Uneven Trades and K/9 vs Swinging Strikes
Uneven Trades
Most owners this season have filled their DL slots and probably have an injured player or two on their bench. As these players return from the DL, it might be a good time to look to make a trade that is a little lopsided, but benefits both parties.
With a player coming off the DL, you will need to open a spot your roster by dropping a player. Hopefully, you have been searching the waiver wire and have accumulated some useful talent on your team. Your players are hopefully better than those available on the waiver wire. Why give away the player you have to drop for free when they could be used to help your team. Instead of dropping the player, look for teams that may need a player that is better than those available on the waiver wire (the team with four starters on the DL may be a nice place to start).
Next look for a small upgrade on your team like trading Mark Teixeira and Carlos Carrasco to get Joey Votto. It doesn’t have to be the owner’s top stars, just an improvement no matter how small. You had nothing to lose since one of your players was headed to the waiver wire any way.
A couple words of caution. A owner may start by asking around to see if anyone is interested in their players before they are available, but hold off on the trade until your players are satisfactory off the DL and ready to play. Some players may have their rehab extended or pitchers may return a shell of their former selves. Also, other players on your team may end up on the DL between the time of the trade and the player is officially off the DL.
These type of trades are really a no lose situation for both owners.
Swinging Strikes vs. K/9
Swinging strike percentage is a great indicator of a pitcher’s ability to strike out a batter. A quick rule of thumb is that K/9 = SwgStr% – 1.5 (if you run a regression, the linear equation of actual formula has an r = 0.81, the quick rule of thumb has an r = 0.80). Using SwgStr%, here are the starting pitchers that are under and over performing their K/9 rates so far for 2011:
| Under Performers | ||||
| Name | SwgStr% | Projected K/9 | K/9 | Difference |
| Rick Porcello | 9.8% | 8.3 | 5.1 | -3.2 |
| Jeremy Hellickson | 10.3% | 8.8 | 5.7 | -3.1 |
| Shaun Marcum | 12.0% | 10.5 | 8.2 | -2.3 |
| Michael Pineda | 12.4% | 10.9 | 8.8 | -2.1 |
| Hiroki Kuroda | 10.3% | 8.8 | 7.0 | -1.8 |
| Over Performers | ||||
| Name | SwgStr% | Projected K/9 | K/9 | Difference |
| Erik Bedard | 8.2% | 6.7 | 8.7 | 2.0 |
| James McDonald | 6.5% | 5.0 | 7.0 | 2.0 |
| Yovani Gallardo | 7.7% | 6.2 | 8.2 | 2.0 |
| C.J. Wilson | 6.5% | 5.0 | 7.9 | 2.9 |
| Bartolo Colon | 5.9% | 4.4 | 8.3 | 3.9 |
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I had a similar situation in a league where after bidding we can actually have more than max roster if you won multiple bids and then you can make your decisions from there as long as you’re at max by Monday’s first game. There was a team decimated by injuries so I essentially traded the depth for better track records:
Pineda-Lind-Delmon-Outman-CoJack-Duffy-Reimold
for
Lester-Choo-Cruz
I positively could not keep three of the players so I parlayed that depth into some star power. It’s a keeper league, but only 2 keepers so it’s always stars and thus trading Pineda doesn’t hurt me long-term.
Long story short, I totally agree with these deals. Things don’t need to be 100% even once we’re in-season. It’s all about manipulating the standings and best using your assets. Always try a trade before just outright cutting an asset.
On a hunch, I thought maybe these lists weren’t random, but rather a function of certain pitches being more conducive to a swing and miss (as opposed to weak contact). While hardly scientific, I just quickly eye-balled the guy’s 2nd most frequent offering, reporting more than one if his secondary pitch wasn’t clear.
Under Performers
Rick Porcello: Change/Slider (heavy fastball usage)
Jeremy Hellickson: Change
Shaun Marcum: Change
Michael Pineda: Slider
Hiroki Kuroda: Slider
Over Performers
Erik Bedard: Curve
James McDonald: Curve
Yovani Gallardo: Slider Curve
C.J. Wilson: Slider/Curve/Change
Bartolo Colon: Slider (very heavy fastball usage)
The very brief summary is that the under-performers tended towards sliders, while the over performers tended towards curve balls. I wonder if there’s a basic reality at play here. Pitches that move laterally (or simply don’t move much and rely instead on velocity change & deception) are less likely to miss the bat compared to pitches that move vertically, though more likely to induce weak contact. Perhaps worth a closer look.
Excellent point, thanks for that. After watching a lot of Bedard, Pineda, Marcum and Kuroda this season this passes the eye test for me as well. I can’t speak to the other pitchers, I don’t think I’ve caught more than one start for any of them.
