The Home Run Derby Curse

We’ve all heard it: the Home Run Derby can ruin a player’s swing and single-handedly cause the player to tank in the second half of the season.

The theory has been utilized to explain the decline of Brandon Inge in 2009, Dan Uggla in 2008, and Justin Morneau in 2007. Perhaps more famously, though, the Home Run Derby has routinely been identified as the culprit for Bobby Abreu’s disappointing second half in 2005 — in which he connected with 18 home runs in the first half and only six in the second half.

Most people within the baseball industry — players, coaches, and writers — now dismiss the theory’s validity. Some players may alter their swings in the event, but as third baseman Brandon Inge said in this article by Jim Caple:

“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.”

Ironically, Inge said that prior participating in the 2009 Home Run Derby and subsequently taking a nosedive in the second half of the season. He became Exhibit A for those providing evidence in favor of the theory.

So, does the Home Run Derby legitimately affect player performance in the second half of the season?

In my mind, if true, the Home Run Derby would affect more than just the overall power numbers for a player. It would alter the entire approach at the plate. The player’s swing would hypothetically become longer, causing his plate discipline and hit tool to suffer. His batting average, on-base percentage, and ISO would all see a significant decline.

I looked at every player who participated in the Derby since the 2000 season. That provided 96 test cases and a sufficient sample size with which to work. Here are the cumulative numbers:

1st Half .304 .394 .278
2nd Half .293 .389 .252

When we analyze the table above, we actually see an overall drop in batting average, on-base percentage, and ISO from the first half of the season to the second half. Perhaps the batting average or on-base percentage could be explained away by BABIP or simply random variance, but the decline in ISO is rather significant. Cumulatively, it is a 26-point drop from the first half to the second half.

Before declaring that abnormal, however, I thought it prudent to ensure that the entire league did not experience a decline in extra-base hits from the first half to the second half within that time frame. Perhaps fatigue caused ISO to drop across the league from 2000 to 2011.

Season 1st Half ISO 2nd Half ISO
2000 .177 .155
2001 .162 .161
2002 .155 .156
2003 .160 .155
2004 .159 .165
2005 .155 .154
2006 .162 .163
2007 .151 .159
2008 .149 .157
2009 .155 .156
2010 .147 .144
2011 .139 .150
TOTAL .156 .157

Not so. It remained relatively consistent over a twelve-season span, as one would imagine.

Again, we are left with data that suggests Home Run Derby participants hit for less power in the second half of the season than they did in the first half. That would seemingly further vouch for the theory’s validity.

The real issue then becomes regression. Players selected to participate in the event are individuals who performed at an elite level in the first half of the season. For many of them, their first-half numbers represented a level of performance significantly out of sync with their career numbers to that point.

Let’s look once again at Brandon Inge. He posted a .247 ISO through the first half, despite having a career .153 ISO and only sustaining a .200+ ISO once in his career (2006). Should we legitimately be shocked that his performance fell off in the second half? Obviously not.

But how much regression should we have expected in that second half? Should we really have expected Brandon Inge to implode and hit .186/.260/.281 with a .095 ISO over a two month period of time? That also seems a little unreasonable.

In short, we probably should expect the numbers to regress across the board in the second half for the Derby participants. How much, however, is another question entirely. Perhaps the Derby participants did struggle more than expected in the second half. Perhaps they didn’t. Perhaps they even performed worse than what their expected regression should have been.

Of course, it should also be noted that not every player needs to alter his swing in the Home Run Derby. Players such as Prince Fielder possess a swing that is naturally built to lift baseballs over the fence. Players such as Joe Mauer or Andrew McCutchen, on the other hand, could attempt to create more lift, as their natural swings are not considered traditional home run strokes. This variation in swing and ability cloud the situation even further.

It is easy to see why the theory regarding the Home Run Derby and ruining players’ swings continues to dominate the discussion during the All-Star Break. The raw numbers indicate Derby participants hit for less power in the second half than they did in the first half, and by a rather significant margin.

