Eno Sarris

enosarris

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Toronto Blue Jays v Tampa Bay Rays

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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Minnesota Twins v Chicago White Sox

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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Texas Rangers v Kansas City Royals

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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Oakland Athletics v Detroit Tigers

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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Division Series - Baltimore Orioles v New York Yankees - Game Five

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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San Diego Padres v Los Angeles Dodgers

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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The dreaded ‘hometown scoring bias’ can go in either direction depending on the situation. Your pitcher is in the midst of a no-hitter? That’s an error. Your hitter is in the midst of a hit streak? That’s a hit. These sorts of situations make it tough to see a pattern through all the noise.

That didn’t stop Doug at HighHeatStats. By taking away errors from home errors for both the home team and away team at each park, he tried to find systematic pro- or anti-error bias in each stadium’s scoring team. It’s a little hard to read the graph, but here’s what he found:

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