Nearly three years ago, I developed and published the original batter xHR/FB rate equation. While I used it during the season to analyze players, it was unfortunately behind the FG+ pay wall and shrouded in mystery. Then almost exactly two years ago, I unmasked the equation and shared it with the entire world. The equation used three components compiled by Jeff Zimmerman and did a fairly solid job of estimating what a hitter’s HR/FB rate should have been (adjusted R-squared of 0.649). Sadly, the data fueling the equation is no longer available, so naturally I decided to create a new equation. A Statcast charged one.

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Power often comes with punch outs. When homers aren’t tied to strikeouts, that’s usually the profile of an elite hitter. In 2016, only six players with more than 250 plate appearances recorded a Hard% north of 40% and a K% south of 20%. One of those six players, David Ortiz, is now retired. Joining Big Papi in that group of hitters was Josh Donaldson, Miguel Cabrera, Matt Carpenter, Jose Bautista and Kendrys Morales.

Using the NFBC ADP data, Donaldson and Cabrera require roughly a top-15 pick for their services with the former carrying an 11.16 ADP and the latter sitting at 15.58. Carpenter has an ADP of 70.58 and Bautista’s ADP is 118.16 after a down year. Lagging way behind this group is Morales with an ADP of 178.21, a minimum pick of 116 and a maximum pick of 213. Yes, Morales’ utility only eligibility at most fantasy sports sites is less than ideal and should be baked into his ADP, but it looks like there’s plenty of wiggle room for a profit. Read the rest of this entry »

This season, I was lucky enough for a couple print publications, Lindy’s and The Fantasy Baseball Guide, asked me to contribute their fantasy preview magazines.  While the quality of both magazines is top notch, print publications have limited room for explanations and no ability for back-and-forth discussions. Today, I am going to go over my contributions which I feel could use more explanation and will answer any questions on my thought process.

Lindy’s

For Lindy’s, I participated in their 12-team mock draft ( standard team except 1 C, 4 OF, 8P) and I picked out of the 3rd position. Here is my team

Position – Name (Round Drafted)
C – Buster Posey (3)
1B – Hanley Ramirez (7)
2B – Rougned Odor (2)
3B – Adrian Beltre (4)
SS – Marcus Semien (12)
MI – Jung Ho Kang (17)
CI – Albert Pujols (10)
OF – Andrew McCutchen (5)
OF – Mark Trumbo (9)
OF – Marcell Ozuna (14)
OF – Matt Holliday (16)
Util – Mike Moustakas (18)
P – Clayton Kershaw (1)
P – Chris Archer (6)
P – Rich Hill (11)
P – James Paxton (13)
P – Michael Pineda (19)
P – Jharel Cotton (20)
P – Andrew Miller (8)
P – Shawn Kelley (15)

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Yesterday, I discussed the debut of Nomar Mazara, specifically looking at the excuse me swings he takes when fooled. The hypothesis is that he’ll be fooled less in the future. More hard contact and fewer grounders should follow. Making contact at any cost is all well and good with two strikes, but even then it’s often sub-optimal. Better to trade a few strikeouts for more doubles.

Of course, Mazara is hardly the only player to tap soft grounders when fooled. Today we’ll look at five distinct cases of players with lofty soft contact rates.

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By all accounts, Nomar Mazara had a successful major league debut. He spent most of the season as a 21-year-old, and his youth showed at times. Overall, he slashed .266/.320/.419 with 20 home runs in 516 plate appearances. He usually batted first, second, or third, although he did finish the season hitting eighth most days.

Some young players swing out of their shoes with no regard to making contact (ahem, Byron Buxton, Joey Gallo). Others, like Mazara, have a more adjustable swing. No matter which type of swing a player possesses, they’re probably prone to being fooled early in their career. As Mazara, Buxton, and others age, they’ll recognize pitches and take better swings. That’s the theory at least – some players simply are what they are.

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In his ninth season, Colby Rasmus is set to join his fourth team, as he heads from one warm climate to another. But despite the fact he’ll be switching home parks, he’s still going to be playing in an orange juice box. On Monday, it was reported that he agreed to a one year contract with the Rays. Coming out of Houston, the knee-jerk reaction is that his fantasy potential, whatever there was left of it, is now kaput. But is that really true? Let’s bring on the park factors to find out what a move from playing half his games in Minute Maid Park (MM, Houston) to Tropicana Field (Tampa, errrr, St. Petersburg) may do to his performance.

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Between the Great Home Run Surge of ’16 and the reams of Statcast data at our disposal, it’s easier than ever to find home runs for our fantasy teams. However, sometimes we don’t even need the fancy stats to uncover 40 home run threats. As the title implies, we’ll discuss three guys such players.

When looking for these sorts of sluggers, it helps to key on three batted ball indicators. Do they have an elevated fly ball rate – preferably at the expense of grounders rather than liners? Do they pull the ball? Do they make hard contact? As a bonus question, is their home park power friendly?

Aside from sharing a batted ball profile, they all have one noteworthy trait in common – fewer than 350 plate appearances at the major league level. Small samples create many kinds of volatility. One of the guys missed all of 2016. Will he be the same hitter in 2017? Scouting reports can take longer than half a season to catch up, especially for players who are underestimated by their opponents.  In other words, what we’ve seen might not be what we get.

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Yesterday, I discussed the fantasy relevant average fly ball and line drive exit velocity (EV) surgers, which overwhelming fueled a spike in HR/FB rate. Let’s now check in on the other side of the ledger — those hitters whose EV declined precipitously from 2015 to 2016.

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We officially have two seasons worth of Statcast data! Mind you, it’s not two full seasons, nor does it include every batted ball. But it’s still highly useful data. Although I continue to work on developing new equations with the data, we could all agree on one thing — harder hit balls are better. This is especially true when considering fly balls and line drives. We care far more about this bucket of batted balls than grounders because I have calculated a correlation of 0.769 between average fly ball and line drive exit velocity (EV) and HR/FB rate. So let’s find out which fantasy relevant hitters enjoyed EV surges from 2015 to 2016 and if those spikes resulted in HR/FB rate increases as well.

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Statcast data is now everywhere and everyone seems to be using it in some form. While detailed pitch information has been available via Pitchf/x, full season batted ball data was missing. Now the batted ball data is leading to some interesting findings, but it’s not a true answer. So far, 12.6% of the batted balls is missing data. I wouldn’t see this as an issue if the missing data was evenly distrusted, but it is biased. I have made a simple correction to the data and now how have available corrected overall data and leaderboards.

I went over the procedure I used to correct the data in this previous article. Here is a quick review of the problem and corrective procedure:

• 12.6% of all the batted balls are missed by Statcast. No bunts or foul balls were counted though.
• Most of the missing data are weak infield popups and groundballs. As a general rule, weak, groundball hitters are missing the most data. For pitchers, groundball pitchers are obviously the ones with more data.
• I found the average value for all detected batted balls fielded by each position.
• If the data is missing, I replaced it with the calculated league average values.

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