Tagged: David Aardsma

Weekend Matters, June 21

I don’t know about you, but I hate getting hurt. I mean, I really hate it when I get hurt. The occurence is far from rare in my life; I’ve had surgery on my left elbow (bone spur), left knee (torn meniscus) and right shoulder (torn labrum) twice. Hell, I even have a metal anchor keeping the loose flap of cartilage down in my shoulder. It wasn’t until recently that learned how to keep a lot of recurring pain away.

With fantasy baseball, though, you start hating it when other people get hurt, namely the big studs on your roster who you drafted early. Last year, I took Jose Reyes as my first-round pick (fourth overall) and not only did Reyes go down for the rest of the year in early May, the rest of my team resembled the Mets M*A*S*H-unit: Ian Kinsler, Nate McLouth, Roy Oswalt, Aramis Ramirez, Joey Votto and so-on and so-on. Now this year, I already had seen Brett Anderson and Miguel Montero miss most of the year when the worst news hit on Thursday:

Colorado SS Troy Tulowitzki was placed on the DL with a fractured left wrist. Expected to miss six-to-eight weeks. My reaction?

Do Not Like.jpgYeah, I guess you could say I was not terribly pleased with that news. But this is a point in the season that can determine whether or not you are, in fact, a good fantasy owner: a top player on your team in a shallow position goes down with a long-term injury. What do you do? How will you recover…if at all?

First things are first: who got hurt? If the answer is a first baseman or outfielder, you’ve got plenty of secondary options available. But, if the player in question is a shortstop or catcher, things get very difficult. Not only are there very few reliable options available in the free agent pool, but if you were to explore a trade for a replacement, the person you’d try to bargain with can command a higher-than-normal asking price. Simple supply-demand logic.

Troy Tulowitzki 1.jpgTo combat this problem, you have to anticipate the catastrophic injury before it happens–and with injury-prone positions like catcher and shortstop, it would behoove one to do so. Every week or so, look through the free agent pool at the positions in question and add whichever players look interesting to your scout team or watch list. As the season goes on, if some players’ production tails off, don’t think you’re obligated to keep them on your watch list. I find that if you remove the failing players and keep the ones who are succeeding, you eliminate unnecessary options that could make you over-think your decision, make you hesitate and eventually lead you into selecting a regrettable choice.

Another thing to keep in mind is to lower your expectations for your replacement player, whether you’re activating him from the bench or picking him up out of the free agent pool. As in the case of Tulowitzki, you almost will certainly not find another shortstop capable of posting an OPS north of .850. The focus should be on finding a player who has shown a history of–and gives you a legitimate reason to believe–putting up above-average numbers. Then there are the usual splits you hopefully already consider when making any personnel move: how Player X does in such-and-such a month? Is Player X a first-half or second-half player? Who is batting ahead and/or behind Player X in the order? You get the picture.

Lastly, consider how much time your injured stud will miss. If he’s on the 15-day DL and not expected to stay beyond that amount of time, picking up a flavor-of-the-week won’t hurt you. But if the fallen soldier in question is set to miss a month or two of action, target more players who have a history of sustained production.

Will there be some compromise involved? Of course. Are you sorely lacking in one category or another? That would certainly come into play during the decision-making process. Also, consider where you are in the standings. If you’re far ahead or way behind, your decision probably won’t carry as much weight as it would if you were a game out of the playoffs or were holding on to first place by a thread.

Personnel Matters

Lineup card 2.jpg

 

Injury Matters

Injuries.jpg

  • OF Nelson Cruz (15-day DL, torn left hamstring; continuing rehab program … may return for June 22-24 series vs. Pirates)
  • 3B Aramis Ramirez (15-day DL, left thumb contusion; began rehab on June 19 with Class A Peoria … eligible to return Wednesday, June 23)
  • SS Troy Tulowitzki (15-day DL, broken left wrist; out until late July-early August)
  • SS Erick Aybar (day-to-day, torn right meniscus; doubtful for June 22-24 series vs. Dodgers … plan of action unknown)
  • OF J.D. Drew (day-to-day, strained right hamstring)
  • OF Carlos Gonzalez (day-to-day, jammed left knee; missed games on June 18-20)
  • OF Austin Jackson (day-to-day, back spasms; missed games on June 18-20)
  • RP Bobby Jenks (day-to-day, soreness; held of out Sunday’s game … White Sox would not disclose further information about Jenks’ condition)
  • SS Derek Jeter (day-to-day, bruised heel; held out of Saturday’s game, but returned on Sunday)
  • C Jorge Posada (day-to-day, hairline fracture in right foot; questionable for June 22-24 series vs. Diamondbacks and possibly beyond)
  • SS Hanley Ramirez (day-to-day, tight right hamstring; left Saturday’s game and sat on Sunday … status for June 22-24 series vs. Baltimore unknown)
  • 3B Alex Rodriguez (day-to-day, hip; manager Joe Girardi said team will be cautious with Rodriguez during June 22-24 series vs. Diamondbacks)
  • RP David Aardsma is NOT hurt or anything, but his wife is expecting their first child any day now (congrats, D & A!), so he may abruptly leave during the middle of game. Also, if his performance isn’t what it usually is, realize that there may be more pressing matters on his mind, so don’t go cutting him on a whim.

