Advanced Analytics Reveal NFL Discrepancies in Team Strength and Awards

The NFL's Hidden Metrics: How Advanced Analytics Uncover Deeper Truths Beyond the Win-Loss Record

This conversation with Aaron Schatz, Chief Analytics Officer at FTN Fantasy and founder of Football Outsiders, reveals that the surface-level narrative of NFL team performance is often a misleading simplification. Schatz’s work with DVOA (Defense-adjusted Value Over Average) and play-by-play data challenges conventional wisdom by highlighting how teams’ underlying efficiency and situational performance diverge dramatically from their win-loss records. The non-obvious implication is that conventional scouting and narrative-driven analysis can lead to significant misjudgments of team strength, creating opportunities for those who understand the deeper statistical currents. This analysis is crucial for bettors, fantasy players, and anyone seeking a genuine competitive edge in understanding the NFL, offering a framework to identify undervalued teams and avoid overvalued ones.

The Illusion of the Win-Loss Record

The most immediate takeaway from Schatz's work is the stark disconnect between a team's record and its actual on-field performance. Conventional analysis often relies on win-loss records as the primary indicator of success, but Schatz demonstrates how this can be a deceptive metric. His DVOA metric, which measures efficiency on a play-by-play basis adjusted for situation and opponent, often paints a very different picture. Teams like the Carolina Panthers, Chicago Bears, and New England Patriots, despite their poor records, are often statistically better than their wins suggest, while teams like the Detroit Lions and Indianapolis Colts have outperformed their records. This discrepancy arises from the inherent randomness of football, particularly in close games, and the extreme variations in schedules.

"There are a lot of teams where their record doesn't really match what the underlying statistics suggest about how good they are."

-- Aaron Schatz

This highlights a critical failure of conventional wisdom: it prioritizes the outcome over the process. A team might win a close game through sheer luck or a few timely plays, masking underlying inefficiencies that will likely lead to future losses. Conversely, a team might consistently play well, generating positive yardage and controlling the game, but lose close contests due to a few turnovers or a missed field goal. Schatz’s analysis suggests that focusing solely on wins ignores the signal in the noise, leading to an overestimation of struggling teams and an underestimation of those playing efficiently but suffering from bad luck. This is where a deeper dive into metrics like DVOA offers a significant advantage, allowing for a more accurate assessment of a team's true strength and future potential.

The NFC's Statistical Anomaly: Seattle and the Rams

One of the most striking insights from Schatz's analysis is his unusually high valuation of the Seattle Seahawks and Los Angeles Rams, placing them among historically great teams based on DVOA. This deviates significantly from many other advanced metrics, such as EPA (Expected Points Added), which tend to rank them lower. Schatz attributes this difference to the specific methodologies of DVOA, particularly its treatment of turnovers and special teams. DVOA downweights turnovers because they are less predictive of future success, and it includes special teams, which are often excluded by other models.

"The market I have Seattle and the Rams much farther ahead of the rest of the league than a lot of other people who do advanced analytics."

-- Aaron Schatz

The implication here is profound: different analytical frameworks, even those considered "advanced," can yield vastly different conclusions. Schatz’s willingness to embrace a methodology that highlights these teams, even when it contradicts broader consensus, speaks to the power of sticking to a consistent, well-reasoned analytical approach. For observers, this presents an opportunity. If Schatz's DVOA ratings are indeed a more accurate predictor of future success, then teams like the Rams and Seahawks might be significantly undervalued by betting markets and public perception. This is a classic example of how understanding the nuances of a metric can reveal hidden competitive advantages, allowing for more informed decisions in fantasy leagues, betting, or simply in understanding the landscape of the league. The conventional wisdom might dismiss these teams, but the data, as interpreted by Schatz, suggests they are contenders of the highest order.

The Quarterback Conundrum: Talent vs. System

A fascinating tension emerges when discussing the impact of quarterbacks, particularly in relation to Schatz's DVOA metric. While Vegas odds and general perception heavily favor teams with elite quarterbacks like Josh Allen, Schatz's model doesn't explicitly incorporate a quarterback's historical reputation or perceived "clutch" factor. This leads to a scenario where his model might rate a team highly despite having a quarterback like Sam Darnold, whose historical performance might not inspire confidence. Schatz acknowledges this as an area for future development, recognizing that betting markets, for instance, clearly factor in quarterback reputation.

