In large-field GPPs, you need home runs and many of them. In fact, if there was only one statistic that you could research for your lineups, it should be how to find homers. As such, let’s look at some trends about when batters hit them and see if we can glean any useful information.

First, here’s the number of homers in 2015 depending on whether the batter is ahead, even, or behind in the count, along with the percentage on the season.

(Data from

Count HR % of HR
Ahead 981 38.91%
Behind 960 38.08%
Even 580 23.01%


hr count broad

Now, let’s get specific. Here’s the same data for each specific count possibility.

Count HR % of HR
3-0 13 0.56%
3-1 130 5.63%
3-2 213 9.23%
2-0 153 6.63%
2-1 177 7.67%
2-2 214 9.27%
1-0 295 12.78%
1-1 314 13.60%
1-2 218 9.45%
0-0 432 18.72%
0-1 245 10.62%
0-2 117 5.07%


hr count specific

In the first graph, it’s important to note the different possibilities of a count – a batter being ahead, even, or behind – aren’t equal. There are 11 different possible count combinations in an at-bat – a batter being ahead can occur six different ways (55%), even can occur three different ways (27%), and behind can occur two different ways (18%).

My point here is that even though there are only two different ways for a batter to get behind in the count, they actually hit home runs just as frequently as a batter ahead in the count. To be fair, batters have gotten behind more frequently after the first pitch than ahead (9,059 vs 6,684). But still, it seems that batters generally hit more home runs from behind than ahead in the count, after adjusting for opportunity.

However, the most frequent home run situation has come off the first pitch of the at-bat – nearly 19% of them, to be specific. And this data would probably even out the even-count numbers if we thought of 3-2 differently. It’s technically classified as the batter being ahead, but because the next pitch will either result in a strikeout or walk if the batter doesn’t swing, I’d actually consider it an even count.

Not to get off track, but that extends even further to how we label something like 2-2. It’s considered even, but again, because it requires fewer strikes to get a strikeout than it takes balls to get a walk, the batter is more behind than even.

Anyway, a a couple takeaways – first, when batters have been significantly up in the count (3-0 or 3-1), home runs haven’t come very frequently. While choosing batters against pitchers with a low WHIP is probably a good idea, if you’re looking for home runs, it might be a good idea to break up the WHIP categories – as in, look for pitchers who give up a ton of hits, as opposed to a ton of a walks.

Regarding the high home run rate on the first pitch, there’s some cool information in this article from The Hardball Times. Here’s an interesting excerpt:

Let’s not forget that BABIP on 0-0 counts is fairly high when compared to other counts. Perhaps pitchers tend to groove first pitch more often to get ahead, perhaps they take a little off their velocity, perhaps the cut flattens out a bit (this is certainly an interesting idea for a PITCHf/x study for another day*). Whatever the case, swinging at first pitch seems to give a hitter better odds than falling into an 0-1 deficit.

*I’m actually looking at this as part of this series, for later this week.

That same site also wrote another great piece on the same topic and included this graph.

swinging on first pitch

The data is screaming at us to find batters who typically swing at first pitches. For the casual baseball fan, it might seem counterintuitive or that the batter isn’t very disciplined. However, what we’ve learned from PITCHf/x data is that that particular stigma isn’t true. Both and have this data (the former in the PITCHf/x search and the latter has plate discipline sections).

Up next in this series (I honestly don’t know how long it’ll be – at least two articles) is how home runs are affected by pitch speed.