Laughing Matters

JOKE.jpgI’m starting to believe that the Blue Jays have it in for The Men Who Laugh, I really do. I draft Adam Lind in the third round and he performs like someone taken in the 30th round. I pick up Jose Bautista as he’s starting to get hot, cut him when he cools down a little, then he goes bonkers the very day I release him for Travis Snider (who gets DL’d the very next day). I hesitate for a day on Ricky Romero and miss out on one of the best pitchers in the AL. Now, I cut a then-slumping John Buck on Monday morning and he proceeds to score 27 points this week while I start Ivan Rodriguez (2 pts) and sit Miguel Montero (24.5 pts).

Still, I managed to pull out a come-from-behind victory, thanks in part to McCutchen and Neftali Feliz, who as a closer, outscored all but one of my opponents players. You know what they say: sometimes it’s better to be lucky than good.

 

– ME

Alphabet Soup in a Numbers Game

Hey everyone, I apologize for the gap in entries. As everyone knows, sometimes life can get in the way every once in a while. That and the martial art of hapkido has some rather painful techniques for the fingers & thumbs. Ouch. I think the length and depth of this entry should make up for it, though.

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For about 99 percent of fantasy leagues, there are only a few set of statistics that we care about. For hitting, it’s usually batting average (BA), home runs (HR), runs batted in (RBI), runs (R) and stolen bases (SB). On the mound, it’s the earned run average (ERA), walks-plus-hits by innings pitched (WHIP), strikeouts (K), wins (W) and saves (SV). Nothing earth-shattering with that. The problem we all encounter is trying to find and acquire those players who can give us the best overall production before anyone else can find them.

This is partially the reason why we have sabermetrics: to accurately gauge a player’s true value and estimate his most likely levels of production in the future.

Albert Einstein.jpgThe problem with all these new-fangled stats and metrics is two-fold: one, there’s about a million different statistics to choose from and two, many of them employ formulas that would give Albert Einstein–let alone Albert Pujols–a strong migraine. After playing fantasy baseball for almost 10 years now, even I still have trouble trying to figure out how to come up with a player’s VORP (Value Over Replacement Player) and what constitutes a good figure.

And that’s the biggest dilemma for most fantasy players: they are too intimidated by the complexity of these metrics to understand them, give up and hope they make the right personnel decisions. When it comes to making my roster choices, I’ve narrowed it down to six categories I feel most comfortable with. Now, these aren’t necessarily the absolute best categories to use, but it all comes down to what an individual feels most comfortable with. So here we go…

(All stats accurate on morning of June 2. Also of note: some of the leaders of these stats are not very surprising. The goal of giving you this information is to help you in waiver-wire decisions or in judging whether a trade is in your favor or not. You should know by now that some times the smallest, seemingly most insignificant transactions hold major implications for the rest of the season…and your team’s chances of making the playoffs.)

Strikeout rate (K%) & walk rate (BB%)

This will probably be the easiest out of all of the statistics I will show you. Quite simply, it measures how often a batter strikes out or walks based on his total plate appearances. While you can usually tell if a full-time player strikes out/walks a lot just by looking at his numbers, it’s more difficult to tell with batters who have far less playing time, or during the first part of the season, where everyone is trying to figure out what level everyone else is at.

For me, it allows me to figure out who is more prone to long slumps and who can still provide value, in terms of steals and runs, when they aren’t hitting well. 

