Archive for Meta Analysis

In 1996, Larry Labadini spent $251 of his $260 auction budget on hitting in the early years of LABR. That meant that his pitching staff was completely filled by $1 hurlers. Labadini finished fourth that year, but his strategy has forever been known as the Labadini Plan. I have been dying to try this in an auction, but never actually went through with it. It’s obviously high risk and there are always too many pitchers I want to own that cost more than a buck. One day though, it must happen.

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On Thursday, I discussed the six hitters whose average fly ball and home run distance increased most from 2012. Today, I will look at the opposite side of the coin, the decliners. While the surgers held onto half of their gains the following year, the decliners held onto about 65% of their decline, as their distance rebounded by just 35% . In other words, a decline in batted ball distance is more real than a surge. Or once again, regression at work, as higher batted ball distances are simply more likely to drop.

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I stunk up the joint last year in AL LABR, one of the longest-going super leagues in the business. I combined terrible draft choices with terrible free agent auction decisions and even a bad trade in order to end up tenth of twelve. I showed poor restraint and discipline at the draft, I lacked the context necessary for making good decisions, and I panicked a bit late in the season. I’m not so proud of that season.

At least this year I feel a lot better about the draft. As Razzball’s Grey Albright said at the after-party, there’s a long way from feeling good to doing good in the final tally. Admitted. But since I actually didn’t even feel great about my draft results last year, I’ll take this as a positive sign.

Let’s take a look at the team and the better decision-making process that went into it.

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In the past, it used to be difficult to determine how legitimate a batter’s HR/FB rate spike was. We could consider factors like the hitter’s age, his career history including minor leagues and any changes in environment such as home park. Unfortunately, it felt more like a guessing game with the conclusion usually being that the hitter was going to regress the following year back to his career levels. But that doesn’t always happen of course and Jose Bautista wants to make sure you know this. Jeff Zimmerman’s average fly ball and home run distance leaderboard makes it easy for us to find exactly the stat we need when on the hunt to validate a power surge. Did the hitter’s distance rise in conjunction with his HR/FB rate like we would expect to see or not?

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As you are no doubt keenly aware, Eno has been furiously posting the RotoGraphs rankers’ position rankings over the past week. This is always an interesting and sometimes surprising exercise as I find players I didn’t think I liked ranked higher than the others, whereas guys I thoughts I liked are ranked lower. Trying to figure out why such was the case is the fun part. But rather than explain why each player I ranked differently was ranked at such spot (don’t worry, Pod’s Picks will soon be upon us!), I thought it would be helpful to explain the various general reasons a player is ranked dramatically differently than others rank that same player. Or, in simpler terms, what leads to a difference in ranking?

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I’m always talking about how good a pitch’s peripherals are, on our podcast, or on the radio, or on twitter, so I thought I’d help you get a sense of makes a good off-speed pitch, when it comes to whiff percentages. Since some of the listings in different places are whiff per swing (more here), and yet our site uses swinging strike rate (swSTR%), I thought I’d make the benchmarks in swinging strikes. You can see the whiff benchmarks in this oft-linked post here. You’ll also get ground-ball rates there, which is an important pitch peripheral that I won’t talk about much today.

So what makes a good pitch, swinging strike wise?
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I am going to finish my fantasy ranking series today (Part 1 and Part 2). Today, I am going to look at how I set up my draft ranking.  It is a little unique, but indispensable for finding draft day values, but I first need to clear up one issue from yesterday.

I am going to go over one problem people are noticing which is how high catchers are in my rankings and the lack of Robinson Cano. Well here are my projections for each. If you disagree, that is a different discussion, I am just looking at the difference in replacement level values:

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In my last article, I went over the initial work for valuing players in a basic fantasy league (12 5×5 teams with 23 roster positions).  Each league has its own unique rules so the procedure may need to be adjusted accordingly. Today, I am going to finish the positional rankings and begin to come up with an overall ranking.

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We are now heading into the third year of the LABR mixed league. In its inaugural 2012 season, I finished in the middle of the pack. Last year, I finished second. If you’re a fan of following trends, then clearly I’m going to finish somewhere in the negative mid-single digits this year. I think that’s good. On Tues night, I endured an exhausting nearly four hour online snake draft…in early February. Yup, LABR likes to draft early so the results could be published in the fantasy issue of USA Today Sports Weekly. Of course, that makes it extremely difficult to draft a real team with so many position battles yet to be fought.

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About four times a season, Eno unleashes the shocky monkeys and a few of us slow-footed writers are forced to enjoy ranking all the players. For the next few days, I am going to go over how I prepared my rankings.

Note: I am trying to keep the amount of math to a minimum. If somewhere you get lost in the procedure let me know and I can explain the procedure in more detail.

The first item to remember is all leagues are not even close to being the same. In my three keeper leagues, two are points based and the other is an AL only league with one pitcher category being Wins+Saves+Holds. Additionally, some leagues have keepers. How the keeper’s “salary” is set determines a their value. Other league options have innings pitched limits (good rates stats needed) or as in the case of my league with W+S+H, an IP minimum is set to keep owners from only using relief pitchers. Catcher rankings can vary quite a bit from a one catcher to  two catcher leagues or even two catcher slots with a 162 game limit as in Ottoneu. For my rankings, I did them off a basic 5×5 12-team league with 23 positions (14 position players, 9 pitchers).

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