This past weekend was Tour Wars weekend in New York City. The committee extended me an invitation, likely by mistake, into its head-to-head points league auction. I’ll take a moment to self-indulge and say it’s pretty surreal to finally, like, reach the pinnacle, in a sense. I appreciate and am endlessly grateful for the kind words folks have extended my way in the past few weeks and months and years.

Rudy Gamble, of Razzball fame and a delightful human being whom I finally met in person Friday night, passed along to me positive feedback about my recent draft recaps (NFBC, TGFBI, Rotoballer mock), which seem to have been a helpful prep tool this preseason for some folks. I endeavor to provide a recap that goes beyond a simple list and self-aggrandizement — it would fundamentally misrepresent my rampant self-doubt. Besides, I think it’s helpful to articulate a plan and, when a plan falls apart, how a plan changes mid-draft.

This draft review differs from previous reviews in that the Tout Wars head-to-head points league spawns from an auction draft and not a snake. (A classic auction, each of the 12 teams was allocated $260 to fill 24-man rosters.) Thus, the structure will vary and may be a bit rough around the edges. Still, let’s give this a shot.

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You may recognize this, the Fringe Five, as an exercise Carson Cistulli once established and, weekly, pursued at the main FanGraphs site. I don’t know what you’re thinking, but I may have an idea. We — Brad Johnson and I, we — don’t expect or even want to replace Cistulli, nor do we intend to replicate or imitate what he could achieve with the written word. What a colossal mistake that would be. I would be lying if I said I never tried. I can sing his praises for days. He was my favorite, and he was probably yours, too.

What we do intend to replicate, however, is Cistulli’s ability to identify market inefficiencies. Fringe Five, while quintessentially Cistullian, was an ongoing exercise in doing so, often successfully, unearthing a list of prominent names that includes Mookie Betts and Charlie Blackmon (and, maybe, one day, Max Schrock). I’m not sure Cistulli would have ever divulged his exact process; in introducing Fringe Five, he only went as far as to say, with emphasis added editorially, the following:

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Allow me to break the fourth wall (more than I normally break the fourth well) and say I’m glad a few of you have enjoyed my recent (mock) draft recaps, especially the format of them. It can be tough to make that kind of content both interesting and informative, so I’m glad it has achieved at least the minimum thresholds in both regards.

(Mock Draft Review: RotoBaller Friends and Family Draft)
(Draft Review: The Great Fantasy Baseball Invitational)

I was fortunate enough for the National Fantasy Baseball Championship (NFBC) to sponsor a “Beat Alex Chamberlain” high-stakes league. It was my first time competing in this specific contest: the $350 Rotowire Online Championship. With that kind of buy-in, I knew I would likely face some sharp competition despite having no prior exposure to any of the other owners.

The league specifications are as follows:

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The Great Fantasy Baseball Invitational (TGFBI) is a league of leagues in which hundreds of fantasy baseball analysts square off against one another in 15-team leagues and overall. Three hundred and fifteen competitors will face off in 21 separate leagues to test their wits and all that jazz.

I wrote about my performance last year here. After bottoming out in April, finishing the month 12th of 15 with only 63.5 points, I had the 3rd-best team from May onward, finishing the season 4th in my league and 51st overall out of 195 analysts. Of the $900 or so I spent on free agent auction budget (FAAB), roughly half was spent on chasing saves — of which I accrued only 22. It was a preposterously bad performance in that regard. Only one other team above me in the overall standings collected fewer saves, and maybe three others had fewer than 40. Otherwise, everyone had 60 or more. It stands to reason a sharper FAAB performance could have vaulted me up the standings.

This year, I’m running down my picks as they happen, almost like a diary, although I won’t publish this until the draft is complete. Still, you can track my train of thought as if it were real time.

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For the fourth consecutive year, my friends at RotoBaller invited me to participate in the RotoBaller Friends and Family mock draft. The draft room was, in a word, terrifying:

1. Vlad Sedler, Guru Elite
2. Nick Mariano, RotoBaller
3. Pierre Camus, RotoBaller
4. Todd Zola, Mastersball
5. Tim Heaney, RotoWire
6. Heath Cummings, CBS Sports
7. Howard Bender, Fantasy Alarm
8. Nando Di Fino, The Athletic
9. Scott Engel, RotoExperts
10. Alex Chamberlain, RotoGraphs
11. Ray Flowers, Guru Elite
12. Real Talk Raph, RotoBaller

I drew the #10 pick (as shown in the draft order above), immediately understanding I might have a difficult decision to make very early in the draft.

This doesn’t need much preamble, but I do want to say one thing: I maintain that a good way to improve as a drafter (for lack of a better word) is to try something you might not ordinarily try or force yourself into an uncomfortable position you might not normally get into. I embraced this discomfort with my first two picks, assembling a base from building blocks I might not normally use given the options available to me. As you’ll see in my concluding remarks, I think I did pretty well.

Also: we were all on a 30-second clock. I don’t know about everyone else, but I was stressed. My internal monologue was utter chaos.

