In my first piece as a contributor to Beyond The Box Score on July 25th, I wrote that Max Kepler’s unexpected power surge would not last. At the time he had hit 10 home runs in 189 plate appearances. In the week following he proceeded to hit five more, earning me playful jokes from some friends and a healthy dose of self-doubt. The small-sample randomness of baseball was mocking me, aiming to shake my confidence just as I started writing for the site.
Kepler would go on to hit just two more home runs in the remaining almost three months of the 2016 season (212 plate appearances). It was a reminder of the peaks and valleys of a baseball season.
Part of the reason I was so down on Kepler sustaining his power was seeing the minuscule home run percentage on similar batted balls that he was generating with his bombs. As found on Baseball Savant, every combination of exit velocity and launch angle has a listed league wide batting average and percentage in which that batted ball has ended up a single, double, triple, or home run. After looking at how small Kepler’s expected home run percentages were, I thought it might be interesting to find his and every other player’s average in this area.
A couple of notes to keep in mind:
- There were 312 players who qualified under my arbitrary five-home run minimum, so use the search function to target specific players or teams.
- The average for all of these players’ AvgExHR% was 59.1 percent.
- Statcast is not perfect — it sometimes fails to track a batted ball. The number of home runs not tracked for each player are listed next to their home run total.
Here now is the 2016 Average Expected Home Run Percentage (AvgExHR%) for every player who had a minimum of five home runs tracked by Statcast:
2016 Average Expected Home Run % (min. 5 HRs)
|Player||Team||HR Total||HRs Not Tracked||AvgExHR%|
|Player||Team||HR Total||HRs Not Tracked||AvgExHR%|
|Steven Souza Jr.||Rays||17||1||83.2%|
|Avsail Garcia||White Sox||12||0||80.8%|
|Travis Shaw||Red Sox||16||0||78.3%|
|Edwin Encarnacion||Blue Jays||42||4||76.6%|
|Josh Donaldson||Blue Jays||37||2||70.8%|
|Melvin Upton Jr.||Padres/Blue Jays||20||1||70.6%|
|Tim Anderson||White Sox||9||0||69.1%|
|Justin Smoak||Blue Jays||14||0||68.9%|
|Jose Abreu||White Sox||25||0||68.7%|
|Hyun Soo Kim||Orioles||6||0||68.0%|
|Todd Frazier||White Sox||40||1||67.0%|
|David Ortiz||Red Sox||38||1||64.5%|
|Rickie Weeks Jr.||Diamondbacks||9||1||64.4%|
|Jung Ho Kang||Pirates||21||1||63.7%|
|Brett Lawrie||White Sox||12||1||61.7%|
|Russell Martin||Blue Jays||20||0||60.9%|
|Troy Tulowitzki||Blue Jays||24||0||59.8%|
|Ezequiel Carrera||Blue Jays||6||0||58.8%|
|Michael Saunders||Blue Jays||24||1||56.5%|
|Jackie Bradley Jr.||Red Sox||26||0||56.3%|
|Hanley Ramirez||Red Sox||30||3||54.6%|
|Adam Eaton||White Sox||14||1||52.2%|
|Kevin Pillar||Blue Jays||7||0||50.1%|
|Xander Bogaerts||Red Sox||21||2||49.5%|
|Sandy Leon||Red Sox||7||0||49.4%|
|Alex Avila||White Sox||7||0||48.4%|
|Alejandro De Aza||Mets||6||0||45.5%|
|Jose Bautista||Blue Jays||22||2||43.4%|
|Devon Travis||Blue Jays||11||1||43.2%|
|Tyler Saladino||White Sox||8||0||41.5%|
|Mookie Betts||Red Sox||31||1||41.4%|
|Brock Holt||Red Sox||7||0||39.3%|
|Justin Morneau||White Sox||6||0||35.8%|
|Chris Young||Red Sox||9||0||34.8%|
|Dustin Pedroia||Red Sox||15||0||33.9%|
|Melky Cabrera||White Sox||14||1||33.2%|
|Dioner Navarro||White Sox||6||1||21.6%|
For a more visual look at the data, here’s an interactive scatter plot:
Since this list is based solely on how well a player’s home runs were hit, we see perennial top-tier sluggers such as Nelson Cruz, Chris Davis, and Miguel Cabrera mixed in with Quad-A types such as Byung ho-Park and Peter O’Brien. That doesn’t really move the needle; we know guys like Park and O’Brien have massive power. The difference between them being an everyday major leaguer or a farmhand is about improving their contact skills and strikeout rate, something this statistic doesn’t care about. This is about game power, which those guys have plenty of.
