Introduction
Our MLB Playoff Chances and matchup pages show numbers like "62% to make the playoffs" or "if the Yankees win this game, their World Series chances rise to 4%." Those aren't guesses, and they aren't a single deterministic prediction of what will happen. They come from actually playing out the rest of the season, over and over, and counting how often each outcome occurs.
This article explains how that process works end to end, and the handful of decisions that are specific to baseball rather than simply carried over from how we simulate other sports.
The Basic Idea: Play the Season Out, Thousands of Times
Nobody can know in advance who will win a given MLB game. But if you have a reasonable estimate of each team's current strength, you can estimate how likely each team is to win any given matchup. The simulator takes that one game-level idea and scales it up to an entire 162-game season.
For every game still left to play, the model doesn't pick a single winner. Instead, it simulates the rest of the schedule as one complete, self-consistent "possible future": every remaining regular season game, the standings and tiebreakers that follow from it, and the entire postseason bracket through to a World Series champion. Then it does that again. And again, thousands of times, each one a full, independent replay of the rest of the season with its own sequence of upsets, hot streaks and lucky bounces.
"No single simulated season is a prediction. The prediction is what fraction of them agree."
If a team makes the playoffs in 6,200 of 10,000 simulated seasons, we report that as a 62% chance to make the playoffs. If they win the World Series in 400 of them, that's a 4% title chance. Every probability on the site is produced this way: a count, divided by the number of simulated seasons.
How One Simulated Game Gets Decided
Each team carries a rating that reflects its current estimated strength, on the same kind of scale chess players have used for decades: two evenly matched teams have similar ratings; a meaningful gap represents a genuinely stronger team. Given both teams' ratings for a specific matchup, there's a well-established formula that converts that rating gap into a win probability. The bigger the gap, the more lopsided the probability, but it never reaches certainty, because upsets really do happen over a 162-game grind.
A simulated game is then decided by drawing a random outcome weighted by that probability. It isn't a 50/50 coin flip; it's a coin weighted exactly to the model's win probability for that matchup. Unlike our NFL model, MLB game outcomes are not weighted by margin of victory -- baseball's run-differential signal behaves differently enough from football's that we calibrate the two sports separately, and the rating update after each game reflects only who won, not by how much.
Home Field Advantage, and When It Doesn't Apply
Home teams get a boost added to their rating before that win-probability calculation, matching the real, measurable home-field advantage seen across MLB history. A handful of games each season are played at a neutral site (an international series game, for example) with a designated "home" team for scheduling purposes only. The model checks each game for exactly this before applying any home-field boost, so a neutral-site game is modelled as the genuinely even contest it actually is.
Letting Ratings Evolve Through the Season
When the model simulates a game deep into a team's season, it uses that team's current rating, not its rating from Opening Day. Ratings update after every game, real or simulated, the same way a chess player's rating moves after every result. A team that gets off to a hot start in a given simulated season has that reflected in its rating for the rest of that same simulated season, rather than every team's fortunes reverting toward its pre-season projection all year. We explain the reasoning behind this choice in more depth in how the NFL season simulator works -- the logic is identical across both sports, only the sport-specific calibration differs.
A 162-game MLB season means simulating vastly more individual games per trial than an NFL season does. To keep that fast enough to run thousands of full-season trials, real completed games are replayed into the model's ratings exactly once, and each trial then only simulates the games that haven't been played yet -- rather than re-simulating already-known results over and over, which would add computation without adding any real uncertainty to account for.
From Games to Standings to a Champion
Once every remaining regular season game in a simulated trial has been decided, the model builds the final standings from those results using MLB's real, current postseason format: three division winners and three wild cards per league, seeded 1 through 6, with the top two seeds earning a bye into the Division Series. Ties are broken using MLB's actual published tiebreaker rules, including the specific procedure used when three or more teams are tied for the last wild-card spots. The bracket is then seeded and simulated the same way the regular season was: current ratings feed a win probability for each matchup, a weighted draw decides the winner, ratings update, and the bracket advances round by round -- Wild Card, Division Series, League Championship Series, and finally the World Series -- to a single simulated champion.
Turning Thousands of Simulated Seasons Into a Single Percentage
Every one of those simulated seasons ends with a complete outcome for every team: did they make the playoffs, win their division, win the pennant, win the World Series. Tallying how often each of those events occurred for a given team, across every simulated season, and dividing by the number of simulations, is where every probability on the site comes from. Run more simulated seasons and the percentages settle down and stop moving much between runs. That's the model converging on a stable estimate rather than reporting noise.
Keeping Comparisons Fair When Testing "What If"
Our matchup pages show how a team's outlook shifts depending on who wins a specific upcoming game. To make that comparison trustworthy, the two scenarios ("if they win" and "if they lose") are simulated using the exact same underlying randomness for every other game on the schedule. That means any game whose outcome doesn't depend on the two teams involved in the game being tested comes out identically in both scenarios, and any difference you see in a team's chances is attributable specifically to that one game's ripple effect, not to unrelated random noise happening to land differently between the two runs.
Kept Up to Date, Every Day
Ratings and results refresh daily as real games are completed, so every simulated season starts from the current, actual state of the year rather than a stale pre-season snapshot. The Playoff Chances page and each day's matchup pages are recalculated from that fresh data every day, which is also why you'll sometimes see a team's chances move even on a day they didn't play. Another team's result, or a rival clearing a tiebreaker, can shift the whole playoff picture around them.
If you want to see this in action, the Playoff Chances page shows the full league output this process produces, and any upcoming matchup page shows exactly how one game's outcome ripples through a team's own season outlook. Or try the MLB Simulator yourself and pick winners for any game left this season to watch every team's chances update live, using this exact process running in your browser.