A while back, I stumbled upon an article discussing converting NFL point spreads to moneyline odds by examining the win percentages of favorites and underdogs at each spread level. Intrigued by the concept but noting that the original data source wasn’t specified, I decided to delve into the analysis myself. Using the statistical programming language R and the nflreadr package, I embarked on a journey to explore this relationship using historical NFL game data.

Analyzing Historical NFL Games with R and nflreadr

To conduct this analysis, I utilized the nflreadr package in R, which provides access to comprehensive NFL data. I pulled data from 4,835 NFL games, encompassing several seasons of play. The primary focus was on the point spreads—the expected margin of victory set by sportsbooks—and the actual outcomes of the games.

The objective was calculating the win rates for favorites and underdogs at each point spread level. By doing so, we can observe how the probability of a favorite or underdog winning changes as the point spread increases or decreases. This information is crucial for bettors and sportsbooks to understand and interpret betting lines.

Below is a summary table of the results:

Key Observations:

As the point spread increases, the favorite’s win rate generally increases. The implied moneyline odds for favorites become more negative (indicating a higher likelihood of winning), while the odds for underdogs become more positive (indicating a lower likelihood of winning). At spreads of 10 points or more, favorites win over 80% of the time. By analyzing this data, we can see clear trends in how point spreads correlate with actual game outcomes, providing valuable insights for betting strategies.

Utilizing the Data for Value Betting

This data is beneficial for bettors seeking value in the NFL betting market. Value betting involves finding discrepancies between the implied probabilities of an outcome (as suggested by the odds) and the bettor’s assessment of the true probability.

How to Use This Data:

• Compare Historical Win Rates to Offered Odds:

  • If a sportsbook offers moneyline odds that suggest a favorite has a 60% chance of winning at a particular spread. Still, historical data shows that favorites at that spread win 70% of the time; there may be value in betting on the favorite.

• Identify Mispriced Odds:

  • Sometimes, sportsbooks set odds that don’t perfectly align with historical probabilities due to market pressures or other factors.

  • By identifying these mispricings, bettors can capitalize on opportunities where the potential payout exceeds the risk.

• Adjust Betting Strategies Based on Spread Levels:

  • Understanding that favorites win more frequently at higher spreads can inform decisions about when to bet on favorites versus underdogs.

  • For instance, at spreads of 7 points or more, favorites win approximately 75% of the time.

Example Scenario:

A game has a point spread of 7 points, and the sportsbook offers moneyline odds of -280 for the favorite. Historical data suggests the favorite wins 76.8% of the time at this spread, corresponding to moneyline odds of approximately -331. The sportsbook’s odds are more favorable than the historical data suggests, indicating potential value in betting on the favorite.

Caveats:

• Sample Size Considerations: Some spread levels have fewer games, which may affect the reliability of the win rates.

• Changing Dynamics: The NFL evolves, and factors such as rule changes or team strategies may impact the applicability of historical data to current games.

• Variance: Upsets happen, and no betting strategy is foolproof. It’s essential to manage risk appropriately.

Applications for Sportsbook Operators

Historical win rates offer a foundational understanding of how often favorites and underdogs win outright at specific point spreads. Sportsbooks can anticipate betting patterns and adjust their strategies by referencing this data. Knowing that favorites win outright at certain rates for specific spreads allows sportsbooks to predict how bettors might wager. 

For example, if favorites historically win 75% of the time at a 7-point spread, sportsbooks can expect more bettors to favor the favorite on the moneyline. With this knowledge, they can adjust moneyline odds to make betting on the underdog more attractive if they foresee disproportionate action on the favorite. This helps balance the betting handle, which is crucial for mitigating potential losses.

Operational Efficiency Through Data-Driven Insights

Historical data is one of several tools that inform the initial setting of odds. It provides a statistical baseline that, when combined with current analytics, helps sportsbooks make more informed decisions. Understanding historical trends also allows sportsbooks to adjust odds more quickly in response to betting activity. If a certain spread attracts unexpected betting patterns, historical data can help determine whether this is an anomaly or part of a broader trend, streamlining the adjustment process.

Risk Management

Knowledge of historical outcomes assists sportsbooks in assessing the potential risk associated with certain bets. This information can inform the setting of betting limits and the need for line adjustments to protect against significant losses. While each game is unique, historical data can help predict outcomes under similar conditions, aiding in overall risk assessment.

Limitations and Considerations

It’s important to acknowledge that historical data is not a definitive predictor of future outcomes. Variables such as player injuries, team dynamics, weather conditions, and other situational factors can significantly impact game results. Therefore, sportsbooks must balance the insights gained from historical trends with current, real-time information to set accurate and competitive odds.

Conclusion

The exploration of NFL point spreads and moneyline odds using historical data reveals significant insights for bettors and sportsbook operators. By analyzing over 4,800 NFL games with tools like R and the nflreadr package, clear patterns emerge showing how the probability of a team’s victory correlates with the point spread. Bettors can leverage this information to identify value bets, comparing historical win rates to current odds to spot discrepancies and potential opportunities.

For sportsbook operators, understanding these trends aids in anticipating betting patterns and adjusting odds to balance the betting handle effectively. It enhances operational efficiency by informing the initial odds setting and streamlining adjustments in response to betting activity. Risk management also benefits from these insights, as historical data assists in setting appropriate betting limits and assessing potential losses.

Note: The data used in this analysis was sourced using the nflreadr package in R, which provides access to comprehensive and authoritative NFL data. The code used in the analysis can be found at https://github.com/bettor-analysis/nfl-spread