The problem lies in determining whether or not the Derby plays a part in the decline. Logic suggests it does not and that simple regression makes much more sense, but perhaps it’s a combination of both.

One thing is clear: this theory will once again become a popular topic of conversation in July 2013, when the All-Star festivities travel to Citi Field in New York.

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J.P. Breen is a graduate student at the University of Chicago. For analysis on the Brewers and fantasy baseball, you can follow him on Twitter (@JP_Breen).

51 Responses to “The Home Run Derby Curse”

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  1. Hunter fan says:

    Why are 96 players a big enough sample size to make determinations? Random chance seems to say I could pick a group of 96 players out of a hat and some non zero percentage of them would experience similar decreases.

    In other words, could you show your work here as to why this is a sample size we can draw conclusions from as opposed to noise?

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  2. Matt says:

    Nice piece, but I think this is a pretty obvious case of selection bias. If you limit your analysis to players who participate in the derby, you’re ignoring the fact that many of these players had abnormally good first halves and simply regressed (to the mean) in the second half.

    In other words, if someone hits 6 homers in the first half, and 18 in the second, (a) they aren’t going to be invited to the derby, and (b) idiot analysts won’t be around to explain the difference as being due to the fact that the batter’s swing was improved because he didn’t participate in the home run derby.

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    • Benjamin says:

      This must be right. The Home Run Derby is essentially a selection of players who hit a lot of home runs in the first half. Some of the participants will be doing their usual thing, but a relatively high percentage will be those who outperformed their talent over the first half. It isn’t surprising that many hit less homers over the second half.

      It also isn’t surprising that there will be a couple of notable stories (Inge, Abreu) of 2nd half collapses. It’s probable that this will happen to some players in any sample of significant size that you pick.

      Moreover, I’m not sure how significant the collective decline actually is. The average ISO drops about 9%, but it’s still decent. Certainly, Inge’s case is not the norm.

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    • JeffMathisCera says:

      Correct correct correct.

      And even if we do assume for a second that these players are tired out by prior performance, we could just as easily determine that they are tired out from their scorching first halves as from the homerun derby.

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    • Tim_the_Beaver says:

      you’re affirming the point made in the post, yes?

      “The real issue then becomes regression. Players selected to participate in the event are individuals who performed at an elite level in the first half of the season. For many of them, their first-half numbers represented a level of performance significantly out of sync with their career numbers to that point.”

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  3. Rob says:

    I seem to remember Hanley Ramirez’s power declining considerably after his turn in the home run derby. Would any care to verify or refute that remembrance for me?

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  4. Well-Beered Englishman says:

    Pause to consider that there was once a time when Brandon Inge could be in the Home Run Derby.

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    • bada bing says:

      FACT: Brandon Inge would have been an all-time great, but he was consistently “pitched to like Babe Ruth”. Poor guy.

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      • asdfasdf says:

        Or when earlier this year he was hitting poorly in AAA on a rehab assignment. He claimed he wasn’t getting anything good to hit. Those AAA pitchers can be real tough on a 10 year MLB veteran.

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      • Ronin says:

        Wait I thought it was because of his knees that he isnt an inner circle hall of famer, or was it mono, or was it chaning positions, or…

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    • Jack says:

      I was utterly shocked by this actually. What next… All-Star Starter Yuniesky Betancourt?

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      • Cidron says:

        naa, Adam Kennedy would be a better SS HR derby rep. YuckNasty actually has a little pop for the position (key there, “for the position”)

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  5. L.UZR says:

    I’d feel more confident if a 1st half/2nd half baseline were established league wide. Maybe the data will show that HR Derby participants regress at the same level as all other hitters in the 2nd half of a 6+ month season.

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  6. Ryan says:

    Selection bias, indeed.

