Methodology: How I Calculate Odds and Projections

At the heart of my projections is the Elo rating system, a method originally developed for chess players, adapted here for NFL teams. Each team's Elo rating changes throughout the season based on game outcomes.

Initial Elo for a New Season:

• A team's Elo from the end of the previous season is partially regressed towards the league average. This accounts for natural year-to-year fluctuations and roster changes.
• I also calculate an implied Elo from pre-season Vegas odds on team win totals. I then use the average of the market-based Elo and the regressed Elo from last season to set the initial Elo for each team.

Updating Elo After Each Game (Completed Games):

• Before a game, I calculate the expected outcome based on the Elo ratings of the two teams, including an adjustment for home-field advantage. A higher-rated team is expected to win more often.
• After a completed game, team Elos are updated. Winning teams gain Elo points, and losing teams lose them.
• The magnitude of the change depends on the game's outcome relative to the expectation:
  • Upset wins (a lower-rated team beating a higher-rated one) result in larger Elo swings.
  • Expected wins (a higher-rated team beating a lower-rated one) result in smaller changes.
  • Ties also result in an adjustment, typically benefiting the lower-rated team slightly.
• A K-factor determines how sensitive the Elo ratings are to recent results.

This dynamic Elo rating provides a constantly updated measure of each team's current strength based on actual game results. I do not factor in things like in-season injuries, trades, or weather.

Once I have each team's current Elo rating and the schedule of remaining games, I use a Monte Carlo simulation to project the rest of the season. This involves:

Simulating Individual Future Games:

For each unplayed game in the schedule:

• I calculate the home team's win probability using the current Elo ratings of both teams (including home-field advantage).
• A random number is generated. If this number is below the home team's win probability, the home team is marked as the winner for that simulated game. Otherwise, the away team wins.

Repeating Many Times:

I simulate the entire remaining schedule in this way to create one simulated season. I then repeat this process 2 million times. Each of these 2 million run-throughs of the remaining schedule provides a complete, unique potential outcome for the season.

After generating 2 million simulated season outcomes, I analyze the results:

• Win-Loss Records & Standings: For each of the 2 million simulated seasons, I tally the final records for every team. These records are based on actual past results combined with the outcomes from the simulated future games.
• Playoff Qualification: Based on the simulated standings and NFL tie-breaking rules (including head-to-head results, divisional and conference records, strength of victory, and strength of schedule derived from the simulated games), I determine which teams make the playoffs for each simulated season.
• Conference Winners: I identify the top seed in each conference in each simulation.
• Draft Order: In each simulation, I determine the draft order for all of the teams that missed the playoffs. I then apply first round draft pick trades, and tally up how often a team gets the first overall pick or a top 5 pick.

By counting how many times each event occurs across all 2 million simulations, I can calculate probabilities:

• Playoff Odds: If a team makes the playoffs in 1.2 million out of 2 million simulations, their playoff odds are 60%.
• Conference Winner Odds: Calculated similarly based on how often a team secures the #1 seed.
• Draft Pick Odds (#1, Top 5, etc.): Based on how often the current owner of a pick lands in those draft slots.
• Estimated Wins: The average number of total wins a team achieves across all simulations.

To determine how a specific upcoming game impacts a team's chances for various outcomes (like making the playoffs or getting the #1 pick), I downselect the 2 million simulations to only those where the home team won the game in question, and calculate the fraction of simulations where the target team makes the playoffs. Then I repeat this for the away team winning. The difference in these two probabilities shows how much that game's outcome impacts the target team's chances.

• Baseline: I first note the team's probability for an outcome (e.g., playoff odds) based on all 2 million simulations.
• Conditional Probabilities: I identify the subset of simulations where Team A wins a particular upcoming game. Within this subset, I calculate the target team's outcome probability (e.g., playoff odds if Team A wins). I do the same for the subset of simulations where Team B (Team A's opponent) wins that game.
• Impact (Delta): The difference in probabilities between these scenarios shows how much that game's outcome "swings" the target team's chances. The "Root For" suggestion points to the game outcome (Team A winning or Team B winning) that most benefits the team you are viewing for a selected projection (e.g., making the playoffs).