On Monday I looked at bust candidates according to the 2015 Steamer projections. I ran the projections through Zach Sanders’ Fantasy Value Above Replacement system and then compared the rankings at each position to early ADP data from NFBC drafts. Today we’ll look at a sleeper candidate from each position. Read the rest of this entry »
I traded for Allen Webster and I’m not sure you should too. You see, I traded for Webster in a 20-team 28-keeper league, and he’s immediately my last keeper. Even if he’s the best last keeper, he’s probably no better than 540th. On the other hand, I spent a real-life asset to get him, so, yeah, I’m going to own some Allen Webster shares.
You’ve heard me yammering about pitch-type peripherals for two years now, and we’ve made some advancements along the way. We established some good pitch-type peripheral benchmarks, and we took a first look at properly weighting each pitch. We’ve started to get a sense of how these things interact when it comes to the shape and speed of pitches. We’re making progress.
It’s worth stepping back and figuring out what the aim is at this point. Because we aren’t trying to rank the best starting pitchers overall, really. We’re trying to find undervalued pitchers before the market realizes that they’re good. So we have to move in the smallest possible samples. And we want to have a list of great pitchers that has some weird names on it as well. Those names, we hope, will soon start to make sense.
So, to that end, I’ve taken each pitch type and looked at only those pitchers that have thrown 100+ in each of those types. I’ve summed the ground-ball and swinging strike rates for each pitch, and then found the standard deviations. I’ve given each pitcher a z-score for his ground-ball rate and swinging strike rate on each pitch type. Then I’ve summed the z-scores for each pitch type, and then for each pitcher.
What we should be looking at is an Arsenal Score. With this way of looking at things, it’s possible to have one dominating pitch and still score well. Or a group of lesser pitches that are all positive.
What we haven’t done yet is nail down what the smallest sample for each pitch is. Or how to weight the pitches. Or how to weight the whiffs versus the grounders. So this may look different once we weight each pitch differently, and if we find a way to weight grounders and whiffs more correctly.
But at least we have a first attempt at it here.
So many things happened. Everyone was traded. Everyone was released. And everyone was signed. It’ll fuel RotoGraphs pieces for weeks to come. You’ll see more in-depth pieces on these guys. But, with the dust settled, it seems like a good time to run all through some of the players that changed addresses, and talk a little bit about how they may have changed their fantasy outlooks for the coming season.
Here is Travis d’Arnaud before he was sent down on June 8th: .180/.271/.273/.241 (BA/OBP/SLG/wOBA).
Here is TDA after his return: .269/.315/.481/.346. He also tore it up (again) at Triple-A.
Here is TDA from August fifth onward: .280/.335/.510/.369. Yan Gomes, FYI, went .278/.313/.472/.340 albeit for the season.
The Astros apparently listened to their analytics team when they acquired Collin McHugh. You see, McHugh’s spin rate on his curveball is exceptional. Maybe it’s not quite that cut and dry, we’ve talked about his move to the four-seamer over the two-seamer was great for him. But read this newest snippet on McHugh and you’ll see that spin rate was huge for his acquisition:
The Astros’ analysts noticed that McHugh had a world-class curveball. Most curves spin at about 1,500 times per minute; McHugh’s spins 2,000 times. The more spin, the more the ball moves during the pitch—and the more likely batters are to miss it. Houston snapped him up. “We identified him as someone whose surface statistics might not indicate his true value,” says David Stearns, the team’s 29-year-old assistant general manager.
I’ve stolen from the movie before. I’ll do so again…
Guarantee? If you want me to take a dump in a box and mark it guaranteed, I will. I got spare time. But for now, for your fantasy teams’ sake, for your daughter’s sake, ya might wanna think about listening to quality content from me.
If you don’t know where this reference is from, then well…just ring your call button, and Tommy will come back there and hit you over the head with a tack hammer.
I actually will play guarantee fairy here, specifically for deep leagues since there are no uber-exciting names that jump out in my below grid. So here goes…
So long as they pitch to a qualifying level of innings without getting hurt or losing velocity (not ballsy enough to leave out these contingencies), I GUARANTEE these starters won’t be any worse next year (although in the grid below I highlighted in different strengths of green/red both starters and relievers):
One thing we know about strikeout and contact rate is that the stats stabilize quickly. In other words, those stats tell us more about future work in those categories quicker than other stats. But players make changes all the time, and it’s not just the first month of the season that we care about.
So who’s changed the most in the last month? Let’s look at the biggest differences in contact rate over the last month, and it what might tell us about these relevant players.
In 1948, Preston Tucker introduced the Tucker Sedan, a revolutionary automobile that innovated several safety features still in use today. The Tucker 48 posed a threat to the major automobile manufacturers of the time, and the company fell into bankruptcy amidst smear campaigns and a lengthy SEC trial, in which Tucker was acquitted on all counts. His story inspired the 1988 Francis Ford Coppola film Tucker: The Man and His Dream, which is an okay movie. Jeff Bridges is really good as Tucker, but it’s a pretty cheesy flick.
In 2014, Preston Tucker (no relation, at least that I know of) is an outfielder in the Astros’ system. Largely overlooked, thanks to Houston’s crazy-deep farm system, Tucker didn’t make a whole lot of top prospects lists this year, but he still got enough attention to slide in at No. 21 on SB Nation’s organizational consensus top prospect list.
On Friday, Jeff Zimmerman published the calculated xBABIP marks using his new equation incorporating Inside Edge data. While we are well aware that we remain far away from a perfect xBABIP equation as some commenters pointed out what’s missing, it’s still a useful metric to analyze. So I have curated a list of hitters whose xBABIP marks are significantly higher than their actuals and could represent good acquisition targets for the final two months of the season.
