Author Archive

Draft Recap: Tout Wars (Mixed H2H Auction)

I wanted to not write too many draft recaps this preseason. I didn’t want draft recaps to simply stand in for analysis. Turns out we might not have baseball until June or July or maybe ever. Draft recaps might be all we have in this pandemic hellscape. (I’m being dramatic, I know. But, also, maybe I’m not!)

I did recap my first-ever Tout Wars draft last year. Honestly, it went poorly. I didn’t click through the link to read what I wrote, but if I try to make it seem like I did well… I promise you, I didn’t. I ended the SiriusXM stream chastising myself for drafting so poorly. It’s true!

I did, however, recover nicely in-season once I finally learned how to use OnRoto and I got a handle on what seemed to me like the optimal roster-building strategy. My year-end roster looked nothing like my drafted roster, and I was able to navigate FAAB effectively enough to wiggle my way into a semi-finals matchup. (I was a benched Kole Calhoun home run away from beating Clay Link and heading to the finals to face Ian Kahn, where he would have annihilated me unceremoniously.)

This year, I’d like to think I fared much better. I actually calculated projecwhatevted points this time! I’m sad we all couldn’t draft in person, but I’m more than happy to draft online at Fantrax in the name of social distancing.

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Potentially Meaningless Draft Recap: TGFBI (Rounds 21-30)

Well… this all feels sort of pointless now. But if you need a distraction from any coronavirus-induced anxieties, let this be a brief respite.

* * *

You can catch up on the first 10 rounds here and middle 10 rounds here. I’ve beaten to death the term “threading the needle” throughout these posts, but it’s an apt description for what I feel like I’ve had to accomplish with my particular strategy. “Walking a tightrope” is another.

The more time I’ve had to sit with my team, the longer I’ve had to disabuse myself of the notion that my team is any good. I still think it is, at least on the hitting side of things, but the pitching is as weak as it has been my last two seasons — or, if not as weak, at least as shallow. Most likely, I’ll be spending most of my FAAB (free agent acquisition budget) chasing pitching replacements, which is what I’ve done most of the last two years, too. Oh well.

Through 20 Rounds
Pos Player Pick #
C Christian Vazquez 13.189
C Tom Murphy 17.249
1B Edwin Encarnacion 11.159
2B Ozzie Albies 3.39
SS Trevor Story 1.09
3B Alex Bregman 2.22
CI Renato Nunez 16.232
MI Elvis Andrus 9.129
OF Jeff McNeil 6.82
OF Oscar Mercado 7.99
OF Justin Upton 14.202
OF Trent Grisham 20.292
OF
UT Nelson Cruz 5.69
 
P Aaron Nola 4.52
P Carlos Carrasco 8.112
P Hyun-Jin Ryu 10.142
P Joe Musgrove 15.219
P Alex Wood 18.262
P
P
P Keone Kela 12.172
P
 
b Ross Stripling 19.279
b
b
b
b
b
b

Let’s wrap this sucker up, stream-of-consciousness style.

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Draft-in-Progress Recap: TGFBI (Rounds 11-20)

Last week, I recapped the first 10 rounds of my the Great Fantasy Baseball Invitational (TGFBI) draft. I’m feeling good about it so far, which is a somewhat predictable feeling to have, since I probably shouldn’t hate my team yet. But it’s more than I can say about last year’s draft, which went poorly. Of course, I’m writing this intro through 14 rounds, and anything can happen in the next six or 16.