"The only reason to have them [the Bills] as the favorite to come out of the AFC is Josh Allen. It's the idea that you have more historical trust in Josh Allen than you do in Drake Maye or Bo Nix or Trevor Lawrence."

-- Aaron Schatz

This reveals a critical debate in sports analytics: the balance between individual player talent and the overall system or team efficiency. While a superstar quarterback can elevate a team, a strong system, coaching, and overall team efficiency (as measured by DVOA) can mitigate the impact of a less heralded quarterback. The conventional view often overemphasizes the quarterback’s individual brilliance, potentially overlooking the contributions of the entire team. Schatz's approach suggests that while quarterback play is undoubtedly important, it's not the sole determinant of success, and a team's overall efficiency, as captured by DVOA, can be a more robust predictor. This creates a strategic advantage for those who can identify teams with strong underlying metrics that might be overlooked due to quarterback perception, or conversely, avoid teams heavily reliant on a single player whose performance is subject to greater volatility.

The Deep Roots of Data: Play-by-Play's Historical Reach

Schatz's dedication to collecting and utilizing historical play-by-play data, going back to 1978, underscores a fundamental principle of robust analytics: the more granular and extensive the data, the deeper the insights. This commitment is not merely academic; it allows for the identification of long-term trends and the validation of metrics across decades. By having this data, Schatz can compare current teams not just to their peers in the current season but to historically great and historically poor teams. This historical perspective is crucial for understanding whether a team's current performance is an anomaly or part of a more enduring pattern.

"We've got back to 1978 now. We have transcribed things off of video. We have found old game books... and it is no longer just mine; I have shared it with Pro Football Reference."

-- Aaron Schatz

The immediate implication of this is that the collective understanding of football history and team performance is being fundamentally enhanced. For those who can access and interpret this data, it offers a powerful lens to evaluate player and team performance beyond superficial statistics. It allows for a more nuanced understanding of how different eras of football compare and how specific strategies or player types have performed over time. This is where true competitive advantage lies: not just in having access to data, but in the sustained effort to collect, clean, and analyze it in a way that reveals patterns invisible to those relying on aggregated statistics or anecdotal evidence. The ability to say, "This team's current DVOA is comparable to the 1985 Bears" is a far more powerful statement than simply noting their win-loss record.

Actionable Insights for the Data-Savvy Fan

• Immediate Action (Next 1-2 Weeks):

  • Re-evaluate Team Rankings: Instead of relying solely on win-loss records, consult advanced metrics like DVOA (available through Football Outsiders or similar resources) to assess team efficiency.
  • Scrutinize Close Game Outcomes: Pay attention to how teams win or lose close games. Are they consistently outperforming their opponents statistically despite the outcome, or are they getting lucky? This distinction is key.
  • Consider Schedule Strength: Recognize that extreme schedules can inflate or deflate win-loss records. Look for teams with easier or harder schedules than their record might suggest.

• Short-Term Investment (Next 1-3 Months):

  • Deep Dive into Quarterback Impact: While acknowledging the importance of quarterbacks, analyze how well-rounded teams perform even with less-heralded QBs. Look for teams with strong offensive and defensive efficiency metrics that might be undervalued due to quarterback perception.
  • Understand Metric Nuances: Familiarize yourself with how different advanced metrics (DVOA, EPA, etc.) are calculated and what their specific strengths and weaknesses are. This will help you interpret discrepancies between them.
  • Follow Historical Data Trends: Explore resources that provide historical play-by-play data. Understanding how teams and players have performed across different eras can offer valuable context and identify enduring patterns.

• Long-Term Investment (6-18 Months):

  • Develop a "Process-Oriented" Betting/Fantasy Strategy: Shift focus from predicting outcomes to identifying teams that consistently execute well, regardless of immediate results. This requires patience but yields more sustainable advantages.
  • Track Analytics in Awards Voting: Observe how analytics are influencing MVP, All-Pro, and other awards. This can signal shifts in public and media perception that might not yet be reflected in market prices.
  • Explore Specialized Data Sources: Invest in or learn to use specialized charting data (e.g., against man/zone coverage, specific run concepts) to gain an edge in understanding team strengths and weaknesses beyond aggregate statistics.