HR/FB Definition
HR/FB represents the percent of fly balls a pitcher allows that are home runs. Needless to say, home runs are bad outcomes for pitchers, so a lower rate is going to be desirable.
The Concept of HR/FB
The concept behind tracking this statistic actually has a lot to do with monitoring luck. As it turns out, pitchers have a minimal amount of control over their HR/FB rate. Some of those fly balls are going to soar into the seats, and others will be caught a few steps from the fence. The difference could be something like the design of the park, a gust of wind, and of course, the strength of the opposing batter.
The league-average rate hovers around 10% and going well above or below that mark is a sign that luck is likely playing a big factor. Few pitchers have maintained rates that are considerably higher or lower than the league norm for an extended period of time.
Why is HR/FB Important?
This is an important statistic for pitchers because it helps remove luck from the equation when evaluating a pitcher’s performance. For instance, an otherwise-talented pitcher with a high HR/FB rate, and a high ERA as a result, should see improvement moving forward. Or, a pitcher with an extremely low HR/FB rate may be outperforming his true talent, and a correction can be expected.
Let’s highlight this concept by looking at the case of Jacob deGrom. Known as one of the best pitchers in baseball, deGrom had a HR/FB rate in 2017 of 16.1%. That’sa very high number, and one that would not be expected to continue. As a result of that high HR/FB rate, deGrom’s ERA in 2017 was 3.53. That would be a perfectly acceptable mark for most pitchers, but deGrom has proven to be much better than that over the course of his career.
Moving ahead to 2018, the HR/FB rate for deGrom dropped considerably. In fact, it dropped even more than would have been expected, all the way down to 6.3%. In turn, his ERA plummeted to 1.70, allowing deGrom to win the NL Cy Young Award in the process. While deGrom also improved his walk rate from 2017 to 2018, the major change in his outcomes was fewer fly balls landing on the other side of the wall.
How is HR/FB Calculated?
If you are intimidated by some of the complex math that comes along with modern baseball statistics, you’ll enjoy the simplicity of calculating HR/FB rate.
HR/FB = (Home runs allowed/Fly balls allowed) * 100
That’s it. You’ll need just two inputs to calculate this rate for yourself. By looking up a pitcher’s number of home runs allowed over a given time period, and the number of fly balls he allowed over that same time period, you’ll be able to quickly calculate this statistic.
What is a Good HR/FB?
Historically, the league average HR/FB rate has been around 10% and major variations from that number are typically the result of luck so it isn’t a stat that is directly used to rank a player’s ability.
Let’s look at two charts which will hopefully clarify how this statistic works. The first includes the ten-lowest HR/FB rates for the 2018 season. The second features the ten-best cumulative rates from a five season span of 2014 – 2018.
Top Ten HR/FB Rates, 2018 Season. (Minimum 1 IP per team game played)
| Player | HR/FB |
| Trevor Bauer | 6.2% |
| Jacob deGrom | 6.3% |
| Trevor Williams | 8.0% |
| Zack Wheeler | 8.1% |
| Kyle Freeland | 8.5% |
| Jake Odorizzi | 8.9% |
| Miles Mikolas | 9.2% |
| Jhoulys Chacin | 9.3% |
| Reynaldo Lopez | 9.5% |
| Mike Foltynewicz | 9.6% |
Top Ten HR/FB Rates, 2014 – 2018 Seasons. (Minimum 800 IP)
| Player | HR/FB |
| Gio Gonzales | 9.5% |
| Justin Verlander | 9.7% |
| Jose Quintana | 10.0% |
| Max Scherzer | 10.1% |
| Jacob deGrom | 10.2% |
| Gerrit Cole | 10.2% |
| Johnny Cueto | 10.6% |
| Madison Bumgarner | 10.7% |
| Clayton Kershaw | 10.8% |
| Jake Odorizzi | 10.8% |
These two charts are a perfect demonstration of the role that sample size plays in HR/FB rate. When looking at just a single season, a few pitchers were able to post numbers significantly lower than league average. However, when increasing the same size out to five years and more than 800 innings, only two players were under 10%, and only by slight margins.
With a big enough sample, virtually every pitcher will move close to the league average mark in this statistical category. Even if a pitcher has been able to maintain a lower than average HR/FB rate over a large sample, as I explain in the next section, it’s likely those differences are due to factors outside their control.
What are the Problems with HR/FB?
One of the notable problems with reviewing HR/FB is that it is not park-adjusted. The dimensions and weather conditions of a pitcher’s home park are naturally going to have a lot to do with how many fly balls leave the yard during the course of a season. A pitcher working in San Francisco’s Oracle Park, which features spacious dimensions and frequently heavy marine air, will usually give up fewer home runs compared to a pitcher who works at the Rogers Centre in Toronto.