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So just as I had hoped when I published my 2017 starting pitcher strikeout rate upsiders using my xK% equation, followed by my recap of my 2016 upsiders, it generated some excellent discussion. I made mention in my recap article, that was also requested in the comments, that I should also compare how xK% did versus the 2016 Steamer projections. It’s not enough to share a list of 12 guys with upside, show that as a group they did improve, with 66% doing so, and call xK% successful. If every forecasting system projected improvement as well without my xK% equation, then perhaps the equation isn’t providing any incremental value. So let’s bring my recap table back, but this time with the 2016 Steamer projections included.

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The NFBC has ADP data up, and in the hopes of finding a player or players to write about this week, I combed over their draft data. It took until just the third page of drafted players to find a couple of players who sparked my interest. Miguel Sano is carrying an ADP of 114.00 with a minimum pick of 73 and a maximum pick of 157. Teammate Byron Buxton is even cheaper on average with an ADP of 145.33, a minimum selection of 104 and a maximum selection of 187. Both players possess their fair share of risk, but both have ample upside I fully intend on chasing at their respective current investment costs. Read the rest of this entry »

Entering 2016, Liam Hendriks was one of my favorite relief sleepers. In fact, I fantasized in my Bold Predictions piece that he’d enter 2017 as an elite closer. And while the results are laughable in hindsight, you can understand why I was bullish on him.

Coming off an outstanding 2015 in Toronto, Hendriks entered a remade Oakland bullpen headlined by a formerly dominant but unequivocally injury prone Sean Doolittle. His other competition included renowned ball four-enthusiast, John Axford, rookie Ryan Dull, and a resurgent but Medicare-eligible Ryan Madson. All possessed as many risks as virtues and it seemed, given the lingering questions surrounding them, that Liam Hendriks’ big fastball, elite command, and strong ground ball rate should have put him towards the top of the queue once the inevitable arm injury befell Doolittle. So what happened?

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Last week I posted the year to year correlations for xStats and their standard variants, and it came up with a few interesting results.  The xStats variants were much more consistent year to year, for better or worse, and in general they were better at predicting future performance. Not by much in some cases, but hey, every bit helps, right?  It made me curious how it may translate to groups of players with smaller sample sizes, so this week I’ve taken these stats to relief pitchers, with those year on year correlations in mind.  Yes, it is frustrating that we only have two seasons to look at, but this is the best we have at the moment so let’s see where it gets us.

As you might remember, vertical launch angle was very consistent (.75) between 2015 and 2016 for all pitchers, and as it turns out this holds true for every innings limit you can imagine.  Whether you want to talk about guys with 30 innings, 200 innings, or anything in between.  Vertical angle appears to stabilize fairly quickly.  So, that begs the question, how does vertical launch angle change batter performance?  Hopefully this chart will answer your questions.

Between roughly 10 degrees and about 35 degrees batted balls have high value, with batting average peaking around 13 degrees, slugging around 25 degrees, and home runs around 27 degrees.  So, if we know vertical launch angle is stable between seasons, and batted balls between 10 and 35 degrees are bad (for the pitcher), then perhaps aiming for pitchers who have average launch angles outside of that zone would be ideal. Read the rest of this entry »

Projecting future closers is always difficult. We can use a number of different frameworks that factor in environment, talent, pitch quality, and arsenals, and still scratch our heads marveling at how relievers are used. It’s a tricky proposition given the number of variables involved. Add to that the changing nature of bullpen roles, it’s not inconceivable, as we saw with Andrew Miller’s usage, that a progressive manager might not use his best reliever in a way that’s conducive to racking up saves.

In fantasy, saves are expensive and the inherent volatility of bullpens can make chasing them on draft day a dubious endeavor. The Chacon Zone’s goal is to identify non-closing relief aces. Those pitchers whose contributions in strikeouts, ERA, and WHIP, despite low innings totals, are significant enough to offset the lack of saves that you’d receive by rostering a closer in his place. By banking on talent, rather than simply opportunity, we can identify cheap relievers not only possessing high floors but also high ceilings should they be thrust into a ninth inning role. Think Edwin Diaz from last year. Luckily for us, the new Splits Leaderboard, provides yet another tool by which we can (attempt to) identify these pitchers. By isolating performance in high leverage situations, we can not only identify talented relievers but those whose managers entrust them in the most pivotal moments.