If this is your first time hearing about TGFBI, you can click my last post in the first sentence for more information. Ditto, some of my pre-draft planning. Otherwise, here’s my roster through 10 rounds:

Through 10 Rounds
Pos Player Pick #
C
C
1B
2B Ozzie Albies 3.39
SS Trevor Story 1.09
3B Alex Bregman 2.22
CI
MI Elvis Andrus 9.129
OF Jeff McNeil 6.82
OF Oscar Mercado 7.99
OF
OF
OF
UT Nelson Cruz 5.69
 
P Aaron Nola 4.52
P Carlos Carrasco 8.112
P Hyun-Jin Ryu 10.142
P
P
P
P
P
P
 
b
b
b
b
b
b
b

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Draft-in-Progress Recap: TGFBI (Rounds 1-10)

It’s draft season, which means it’s also draft recap season. Last year, I partook and subsequently recapped a few of my drafts. Folks seemed to enjoy them and/or find them beneficial. That’s good!

Incidentally, and unfortunately, all the drafts I recapped turned out terribly, and all my good teams (my league-winning National Fantasy Baseball Championship (NFBC) Online Championship team, my 3rd-place Tout Wars team, etc.) I let slumber. One of those terrible teams was my Great Fantasy Baseball Invitational (TGFBI) squad. I’ve returned to fight off my demons.

I will say: I feel much more well-prepared than I did last year. I feel more cogent, more lucid. Last year, I barely prepped. I was overconfident because of my 2018 success, in part, but primarily I was overwhelmed and burned out. I held firm convictions about hardly any player, which goes against every fiber of my fantasy baseball being.

This year, the opposite. I’m eager to correct my flaws from last year, starting, first and foremost, with actually preparing. Doesn’t mean I won’t totally botch this draft. I don’t fancy myself particularly good at 15-team leagues, excelling instead at 12-teamers, especially auctions. But, hey, no excuses. At least this time, someone else, instead of my own damn self, will have beaten me.

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Which Statcast Measures Correlate Best? 2019 Refresh

A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.

Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.

Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.

OK, here’s the goods. For 2019 hitters:

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Quantifying the Benefit of Spray Angle to xwOBA

Expected weighted on-base average (xwOBA) is one of Statcast’s most important additions to the Sabermetric sphere. It’s a simple premise — estimate a hitter’s deserved production based, simply, on his combinations of exit velocity (EV) and launch angle (LA) — with robust implications and applications. It’s remarkable how powerful the metric is with just two inputs.

However, the metric is not without its faults (or complaints from those who use it). Its simplicity is beautiful but inherently and knowingly lacking, accounting minimally or not at all for:

  1. spray (lateral) angle (touched upon here),
  2. a player’s foot speed (discussed more thoroughly here),
  3. park factors, and
  4. opposing defense.

None of this necessarily serves as an indictment of xwOBA. The number of inputs you include affects the purpose you want it to serve. That is, do you want it to be descriptive or predictive? How about both? Maybe defense shouldn’t be included, then, if we can’t reasonably expect a hitter to face the same caliber of defense each year, something that is out of his control.

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Upgrading a Deserved Barrel%

New year, new deserved barrels metric. In October, I took a crack at devising a “deserved barrels” metric in which I took the basic components of a barrel — a hitter’s exit velocity (EV) and launch angle (LA) — and determined the capacity in which the components relate to Statcast’s barrel rate metric (barrels per batted ball event, or “Brls/BBE %” on Baseball Savant). I included squared terms (EV2, LA2) assuming the relationship is not linear. (A launch angle that’s too steep is detrimental, for example.)

Further offseason research led me to additional insights:

There exist many measures of contact quality; barrel rate captures how often a hitter produces high-quality contact. (Hard-hit rate functions similarly but ignores launch angle, to my knowledge, making barrel rate arguably superior.) It only made sense, then, that the latter finding above — that launch angle tightness matters to batted ball quality — should be incorporated into my deserved barrels work somehow.

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Converting ADP to Auction Values

Oftentimes, I write out of inspiration. This time, I write simply to write, because the subject happened to creep up into my thick ol’ skull without provocation, which I guess is a type of inspiration in and of itself but not wholly what I had in mind. No one specifically needs this post right now, or maybe everyone does. I don’t know.

