The LABR Mixed draft went off last night (or will begin in two hours as I being to type this), and I am hoping not to once again have to explain why I selected Billy Hamilton in the second round. Anyhow, I wanted to share an important piece of draft preparation that I have never read as being advised, but I do perform heading into every snake draft.
Three years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with standard deviation of distance and average absolute angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.
Three years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with standard deviation of distance and average absolute angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.
Yesterday, I used Alex Chamberlain’s xBABIP formula to determine which hitters most underperformed in the BABIP department, if you believe in xBABIP, of course. Today, I check in on the other side of the coin — those hitters whose xBABIPs were well below their actual BABIP marks. Since we’re dealing with an equation here that still has much room for improvement (it’s r-squared is the best I’ve seen, but still only in the mid-0.40 range), it’s possible, heck quite likely, that it’s missing things.
So let’s not take what it says as gospel, but it would be foolish to ignore what it suggests. Though you might not agree that these hitters all have the type of downside xBABIP hints at, everyone should agree that there’s far more downside here than upside and elevated risk if fantasy owners are paying for a repeat BABIP, or close to it.
Seemingly every year, we try to develop a new hitter xBABIP equation. Obviously, the goal is to improve upon the previous iteration because BABIP is really hard to predict. We can be reasonably sure that some hitters own high BABIP, average BABIP or low BABIP skills, but that’s about it. In any given year, the metric could swing wildly. As more granular data continues to be made available to us, we could use it to keep bettering our ability to predict BABIP and understand which underlying skills drive it.
Yesterday, I whipped out my xHR/FB equation and compared its calculation to every player’s actual HR/FB rate to come up with a list of eight hitters destined for a HR/FB rate spike off their 2015 marks. Today I visit those on the opposite end of the spectrum, the guys who outperformed their xHR/FB rates the most.
How does one project a power breakout? It is difficult, perhaps impossible, to develop a system that more often than not uncovers a player due for a power spike. So rather than sift through an array of underlying metrics searching for clues, there’s an easier way. It’s the same thing we do when we look at a hitter’s BABIP and compare it to his xBABIP or check a pitcher’s BABIP and assume better/worse fortune the following year will lead to improved/decreased performance.
Yesterday, I laid out the framework for how I go about projecting players entering MLB from a foreign league. This year, two hitters from the KBO League of South Korea will be making their debuts this year, Byung-ho Park of the Twins and Hyun-soo Kim of the Orioles. Refresh your memory of each of their statistics by reviewing yesterday’s post linked to above. Now that you’re back, let’s get to projecting.
This year’s new faces from foreign leagues on the offensive side come to us from the KBO League of South Korea. First baseman Byung-ho Park was signed by the Twins and is expected to serve as the team’s primary designated hitter, as Joe Mauer is entrenched at first. The Orioles signed outfielder Hyun-soo Kim and he figures to play left field on an every day basis, though as a lefty, could end up being platooned. We have precious few players historically who have come over from the KBO League to look to in order to assist with our translations. So forecasting these two players is extremely difficult. They are essentially just educated guesses, and although all projections technically are, these are far less educated ones!
Last year, I unveiled the xHR/FB rate equation I developed that I use to help guide my HR/FB rate forecast for my Pod Projections. We’re all familiar with the average batted ball distance component of the formula. Also included is the hitter’s standard deviation of distance (SDD) of his fly balls and home runs, which has a rather high year-over-year correlation, though not as hefty as distance. That means that the leaders generally remain near the top and the bottom dwellers will rarely surge into the top quartile.
