New Podcast: S3E2: Tanner Roark: MacGyver of Pitching

Frank is joined by Stuart Wallace (@TClippardsSpecs) of District Sports Page to discuss the current state of the Nationals, including Denard Span’s resurgance, Doug Fister’s dominance, the hills of Las Vegas, Why injuries happen to pitchers, Belgian vs. IPA beers and just how the hell does Tanner Roark do what he does!?!

Get To Know a Stat: wOBA and wRC (aka Lombo vs. Danny Part 567)

UPDATE:  This quote:

 

Welcome to Get to Know a Stat!  Once a Week (or so) I intend to take an advanced baseball statistic and present it to you in a way that’s understandable.

This week, I want to look at a stat called Weighted On Base Average, something mentioned by Court a few weeks ago in his Holding Court column.  A lot of folks hold on to batting average as the end all/be all of comparing batters despite the fact that there are many other metrics to look at-often giving a more complete picture of what is going on.

To give you a basis for what I’m talking about, allow me to parrot some of the more insane arguments I’ve heard/read to start Steve Lombardozzi over Danny Espionsa at second base.  Note:  [Insert my usual disclaimer of love for Lombo as a utility player even if I disagree with the position he should be a starter].  The case for Steve Lombardozzi goes something like this:

  • He’s just as good a fielder as Danny Espinosa [Not at all true, but we’ll deal with that another day]
  • Danny Espinosa strikes out too much, and Steve Lombardozzi doesn’t strike out nearly as much
  • About a week ago Danny Espinosa was only batting .155 (currently .185) and Lombardozzi was batting .365 (now .235)
  • Danny Espinosa can’t hit with runners in scoring position, and gets no RBIs.  Lombo is “scrappy” and “clutch.”

Now usually, I can go through the whole litany of reasons that is insane.

  • You can start with the fact that Lombo hasn’t nearly had the plate appearances Espinosa has had (so it’s likely that his average will drop-which it did recently).
  • You can point out that while Lombo doesn’t strikeout as much as Espinosa, he doesn’t draw walks (he has only one) which indicates maybe he doesn’t have a great eye, but just makes contact outside the zone (bad contact that leads to ground outs).
  • You can also take a look at Total bases which is the total number of bases a player gets per hit (a HR is 4, Triple 3, Doubles 2, Singles 1).  Espinosa’s total bases double that of Lombardozzi’s- meaning he’s getting much bigger hits than Lombo, who hits a lot of dribbler singles that squeak through.

You can do all that, and I can do all that, but it might be better to look at something come up with by Tom Tango called Weighted On Base Average (wOBA).

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Holding Court: An Ode To Sabermetrics

Welcome to “Holding Court.”  Written by Court Swift (@RCourtSwift) one of the most knowledgeable Nationals (and everything) fans I know.  He’ll be writing a columns for us that not only get you up to speed on some baseball things, but also offering his sage like opinion on those same topics.
 
THE JOURNEY FROM IGNORANCE TO BLISS (An Ode To Sabermetrics)

“…There’s no singular way to watch and analyze baseball, and far too many people want to treat the sport like one giant math equation.” – Michael Hurley, CBS Boston

“Fight it if you like, but baseball has become too complicated to solve without science. … WAR represents a chance to respond to the complexity of baseball with something more than ideology or despair.” – Sam Miller, ESPN

Back in February, These two competing articles got Court thinking about his baseball fandom and how he went from an Old School to a Newer School baseball fan. 

When the Montreal Expos relocated to the District, I was single, living in Cleveland Park, and a Reds fan. I was a baseball purist who hated the DH, loved stolen bases and loathed strikeouts. The Moneyball phenomenon, to me, glorified the American League game – the Earl Weaver game of waiting for a big homer while emphasizing walks and ignoring strikeouts.

By the time Ryan Zimmerman’s walk-off home run won the first Opening Night in Nats Park in 2008, I had left the Reds behind, moved to Columbia Heights, and engaged to the love of my life (who I met at a Nats game). I was also a convert to the world of sabermetrics.

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Pythag Follow Up: What About Last Year?

On Monday’s podcast, Ouij came on the show to talk about his model and method for predictive analysis.  Specifically, we discussed the Pythagorean Win/Loss Expectation formula.  After it was over, I wanted to know: Just how does it work, and more importantly… how well does it work?

It’s not much, but I decided to run the 2012 standings through the P-formula to see how well the expectations matched up to what happened.  Some very surprising (and not surprising at all) results followed.

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Get To Know A Stat: Two Simple Bat Stats

Welcome to Get to Know a Stat!  Once a Week (or so) I intend to take an advanced baseball statistic and present it to you in a way that’s understandable.

We’ve spent the last few Get To Know A Stat posts discussing pitching stats.  This week we’re going to look at a few batting stats, some of which will make appearances later this week.  Nothing too complicated, but hopefully they can help us answer some questions we might have about batters.  Let’s start with power.

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Get to Know a Stat: Some Pitching Averages Worth Knowing

Welcome to Get to Know a Stat!  Once a Week (or so) I intend to take an advanced baseball statistic and present it to you in a way that’s understandable.

Real life gets in the way this week, so rather than delve into an advanced stat like last week, I thought I would run through a few of the fairly basic ones that can still prove very useful in evaluating a pitchers.

The way I’ve come to view stats is not as some all-encompassing answer about who is the best player, but a variety of tools that let you dig into the truth of situations a little better.  ERA might be the sledgehammer that can break things open, but the more finely crafted FIP lets you dig in a little more carefully without breaking the whole thing open. As such, I’m just going to highlight a few of the other stats that can help you get a more complete picture of what a pitcher is doing. 

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Get To Know a STAT! FIP explained

Welcome to Get to Know a Stat!  Once a Week (or so) I intend to take an advanced baseball statistic and present it to you in a way that’s understandable.

To get started with advanced stats, I want to introduce a fairly easy one to wrap your heads around- FIP, or Fielding Independent Pitching.  This is a pitching statistic and it’s supposed to answer what ought to be a very straight forward question:  How much of the pitcher’s success is actually due to the pitcher, and how much is due to the pitcher getting help?

The basic stat that most people look at first with pitchers is ERA or Earned Run Average.  This is the number of runs a pitcher “earns” over a nine inning game.  (Note: not all runs are earned.  If a fielder makes an error that eventually leads to a base runner scoring that run doesn’t count against the pitcher.)

Here’s the formula:

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