Something I do see and have seen before, however, with frequency, are mentions of such-and-such player rising or falling in the ranks, usually by virtue of average draft position (ADP). ADP is a measure of a player’s rank by aggregating data for a whole boatload of snake drafts. It’s a good way of assessing a player’s market value.

The problem with ADP is, unless you have completed research nearly identical to this, you can’t possibly be expected to know how a player’s ADP rank might equate to a dollar value at auction. Having this knowledge, this intuition, is arguably helpful in understanding how much you’re staking on any particular player. Moreover, changes in ADP become easier to digest. Possibly. For me, it does. If you’ve never participated in an auction draft before, maybe it doesn’t.

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2019 Statcast Park Factors (and the Importance of Spray Angle)

Last year, I took a stab at developing what might be loosely defined as park factors using Statcast data. (I called them park “impacts” because they lacked the requisite rigor to be true factors, although it’s all semantics, truly.) I sought to use Statcast’s expected wOBA (xwOBA) metric, specifically on batted ball events (BBEs), such that we would have a measure of xwOBA on contact (or xwOBAcon). This metric accounts for exit velocity (EV), launch angle (LA), and little else — which makes it perfect for this purpose.

The difference between actual and expected wOBA on contact indicates the amount of luck, whether good or bad, a hitter might have incurred on a particular batted ball event. In other words, given ‘X’ exit velocity and ‘Y’ launch angle, what is the most common wOBA outcome, and how much did the actual wOBA outcome differ from it?

The beautiful part about xwOBAcon is it strips away all other context. It removes elements that confound other park factor calculations, such as hitter and pitcher quality or even sequencing (vis-à-vis run-scoring). Except for fielding. Can’t control for fielding, unfortunately.

With this approach, we have the exit velocity. We have the launch angle. We have historical results for that particular combination of EV and LA to use as a benchmark. And then we compare.

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Let’s Talk About Launch Angle Generally

Edit: Further investigation has brought to my attention that the results presented below are slightly askew, although not incorrect. All discussion below regarding hit frequency (BABIP) and contact quality (expected wOBA on contact, or xwOBAcon) should have been framed specifically in the context of non-home run batted ball events. This is significant, because home runs are a big deal, but it’s also insignificant. Allow me to explain.

When we re-include home runs, the relationship between launch angle tightness (stdev[LA]) and contact quality weakens dramatically. I think it comes down to the graph shown in the middle of the post below. Removing home runs narrows the range of productive launch angles, thus making a tighter range of launch angles (confined primarily to line drives) more appealing. When you include home runs, it expands the range of productive launch angles to include productive fly balls in addition to productive line drives. There’s literally more margin for error when we reconsider home runs, making a tighter range of launch angles was valuable.

That doesn’t mean launch angle tightness isn’t important! If anything, removing home runs was a nifty way to demonstrate this fact.

Anyway, I have updated this post with red text to clarify that references to contact quality exclude home runs — and that the findings from this post are technically correct, just through a certain lens.

* * *

Last week, I published some work regarding launch angle “tightness,” aka a hitter’s ability to replicate his average angle as closely as possible as often as possible. Effectively a measure of consistency, I found launch angle tightness (consistency, variance, whatever you want to call it) bore a moderately strong relationship with batting average on balls in play (BABIP).

Truth be told, I began to question my finding almost immediately for reasons I’ll discuss shortly. After inquiries from The Athletic’s Eno Sarris, FantasyPros/PitcherList’s Nick Gerli, and even Cody Asche (this is the mildest of brags) that echoed my internal self-doubting dialogue, I dove into the question further.

Ultimately, the best explanation for the importance of launch angle consistency is to simply elaborate upon launch angle generally. So, consider this a de facto primer on launch angle. It’s probably not the first and certainly won’t (or shouldn’t) be the last. But in the context of my post from last week, it simply makes sense to bring the conversation full circle and wrap it up nicely with a bow. And the final result is gratifying, I hope.

Enjoy (or not, I’m not your dad):

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