Highest K% — BB%

  1. Mark Reynolds (39.6) — Chipper Jones (20.3)
  2. Colby Rasmus (36.4) — Kevin Youkilis (19.1)
  3. Will Venable (36.0) — Josh Willingham (18.8)

Lowest K% — BB%

  1. David Eckstein (2.7) — Adam Jones (2.3)
  2. Jeff Keppinger (5.2) — Aaron Rowand (3.0)
  3. A.J. Pierzynski (6.4) — Ryan Theriot (3.2)

 


Vladimir Guerrero 2.jpgO-Swing Pct. (O-Sw%) & Z-Swing Pct. (Z-Sw%)

These stats are fairly straight-forward, too–how often does a batter swing at a pitch outside or inside the strike zone–but it carries more weight than the previous metric. Batters who tend to have higher O-Sw percentages are the ones who expand their strike zone, therefore increasing the likelihood of putting themselves in pitcher-favorable counts, making poor contact and/or striking out. In short, this shows how well-disciplined a hitter is.

The caveat here is that not all pitches inside the strike zone are very hittable and not all pitches outside the strike zone are unhittable.

 

Highest O-Sw% — Z-Sw%

  1. Vladimir Guerrero (50.4) — Josh Hamilton (80.9)
  2. Pablo Sandoval (43.3) — Guerrero (80.8)
  3. Jeff Francoeur (43.2) — Francoeur (80.5)

Lowest O-Sw% — Z-Sw% 

  1. Daric Barton (15.1) — Brett Gardner (43.4)
  2. Bobby Abreu (15.3) — Abreu (48.9)
  3. Marco Scutaro (15.6) — Elvis Andrus (49.8)

Contact rate (Ct%)

Once again, here’s another verrrrrry easy stat to understand (noticing an underlying theme here?). But just for the point of stating the obvious, this stat measures how often a batter makes contact with the ball on every swing. Now that wasn’t too hard, was it? And the leaders…

Highest Ct% — Lowest Ct%

  1. Juan Pierre (96.3) — Reynolds (63.5)
  2. Luis Castillo (95.8) — Justin Upton (69.1)
  3. Scutaro (95.4) — Ryan Howard (69.7)

OK, Player X almost always makes contact on every swing while Player Z looks like he’s up at the plate with half a broomstick. So what? Well, Mr. You’re-So-Smart, if you notice the pattern of player types at each end of the list, you’ll notice that this significantly impacts two major fantasy categories: runs and RBIs.

First, you’ll see that the guys at the top of the Ct%-leaderboard are mostly table-setters: the guys whose incredible ability to put the bat on the ball is their primary reason for gainful employment. In most instances, the guys who make more contact stand a better chance to get on base, swipe a few bags (provided they have the speed and awareness necessary) and score runs! The players at the bottom of this barrel are, for the most part, the hard-hitting run-producers who sacrifice a controlled, accurate swing for a faster, more powerful and less-accurate hack in order to drive the ball.

If your team is greatly lacking in runs scored, start looking for any free agents who swing and miss less than 16 percent of the time (86 Ct%) and (don’t forget!) bat in front of players who can reliably drive them in. Should your team be deficient in RBIs, take the opposite approach. And should you find a player who combines both a high Ct% and a favorable figure of the next stat, well, you better not let him go…at least without getting someone at least just as good in return.

Isolated power (ISO)

Corey Hart 1.jpgMost of us know that a guy with high slugging percentage is the guy you want if you’re looking for home runs, RBIs and total bases. But SLG is flawed in two ways: one, guys with high batting averages pumped up by lots of singles (see Suzuki, Ichiro) can sometimes appear to be semi-sluggers, or players mired in slumps will overly defleat their SLG. Secondly, SLG treats a triple the same way as a double or a home run when, in fact, a triple is more the result of a player’s speed rather than power. ISO helps whittle away some of the mitigating factors that go into SLG.

Now here comes the hard part, the first formula of the entry. The simple version is taking the SLG and subtract the BA from it: ISO = SLG – BA (ex: .658 – .347 = .311, Miguel Cabrera). The more advanced formula goes a little like this (remember, do the work inside the parenthesis first: ISO = (2B + 3B + (HR*3)) / AB (ex: 10 + 3 + (10*3) = 43 / 164 = .262, Jason Heyward).

As far as gauging a an acceptable figure, a slightly above-average ISO falls somewhere between the .175-.200 mark while an average figure is around .150-.175 or so. The leaderboard I’m showing you is from FanGraphs.com, which uses the traditional formula.

