I was extremely excited to find that our saber-partner, Fangraphs, is now hosting splits data on their site. Lefty/righty splits are something that fascinate me. Ever since I was a little kid, I was awed by the idea of having a “lefty masher” on the bench, just in case an opposing manager brought in a LOOGY, allowing you to pull a quick-one and play for the extra-base hit. However, lefty/right analysis has advanced since my adolescence, and I think this post from MGL is a must read for what I’m talking about. In it he says:
IOW, how a batter does against RH pitchers informs us on how he will likely do against LH pitchers and vice versa. Why? Because there is not much of a spread in true platoon splits among ML baseball players yet there is a large spread in overall true hitting talent among ML baseball players. So if we see a large platoon split, like for a player like [Ryan] Howard, it is likely a fluke. If a player does really well versus RH pitchers but terrible against LH pitchers, both the “really well” and the “terrible” numbers are likely fluky and the “truth” is somewhere in between.
Howard has a .719 OPS in the last 4 years versus LHP. How would we estimate his “true” OPS versus LHP? You might be tempted to just use the .719, which is not too good or you might be tempted to use the .719 and then regress that toward the league average for a LH batter of Howard’s physical characteristics, which might be around the same or a little higher – I don’t know. Both of these methods would be wrong. You cannot ignore the fact that he also hit 1.052 in OPS versus RHP over the same time period (last 4 years) and in many more PA. This suggests that he is a very good hitter overall (which he is) and that the .719 is somewhat of a fluke.
So MGL’s main thesis is that the large discrepancies we see in some players is due to a smaller sample size relative to their overall performance, which is a more useful indicator of their talent and can be applied to platoon splits via regression to come up with a more “stable” number. MGL goes on to do this for Howard, getting an OPS of .805 versus lefties, rather than his actual .719 over the past four years.
The question I then have to ask is: how long do we have to wait to believe that a noticeably large split is due to a real ability to mash one side and a true inability to hit even close to as well (relative to the other side) against the other?
Unfortunately, we don’t have league leader/sortable data on Fangraphs pertaining to splits just yet, but I thought about all the guys that announcers had told me stunk against their same-handed pitching counterparts and looked them up. Here are some interesting names sorted by wRC+ with their same-handed numbers first, opposite second, and plate appearances in parentheses:
Some extreme differences there, and these are only the ones that hit me from memory. Obviously MGL and others are not saying that some players don’t hit better against opposite hand pitchers; this is clearly true. The question is how much.
I’d also like to see how players do split wise over the course of a career. Obviously the skill of hitters diminishes over time, but it’d be interesting to see if the splits are larger. Ryan Howard certainly is not “old” at 30 years old, but his skill set and physical size certainly have shifted over the years. While he was never slender, Howard is certainly a “bigger” guy than he used to be, and probably a good amount slower. Besides that, as players get older they tend to lose some hand-eye skill and bat speed, an effect that may be magnified when facing a pitcher of the same handedness. Here are Howard’s wRC+ from 2006-2009, going from overall to versus lefties and then versus righties.
2006: 166, 133, 182
2007: 140, 110, 159
2008: 123, 91, 143
2009: 141, 71, 178
As you can see, that’s a 49 point difference in 2006, same in 2007, 52 in 2008, and 107 in 2009. I doubt the difference will be as big in 2010, because I’m sure we can charge a big chunk of the 107 to some luck, but we shouldn’t be surprised to see a difference of around 75-90 points.
There’s still a good amount of work to be done in this area of research, and we should be excited for Fangraphs’ new tool. I think the next step is looking to the data to find similar traits in players, both physically and in their numbers, to determine whether their relative success versus opposite-handed pitchers would be indicative of future success, or just white noise. I’m anxious to find out.