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  7. mcbrown says:

    I am feeling deja vu for some reason…

    Here is a suggestion that would still be fraught with problems but is at least a new approach.

    Step 1. Crowd source a list of players who were named as possible participants but did not actually participate. They are presumably similar to the players who did participate in that they hit lots of HRs in the first half, but dissimilar in that they did not actually take any of the life-threatening derby swings.

    Step 2. Compare the second half performance of this sample of players to the sample of actual participants.

    Step 3. Profit.

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    • Aaron (UK) says:

      Bil-ly But-ler! Bil-ly But-ler!

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    • Elias says:

      Or just skip the crowdsource and use the smallest number (or rate) of HR by a player in the derby in the year as a threshold. I did something like this after the Abreu debacle and found some very small effects of the derby on power, but not enough to worry.

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    • sleepingcobra says:

      Josh TweakerTough Reddick got snubbed! Best RF in the AL!

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  8. Jason H says:

    You need to compare the league-wide DISTRIBUTION of player’s first to second halves to the DISTRIBUTION of HR Derby participant’s first halves and second halves. You cannot just look at the league wide means and declare that the HR Derby participants are significantly different. If the data has a lot of dispersion then your tiny sample size of HR Derby participants will fit comfortably inside it. In that case no explanation is needed (e.g. regression and lucky first halves for the HR Derby participants).

    I’m willing to bet this is just random variation. Your regression explanation makes intuitive sense, but there are plenty of All Stars that don’t fit narrative. Players don’t make the All Star team and HR Derby just because they’ve hit a lot of home runs in the first half. Prince Fielder made the All Star team and participated in the HR Derby because he’s Prince Fielder. He has underperformed in the first half this year. Also, in the general population it is expected that half of all players will over perform in the first half.

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  9. adohaj says:

    I wonder how the second half performance of derby participants compares to other players who hit “x” amount of first half home runs.

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  10. Mike P says:

    Shortly after the All-Star break in 2009 Inge hurt his knee, but being the gamer that he was(barf), he played with it injured because the team had no one else, he couldn’t hurt it any worse is what he claimed the doctors told him, and to get it right would have taken 8-12 weeks and he didn’t want to miss that much time.

    Couple that with the fact he played over his head in the first half then got hit with the double whammy of an injury and probable regression to the mean in the second half. I only mention this because he seems to be the poster boy for this theory and the fact he was a huge contributor to the September collapse that year.

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  11. Hurtlockertwo says:

    I suspect that home run derby guys don’t get three days rest like the rest of the league, that may make a difference too.

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  12. Evan says:

    In terms of your methodology, would you consider Cano to have participated in the Home Run Derby this season?

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  13. CommonCents says:

    Why not compare individual post HR Derby 2nd half numbers to career numbers? Other than the large amount of work it would be…

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    • elkabong says:


      You can click on any column heading to sort. First Half+ and Second Half+ are comparisons of each player’s respective half to their career numbers. Initial sort is by Second Half ISO+. The numbers at the very top of each column are the averages for all 96 Derby participants.

      Interestingly enough, the collective AVG+ and OBP+ for each half is about the same, but the ISO+ is 123 for first half and 110 for the second half. As many have already stated, this higher ISO+ creates a selection bias. Those with higher ISO+ in the first half are more likely to be selected for the Derby. Only 10 of the 96 Derby participants had a First Half ISO+ less than 100 (i.e., an ISO less than career average), while 42 of 96 had a Second Half ISO+ less than 100.

      I think these comparisons are a good start, and would like to see research comparing the distribution of these 96 players to First and Second Half distributions of the entire league.

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  14. Rob says:

    Comapre HR Derby participants to All Stars in general.

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    • TheGrandslamwich says:

      This also has selection bias issues as all All Stars usually have had especially good first halves.

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      • Monty says:

        I think that’s his point. Like mcbrown above, the goal would be to analyze players who had a strong first half of the season, and compare their 2nd-half numbers to the 2nd-half numbers of Derby participants.