Highest ISO — Lowest ISO

  1. Jose Bautista (.344) — Ryan Theriot (.029)
  2. Corey Hart (.331) — Pierre (.030)
  3. Justin Morneau (.313) — Castillo (.035)
  4. Miguel Cabrera (.311) — Andrus (.038)
  5. Scott Rolen (.302) — Gordon Beckham (.042)

Batting Average, Balls In Play (BABIP)

Ubaldo Jimenez 1.jpgWhen this stat was first introduced, most people assumed that this would be a great tool in assessing a hitter’s value. But when the stat was explored a little more closely, it was revealed that there are way too many variables involved with this stat to have any strong corelation to a hitter’s performance. Buuuuuuuuut, this new metric did have its usefulness with pitchers and team defense.

In its essence, BABIP demonstrates how effectively a defense can turn balls hit in the field of play into outs, and in a round-about way, how difficult it is for a batter to make solid contact against a pitcher. This cuts out obvious things such as home runs, strikeouts and walks. The way you get this figure is pretty similar to getting a batting average, only with a couple wrinkles: BABIP = (H – HR) / (AB – K – HR + SF) (ex: [45 – 1 =] 44 / [268 – 70 -1 +0 =] 197 = .223, Ubaldo Jimenez).

The lower the number, the better it is for the pitcher and the higher, the better for the batter, with a league average hovering around the .300 mark. Rule of thumb (tntried to find a cleaned-up Boondock Saints link for that term, but couldn’t get one!) is that if a pitcher’s BABIP is either extremely low or high, he’s gone through a fairly (un)lucky stretch and is due for a return to the mean later on that season or the next. And should you notice one of your pitchers sporting a really nice BABIP, but is walking more and striking out fewer batters than usual, much like the second-ranked starter on the following list, it may be time to see if there are any takers for this particular hurler.

Highest BABIP — Lowest BABIP, Starters

  1. Justin Masterson (.404) — Jimenez (.223)
  2. Brian Matusz (.359) — Tim Hudson (.225)
  3. Zach Duke (.359) — Livan Hernandez (.229)
  4. Gavin Floyd (.355) — Jason Vargas (.236)
  5. Wandy Rodriguez (.351) — Matt Cain (.237)

Highest BABIP — Lowest BABIP, Closers

  1. Chad Qualls (.476) — Jose Valverde (.159)
  2. Bobby Jenks (.450) — Mariano Rivera (.182)
  3. Brian Wilson (.424) — Manny Corpas (.186)
  4. Heath Bell (.387) — Jonathan Papelbon (.196)
  5. Matt Lindstrom (.372) — Rafael Soriano (.218)

Fielding Independent Pitching (FIP)

The last stat for the day is probably also one of the more telling when it comes to pitching. You know how you see a pitcher’s ERA and you absolutely know that he is much better/worse than what it says? Well, this nifty metric helps trim away the grizzle and fat. Basically, what this stat does is eliminate the things pitchers cannot control and zeros in on the things he does: strikeouts, walks, home runs and hit batters (similar to the “Three True Outcomes” for hitters). The formula, though, is a little difficult to digest, though: FIP = (13*HR + 3*(HBP + BB – IBB) – 2*K) / IP +3.10

I’ll give you a minute to process that jumble of letters, numbers and other doo-wackies.

Javier Vazquez 1.jpgOK, done yet? Good. Now I would absolutely love to tell you how the creator of this stat, “Tom Tango” (yes, that is an alias), but I just don’t think I have the requisite brain power to figure that out. The good thing about this stat is that it operates at the same scale as ERA; someone with a 3.00 ERA is really good, someone with a 4.25 is OK and someone with a FIP above 6.00 is probably Javy Vazquez as a Yankee.

Highest FIP — Lowest FIP, Starter

  1. David Huff (6.01) — Roy Halladay (2.39)
  2. David Bush (5.66) — Francisco Liriano (2.41)
  3. Rich Harden (5.56) — Jimenez (2.62)
  4. Wade Davis (5.49) — Josh Johnson (2.69)
  5. Freddy Garcia (5.41) — Adam Wainwright (2.73)

Highest FIP — Lowest FIP, Closer

  1. Trevor Hoffman (9.06) — Jonathan Broxton (0.63)
  2. Papelbon (4.98) — Matt Thornton (1.08)
  3. David Aardsma (4.40) — Wilson (1.43)
  4. Francisco Cordero (4.34) — Carlos Marmol (1.79)
  5. Qualls (4.19) — Bell (1.98)

Well, hopefully you were able to get some useful information out of this. Like I said before, there are a lot of other statistics out there, and some may be easier to understand for some more than others.

 

– ME