Archive for fantasy baseball

So, what is Ottoneu?

Ottoneu is a smarter, better brand of fantasy baseball.  By design, it’s a fantasy platform engineered for the hardcore baseball fan that doesn’t enjoy the term “off season”.  Ottoneu is the perfect fit for FanGraphs readers looking to join a competitive fantasy baseball league with a lot of cool features, a sabermetric-economy, and a massive community of raving fans.

You can read first-year feedback from Ottoneu players here.  You can also learn a lot more about the game from the FAQ, but here are the top 10 reasons you should consider joining or moving your fantasy baseball league to Ottoneu this year:

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With two months to go, we’ll do one more reboot of the tiered shortstop rankings. Perhaps next month we’ll do some keeper rankings. The rankings below were derived by running our rest-of-season depth chart projections through the z-score method with a few names getting moved around by moi. Read the rest of this entry »

One time, I made some bold predictions about the 2015 season. They were, by and large, pretty terrible, as most bold predictions usually go. However, not all of them were complete losses, and it seems appropriate anyway to review them at the season’s midway point. In addition to brief analyses for each player where applicable, I also provided completely arbitrary percentages of probabilities that my remaining predictions will hit.

1. Giancarlo Stanton finishes outside the top 10… outfielders.

Dude swings so hard, he fractures his wrist. It’s incredible, and aside from getting hit in the face by a Mike Fiers fastball last year, Stanton has suffered non-freak injuries in all of his professional seasons that have caused him to miss significant time — hence, the basis of this prediction. He ranks third of all outfielders, according to the ESPN Player Rater, and six weeks of missed time gives me a solid chance here. A wrist injury is by no means a death knell for a hitter’s power, but there’s reason to believe, aside from reasons related to regression, he doesn’t match his production through the season’s first three months — as in he hits closer to 10 or 12 home runs rest-of-season rather than 15 to 20.

Probability: Read the rest of this entry »

Rubby de la Rosa twirled a gem Monday night.* It was a night of several unlucky fantasy pitching performances: Corey Kluber, Chris Sale, Matt Harvey and John Lackey together struck out 34 batters and allowed only three runs in a combined 32 innings pitched, and the Twitterverse made sure everyone knew.

Yet there was de la Rosa, plugging away against the Marlins (and against Dan Haren, who admirably tossed eight innings of two-run ball in defense), needing only 94 pitches to get through a full nine innings of play. Rubby, too, settled for a no-decision, his due to a poorly timed two-run homer allowed to J.T. Realmuto in the 7th inning.

I was reluctant to invest in Rubby at first, remembering his wholly disappointing 2014 season. (In his defense, he was never really a touted, let alone highly touted, prospect, even within the Dodgers and Red Sox systems, so the term “disappointment” is used loosely here.) However, I have bought several shares of Rubby in the past couple of weeks due in large part to a smattering of injuries but also a series of respectable performances by de la Rosa.

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Last week, I introduced xISO, a metric that calculates a player’s expected isolated power based on his batted ball profile (per FanGraphs’ recently added batted ball data courtesy of Baseball Info Solutions). Having looked at a handful of underachieving National League outfielders for its induction, I’ll expand the analysis of xISO here today.

I’ll reiterate some key points. I used all 12 years’ worth of batted ball data for all player-seasons in which a hitter qualified for the batting title. The OLS regression specified pull rate (Pull%), hard-hit rate (Hard%) and fly ball rate (FB%) as explanatory variables and produced the following equation, which I deliberately omitted last week:

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You may have noticed that FanGraphs now feeds batted ball data, courtesy of Baseball Info Solutions, into its leaderboards. The day the data appeared, my mind buzzed with ways they could be useful in improving our understanding of a hitter’s batting average on balls in play (BABIP).

Mike Podhorzer already augmented previous attempts at devising an equation for expected batting average on balls in play (xBABIP) for hitters by incorporating elements of a hitter’s power, speed, plate discipline and batted ball tendencies. So, with fresh numbers in hand, I embarked on a journey to further improve the ever-evolving xBABIP. However, I sought to do so by using only batted ball data. Basically, I intended to develop a convenient xBABIP equation, one that can be computed using almost entirely variables found on the same page.

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Let’s cut straight to the chase. Take a look at the statistical snapshots below:

Obviously, Player 1 is benefiting from a higher batting average on balls in play while Player 2 is getting burned a bit by his. Still, take away their triple-slash lines (but leave the isolated power) and you have two players with almost identical numbers, down to the six steals on eight attempts and the meager isolated powers (ISOs). Where they differ a bit is in plate discipline: Player 1 has a much healthier walk rate than Player 2 and a couple fewer strikeouts. So while Player 1 is benefiting from the a higher BABIP, he can also reasonably be expected to post a marginally higher batting average and noticeably higher on-base percentage. Most importantly, the two hitters are eligible at the same position and are, thus, substitutable.

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Chris Carter is off to a bad start. This is an unambiguously true fact. Jeff Sullivan, among other Twitterers (Tweeters?), have succinctly illuminated this unambiguously true fact:

selection of players with ISO of .000 Curtis Granderson Brandon Phillips Desmond Jennings Logan Morrison Chris Carter

— Jeff Sullivan (@based_ball) April 21, 2015

Carter has supplemented the zero extra-base hits with four singles, 21 strikeouts and nary a run batted in to speak of through 51 plate appearances. (FYI, that’s a 41.2-percent strikeout rate.) His atrociously bad start is the talk of the Twitter town, and it has Astros Manager A.J. Hinch dropping him in the batting order. As of last night, Carter’s ownership had dropped to 67.7 and 73 percent in ESPN and Yahoo! leagues, respectively, as owners have jumped ship.
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If fantasy baseball were a marathon, we’d all have run about a mile and a half. Most races don’t even have their volunteers stand with trays of water cups this early on, so you’d better pace yourself if you’re already tired. I think I forgot to stretch.

Here’s an obligatory sentence reminding you about the caveats about small samples while attributing a shred of validity to them. OK, now that the formalities are out of the way, let’s talk ownership trends. National League outfielders are a promising bunch, especially in regard to the youth movement. I’ve been sold on a handful of them prior to the start of the season, and I’m surprised by their meager ownership numbers. They aren’t completely unowned, as my misleading title alleges, but they’re close enough.

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Last week, I identified potential stolen base streamers for daily fantasy leagues and weekly leagues with daily transactions and lineup changes. I used a pitcher’s career caught-stealing and pick-off rates as criteria to determine if a particular matchup was primed for streaming a speedster with the hope of him stealing a base (or two or four).

I like how it turned out, but it felt hastily constructed. A pitcher’s career rate seemed too broad a scope, especially considering the possibility that a pitcher can get better (or, perhaps, worse) at limiting steals and picking off runners over time.

With a little more time and care, I fleshed out everything a bit more and added an additional criterion: catcher effectiveness, which can be most obviously measured by caught-stealing rate. But I think there also is merit to calculating the frequency at which runners attempt to steal on a catcher. In a sense, it measure runners’ perception of a catcher’s skill, especially for those at the tails of the distribution.

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