Last week, we released a baseball simulator from FanGraphs Lab. This week, we’re adding home field advantage to the simulator. You can toggle HFA on and off using a new menu option:
The selected home field advantage will then be used in any simulation you perform. But how do we calculate home field advantage in this simulation? Let’s go through it.
You are probably familiar with home field advantage expressed in terms of winning percentage. From 2000-2010, the home team’s winning percentage increased to 54%. Over the next ten years, it dropped to about 53%. In recent years, it has dropped to 52-53%. Since our simulation works at the plate appearance level, however, we could not look at game results to measure home field advantage. Instead, we used PA level data to understand how much playing at home affects the level of each outcome in our simulator.
We took data from 2022-2025, the global DH period, and used it to fit three different home field advantage models. First, we simply compare the rate of each result achieved at home and on the road, without considering the identity of the batter. Next, we entered a logistic regression taking into account the bettor’s identity and the home/away class variable. Finally, we included another functional regression using batter identity, pitcher identity, and home/away. The three models produced similar values, within the margin of error, which gives us confidence that the methodology is sound.
In our sample, home field advantage appears to be most effective for strikeouts, walks, and home runs. Home hitters hit less, (unintentionally) walk more often, and hit home runs more often. They also hit twice as often, although that effect size was objectively small. We found no statistically significant differences in hit-by-pitch, single, triple, or “in play, out” rates, although it is important to note that for triples in particular, the sample size was too small for greater accuracy.
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We use the home field advantage factors we calculated in the step above to adjust the baseball simulation engine. We make these changes to the odds ratio space by taking the originally calculated probabilities for each outcome, dividing them into odds ratios, correcting them for the home field factor, and re-adding them to the teams. This method incorporates various effect sizes based on the expected matching results. Luis Arraez doesn’t get the same momentum in home runs as Aaron Judge, in other words.
We tested this method of calculating home field advantage by having similar teams face off, both with and without home field modifiers. The home team’s winning percentage averaged between 52% and 52.5%, which is exactly what we expected given the relationship between the observed change in the frequency of each result and the actual winning percentage. In other words, we feel that our model is well calibrated to real-world data over recent years.
By default, we divide our calculated home field adjustment in half and assign a bonus to home field results and a penalty to batter away results. If you’d like to use your home field version, however, you can change the weighting scale from that default weight of 0.5:
Moving that slider will increase or decrease the home and away modifiers added to the simulation. You can also use your own custom weights for each event type, although you obviously shouldn’t need to; We thought it was too good to be able to, so we left the feature in. As a word of caution, moving these sliders in large numbers can produce unrealistic results; if your multipliers mean that the home team hits 50% more singles than the away team on average, the simulated game won’t look much like baseball.
The rest of the acting is unchanged; you can see a description of its current features here. As before, this is still very much a beta product. If you see any issues or have feature requests, just let us know, either in the comments here or by using the Lab feedback button.
