Predicting Home Runs is Eaaaaaaaaaasy. No, Seriously:

Predicting Home Runs is Eaaaaaaaaaasy. No, Seriously:

NARRATOR: Predicting home runs is not easy

Alright, I lied a little. It’s not exactly easy. That’s because some simple math shows us that even the best home run hitters in baseball (like, the absolute very best) average a ding dong on only 6-7% of their plate appearances.

via GIPHY

The average game might allow each hitter 4-5 plate appearances. Since there’s yet to be an MLB player in league history who hits a home run on 20-25% of their plate appearances (PAs), you can’t guarantee a homer each night from anyone. Put simply: elite power hitters can still go quite a few days between homers.

It is simple to predict who is most likely to go yard, though. That’s because Major League Baseball provides us with a rich data set to consider thanks to Statcast. We can use the Statcast data to figure out what a home run hitter might “look” like in data form.

While important for season long, this relative HR hitter “profile” could prove vastly more important for DFS tournaments – where HR points can make the difference between winning big money and going home empty-handed.

The rest of this post will be dedicated to showing you what a home run hitter’s Statcast “profile” looks like, as well as 5 players who might be going under the radar in 2019 drafts (by NFBC ADP) based on their implied power ceilings as a result.

2018’s Home Run Percentage

First, it’s pretty simple to calculate the rate at which a hitter goes deep. Take Joey Gallo, for example. The Texas Rangers’ outfielder hit 40 home runs in 577 plate appearances. If we take HR/PA (or 40/577), we see Gallo’s HR% was 6.93% in 2018. I think calculating HR% is an important distinction, because a player who hits 20 HR in 400 PA is not the same as a player who hits 20 HR in 600 PA.

By repeating this process for every player who saw a large sample of PAs in a season, we can start to build a case for who the most elite HR hitters are.

What exactly is large enough, you ask? I settled on 200 PAs – about one-third of a season. I’m open to widening that, but for the loose purpose I set out to prove, I think this is sufficient.

There were 329 players who had at least 200 plate appearances and 150 batted ball events in the 2018 MLB season (In addition to Statcast, I pulled statistics from Fangraphs using their table CSV export function for leaderboards). The average HR% for the players in the entire sample? Just 3.15%!

By one metric alone, Gallo appears to be an elite power hitter, for sure.

What About Statcast Data?

We cannot use one simple ratio metric to build a true profile of what a HR hitter looks like, though. We can, however, start to layer in some batted ball data for each player in the sample to get a more sophisticated look. Here’s the data points I considered:

  • Exit Velocity: in MPH, how fast each batted ball comes off the bat
  • Distance: in feet, how far a batted ball travels.
  • Barrel %: put simply, how often the player hits balls “optimally”

Players who hit balls that travel fast will also typically hit balls that travel farther. They are also likely to hit balls “optimally” – that is to say, in such a way that they net a positive result for their team more often than not.

When all of this comes together, we have a hitter who is valuable for your fantasy teams and DFS lineups (as well as his actual club!).

The Statcast Home Run Hitter “Profile”

I looked at both the averages for the entire 329 player sample, as well as the upper quartile, or top 25%. Here’s where those averages fell:
We see the top 25% (about 83 hitters) is only marginally better on average in terms of average and maximum exit velocity as well average and maximum batted ball distance. The average’s start to move a lot in terms of barrel % and HR% in that top quartile, though.

So what does an elite HR hitter’s Statcast profile look like? Back to Joey Gallo:As an MLB season progresses, you’ll want to monitor this data somewhat loosely if you can for fantasy purposes. Did a team just promote a new outfielder? Is he hitting a lot of homers?

By using this data, you can start to suss out whether or not he’s just getting lucky, or if he meets the profile above.

The Diamonds In The Rough?

In NFL, the biggest hurdle in the way of an elite athlete putting up elite fantasy production is typically opportunity to touch the ball. Without enough carries (for RBs) or targets (for WR/TE), their playmaking skill set is not likely to matter as much as it should for your fantasy teams.

In baseball, the factor that affects opportunity to hit is simply playing time. If a player isn’t routinely finding his way into a starting lineup, he’s hard to count on for season long formats.

Today, there are a handful of players who a.) are forecasted for decent playing time and b.) have really strong Statcast power profiles that are being overlooked even now, literally the day before the MLB Opener for the 2019 season. These guys could also make sense as “cheap” DFS plays – their salaries are likely to be lower as things get started.

Here’s 5 of them, by NFBC ADP:

1.) Randal Grichuk, OF, TOR (ADP 243.29)

Grichuk right now projects to hit 5th in the Blue Jays order, but he’s being drafted somewhere around the 20th-21st round in 12 team leagues because public sentiment on Toronto isn’t exactly high at the moment. In 2018, his HR% was 5.41% in 462 total PAs.

2.) Tyler White, 1B, HOU (ADP 261.05)


White projects to be near the bottom of the order for Houston, which has a lineup loaded from top to bottom, to be sure. If he’s able to replicate his HR% of 5.06% in 2019, he could be an interesting option for DFS tournaments as well as season long fantasy.

As you can see, there’s some holes in this profile – his maximum exit velocity and distance falls shy of the profile thresholds. But there’s a reason he’s ranked where he is currently, right? His ADP puts him in the 21st-22nd round in 12 team formats at the moment. I’d consider him a strong watch list candidate at the moment.

3.) Daniel Palka, OF, CWS (ADP 330.21)


Palka is slated to hit 6th according to MLB.com for Chicago this season. His average batted ball distance falls just shy of the average threshold for the sample, but every other metric looks fairly elite, here.

He had 449 PAs in 2018, so this isn’t one of the smaller profiles in the sample, either. His ADP is basically free – monitor Palka closely as the season starts for sure.

4.) Avisail Garcia, OF, TB (ADP 373.7)


Coming off an injury plagued 2018 campaign in Chicago, Garcia joins the Rays for the 2019 campaign. He’s expected to be their DH and hit 5th at the moment. His 2018 season included 385 PAs.

5.) Mark Trumbo, OF, BAL (ADP 542.19)


Baltimore is radioactive for fantasy this season. No one seems to think 2018’s 47 win club has gotten much (if any) better since then. Still, this is one of thirty teams that will play 162 games in 2019. There’s value to be found for fantasy here, as gross as it might feel.

Trumbo is coming off a disappointing 2018 season from a raw total perspective, but he’s projected to hit clean up, or in the 4th spot of the order. He still managed to post a HR% of 4.75% in 358 PAs in 2018.

Conclusions & 2019 Resources

Statcast data shows us that home run hitters have an exit velocity of about 90 mph, and average about 172 feet as their batted ball distance throughout a season. Their Barrel % is at least 7.08%, and ideally it’s even higher!

For 2019, I’ve partnered with the team at Occupy Fantasy to help write some articles about best values for MLB DFS slates this season.

One of the things that the team at Occupy does best is use the Occupy Model to find diamonds in the rough like the ones above each evening.

Statcast data is a factor that plays a heavy role in determining which players you should consider on a slate. Several players have won DFS GPP’s using this model – one has even become a DFS millionaire! I highly recommend you give Occupy a chance if you want to improve your research for lineups and your contest selection skills.

Thanks for reading!