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.

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).

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Hunter fan
11 years ago

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?