ASK ROTOMAN: My League Is Using New Categories. Help!

Dear Rotoman,

My 5×5 Rotisserrie – 10 team NL-Only Yahoo League is switching categories this year:  New Categories are XBH, OBP and E, replacing HR, BA & SB to go with RBI and R for five categories.  In pitching we are keeping W, Sv, ERA & Whip and replacing K with K/BB.  How do I project what I will need in categories without a previous history of scoring?

“Categorically Insane”

Dear CI:

Wow. I’m a big fan of experimentation and innovation, and I love the fact that your league is jumping into it head first, but I’m sorry to inform you that you are uncorking an Albert Belle bat’s worth of complication with your changes (the least of which is projecting how much of any category you’re going to need to win). Here’s why:

Valuing stats is easy. Knowing how many you’ll need to win isn’t, but isn’t necessary unless your league doesn’t allow you to trade. And even then you’ll be better off knowing how much each player is worth than targeting category totals.

Your goal is to amass value, which means buying stats that others are undervaluing. Targeting category totals too often leads to teams overbidding to reach their goals.

Obviously, there is a point when too much is too much, when you have way more steals or saves than you can gain points for in roto scoring, but common sense should be enough to guide you there. In the meantime, collect value.

The problem for your league is that some of your changes are provocative and disrupt the way we usually play the game.

Not XBH, which is just like HR, only it rewards Doubles and Triples hitters. And not OBP, which is just like BA, but rewards guys who take a walk. But Errors? Hell yes.

Errors is a backward category. The lower the number, the better. The problem is that fielders make errors not only in proportion to how many they make, but by how much they play. The more they play, the more errors they make.

More playing time has long been a key strategy for 5×5 roto. You want to win the AB race, even though AB isn’t a category, because the more AB your team puts up the more Runs and RBI and HR it will accrue.

So, if we look at the top 15 NL shortstops last year in fewest Errors allowed (200 AB minimum), they averaged 621 innings played and 7 errors (84 innings per error), while the top 15 NL shortstop last year in Offensive contribution (not including steals, which you’re replacing), averaged 990 inning played and 12 errors (83 innings per error).

As you can see, there’s almost no difference in quality as a group, but the heavier offensive contributors play more and hurt more in your Error category.

While there are clear winners (Troy Tulowitzki, maybe Jose Iglesias) and losers (Jonathan Villar! Dee Gordon!), it isn’t clear to me how you go about choosing whether to roster Brandon Crawford, good defender but makes errors because he plays a lot and is a marginal offensive talent, or Daniel Descalso, who played much less, contributed less offensively, but hardly made half as many errors.

And since the player pool determines the value of players, every change to the pool has the potential to shift all the prices. Fascinating stuff. And good luck with it.

(SIDEBAR: To value the reverse category you would credit each player with Each Error He Didn’t Make. So, Starlin Castro made the most errors as a SS in the NL last year, with 22. Every other player Didn’t Make 22-the number of errors he did make.)

Converting from Strikeouts to Strikeouts Divided By Bases on Balls is a whole ‘nother matter. Here you’re switching from a quantitative stat that measures playing time almost as much as quality, there are many leagues that play with IP as their fifth 5×5 category rather than strikeouts. Put this together with ERA and WHIP, also qualitative stats, and you’re almost begging for teams to try pare their innings pitched to a minimum.

Remember that no starters earn Saves, and few closers rank highly in Wins, so you’re basically measuring pitchers on their quality innings. I’m a bit skeptical about this innovation being a good idea, but if you have a stringent minimum IP limit it might work.

Still, if you’re playing with real Yahoo rosters, guys who qualify as SP but work in relief are going to be gold.

To get back to your question. In standard roto leagues, a good benchmark for last place in the qualitative categories is the major league average. Players who do better than that are some roto help. In your somewhat smaller league the right number is going to be better. To figure out K/BB I recommend sorting last year’s stats based on different IP threshholds.

With a minimum IP of 40 last year, 22 of the Top 30 pitchers in K/BB were relievers.

 

2 thoughts on “ASK ROTOMAN: My League Is Using New Categories. Help!

  1. So part two of the question:

    I just finished reading Larry Schector’s book and my original question stems from the subject regarding Standings Gain Point v Percentage Gain Value. (I am sure you see where the question is going.) Valuing players is in part based on historical standings. I am going to do my projections and values based on the historical data from the previous seasons for known categories with some type of pro-rated value applied to the new categories. Which valuing method would work better since I am ‘winging it’ in four of 10 categories?

    Regards,
    Categorically Insane

    • I’m on record going back a really long time saying that Standings Gain Points are not invalid, but rather a lot of work for a degree of improvement that comes into play only if you make a really big miscalculation on draft day. That is, fine tuning your values for SGPs is a very blunt instrument when you have really good league-historical standings. In part because league dynamics are constantly changing, and even if you’re in a league with a 30 year history, much more has changed from year to year than the standard variance that would affect SGPs.

      Since you have historical data for each player in your pool, you will learn far more simply to compare his production in each category (including the new ones) to the total in each category and create rankings for the individual cats and for the combined total they offer. This way you won’t be winging it at all, and you won’t be wasting time trying to attain a mathematical precision that isn’t likely to make any difference for your team.

      I have to admit, I haven’t read Schechter yet on SGPs. If he opens my eyes to something I’m not seeing I’ll bring it up here.

      Cheers,
      Peter

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