MLB finalized the 2017 Home Run Derby field on Monday: Charlie Blackmon, Mike Moustakas, Giancarlo Stanton, Aaron Judge, Gary Sanchez, Cody Bellinger, Justin Bour and Miguel Sano all announced their intentions to participate in the event, which will take place on Monday, July 10 in Marlins Park.
Every year, with Derby talk comes the idea that the event “hurts players’ swings” and could potentially be the reason why participants tend to have bad second halves to their seasons. This theory is used to explain the drop-offs in performance from many sluggers. This notably occurred in 2009, when Brandon Inge struggled down the stretch. Inge hit just six home runs (40 wRC+) in the second half, after swatting 21 bombs (128 wRC+) in the first half.
Inge’s Derby performance wasn’t much to write home about, either. He hit zero homers during the event in Busch Stadium that year, finishing in last.
"We're professionals. As Albert Pujols or Ryan Howard said, you can make adjustments. It won't stick with you anyway. Someone once told me it takes 30 days for muscle memory to become habit. I wouldn't think that few swings in one night would affect you."
A few studies have been done on the Home Run Derby “curse,” including this one by J.P. Breen of FanGraphs in 2012. Most studies have concluded that the Derby itself does not impact players. Yet, Dave Cameron wrote in the Wall Street Journal in 2009 that the curse is real, with one player in each of the past four years (2005-2008) who hit at least 10 home runs in the event went on to see their power disappear in the second half.
With all this in mind, I decided to conduct my own study with more up-to-date information. I found Breen’s article to be especially interesting, considering he looked at a player’s entire slash line (as opposed to just their power numbers) because he felt their entire approach would change as a result of the Derby.
I slightly deviated from Breen’s study by using more of a power-production focus. For my study, I looked at three numbers — isolated power (ISO), wRC+ and HR/FB rate — in order to see any changes. Isolated power seemed like an obvious statistic for this work (Breen used it as well), as it shows specifically the number of extra bases a player averages per at-bat. Basically, it removes singles from a player’s slugging percentage. I used wRC+ for this study to follow up on Breen’s indication that a player’s entire approach changes; the stat encompasses all of a player’s offensive contributions (as opposed to just power) and adjusts for park-and-league factors. Lastly, I used HR/FB rate, a stat that I could not find utilized in any previous study of this “curse.” For most players, HR/FB rate should eventually regress towards the MLB average over the course of a season. Of course, some batters are stronger than others and will be able to hit more fly balls out on a consistent basis, but a higher HR/FB rate tends to be an indication of luck. I will mainly be focusing on that number throughout this article.
Before we dig into the Derby participants, I want to provide a quick background on my parameters and the MLB-average HR/FB rates over time.
First, I used player data from each of the past 10 Home Run Derbies (2007-2016), giving me a total of 82 data points. (Oddly, MLB expanded the Derby from the usual eight participants to ten participants in 2014 before returning back to eight participants in 2015.) I removed two pieces of data from this set — 2012 Jose Bautista and 2014 Troy Tulowitzki — because they were injured for a large majority the second half and would skew the data. Are their injuries because of the Home Run Derby? Maybe, but assuming that is true would be unfair to the overall data because both of them posted HR/FB rates of 0.0 percent in the second half. This left me with a nice round 80 data points, so I am not complaining.
With that out of the way, I want to show you the MLB-average HR/FB rates from 2007 to 2016.
League average HR/FB, by year
As you can see, there has been a bit of fluctuation throughout the years, and as you are probably aware, the MLB-average HR/FB rate began rising during the 2016 season. Of this group, the median MLB-average HR/FB rate was 10.1 percent. I will reference this figure throughout, so be aware that this is where it is coming from. Let’s begin, shall we?
First thing is first: the players that are selected to the Home Run Derby are always those who have hit a lot of homers in the first half of the season. That number is backed up by the fact that just one (let’s bold and italicize that) of the 80 data points posted a first half HR/FB rate of below the 10.1 percent median. That was 2014 Yoenis Cespedes, who fell just 0.1 percentage points shy with a 10.0 percent HR/FB rate after hitting 14 home runs in the first half.
That makes 79 of 80 — or 98.8 percent — of Derby participants over the last 10 years already “lucky” from their first half numbers by what would be considered an inflation fly balls to home run rate.
What’s more is that these players weren’t just lucky in the first half; they were extremely lucky. An outrageous 41 of the 80 participants — just over half — posted first half HR/FB rates of above 20 (!!!) percent. For an outrageous comparison, in 2014, just six of 146 qualified hitters (4.1 percent) kept their HR/FB rate above 20 percent for the entire season. In a normal year, we see between 10-15 hitters do this. Regardless, it shows how rare it is to have this high of a HR/FB rate.
That’s why it all has to come crashing back down to Earth.
For the most part, hitters were able to keep their HR/FB rates above the MLB-average in the second half of these seasons. After all, most of these players are big-time sluggers; it is literally their job to have an inflated HR/FB rate. Otherwise, they probably would not have even been selected for the Derby in the first place. So, only 10 hitters fell below the 10.1 percent threshold I am using during their respective second halves.
What about the number of hitters that posted second half HR/FB rates above 20 percent? It’s 17. Remember, 41 hitters posted a HR/FB rate above 20 percent for the first half. And, to show you how random HR/FB rates can be, of the 17 hitters who posted a HR/FB rate above 20 percent in the second half, five did not post HR/FB rates above 20 percent in their respective first half. This means that only 12 of the 80 hitters were able to keep their HR/FB rates above 20 percent in both halves of the season.
This is why we see a huge drop in production from the first to the second half for these players.
Home Run Derby participants
|Statistic||First Half (avg)||Second Half (avg)|
|Statistic||First Half (avg)||Second Half (avg)|
To what extent does the Home Run Derby play into this drop in performance? I’m going to say little-to-none. More often than not, players selected to the Derby are those who are having extremely lucky first halves. That’s why they have been selected in the first place! I’d like to look at the average second-half HR/FB rate. Notice how it did not come crashing down; it “merely” dropped by three percentage points. This shows that the hitters who are selected for the Derby tend to continue to hit home runs at a good pace (going back to the point about renowned sluggers being selected), but they also tend to lose the first half luck that they had for the first 3+ months. If we saw a sharper decrease in the figure, we may be able to conclude that the Derby does have an impact; the fact that it remains safely above the league-average keys us into the fact that these players still do what they’re supposed to: hit homers.
Going back to what Breen said, many of these hitters who choose to participate often don’t have the swing necessary for a competition like this. Of course, a player with natural loft in their swing, à la Josh Donaldson, doesn’t need to change his swing to do well in the event, whereas a player like Chase Utley, who hits more line drives, may need some bigger changes. That’s why we see such huge outliers.
A note to Blackmon, Moustakas, Stanton, Judge, Sanchez, Bellinger, Bour and Sano regarding the Home Run Derby: you have nothing to worry about beyond some good ole’ fashioned luck.
Devan Fink is a Featured Writer at Beyond The Box Score. You can follow him on Twitter @DevanFink.