Methodology

This site runs thousands of full-season simulations on each season in the history of a sports league to understand the roles that variability and luck play in determining the final standings and champions. For each season, we replay the actual schedule using a statistical model based on team strength - specifically, Elo ratings - to determine the final standings. From there, we determine the champion using the actual format using in the league, whether that be a playoff bracket, a knockout tournament, or simply point total.

The goal isn't to rewrite history, but rather to measure how things could have turned out differently. We can only play the season once in reality, but these simulations aim to uncover how we would have expected the season to play out if we could replay the season thousands of times. Which teams were lucky? Which teams made historic runs through the playoffs? Which teams endured historic collapses? Who are the unlikeliest champions of all time? These simulations offer a data-driven exploration into how much randomness shapes the seasons we remember and how close we came to remembering something different.

An Elo rating is a number that represents a team's current strength based on how it has performed in past games. Higher Elo ratings indicate stronger teams while lower ratings indicate weaker teams. Elo ratings are constantly being adjusted to reflect the team's current strength. After each game, teams always earn points for a win and lose points for a loss. The number of points the team earns or loses depends on how likely the game outcome was and the margin of victory.

Elo ratings have two key properties:

• Zero sum: The number of points a team earns by winning a game equals the number of points that their opponent loses; this guarantees that the average Elo rating for a league is always constant.
• Self-correcting: If a strong team suddenly loses key players (due to injury, for example), their Elo rating will not immediately reflect the drop in strength. Only when they start losing unexpectedly or by large margins will their Elo rating begin to decrease quickly, eventually converging toward their true new strength.

We run two different types of simulations, both using a Monte Carlo approach. The first replays an entire season from scratch. The second replays only the playoff bracket for leagues that have a dedicated postseason.

Each full-season simulation replays the whole season using the actual schedule from that year. For every game, we estimate the probability that each team would win (or draw, where applicable) based on the:

• Elo ratings of the teams involved in the game
• Value of home advantage in the league that season

We simulate games probabilistically, one at a time, updating the standings according to the simulated winner from the Monte Carlo simulation. After each simulated game, we update each team's Elo rating based on the actual result of the game, ensuring that win probabilities track the true arc of the season. Once the season is complete, we crown the champion using the league's format for that year. Leagues with a postseason use the simulated standings to build the bracket and simulate the playoffs. Leagues without a postseason have their champions crowned based on the final standings.

In contrast, the playoff simulation begins with the actual playoff bracket from the final regular season standings. Only the teams that actually qualified for the postseason can win the simulated title. We then simulate each round of the playoffs using the same Elo-based win probabilities to determine the outcome of every playoff matchup and, ultimately, the champion.

Whenever a postseason is simulated, each team carries its end-of-season Elo rating into the first simulated postseason game. Because the simulated playoffs will differ from the playoffs in real life, we simulate both the winner and the margin of victory for each game. The simulated winner advances the team one game closer to the championship, while the margin of victory determines how much each team's Elo rating moves. As a result, both Elo ratings and the postseason path evolve uniquely in each simulation.