Archive for the ‘Roto Relevant Research’ Category

Everyone’s got a list. Something something about buttholes and opinions, I know.

But when Chris St. John makes his prospect list, it’s a little different. He brings all of the prominent prospect lists together to make one list to rule them all. It’s a great way to do things if you want to know what the industry consensus is on a guy, and it’s the way a statistician would make a list.

Hidden with the folds of this list are some very interesting players, too. The players that defy consensus. The volatile prospects. And those players, well, there’s where our ‘research’ comes in this week.

St. John already wrote up a nice article about which players were notable in each list for their inclusion, exclusion, and ranking. Articles are nice and all, but tables and lists are better, right? So I took his article and made a pivot report of the article. That’s how much of a nerd I can be. What follows is a list of the players that were mentioned the most — positive, negative, whatever — and could therefore be thought of as the ‘non-consensus’ prospects, or the most volatile prospects.

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We’ve done pop-ups here before, but let’s really do them this time, right? I mean, let’s get it right this time at least. Because last time I was writing about IFFB%, which I thought was infield fly ball percentage. Turns out that’s infield flies divided by fly balls. That’s a little strange.

Steve Staude on FanGraphs is a proponent of infield fly balls divided by balls in play. Freed from the shackles of fly balls, we can get a sense of the pop up as a sustainable skill — IFFB% only has a .37 year-to-year correlation, but pop up percentage (PU%) is better, around .63. That’s better than the year-to-year correlation on home runs (.41)! We have a stat — FIP — that treats home runs as a skill that’s wholely under the control of the pitcher, and yet infield pop-ups are better correlated season to season.

Staude’s excellent initial work on the subject is not incredibly fantasy-relevant, but it does go some distance towards explaining park effects better. Look at the parks that showed a PU% that was more than .5% higher than IFFB% — Anaheim, Wrigley, Citi, Tampa, Detroit — and you get a list of the ‘secret’ pitcher’s parks. Of course, Milwaukee, Detroit and Yankee Stadium are on that list, too.

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Look at Adam Dunn. He’s just sliding in your draft and you’re so tempted to take those 40 home runs to the bank. But that .200-ish batting average is going to hurt. Look at Adam Dunn. Look at him.

Those of us in 5×5 fantasy leagues don’t have the benefit of embracing high-walk, high-power sluggers — at least not without ramifications — and so we’re always on the lookout for power paired with batting average. It’s why Cardinals’ prospect Oscar Taveras has so many drooling over his future. It’s why we love Albert Pujols so much. That doesn’t mean these guys are easy to find.

We might have a new tool. Well, a new thing to watch out for at least. A warning: this is not scientific journal level research. This is a thing I found after talking to a baseball player that knows his craft.

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You’ll see many decent analysts trot out the strikeout-to-walk ratio, and it’s a fine number. Strikeout three for every walk and you’re be valuable, no matter what. But we’re always trying to shave the margins in fantasy baseball, and one margin comes with that mathematical sign: don’t divide strikeouts by walks… subtract walks from strikeouts.

It’s as easy as an exercise in hypotheticals. Suppose you have two pitchers that strikeout three for every walk. One of them strikes out a third of the batters he sees and walks eleven percent. The other strikes out eighteen percent of the batters he sees, and walks six percent. You already know which one you want in fantasy, and it’s true in real life, too: the one with the elite strikeout rate is the one you want.

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Some of the most-used baseball truisms are a variant of “pitching to the corners.” Scouts even differentiate between command and control when they say that some pitchers can put the ball in the strike zone and some can put the ball exactly where they want.

But much of baseball analysis is on/off when it comes to the strike zone. Was it as strike or wasn’t it? Was it walk or wasn’t it? It stands to reason that there might be pitchers falling between the cracks of our pitching metrics that are still adding to their value by showing great command. And, further, it stands to reason that those pitchers might show better numbers in the future — if they keep ‘pounding the zone’ in the right places, it’s sure to show up in his ERA and WHIP eventually, right?

Thanks to Jeff Zimmerman and Bill Petti at FanGraphs, we have the beginnings of an inkling about these pitchers now. The two developed a metric called ‘Edge%’ which tracks how well a pitcher can throw to the edges of the strike zone. It’s a simple concept, and executed well: who hits a defined sliver of the strike zone best? And what does that mean?

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Pitchers are a necessary evil to most fantasy players. They get hurt more often than hitters, and they stay hurt longer. While it’s extremely rare to lose a hitter for the entire season, it happens all the time with pitchers. But we need them, so here we are.

The holy grail for fantasy, then, is being able to predict pitcher injury. Well, that factors in pretty well with the real-world success of the White Sox for example, but this is a Roto column, so there.

Tom Verducci had a theory — pitchers with big jumps in innings from year to year were more susceptible to injury. Turns out it’s not really true. Jeff Zimmerman and I showed a case for sliders and curveballs adding to injury risk, but the added risk is a) not true for everyone and b) incremental. Zimmerman found a similar effect for wild pitchers after Billy Beane mentioned it. These things all have a little bit of truth to them, but they aren’t enough of a rack to hang the hat of our fantasy hopes upon.

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Whether we like our players to stroke dongs or jack taters, we all know our fantasy-based desire for the stat borders on the lecherous. The problem with the home run is that it’s such an I/O situation: it’s either a home run or it’s not. And using stats like isolated slugging percentage to try and suss out changes in a player’s power profile can be confounded by the fact that any ball that lands in the park is then subject to the interaction between the fielder’s grace and the batter’s speed. Doubles don’t always turn into home runs. Sometimes doubles are actually stretched-out-singles or boffed grounders in the outfield.

Are there peripheral stats for batters that can help us predict power surges? Or at least some numbers that can help predict which power surges will stick?

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Archive for the ‘Roto Relevant Research’ Category

Roto-Relevant Research: Volatile Prospects

Posted by Eno Sarris under Roto Relevant Research on Apr 04, 2013

Roto Relevant Research: Pop Ups, For Real

Posted by Eno Sarris under General, Jered Weaver, Roto Relevant Research on Mar 28, 2013

Roto-Relevant Research: Power and Batting Average

Posted by Eno Sarris under Adrian Gonzalez, Jose Bautista, Paul Goldschmidt, Roto Relevant Research on Mar 21, 2013

Roto-Relevant Research: Strikeouts And Walks

Posted by Eno Sarris under Joe Blanton, Roto Relevant Research, Yu Darvish on Mar 07, 2013

Roto-Relevant Research: The Edge

Posted by Eno Sarris under James Shields, Max Scherzer, Roto Relevant Research on Feb 28, 2013

Roto-Relevant Research: Pitcher Injuries

Posted by Eno Sarris under CC Sabathia, Cliff Lee, Roto Relevant Research on Feb 21, 2013

Roto-Relevant Research: Home Runz

Posted by Eno Sarris under Andrew McCutchen, Buster Posey, Chase Headley, Roto Relevant Research on Feb 14, 2013

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