11/05/17

The latest episode of “The Sleeper and the Bust” is brought to you by Out of the Park Baseball 18, the best baseball strategy game ever made – available NOW on PC, Mac, and Linux platforms! Go to ootpdevelopments.com to order now and save 10% with the code SLEEPER18!

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Special Thanks to James Gale who produced the live show in Arizona!

Notable Transactions/Rumors/Articles/Game Play

• Justin Upton back to LAA (1:30)
  • Pursuing Mike Moustakas, too?
• Masahiro Tanaka opts in (6:38)
• Miguel Sano leg surgery (10:06)
• Dustin Pedroia knee surgery (14:55)

Playoff Stock Watch

• Cody Bellinger (17:55)
• Chris Taylor (20:48)
• Gregory Bird (23:52)
• Javier Baez (31:00)
• Charlie Morton/Justin Verlander (36:12)
• Yu Darvish (41:36)

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Niv Shah, creator of ottoneu, was kind enough to provide me with the end of season rosters for every ottoneu league/team, and I’ve been playing with the data on and off for the last week. For today’s post, I’m going to start simply and look at some of the players most often owned by ottoneu champions, the players most drafted by champions in first year leagues, and some of the average finishes by player in those first year leagues.

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I have spent the past few years creating a family of stats that I’ve called xStats. These stats use Statcast batted ball metrics to analyze each player, which I then manipulate and export in a manner I hope is useful for fans and analysts.

Exit Velocity and launch angle data are good, and I include those, but they aren’t yet intuitive for more baseball fans so I have set forth to display my data in terms of numbers that are more relatable. Namely the standard slash line numbers. I have expected batting average, on base percentage, slugging percentage, batting average on balls in play, and weighted on base average. For pitchers I have bbFIP, which is an ERA scalar. Today, though I’m only going to be looking at batters.

These stats are available, but they don’t help much unless you know how well they are working. To that end, I have created the following table, which compares the regular, standard slash line to the xStats slash line. Read the rest of this entry »

I received some really interesting comments on my previous article on RBI luck, and that has steered me toward several new avenues of related research. Commenter Bill identified that several of the unlucky batters who had fewer RBI on home runs than I expected batted second in the order. Intuitively, it makes a lot of sense for batting order to influence a player’s opportunities for RBI, but that issue can be a bit difficult to disentangle from the quality of the offense he is a part of. Still, I think it is possible, and I’ve made an attempt to do so with a model that chains some league average rates to a hypothetical power hitter’s expected batting outcomes.

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Let’s stick with my xBABIP equation and discuss the group of 10 hitters I identified as potential 2017 BABIP surgers. These were the guys whose xBABIP marks were significantly above their actual BABIP marks.

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Happy Game 7 Day!

About a year ago I released my (2017) Top 50 Fantasy Prospect rankings using the Prospect Scorecard to weight a variety of important variables in the context of fantasy baseball.  Today I’m publishing an (early) expanded list of the Top 100 Fantasy Prospects for 2018 for both Ottoneu’s FanGraphs Points leagues (where wOBA is a key measure on offense) and Roto leagues (5 x 5).

A few quick notes before we begin:

• Since “Cost” is league-dependent (auction salary, keeper round, etc.), I’ve ignored it here for simplicity by keeping it constant for every prospect listed. Feel free to use the Scorecard to make changes that reflect true player costs for your league, which will impact these rankings.
• These rankings below are intended to represent the 100 most valuable prospects for fantasy leagues (depending on scoring format).
• It’s quite possible I’m missing an obvious player that should be ranked, so let me know in the comments.  We can discuss the specific rationale for player rankings in the comments, too.  Player ages are current ages.
• For a lot more prospect resources, check out the Ottoneu community.

Here are the (early) 2018 Top 100 prospects for the linear-weights-based FanGraphs Points scoring format (a good proxy for those in OBP leagues):

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In what has become an annual tradition, today I will hold myself accountable to you the reader by publishing the results of my 2017 fantasy season.

This year, I finally feel like I finally whittled down to a manageable number of rosters – one dynasty, two ottoneu leagues, two keepers, one redraft, and one sim league (mostly played over the winter). While I did woefully under-manage my ottoneu rosters – especially on the trade front – I gave nearly all my teams the attention they deserved. That wasn’t happening when I played in over 10 leagues.

On the Daily Fantasy front, I was able to rebound from a bad start to the year to post only a slight loss – less than the amount of money I spent on rakes. That’s… something. Although I didn’t manage to land any big scores, I did go on a nice streak of more modest wins in late-August. Overall, I lost about $75. I plan to reinvent my process next year.

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Sometimes it’s obvious when a hitter’s BABIP is due for a rebound or regression, but that isn’t always the case. For the first time before this season, I decided to discuss some of the hitters whose BABIP marks looked relatively sustainable, but my new xBABIP equation disagreed. Let’s see how this group ended up doing.

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