Archive for Giancarlo Stanton

Exploring Statcast’s Estimated Swing Speed

My favorite part of this year’s World Baseball Classic, aside from the baseball, obviously, was the television broadcasts’ frequent reference to players’ swing speeds. I was floored, even if only because I didn’t know (but should’ve known) we had the technology capable of measuring it. Regarding Major League Baseball and Statcast’s adoption of such a metric, a little birdy told me I shouldn’t hold my breath. Disappointed, I moved on.

Then yesterday, while fooling around in Baseball Savant’s Statcast database trying to diagnose the misalignment of Miguel Cabrera’s outcomes with his peripherals, I noticed the database query’s “sort by” function offered an option to sort by “estimated swing speed.” A quick Google search indicates to me the Statcast and MLB Advanced Media team(s) has (have) yet to formally announce this; sprint speed has been the more exciting recent development, apparently.

Not to me! I quickly got to work querying the data. I also quickly learned downloading the raw data files that underpin the swing speed summaries previously linked do not include swing speed, which is unhelpful. In other words, swing speed is not communicated to us from Baseball Savant’s organs on a play-by-play basis. I imagine this is by design. So, I was resigned to running a single query that summarized swing speed data at a high level: the average swing speed for every hitter with at least 100 at-bats in a given season, from 2015 through 2017.

Here’s what I found.

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Buying Generic: Aged Bias

On Wednesday, we took a look at two 2B who appear very similar while possessing vastly different average salaries throughout the Ottoneu universe. While Kipnis and Forysthe appeared nearly identical in many ways, today I want to look at two outfielders who are similar in several regards, but not nearly as identical as Wednesday’s test-cases.

2016 Results
Name 2017 Age Ottoneu Avg. BB% K% ISO BABIP AVG OBP SLG wOBA wRC+
Mr. Name Brand 27 $52.49 10.60% 29.80% 0.249 0.290 0.240 0.326 0.489 0.344 114
Mr. Generic 37 $14.72 8.20% 16.70% 0.215 0.253 0.246 0.322 0.461 0.335 109

One of the major differences between today’s Mr. Name Brand and Mr. Generic is their respective ages. Why is this important? If you’ve played Ottoneu (or any dynasty slanted format) for any amount of time, you will likely find that the majority of owners are biased against the aged. While age is typically viewed as a premium, this can be detrimental when pricing comes into play as owners will chase after the shiny, younger, new toys. Given the 10 year age difference between our name brand and generic option, it is likely that these two will never be considered in the same tier.

The $38 price difference across Ottoneu leagues also confirms this – and while age is not the only reason for this price gap – it should help us determine that Mr. Name Brand is priced as if he is one of the most elite players in Ottoneu. While both are power hitters, our name brand option displays otherworldly power, but also strikes 13% more than our generic option. He also had 40 points of BABIP on Mr. Generic. Let’s dig a little deeper.

xStats Differences
Name FgP/G ISO xISO Diff BABIP xBABIP Diff wOBA xOBA Diff
Mr. Name Brand 5.22 0.249 0.234 15 0.290 .318 -28 0.344 .351 -7
Mr. Generic 4.77 0.215 0.213 2 0.253 .306 -53 0.335 .367 -32
SOURCE: xStats.org

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MASH Report 8/29

Some interesting injuries to go over this week….some new things, but also a few updates on older injuries. Many of you are either just starting your league playoffs or it’s coming soon, so the most recent news is going to be of value. That being said, let’s get into what I have seen for the MASH report. If you’ve read this for the past couple of weeks, you know that I try to be responsive to the “Comments” section. If there’s someone specific you feel I missed, please jump in. I never want to over-promise/under-deliver, but I will make every effort to get to your question.

Andrew Benintendi, the Red Sox’ rookie phenom, definitely dodged a bullet with “only” a knee sprain, according to manager John Farrell. It is interesting to note that he says there’s no “structural damage” but it is a ligament sprain. But if they are saying he could still return, then that tells me it’s a minor (grade 1) sprain…remember that time you “rolled” your ankle trying to cross that dude over on the court? You hurt real bad for a while but then you were better? Yeah, something like that.

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MASH Report 8/22/16

Let’s get to the injury updates/analysis….

Jung Ho Kang is on the 15-day DL with a left shoulder injury. No official word as to what he hurt, but the way he landed on it gives me some pause. Hopefully it is minor, and he did run off the field afterwards. These impact injuries could be anything in such a complex joint as the shoulder, from a collarbone fracture to a ligament sprain to a labral tear. Yes, non-pitchers can tear their labrum too. Tough timing because he was starting to hit the ball well.

