Archive for Meta Analysis

Last week, I reintroduced my xK% equation, this time, with updated coefficients. The equation’s components were the exact same, so there was nothing new or exciting to report. However, on the following day, I published the top 10 over/ underperformers from 2011 to 2016 as a fun little exercise to learn who has broken the model. I then attempted to figure out if there was a common theme among the over/underperformers and after performing some additional research and calculations, settled on a possible explanation — changeups are bad for strikeouts. Turns out, I was actually onto something and that something was already discovered two years ago.

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Last year, Tout Wars was nice enough to allow me to join their Head-to-Head league which I preceded to barely win. I just got informed that I will not be in this league anymore. Instead, I was upgraded and will join fellow RotoGraphs author, Al Melchior in the 15-team mixed auction league. Previously I’ve stepped through my preparation process for these industry leagues but this year I can’t. We are picking our player in New York City right before the season starts on March 25^th.  Besides Tout Wars, most local leagues will be drafting this weekend just before Opening Day. Anything I write about my procedure leading up the auction will be useless. Instead, I will write about my preparation over the next two months leading up to the auction.

Unlikely last year, this year’s preparation focuses on an established league with the same good owners. It will be a different animal to conquer than last year the league with some unique rules (H2H and Roto), which I exploited as much as I could. The new league is different in that it has been very constant.  Many of the same owners stay around for years and it has few if any rule changes. I will first begin my prep by breaking down the other information from past auctions.

Note: I know reading about another person’s team is unexciting and some information might be not applicable. I’ve added “Key Points” to summarize how the procedure can help individual owners in their own leagues.

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Last year around the cut deadline, I took it upon myself to lead the charge that you’re keeping too many players on your ottoneu team. With the general premise that, despite our best efforts, leagues are heading into their annual auctions with less than optimal keeping habits.

This was not meant as some form of tell all. Certainly it’s reasonable to think that teams should keep some star prospects, or a slightly overpaid Mike Trout if they think they can trade him. However, with the keeper deadline a week and a half away, I want to take some time to update this study for completeness and also update some potential shortcomings.  Read the rest of this entry »

Let’s finish up new xK% equation week with a look at which starting pitchers gained and lost the most with the new coefficients versus the old ones. Though all of the coefficients increased, while the intercept is now a higher negative number, the L/Str and F/Str coefficients increased more dramatically than those for Str% and S/Str. So, one would imagine that a pitcher relying more on looking and foul strikes, as opposed to swinging, would get a relative boost using the new equation.

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Yesterday I continued new xK% equation week by discussing the 10 pitchers that have overperformed and underperformed the metric the most since 2011. While I calculated the group averages, pulled in fastball velocity, and most frequently used secondary pitch, the sample size was far too tiny to conclude anything. So at the request of commenter JUICEMANE, I have decided to do a larger study in an attempt to explain why some pitchers consistently over- or underperform the xK% equation. Do the players within each group on either side have anything in common with their groupmates?

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Yesterday I rolled out an updated version of my pitcher xK% equation, which estimates what a pitcher’s strikeout rate “should be” given various strike and strike type metrics found at Baseball-Reference.com. With my data set, I put together a table calculating historical averages during the time period (2011-2016) I compiled data for. I’ll share the top 10 pitchers that have outperformed and underperformed their xK% (so if a pitcher outperformed by 2% in 2011 and 3% in 2012, I’ll be looking at the total of 5%, rather than the average of 2.5%), and we’ll try to figure out what, if anything, the pitchers in each group have in common with each other. So let the fun begin!

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The best equation I have ever developed from an adjusted R-squared perspective is pitcher xK%. That equation yielded a 0.913 R-squared, so it didn’t exactly need any tweaking. However, I was on an xEquation rush recently and decided to develop new ones, as well as update old ones. So we’ll begin with xK% and dedicate my week’s posts to looking at the new version of the metric.

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My article today spawned after listening to The Sleeper and the Bust podcast when Paul Sporer interviewed NBFC’s Main Event champion Rob Silver. The entire podcast is a must listen, but one part sparked my interest. Rob mentioned he uses infield flyball rate plus strikeout rate minus walk rate to value pitchers (55:45 point). Silver successfully targeted Kevin Gausmann, Marco Estrada, and Rick Porcello late in his draft by using this stat combination. I will create the same filter to find 2017 sleepers.

There is no easier ball to catch than the infield fly. It’s an easy out. In those few instance when they errantly fall to the ground, a fantasy owner shouldn’t worry since the rest of the inning’s runs won’t count because of the error.

Besides being an easy out, a player’s infield fly rate stabilizes with just over a half season’s data. While infield flies don’t stabilize as fast as strikeouts, they do become stable within a season.

Infield fly rate (IFFB%), especially as we represent it here at Fangraphs, misleads the user. The IFFB% listed indicates the percentage of flyballs (FB%), not all batted balls, which are hit in the infield. To get the infield popup rate, the IFFB% must be multiplied by the FB%. The confusion doesn’t end yet.

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Groundball Pitchers Suppressing ERA

I am going to play with fire and refute a Dave Cameron comment. In a recent article about Brad Ziegler, Cameron said:

So Ziegler basically breaks every mold you can think of. And he even breaks our models. His career FIP is 3.38, but his career ERA is 2.44, almost a full run lower. Part of that is that groundball pitchers get to count more of their runs as unearned because there are more errors on groundballs than on flyballs, so ERA systematically is biased in favor of groundball pitchers.

After the work I just did on pERA, I was worried about its validity. Previously, I found that groundball and flyball pitchers exponentially suppress their ERA as they move to extreme ends of the batted ball spectrum.

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Three years ago, I shared with you an updated version of my xK% equation. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, along with his overall rate of strikes thrown. With an adjusted R-squared of 0.913, it explains a very high percentage of a pitcher’s strikeout rate. Its best use is early in the season when the plate appearance (the K% denominator) sample size is still small, as xK% uses total pitches as the denominator, so we can reach a reasonable sample size to analyze much more quickly.

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