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      • Hamilton Marx says:

        Exactly. It’s like the “Verducci Effect” theory on pitching. Of course they are due for a slight regression when you are looking at players who are performing at a high level.

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  15. Allan G says:

    Gregg Zaun has repeatedly said on Blue Jays telecasts that if the home run derby negatively affects swings, then players would do poorly every single game because batting practice always ends up as a glorified home run derby at the end.

    Makes sense.

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  16. Will says:

    To take it a step further:

    How about Home Run Derby participants vs. other all stars since 2000 to testthe regression theory?

    Or Home Run Derby participants vs. themselves in years they didn’t participate in the HRD?

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    • Scott says:

      How about taking home run derby participants vs all other players that had equivalent home run totals by the all-star break. Obviously, with only 8 participants, there are other home run hitters that don’t participate in the derby. If the non-participating home run hitters have similar drop-offs, then it’s probably a better indicator of late season regression due to fatigue. If non-participating home run hitters maintain their performance, then it indicates that there might be something to the curse of the derby theory.

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  17. Eric R says:

    Would it be possible to make the 96 player data set available for others to play with– I’m sure I could dig them up online, but if someone just handed me a list of names [with lahmanID?] and years, I’d probably be more inclined to do something with this :)

    Likewise for many of the others commenting on this thread I’m sure.

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  18. Bigmouth says:

    I really like Rob and Common Cents suggestions for possible comparisons.

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  19. Matt says:

    Anecdotally, Adrian Gonzalez’s swing has not looked the same since his clinic last year and his power still has not returned. Again, small sample size of 1, but it’s stuck in my head for the last year.

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  20. Jeremiah says:

    Can we look back and find ZIPS rest of season projections for HR Derby participants? It would at least be a start.

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    • mcbrown says:

      But what if ZiPS already knows about The Curse???

      Trumbo is only projected for 14 more homers this season…. ZOMG THE CURSE!!!

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  21. Tomrigid says:

    Reaffirming and restating the conclusion of the post: hitters are not worse after the all star break; they are better before it.

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  22. pft says:

    The fallacy that population averages apply to all individuals lives on.

    Just because the group shows no effect does not mean certain susceptible individuals will have no effect. It may be due to injury or a minor change in a players swing. Anyone participating in the HR Derby is simply rolling the dice.

    A-Gon coming off shoulder surgery last year participated in the HR Derby. His power numbers fell off a cliff in the 2nd half and he said his shoulder acted up. In fact, his power numbers have not recovered and since last years HR Derby he has 16 HR in 607 AB. Yikes. Maybe just a coincidence, but…..

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  23. Eric R says:

    I only grabbed six years of data [2005-2010], but here are the average pre and post ASB HR totals by the HR Derby round the player made it to:

    Finals: 17.8/12.3
    2nd: 21.8/16.0
    1st: 20.5/19.4

    So, guys who didn’t hit enough to advance past the first round hit 95% of the HRs in the second half as the first; guys who made it to the second round, but did not advance, hit 73% of the HRs in the second half as the first; guy who made it to the finals, 69%.

    So maybe alittle HR derby doesn’t hurt much, but a lot of it is more detrimental.

    Since the top two buckets had 12 players in each and the bottom 24, lets even split that one in half. 11 of them hit four or more HRs in their one round. They averaged 21/12 [57%], the other thirteen 20/25 [125%].

    So guys in multiple rounds or who atleast hit a decent number of HRs in the first round took a pretty big hit in the second half and the rest did OK…

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    • BronxBomber says:

      So, essentially, Robinson Cano is going to hit 32 home runs in the second half.

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      • Eric R says:

        Maybe. SSS still applies to my data and even if it didn’t, maybe it’d be more like saying, “if we were to play-out the remainder of the 2012 season 1M times, Robinson Cano would probably average around 32HR”.

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