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MASH Report 8/15/16

Symbolic of the Cardinals’ chances of winning the NL Central division this year, we see a significant change in their DL status. We will start with them:

Matt Holliday now has a fractured thumb. This involves his right hand, and depending on what they discover today/release in the news later, could be season-ending. Don’t dump him yet until you get verification on this, but even if it takes 3 weeks to allow the bone to heal, he will still have to get grip strength and then batting timing back. Consider him to be a playoff hold.

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The Marlins Outfield is Surprisingly Good

I mean, it’s not that surprising. It’s the only outfield with a Giancarlo Stanton, which would be enough to elevate any outfield out of the cellars. It has a Christian Yelich, too, who hits the ball hard but had been somewhat of a fantasy disappointment, having failed thus far to live up to any kind of power potential he once had.

Still, the Marlins are the game’s second-best offensive outfield, per wRC+ (weighted runs created). That’s kind of surprising. I mean, we knew the Pittsburgh Pirates’ outfield, which currently ranks first, would be good. And we probably thought the Chicago Cubs’ outfield, with all its talent and depth, would generate solid offensive numbers, but they’re only 10th.

While it’s a feel-good story riding on the high of a finally-vindicated Marcell Ozuna, it doesn’t look entirely sustainable. But it doesn’t mean we can’t dream, and there are some reasons to be optimistic about the already-established Stanton and Yelich as well as the still-young Ozuna.

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Reviewing Scott Strandberg’s 2015 Bold Predictions

Last year, I hit .300 on my Bold Predictions, which is about what I aim for. That feels like the boldness sweet spot. This year, not so much. It saddens me to report that I was overly bold this year, getting just one Bold Prediction right. However, one other was incredibly close, so I’ll say that I hit .150 this season. Still, I can and will do better next year.

I have failed you, dear readers.

1. Jorge Soler is a top-ten outfielder.

Well, he was a top-100 outfielder, slotting in at No. 99 on the season. Soler battled his fair share of injuries this year, but even when he was healthy enough to play, he hit just .262/.324/.399, with 10 home runs in 404 plate appearances. For the year, he registered a 0.0 WAR. Here’s hoping he turns it around next season.

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Updated Hitter Shift Data: Effect on xBABIP

It’s official. 100% of my value is bothering @jeffwzimmerman to run data and then use that data for said content. I did just that this morning for updated shift data. I believe the last time he posted related content on RotoGraphs was here back in May looking at: Early Hitter Shift Data.

It was because of this post and his balls in play (groundball and pop-up rates) that led me to believe Albert Pujols was going to drop off/continue his downward trend. I guess I pushed out the fact that he was the best hitter of his generation. While the .375 BABIP (from the above link) against shifts did not last, he’s at a very respectable .284. Between this and additional discipline (best contact rate since 2008 and hacking less at stuff outside of the zone), he is able to hover around a .280 BA, which remains elite in conjunction with 30HR, 90R and 100+RBI.

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Bizarro Bold Predictions

The discussion below does not involve bizarre predictions. In fact, it doesn’t even include bold predictions. Essentially, the discussion is just the opposite of my bold predictions post last week. Or opposite in that I’ll be discussing players I dislike compared to the average as opposed to players I like. As for what ‘the average is,’ I’m using the expert consensus rank (ECR) from FantasyPros.com. If you’re still confused on the premise of this post, maybe the image below will help.

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How to Handle Different Categories: OBP

This is a subject I tackled last year, but the basic idea for this article is that people playing in leagues that use non-traditional roto categories may not be equipped with proper draft materials. Almost any set of rankings you can find online are compiled with the standard 5×5 roto categories in mind. Some sites may have rankings published specifically for leagues that replace batting average with OBP, but the vast majority assume standard categories. If you’re using a set of rankings from a site or magazine, you’re doing yourself a disservice unless those rankings were designed for OBP leagues.

I’ve compiled my own projections for 242 hitters to generate my own rankings, and I’ve generated rankings specifically for standard 5×5 leagues and leagues that have replaced batting average with OBP. I want to highlight the players who gain the most in OBP leagues and the players who are hurt the most when OBP replaces average. I base my rankings off a number Zach Sanders calls Fantasy Value Above Replacement (FVARz). Essentially I assign each player a value for each category and add them all up while adjusting for positional scarcity.

Below I’ve included two lists; one shows the players whose FVARz increases the most when OBP replaces average, and the other shows the players whose FVARz decreases the most. I’ve also included their ranking among hitters and auction values for both batting average and OBP leagues. Over on a site of which I’m the managing editor, TheFantasyFix.com, I’ve posted how the rankings and auction values change for all 242 hitters I have projections for when the switch is made to OBP. You can find that here. All values are based on 10-team leagues with 25-man rosters, 13 starting hitter slots and three bench slots. If you have questions about the methodology, the charts or anything else, hit me up in the comments. Read the rest of this entry »