In the wake up the Troy Tulowitzki trade, it’s an all-Rockies waiver wire edition this week. That’s because the speculation is that the newly acquired Jose Reyes won’t be spending very much time in Colorado and may very well be shipped right back out. The two names highlighted here could be manning shortstop the rest of the way. Or not, and remain worthless, if Reyes stays in Colorado.
As we head into the trade deadline, this time of year marks the unofficial fantasy league deadline for when trades might still have some effect on the standings. So let’s use the batted ball distance leaderboard to identify those batters with strong distance marks, but HR/FB rates that don’t quite measure up. These seven hitters all seemingly have some upside over the final two months, assuming they sustain their distances.
If you didn’t consider either the Aramis Ramirez or Scott Kazmir trades last week the first blockbuster of this year’s deadline deals, then you can certainly cross that event off your list now. Johnny Cueto leaves the only team he has played for and travels West to the darling Kansas City Royals. Does Cueto’s value “receive a big boost”, as one popular fantasy news site suggests?
Let’s take a gander at the American League starting pitcher leaderboard for SwStk% over the last 30 days. Since that time span typically comprises just four to five starts, such dominance could get lost in the full season numbers. So I’ll highlight some of the more interesting names among the leaders.
I feel like I’m becoming a broken record when I say that once again, injuries have opened up opportunities and as usual, play a role in this week’s dive into the free agent pool.
In 2013, I began polling you readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the underperformers versus the overperformers during the pre-all-star break period. You can view the results of the first experiment here. For some strange reason, it appears I never actually calculated the results of last year’s experiment, but here was the original poll.
I came up with this idea given my faith in using SIERA over smaller samples, rather than ERA. I generally ignore ERA completely during the middle of the season and it’s interesting to see how everyone else thinks. Will the SIERA outperformers continue to outperform, perhaps due to continued strong defensive support or will the magic vanish? And is it just bad luck that is due to reverse course for the SIERA underperformers or are they being hurt by something that should continue to play a role the rest of the way?
My population group consisted of 97 qualified starters, which included some that are no longer in a rotation. I have decided to only include those who will remain a starter. Group A is composed of the 10 largest SIERA outperformers, while Group B is composed of the 10 largest SIERA underperformers.
Group A – The SIERA Outperformers
| Name | IP | ER | K% | BB% | BABIP | LOB% | HR/FB | ERA | SIERA | ERA-SIERA |
|---|---|---|---|---|---|---|---|---|---|---|
| Chris Young | 87 | 29 | 16.0% | 6.9% | 0.212 | 77.3% | 6.9% | 3.00 | 4.80 | -1.80 |
| Zack Greinke | 123.1 | 19 | 22.8% | 4.3% | 0.233 | 89.5% | 6.3% | 1.39 | 3.17 | -1.78 |
| Hector Santiago | 108.1 | 28 | 22.0% | 7.6% | 0.244 | 88.9% | 8.4% | 2.33 | 3.95 | -1.62 |
| Yovani Gallardo | 113.1 | 33 | 16.8% | 8.5% | 0.267 | 77.4% | 6.6% | 2.62 | 4.22 | -1.60 |
| Shelby Miller | 113.2 | 30 | 20.4% | 7.5% | 0.276 | 78.8% | 5.9% | 2.38 | 3.73 | -1.35 |
| Nick Martinez | 97 | 37 | 13.4% | 8.0% | 0.286 | 76.3% | 9.3% | 3.43 | 4.76 | -1.33 |
| A.J. Burnett | 119.1 | 28 | 20.5% | 6.8% | 0.314 | 81.7% | 4.7% | 2.11 | 3.43 | -1.32 |
| Sonny Gray | 123.2 | 28 | 22.4% | 6.2% | 0.249 | 79.0% | 4.8% | 2.04 | 3.25 | -1.21 |
| Kyle Gibson | 113.2 | 36 | 16.7% | 7.7% | 0.268 | 80.6% | 13.4% | 2.85 | 3.95 | -1.10 |
| Scott Kazmir | 101.1 | 28 | 23.3% | 8.3% | 0.265 | 77.2% | 7.9% | 2.49 | 3.59 | -1.10 |
