfastR, which provides play-by-play data with EPA calculations back to 1999. Pro Football Reference offers traditional stats. NFL Next Gen Stats provides tracking data (speed, separation). Paid sources include PFF (grades and advanced stats), Sports Info Solutions (charting data), and TruMedia (comprehensive platform). For beginners, nflfastR has everything needed to start.

Question 10

How accurate are NFL prediction models?

Accepted Answer

NFL prediction models typically achieve 65-70% accuracy against the spread for the best public models, compared to the ~52.4% needed to profit. Point spread predictions average 10-13 points MAE (mean absolute error). The NFL has high variance due to small sample sizes (17 games), injuries, and week-to-week variation. Machine learning models can improve accuracy but face overfitting risks. Combining multiple models often outperforms any single approach.

Question 11

What is success rate in football analytics?

Accepted Answer

Success rate measures the percentage of plays that achieve 'success' based on down and distance: gaining 40% of needed yards on 1st down, 60% on 2nd down, and 100% on 3rd/4th down. Unlike EPA which can be skewed by big plays, success rate measures consistency. Teams with high success rates sustain drives better. It's particularly useful for evaluating running games and offensive lines.

Question 12

How do NFL teams use analytics?

Accepted Answer

NFL teams use analytics for: fourth-down and two-point conversion decisions, player evaluation and draft picks, contract negotiations and salary cap management, game planning and opponent tendencies, in-game strategy adjustments, and practice optimization. Teams like the Eagles, Ravens, and Browns have been leaders in analytics adoption. Most teams now have dedicated analytics departments with data scientists and former players.

Frequently Asked Questions

Getting Started with Analytics