Great idea. I may have to be an off season project
Agree 100%. This is the way I do “business” when trying to make deals.
It should not be a question of “value”. The question you need to ask yourself is “Is your team better?”
Its a good point and I am sure you do what I am about to say but just to comment on it, you have to make sure you are going to get the best value that you can out of it too. By either checking in with other GMs or doing some counter offers on the current deal.
I think there may be more to be said regarding a direct link between k/9 and swinging strike % since pitchers don’t necessarily throw the same percentages of each pitch type on every count. It would be interesting to see (1) if certain pitchers always strike out more than their swinging strike % would suggest they should (showing an ability to throw their best pitches at the right time), and (2) how well pitchers can induce foul balls early on in a count, therefore allowing them to get strikes without using their better swinging strike pitches.
I haven’t seen anything on either of these subjects (nothing on a quick google search) but if anyone has I’d appreciate passing it along.
I just ran the career numbers for Bedard and Gallardo. Bedard’s k/9 is 1.06 higher than the expected for his career, Gallardo’s k/9 is 2.07 higher than the expected for his career.
So it seems from my brief look into this that over/under performing this regression is sustainable over a season/career.
Nice work. It may be nice to see when the swing and miss pitches are used.
This sort of thinking netted me James Shields for Paul Maholm and Daric Barton about 6 weeks back in my 20-team lwts points league. Wow that guy was desperate for 1B help.
Can someone please tell me why FanGraphs contributors are so obsessed w/ SwSrt%? I also love the plate discipline stats, but why so much focus on SwStr% How many more times do I have to see Kuroda on a list like this? News flash, he’s not gonna achieve 8 K/9, never has probably never will.
Why do you all insist on ignoring the other plate discipline metrics? I’m not going to take the time to check right now but willing to bet that all of the guys listed as “under performers” have a higher Z-Swing% than the so-called “over performers.” Have you watched Colon pitch this year, he gets backward Ks v. lefties using his 2-seamer that starts at the hitter and grabs the inside corner.
What does FanGraphs have against backward Ks? Maybe I should take a sick day soon, run some regressions and then post something formal on this topic? Or better yet, one of you could do it!
Because the r^2 of SwStr% vs K% is exceptionally high (80+), while the CallStr% vs K% is essentially 0. There is no correlation over the population that called strikes effect strike-rate. Doing my own regressions a few weeks back, I actually found that Contact% correlated even stronger to K% than SwStr%, even though Contact% and SwStr% correlated to an almost .95 r^2.
The other factor would be Foul%. I’ve seen regressions where Foul% has an r^2 in the low double-digits with K%, so that’s another avenue.
What I’d really be interested in is looking at K/9 vs K% (K/PA) within buckets, to try and see the biggest single factor for the difference in the two. Obviously, the more batters you face per-Inning, the more chances you have to strike somebody out, so K/9 can be very misleading.
“Obviously, the more batters you face per-Inning, the more chances you have to strike somebody out, so K/9 can be very misleading.”
This statement is the most recent line of bull I’ve heard, and I’ve been hearing a lot lately.
There are only 3 outs per inning, so no matter how many batters a pitchers faces in an inning, he only has the potential to strike 3 guys out, without passed balls/wild pitch 3rd strikes.
If a pitcher’s stamina were infinite and there were no bull pens, this may be an accurate statement, but in reality, the more batters you face the, shorter the pitchers outing will be, which will pull the K/9 back in line.
Cool, I just got Marcum (and Brett Anderson) for Gallardo (and Justin Smoak).
You just traded Gallardo and Smoak for Marcum and Anderson? Just curious as to why?
bartolo gets the called third strike a lot. according to all the statistics, it is supposed to be unsustainable. But, watching all his starts, he seems to be able to repeat it with that 2-seamer that moves a ton from inside to nabbing the inside corner on lefties. he is able to do it every single start and multiple times per start, so it seems like a skill rather than getting lucky with the ump’s K-zone or the hitter being too patient. perhaps in his special case, because of the movement on the pitch and the fact that he pitches for the called strike instead of the swinging strike, he can sustain the K rate.
You have to get to a 2 strike count in order to get a player to strike out. The list of under-performers are pitch-to-contact type pitchers, which means they get to fewer 2 strike counts, which is why their SwStr% does not correlate into as many K’s.
If a pitcher gets a swinging strike on strike 1, and gets a grounder to 2B on strike 2, his SwStr% for that batter is 50%.
If a pitcher gets a batter to hit 2 fouls balls, and gets a swinging strike on strike 3, his SwStr% is 33%.
So the SwStr% stat can be misleading within itself. If you want to know who is under/over performing their K/9, run a comparison of 2 strike counts to total K’s, then compare that to SwStr%.
yes. keep it coming