We are pleased to announce that the latest addition to FanGraphs Lab is a baseball simulator:
You can click on any of the day’s games to pre-populate rosters, or create your own using current players. The simulator will then simulate the game 10,000 times.
Let’s see how this works:
- If you select one game of the day, we pull the rosters for each team. First we check if there is a confirmed lineup, and if there isn’t, we use the RosterResource platoon lineup based on the assignment of the first opposing pitcher.
- Next, we construct the probabilities of the following events for each batter/pitcher rating: single, double, triple, home run, walk, at-bat, hit, and play, out. This is done by taking a ZiPS or Steamer platoon projection for both the batter and the pitcher, depending on the projection system you choose. For example, if the batter hits right and the pitcher throws left, we will draw the batter’s guess “vs L” and the pitcher’s guess “vs R”. In Steamer, we go further, decide whether a pitcher is a starter or a reliever, and draw a specific “vs L as Reliever” theory.
- Once we have the statistics for each team’s pitcher and batter, we calculate the odds ratio (including the league averages), and now we have the probability of each batter/pitcher matchup occurring.
- We also capture each batter’s Speed Score, stolen base average and stolen base attempts per inning.
- We use all that data to simulate the game. We run 10,000 simulations, and show the results.
The result set you get is solid.
You can see each team’s percentage chance of winning, including the distribution of all fixed games, as well as the number of team statistics and corresponding histograms:
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And we have average stats for each batter and pitcher:
And if you click on a specific line for a batter or a pitcher, we have a histogram of the results for each simulation:
We also include a matching grid, which shows all the possibilities we use for each matching:
We show three sample games out of 10,000 simulations:
Finally, if you click on any play, it will show you the probability of events for that match:
The simulator also lets you do fun things like see what would happen if a team composed entirely of Shohei Ohtanis faced off against another team composed entirely of Shohei Ohtanis. The possibilities are almost endless.
It’s worth asking how we know the simulator is doing a decent job of simulating baseball.
First, we recreated our win expectation tables using the simulator. If the simulator does a good job, we can expect to have the same winning expectation based on the outing, inning, and game results that we see in real baseball. As you can see, these were almost identical, with some noise due to the sample size. Even 24 million plays are not enough to remove all the noise:
We also compared the simulator’s off-season results to Steamer’s off-season predictions:
Steam RoS Simulation vs Steamer RoS Playoff Odds
| The team | Acting W | Playoff Odds W | The difference |
|---|---|---|---|
| The Dodgers | 103.3 | 100.7 | 2.6 |
| The brave ones | 90.6 | 89.9 | 0.7 |
| The Yankees | 89.0 | 87.6 | 1.4 |
| Sailors | 87.1 | 84.3 | 2.8 |
| Phillies | 86.0 | 85.5 | 0.5 |
| The guards | 85.8 | 84.2 | 1.6 |
| Tigers | 85.3 | 84.8 | 0.5 |
| The guards | 85.1 | 79.9 | 5.2 |
| Cubs | 84.0 | 84.4 | -0.4 |
| Pirates | 83.5 | 85.0 | -1.5 |
| Padres | 83.2 | 83.2 | 0.0 |
| Radiation | 83.2 | 81.2 | 2.0 |
| The Royals | 82.2 | 78.6 | 3.6 |
| Diamondbacks | 82.0 | 84.1 | -2.1 |
| Red Sox | 81.8 | 81.9 | -0.1 |
| Blue Jays | 77.8 | 81.5 | -3.7 |
| Twins | 79.1 | 80.5 | -1.4 |
| The Orioles | 79.4 | 81.2 | -1.8 |
| Athletics | 78.2 | 78.1 | 0.1 |
| Giants | 78.4 | 78.9 | -0.5 |
| Red | 77.3 | 78.4 | -1.1 |
| Cardinals | 77.5 | 77.6 | -0.1 |
| The Astros | 76.6 | 77.9 | -1.3 |
| Angels | 75.2 | 76.0 | -0.8 |
| Marlins | 74.9 | 75.8 | -0.9 |
| Foreigners | 73.1 | 72.3 | 0.8 |
| White Sox | 68.7 | 67.7 | 1.0 |
| The Rockies | 60.8 | 61.3 | -0.5 |
In addition, the league averages all look reasonable in terms of a standardized distribution of results:
Baseball Simulator rating (real stats for 2025 and 2026)
| The source | 1B | 2B | 3B | HR | BB | HBP | K | SB | CS | R/9 |
|---|---|---|---|---|---|---|---|---|---|---|
| Steam RoS | 14.45% | 4.43% | 0.39% | 2.99% | 9.84% | 1.07% | 21.60% | 2.03% | 0.59% | 4.76 |
| A steamer | 14.76% | 4.50% | 0.42% | 3.32% | 8.75% | 1.02% | 21.31% | 2.03% | 0.59% | 4.83 |
| ZIPS | 14.08% | 4.57% | 0.58% | 3.07% | 8.77% | 1.28% | 21.77% | 2.00% | 0.59% | 4.70 |
| Statistics for 2025 | 14.28% | 4.23% | 0.34% | 3.09% | 8.41% | 1.05% | 22.22% | 1.88% | 0.54% | 4.52 |
| Statistics for 2026 | 13.79% | 4.13% | 0.30% | 2.68% | 9.89% | 1.11% | 22.63% | 1.94% | 0.53% | 4.50 |
Finally, let’s take a look at how the full season stats stack up in the simulator compared to Steamer’s full season revealed stats:
Full Season Projected Statistics vs Full Season Simulated Statistics by Player
| Metric | IR-Squared |
|---|---|
| AVG | .967 |
| OBP | .913 |
| SLG | .946 |
| K% (Batters) | .990 |
| BB% (Batters) | .934 |
| K% (Pots) | .965 |
| BB% (Pitchers) | .965 |
| HR% (Pitchers) | .891 |
| ERA (Starter) | .934 |
| ERA (Reliever) | .384 |
Everything looks good here, except for the ERA release, which will be a point of future investigation. We treat inherited runners fairly, but reliever ERA is notoriously difficult to project due to sample size. It may be that this is just one of the differences between using a simulation and a pure predictive model.
This is a very beta product (that’s why we have it as part of the Lab), so if you see any issues or have feature requests, just let us know, either in the comments here or using the Lab feedback option.