| Total | 1100.2 | 296 | 19.6% | 7.1% | 0.262 | 80.9% | 7.3% | 2.42 | 3.84 | -1.42 |
Group B – The SIERA Underperformers
| Name | IP | ER | K% | BB% | BABIP | LOB% | HR/FB | ERA | SIERA | ERA-SIERA |
|---|---|---|---|---|---|---|---|---|---|---|
| Kyle Lohse | 108 | 74 | 17.0% | 4.9% | 0.312 | 64.2% | 15.4% | 6.17 | 4.07 | 2.10 |
| Rick Porcello | 100.2 | 66 | 18.1% | 5.3% | 0.331 | 64.8% | 14.5% | 5.90 | 3.86 | 2.04 |
| CC Sabathia | 100.1 | 61 | 19.3% | 4.6% | 0.332 | 69.0% | 17.1% | 5.47 | 3.65 | 1.82 |
| Drew Hutchison | 98 | 58 | 21.0% | 6.9% | 0.358 | 65.2% | 9.7% | 5.33 | 3.75 | 1.58 |
| Rubby de la Rosa | 110.1 | 62 | 21.1% | 6.2% | 0.299 | 70.6% | 20.2% | 5.06 | 3.51 | 1.55 |
| Carlos Carrasco | 108.1 | 49 | 27.7% | 5.2% | 0.339 | 70.3% | 11.0% | 4.07 | 2.73 | 1.34 |
| Taijuan Walker | 102.1 | 55 | 22.9% | 6.2% | 0.307 | 68.9% | 13.1% | 4.84 | 3.50 | 1.34 |
| Matt Garza | 99 | 61 | 15.3% | 7.4% | 0.317 | 65.3% | 16.0% | 5.55 | 4.38 | 1.17 |
| Jeremy Hellickson | 94.2 | 53 | 19.4% | 6.6% | 0.309 | 69.2% | 11.6% | 5.04 | 3.91 | 1.13 |
| Kyle Kendrick | 103 | 68 | 12.0% | 6.4% | 0.287 | 70.3% | 16.8% | 5.94 | 4.89 | 1.05 |
| Total | 1024.2 | 607 | 19.4% | 6.0% | 0.319 | 67.8% | 14.6% | 5.33 | 3.82 | 1.52 |
Amazingly, the two groups have nearly identical SIERA marks, but a nearly three run difference in ERA. The outperformers have exactly half the HR/FB rate as the underperformers, along with a lower BABIP and significantly higher LOB%.
It’s rankings update time! We’re coming up to the midpoint of the season, which means there are only a couple of months left of baseball to be played ::sad face::.
My usual caveat on how I rank pitchers follows:
It’s essential to remember that ERA is not a skill. It’s just a result. So I don’t really care what a pitcher’s ERA is at the moment. What I’m really interested in is their peripherals and any changes in pitch mix and/or velocity. And even if their peripherals have changed, you then have to ask yourself if its sustainable. Again, more likely is that what we initially forecasted is what is going to be posted the rest of the way, though obviously this is not always the case.
As a reminder, the tiers are named after the best characters from the FXX show, Man Seeking Woman.
Today, I’ll do something I will rarely do. That is, recommend not one, but two pitchers returning from Tommy John surgery. I typically ignore such returnees, as our own Jeff Zimmerman’s research confirms what we already knew anecdotally – that in their first year back, they suffer serious performance declines. But in a deep league where you’re scraping the barrel and choosing between a near guaranteed mid-4.00 ERA just for the potential for a couple of wins and some strikeouts, the possibility, even if small, of someone league average or better instead looks more intriguing.
As you are likely (hopefully) aware, I’m not a fan of small sample size analysis. In fact, it could be argued that I’m far too patient, requiring the sample size to be quite significant before I change my opinion/projection on a player. But a pitcher’s SwStk% is different. It’s a per-pitch metric, so it stabilizes rather quickly and conveys very useful information. So with that in mind, let’s browse through the SwStk% leaders over the last 30 days and look to uncover any surprise names.
For those of you who have read my many snake draft and auction recaps, you may recall that I rely on the same strategy season after season. No matter the format, I load up on hitting and pay significantly less for pitching than my competition. There’s a reason I continue to follow this strategy — it works. For me, at least. I have been quite successful following the plan, and when I don’t finish in the money, it’s typically because my offense was filled with busts, rather than my pitching performing like the weak staff I presumably paid for. Unfortunately, the strategy may finally have to be tweaked, as our competitors have official smartened up.
