How Favorites Are Determined for the Super Bowl
Bookmakers, analytics models, and media panels decide who is favored to win the Super Bowl by combining quantitative metrics and expert judgment. Key inputs include regular-season performance, strength of schedule, injuries, roster changes, and advanced analytics such as EPA, DVOA, and player WAR. Point spreads and over under lines reflect implied probabilities, while win probability models translate those inputs into win percentages. This article explains the frameworks behind these assessments and how to interpret them, using verifiable league data and methodological context rather than transient game-specific narratives.
Point Spread Basics
The point spread is the primary tool used to set betting favorite status. It is designed to make a contest between two uneven teams economically balanced by awarding or conceding points. The favorite must win by more than the spread for a bet on them to cover; the underdog covers if they lose by fewer points or win outright. Oddsmakers begin with margin of victory projections from models and adjust based on money flow, public perception, and risk management.
How Lines Are Set
- Early numbers reflect power ratings, offensive and defensive efficiency, home field, and rest advantages.
- Sharp bettor activity can move the line before the public reacts.
- Weather, injuries, and travel factors can cause in-game adjustments, especially for wild-card matchups.
Implied Win Probability
Converting spread to implied win probability requires a standard normal distribution assumption. For example, a spread of –7 roughly equates to an implied win probability in the high 70s percent for a typical scoring environment, though actual conversion depends on the exact points distribution and volatility. Teams listed with negative spreads are the favorites; those with positive spreads are the underdogs.
| Point Spread | Implied Win Probability (Typical) | Source Type |
|---|---|---|
| –1.5 to –3.5 | 60–70% | Oddsmaker model ranges |
| –4.5 to –7 | 70–80% | Oddsmaker model ranges |
| –8 to –10.5 | 80–90% | Oddsmaker model ranges |
| –11 and beyond | 90%+ | Historical Super Bowl edges |
Win Probability Models
Advanced win probability models synthesize past performance, season trends, and matchup specifics to estimate the likelihood each team will win. Inputs often include season-long efficiency metrics, rolling form, roster strength, coaching decisions, and in-season injuries. Teams with higher model-derived win probabilities are commonly labeled favorites by media and betting markets alike.
Key Model Components
- Offensive and defensive EPA per play, adjusted for context.
- Strength of schedule measured through opponent win-adjusted records.
- Injury impact scores that discount production from key players.
- Home-field advantage quantified from historical win rates.
Public vs. Expert Perceptions of Favoritism
Media perception of who is favored to win the Super Bowl can differ from betting market conclusions. Fans often anchor on marquee names or recent Super Bowl appearances, while experts weigh efficiency metrics and sustainability. When narratives diverge, markets usually move first on roster changes or coaching hires, whereas public perception may lag behind headlines. Understanding this gap helps interpret forecasts and punditry more critically.
Notable Historical Examples
Historical Super Bowl favorites illustrate how spreads and narratives interact. Some heavily favored teams delivered convincing performances, while others saw tighter contests or upsets. The table below captures a few well-documented cases, focusing on pregame spread and the eventual outcome as verifiable reference points.
| Season (Game) | Point Spread (Favorite) | Favorite Team | Result |
|---|---|---|---|
| 2018 (LII) | –3.5 | Philadelphia Eagles | Upset win, +3.5 covered |
| 2019 (LIII) | –3.5 | New England Patriots | Covered, one-score victory |
| 2020 (LIV) | –3.5 | Kansas City Chiefs | Failed to cover, one-score loss |
| 2021 (LV) | –2.5 | Tampa Bay Buccaneers | Covered, two-possession victory |
| 2022 (LVI) | –2.5 | Los Angeles Rams | Failed to cover, three-possession loss |
How to Interpret Who Is Favored
Being favored to win the Super Bowl means that, according to current data and market consensus, a team has a higher expected probability of victory than its opponent. This can be expressed through a point spread, a win percentage, or an odds format. However, a favorite designation is a snapshot based on available information and can shift with late injury reports, practice updates, or market positioning. Responsible interpretation combines quantitative models with qualitative context and acknowledges uncertainty.
Key Takeaways
- Point spreads convert into approximate win probabilities, often aligned with model ranges.
- Injuries, roster moves, and home field can meaningfully shift favorability.
- Models and markets may align or diverge; both offer useful perspectives.
- Historical spreads do not guarantee outcomes, as underdog stories are common in the Super Bowl.
- Clear methods and transparent data sources improve the usefulness of favorite assessments.
Whether you are evaluating analytics models, betting lines, or media narratives, focusing on verifiable inputs and calibrated probabilities will yield a more durable understanding of Super Bowl favorites over time.