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At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

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Mike Tauchman deserves nothing less than the clickbaitiest of headlines. He’s my favorite player nearly no one has heard of or cares about, a name I draft that genuinely forces people to Google his name, a Triple-A hitter not only too old to be a prospect but also maybe too old to be a post-hype prospect, if he ever were a prospect, which he never was.

No one has heard of or cares about him because of any combination of: (1) he is not and never was a prospect; (2) there are a fair number of actual prospects in Colorado’s actual farm system who are actually exciting; (3) prospect status notwithstanding, he has no path to playing time because the Rockies habitually bury their actually exciting talent. At 28, Tauchman ain’t getting any younger, and I ain’t either. He deserves all the hype he can get, and I’m here to dish it out.

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On Monday, I wrote about my first foray into ottoneu. This post carries the same warning: This genre of post may not appeal to most readers. I don’t want to waste your time if it’s not your thing. Hereafter I’ll dissect my performance in the first annual Great Fantasy Baseball Invitational organized by our Justin Mason. For the uninitiated, TGFBI is a multi-league tournament of sorts among fantasy analysts, all competing in separate leagues and also overall (thanks to standings compiled by Smada).

Again, this is all about accountability. It’s easy to chalk up your W’s and ignore your L’s. I also think some folks might be interested in seeing how an analyst might actually implement the advice they offer. I’ll be the first to admit having a platform does not make me an “expert” by any means. I research and write to learn more about baseball and fantasy baseball and to be the best fantasy baseball player I can be. I’m not there yet. I’m my own worst enemy, as I’ll show below. Ultimately, I hope taking a fine-tooth comb to my season might help me grow as an owner and, perhaps, help others as well through insight and reflection. (Or maybe you’re reading just to be entertained! That’s fine, too.)

Same word of advice as last time, to myself and everyone: always, always make sure you understand the league rules and scoring format. This is something I screwed up in ottoneu, and it’s something I screwed up in TGFBI. Namely: TGFBI did not impose an innings limit. That’s a huge deal. In 15-team leagues, it’s difficult to actually blow through a 1,400/1,500/whatever-inning limit while accruing worthwhile ratios, but you could do it if you set your mind to it. I wouldn’t recommend it; it requires nearly or fully punting saves. Still, at a certain point last year, I decided to embrace it when my pursuit for saves proved itself entirely fruitless.

League finish: 4th of 15
Overall finish: 51st of 195

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Warning: This genre of post may not appeal to most readers. I don’t want to waste your time if it’s not your thing. Hereafter I’ll pull back the curtains to review of performance in two “expert” leagues: FanGraphs Staff leagues #1 and #2, both constituting my first foray into the ottoneu world.

This is about accountability, which is something I am, as some would say, “all about.” It’s really easy to parade your victories; it’s more difficult to advertise and own your losses, both regading overall performance (league titles and return on investment) and also player-specific analysis. I’m eager to remind readers I called a Jose Ramirez breakout in 2015 (and, again, in 2016, when it actually happened), an Austin Barnes “breakout” in 2017, and Luke Weaver and Madison Bumgarner implosions prior to 2018. What I decline to admit, though, is, in the posts to which I just linked, I declared I’d fade Justin Verlander hard in 2016, when he won 16 games with a 3.04 ERA, or that I thought Chris Davis might out-earn Giancarlo Stanton. Sometimes, The Process^TM finds diamonds in the rough; other times, it mistakes turd-shaped rough for diamonds.

I can chalk those L’s up to a lack of experience and knowledge. I’ll readily admit some of my analyses from only a couple of years ago make me cringe. But I also know that even great calls can fall victim to variance or misfortune (which is why I refreshed my Ramirez breakout pick from 2015 for 2016, and my Barnes breakout pick from 2016 for 2017 — and, spoiler, probably again for 2019). Some losses are unearned, akin to a quality start with a bullpen implosion. Others are downright bad. But, I stand by them! I once believed them. It’s just how it goes.

It’s my first time doing this. Just figured it was high time to hold myself accountable and try to learn from my league-specific performances, both profitable and otherwise.

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It seems as though each year, fantasy baseball analysts, “professional” and amateur alike, hone in on a new — or, if not new, then relatively untouched — metric or data set for their endlessly eager consumption. In 2015, FanGraphs introduced batted ball data to its leaderboards. In 2016, Statcast data was unveiled, although it arguably didn’t become popular until 2017, and before the 2017 season FanGraphs changed the game with its splits leaderboard. Baseball Prospectus has introduced myriad new metrics, too — DRA in 2015, DRC+ last year, etc. — and we began to lean into pitch-specific performance analysis last year. (The latter-most topic is relevant to what follows here.)

I recently joined Christopher Welsh and Scott Bogman of In This League on their podcast. I thought one of the evening’s questions was particularly topical and prescient (and I paraphrase): What will 2019’s it metric be? The question was asked with pitch values, something I’ve seen garner increasing attention on Twitter, in mind.

You can acquaint yourself with pitch values directly from the man who created them:

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