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Earlier this year I made my first attempt at integrating horizontal and vertical launch angle in addition to exit velocity.  As I explained in my first post, I split batted balls into 5 degree by 5 degree launch windows, then split each window into buckets based upon the exit velocity, and in doing so I created an array of buckets that is about 20 units wide, 36 units deep, and 60 units tall. So, in other words, each batted ball could fall into one of 43,000 buckets, give or take. I then found the success rates of each bucket, how many go for singles, doubles, triples, home runs, errors, or outs, and I assigned each ball in the bucket the same probability.

This system has some strengths and quite a few weaknesses.  For its strengths, it is easy to implement and debug, the code runs pretty fast (it can classify about 50k batted balls per second while running on a modest computer), and it gave results that both made sense and outperforming similar stats.  However, this method draws a hard and firm line between potentially similar batted balls merely because one crossed an arbitrary threshold and landed in an adjacent bucket which could have radically different average success rates.  This problem is best expressed visually with a chart for Value Hits. Read the rest of this entry »

Yesterday, I wrote up Rich Hill’s 2016 season, declaring him The Leader of Baseball’s Movementarians. In researching the piece, I dove deep into Baseball Prospectus’ Pitchf/x leaderboards. I wanted to find out what makes Hill’s fastball so dominant, despite its unremarkable velocity, and his curveball so effective, given its unimpressive whiff rate. I’m not an expert in pitching mechanics. I can’t breakdown video or tell you much about grips. For that, talk to Eno. But I found that Hill enjoys two distinct advantages that make his (essentially) two-pitch arsenal play up.

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Yesterday, FanGraphs made public its splits leaderboard, which the authors have been able to test and refine in private for some time now. It’s an incredible tool. If you haven’t checked it out, you should. If you haven’t thanked Sean Dolinar for building it, you should. (If you have any preliminary feedback, leave it in the comments and I’ll pass it along.)

There are a seemingly infinite number of ways to cross-cut data in endlessly fascinating ways. Splits by handedness, situation by outs, situation by leverage, situation by defensive alignment (shift or no shift!) — the list goes on. But the thing that most interested me immediately was understanding the implications of more granular batted ball data.

Two tools I once refined/created — xBABIP and xISO — rely almost exclusively on Baseball Info Solutions (BIS) batted ball data. Yet they were limited in their capabilities because of the limited nature of the data: we knew each hitter’s contact quality (hard/medium/pull) and contact direction (pull/center/oppo) but now how the trios intersected. But, ah, the splits leaderboard.

The following tables depict the batting average on balls in play (BABIP), isolated power (ISO), and home runs per fly ball (HR/FB) in 2016 by each cross-section. Read the rest of this entry »

The leader is good, the leader is great. We surrender our will as of this date.

Rich Hill’s renaissance has been remarkable in so many ways. Despite justified skepticism entering 2016, he finished the season as the 14^th most valuable starting pitcher, according to our auction calculator. And he did so tossing just 110 innings over 20 starts. He made contributions in ERA, WHIP, and when he pitched, strikeouts, that were so substantial, that he ranked among the back end of #1 or the very best of #2 fantasy starters. And no intractable three-eyed blister with its own Instagram account could stand in his way.

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Matt Moore didn’t post jaw-dropping numbers in 2016, but the year should be viewed as a success. He had a rocky 2015 season coming back from Tommy John surgery, and he set a new career high with 198.1 innings split between the Rays (130.0 innings) and Giants (68.1 innings) in 2016. Staying healthy enough to knock on the door of 200 innings is a success in its own right. The surface stats and ERA estimators weren’t great, and he finished 60th among starting pitchers, but I’ll once again be firing up the hype machine for the once highly-touted prospect. Read the rest of this entry »