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.
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 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.
The 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%
- Mark Reynolds (39.6) — Chipper Jones (20.3)
- Colby Rasmus (36.4) — Kevin Youkilis (19.1)
- Will Venable (36.0) — Josh Willingham (18.8)
Lowest K% — BB%
- David Eckstein (2.7) — Adam Jones (2.3)
- Jeff Keppinger (5.2) — Aaron Rowand (3.0)
- A.J. Pierzynski (6.4) — Ryan Theriot (3.2)
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%
- Vladimir Guerrero (50.4) — Josh Hamilton (80.9)
- Pablo Sandoval (43.3) — Guerrero (80.8)
- Jeff Francoeur (43.2) — Francoeur (80.5)
Lowest O-Sw% — Z-Sw%
- Daric Barton (15.1) — Brett Gardner (43.4)
- Bobby Abreu (15.3) — Abreu (48.9)
- 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%
- Juan Pierre (96.3) — Reynolds (63.5)
- Luis Castillo (95.8) — Justin Upton (69.1)
- 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)
Most 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
- Jose Bautista (.344) — Ryan Theriot (.029)
- Corey Hart (.331) — Pierre (.030)
- Justin Morneau (.313) — Castillo (.035)
- Miguel Cabrera (.311) — Andrus (.038)
- Scott Rolen (.302) — Gordon Beckham (.042)
Batting Average, Balls In Play (BABIP)
When 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
- Justin Masterson (.404) — Jimenez (.223)
- Brian Matusz (.359) — Tim Hudson (.225)
- Zach Duke (.359) — Livan Hernandez (.229)
- Gavin Floyd (.355) — Jason Vargas (.236)
- Wandy Rodriguez (.351) — Matt Cain (.237)
Highest BABIP — Lowest BABIP, Closers
- Chad Qualls (.476) — Jose Valverde (.159)
- Bobby Jenks (.450) — Mariano Rivera (.182)
- Brian Wilson (.424) — Manny Corpas (.186)
- Heath Bell (.387) — Jonathan Papelbon (.196)
- 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.
OK, 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
- David Huff (6.01) — Roy Halladay (2.39)
- David Bush (5.66) — Francisco Liriano (2.41)
- Rich Harden (5.56) — Jimenez (2.62)
- Wade Davis (5.49) — Josh Johnson (2.69)
- Freddy Garcia (5.41) — Adam Wainwright (2.73)
Highest FIP — Lowest FIP, Closer
- Trevor Hoffman (9.06) — Jonathan Broxton (0.63)
- Papelbon (4.98) — Matt Thornton (1.08)
- David Aardsma (4.40) — Wilson (1.43)
- Francisco Cordero (4.34) — Carlos Marmol (1.79)
- 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.