If you are looking for analysis here, sorry but there is none. What follows though is the information that might help others do their analysis. At the very least, you’ll have better information on shifted hitters and which teams which employ the shift the most.
Top 30 Players (Basically, any slow left-handed hitter)
Yesterday, I took our first look at the batted ball distance leaderboard to identify hitters who could enjoy a surge in HR/FB rate. Today I’ll check in on the potential decliners. This could perhaps be your sell candidates. I’m going to ignore the obvious ones that have still posted strong distance marks, like Bryce Harper at at 35.5% HR/FB rate, backed by a 323 foot distance. Obviously, he’s going to regress, but that big distance suggests a career year in the power department.
We’re not a month into the season, so it’s finally time to start putting the hitter batted ball distance leaderboard to work. Back in late January, I unveiled my xHR/FB rate equation, which included three components, batted ball distance being one of them. Unfortunately, I don’t have the data for angle and standard deviation, so I cannot calculate xHR/FB rate marks yet and share the biggest discrepancies. However, simply looking at batted ball distance could do a reasonably decent job at identifying those who might be in for a HR/FB rate surge or decline. We’ll start with the potential surgers. These are the hitters whose distance is top notch, but for whatever reason, have posted mediocre or poor HR/FB rates.
Man, I am having a blast with the Baseball Info Solutions batted ball data that was recently added to the batted ball leaderboards. Sure, there are reasons to complain: the batted ball spray and contact quality statistics lack context, leaving you in the dark about how spray and contact intersect. For example, there’s Hard%, and there’s LD%, but how many of a hitter’s balls in play are hard line drives? (You can actually find this data on individual player pages under the “Splits” tab — just not on the leaderboards.)
Just because the available data aren’t as granular as one might wish they were doesn’t make them worthless or unusable. Yesterday, I demonstrated that we can still achieve small gains in our understanding of batting average on balls in play (BABIP) using the new data.
You may have noticed that FanGraphs now feeds batted ball data, courtesy of Baseball Info Solutions, into its leaderboards. The day the data appeared, my mind buzzed with ways they could be useful in improving our understanding of a hitter’s batting average on balls in play (BABIP).
Mike Podhorzer already augmented previous attempts at devising an equation for expected batting average on balls in play (xBABIP) for hitters by incorporating elements of a hitter’s power, speed, plate discipline and batted ball tendencies. So, with fresh numbers in hand, I embarked on a journey to further improve the ever-evolving xBABIP. However, I sought to do so by using only batted ball data. Basically, I intended to develop a convenient xBABIP equation, one that can be computed using almost entirely variables found on the same page.
More matter with less art, we hear some spoilsports telling us. We came for data; give us data. Fair enough, and here you go. The thing is, though, you never know. Sometimes you complain about the menu and you wind up with manna. Other times, you complain, and you get so much of what you’re after that, as God so trenchantly put it, “it come out of your nostrils and it be loathsome unto you.” Keep us posted as to which it is; we know you won’t be bashful about it. Read the rest of this entry »
Yesterday, I dove into the Baseball-Reference S/Str (swinging strike rate) metric and identified those starting pitchers who have enjoyed the biggest surge compared to last season. So naturally, today I’ll check in on the other side of the coin — those starting pitchers who have suffered through the largest decline in swinging strike rate. Obviously, knowing that a pitcher is inducing fewer swings and misses is worrisome, but we’ll see if there’s any hope for a rebound.
A week ago, I identified the starting pitchers whose xK% marks were most above their actual strikeout rates. The swinging strike component is the most significant in my xK% equation, so let’s take a look at which pitchers have enjoyed the biggest surge in their S/Str (Baseball-Reference metric) this season. The 2015 numbers do not include yesterday’s starts.
There is no stronger or more unpleasant current in the emotional maelstrom that is Fantasy Baseball than Dumper’s Desolation. This is the feeling you get when you are forced to give up on a player and jettison him, whereupon one of your opponents rescues him, and he (the player, but also the opponent) prospers. As an example, if one is needed: It’s Memorial Day, 2014. You paid good money for Jedd Gyorko at the start of the season ($14, if you happened to buy him in the Tout Wars mixed auction). He’s rewarded you by hitting about .170, and it’s not a subtly-potent .170. You’ve long since banished him to your reserve roster, and replaced him with a second baseman who’s not hurting you as badly but also not helping you—Gordon Beckham, let’s say. As far as you know, there’s nothing wrong with Gyorko physically; he’s just performing poorly. Now someone else on your roster is coming off the DL, and you need to free a spot. So you bitterly but confidently mutter “good riddance” and put Gyorko on waivers. Someone else grabs him and stashes him. A week or two later, Gyorko goes on the DL. He’s got severe plantar fasciitis, which is news to you and everyone else besides Gyorko and his podiatrist. A month or so after that, Gyorko, much refreshed, returns, and you watch helplessly as he posts a .260/.347/.398 slash line for the last two months.
An experience to be shunned at all costs, right? Yet in most leagues, dumping is a way of life; in fact, the better and more sophisticated the owner, the likelier (s)he is to have a roster that forces a difficult choice. And in Fantasyland as in the rest of the universe, Newton’s Third Law applies, and you’re going to have to control the equal and opposite reaction when it’s time to upgrade. Read the rest of this entry »
As we near the end of the month, our samples remain far too small to perform any serious analysis on. But, that’s really only true when we’re using plate appearances or innings pitched as our denominator. My xK% equation is based on per-pitch metrics, which stabilize much more quickly than anything based on innings. No, I don’t know the actual stabilization point, but since a pitcher has thrown more pitches than he has innings pitched, that’s what’s going to happen.
So let’s take a look at those starting pitchers whose xK% marks are most above their actual strikeout rates. These are the guys with significant upside who should enjoy a strikeout rate surge in the near future.