The names that stand out are some of the players near the bottom of the list. Here are some takeaways:
Max Kepler — 24.0 percent on 17 HRs (Rank 310 of 312)
He was the original inspiration for this exercise, but it was still surprising to see Kepler so close to the bottom. He had one mammoth shot with an expected home run percentage of 95, but 10 of his 17 dingers were 20 percent or lower. It was his rookie campaign, so it’s not like his batted ball tendencies are set in stone, but it will be interesting to monitor.
Didi Gregorius — 27.0 percent on 20 HRs (Rank 308 of 312)
Every single one of Gregorius’ 20 home runs in 2016 was to the pull side, with 11 of those twenty coming at Yankee Stadium where he seemed to take advantage of the short porch. Nine of his 20 home runs had an expected home run percentage of 10 percent or less.
Mookie Betts — 41.4 percent on 31 HRs (Rank 283 of 312)
Another instance of a park helping the overall home run total. Of his 31 home runs, Betts hit 17 of them at Fenway Park. Here’s the visual of where they landed, with Fenway on the left compared to the relatively neutral dimensions of Kauffman Stadium on the right.
While nine of his 31 home runs had an expected home run percentage of 70 percent or more, 11 of them were 15 percent or less. Quite a few wall-scrapers for Mr. Betts.
Jose Bautista — 43.4 percent on 22 HRs (Rank 274 of 312)
At first glance, it’s rather shocking to see one of the game’s premier sluggers so far down on this list and so far below the league average, but when you remember that he spent the 2016 season as a 35-year-old constantly battling injury to his lower half, it makes more sense. Seven of his 22 home runs had an expected home run percentage less than 10 percent. The fact that he remains unsigned is an indicator that teams are wary of giving big money to an aging corner outfielder, and this number gives further credence to their hesitation.
Brian Dozier — 59.2 percent on 42 HRs (Rank 168 of 312)
The "will they or won’t they" between the Dodgers and Twins regarding a Dozier trade has been a constant this offseason. On his debut as a co-host of Effectively Wild, Jeff Sullivan of FanGraphs contemplated how teams looking at a smaller player like Dozier would evaluate his power spike in 2016 (starts around the 35:00 mark):
Dozier’s power took off, but if you’ve ever watched Brian Dozier hit a home run, then you’ve watched Brian Dozier hit every one of his home runs. They’re not upper tank shots that he’s hitting, they are those wall-scrapers that it seemed like those slighter, smaller, mediocre power guys, they were just hitting more of them.
Jean Segura had a similar kind of power spike, where no one thinks of Jean Segura as a power hitter, but for whatever reason that tier of players seemed to be able to get that extra 5 or 10 feet. And I don’t think that teams know what to do about that.
I probably wouldn’t have paid much attention to Dozier’s place on this list were it not for Sullivan reflecting on how teams will view his home run surge. After listening I looked up where he placed, and not only was he right next to Jean Segura, ranked 168 and 169 respectively, but their average expected home run percentages were 59.2 and 59.1. (As mentioned earlier, the average of this list is 59.1 percent.)
Is there a ton to learn from this information? I don’t think so. I mean, Mookie Betts isn’t going to have a new home ballpark anytime soon. Max Kepler was a 23-year-old in his rookie season, so while his 2016 power may have been a bit of an illusion, we can’t say that the tool won’t develop. One season’s worth of home run batted ball data is still a pretty small sample size so it’s not fair to dismiss the home run power of players near the bottom of this list.
The point here is not to delegitimize anyone. That 340-foot fly down the line that barely creeps over the wall is still a home run in the record books. This is just another tool we can use to look at the overall home run output of a player to help gauge if that production is undeniable or maybe aided by some luck, be it park factors or weather conditions.
As the data collected by Statcast grows we’ll find out what it means. Maybe it can be a tool to better inform or predict when a player’s power production will start to decline. Maybe it will have no year-to-year meaning whatsoever. Either way, it’s fun to dig in and see how those taters get mashed.
. . .
Chris Anders is a contributor to Beyond the Box Score. You can follow him on Twitter at @mrchrisanders.