Quick looks

3/15 games

I had full game information and write-ups on each of the following three pitchers but my computer did a restart and the information was lost. Here are the condensed versions from what I remember.

• Lucas Giolito: He was a mess. His velocity is still down from his minor league reports by about 3 mph. He couldn’t throw his curveball near the strike zone. He only lasted 2/3rds of an inning with his replacement, Chris Beck, showing more promise. I am not rostering Giolito in any redraft league and recently traded Giolito for Reynaldo Lopez and Curtis Granderson in an industry 20-team dynasty league.
• James Paxton: Looked similar to 2016. No issues here.
• Cody Reed: Not ownable in redraft leagues. He throws, not pitches, with a low 3/4 arm angle which is devastating to lefties but righties can tee off on him (.131 ISO vs LHH, .385 ISO vs RHH in ‘16). Also, he can’t throw is his change for strikes (35% Zone%), so he will have issues keeping righties from waiting on the fastball. Now, if he can get ahead, his two breaking pitches, change and slider, can get some swings-and-misses so he’ll get some strikeouts. I can see the pieces which have scouts hoping but he has not put them together yet.

3/16 games Read the rest of this entry »

This past Thursday, I spoke at the SABR Analytics conference on my per pitch valuations (pERA).  I originally created them to form an understandable framework for comparing prospect pitching grades and major league results. Some byproducts of the work became useful like the effects of dropping a pitch. Today, I will make available new information I provided at the conference.

For the readers who aren’t familiar with the original work, it can be read in its 2500 word entirety in this previous article. Here is a summary.

• The key is to give each pitch an ERA value (pERA) based on the pitch’s swinging strike and groundball rates. All the values are based on the average values for starting pitcher. Closers will have higher grades because their stuff plays better coming out of the bullpen.
• The pitcher’s control is determined from their walk rate which is separate from the pitch grades.
• Each pitch is placed on the 20-80 scale with 50 being average, 80 great, and 20 horrible.

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Luck, as Branch Rickey famously observed, is the residue of good fortune, and it seems to us that a lot of what we Fantasists do amounts to determining who’s been lucky and who hasn’t. This is the stock-in-trade of one genre of preseason Fangraphs article that we, for two, are suckers for: Player A, the article will assert, had bad (or good) Fantasy-relevant numbers last season, but a massage of those numbers or an examination of more granular stats suggests that his performance wasn’t as bad as (or was worse than) his Fantasy outcomes.

The closer look or the more granular stats, the article will continue, reflect the guy’s true performance, whereas the Fantasy numbers are artifactual, and largely produced by the guy’s luck. Since luck evens out, the article will conclude, the guy will do better (or worse) than people who haven’t looked closely at the numbers think, and will be worth more (or less) than the market thinks he is.

One of our relatively accurate forecasts of our rather pitiful 2016 season derived from this approach. At mid-season, we opined that Danny Salazar (first half ERA: 2.75) would decline sharply thereafter, whereas Carlos Rodon (first half ERA: 4.50) would improve significantly. And so it turned out. We reached these conclusions by asking: which starting pitchers, if any, were in the highest (i.e. worst) quartile of Batting Average on Balls in Play and Home Run to Fly Ball Ratio, and in the bottom (i.e. best) quartile of Hard-Hit Ball Percentage?

And which starters, conversely, were in the lowest quartile of BABIP and HR/FB and the top quartile of HH%? Our reasoning wasn’t abstruse: if a guy’s not getting hit hard, and yet is giving up a disproportionate number of hits and home runs, maybe he’s been unlucky, and if his only problem is that he’s been unlucky, maybe his luck will change. And, on the other hand, maybe the luck of a guy who’s getting hit hard but doesn’t yet have the scars to show for it will run out. Read the rest of this entry »

We’ve been rolling through our ottoneu FanGraphs points league rankings. This year the rankings will include values from myself, Justin Vibber, and Chad Young. We are presenting our individual dollar values, the average of all three individuals, plus the ranking of that average. In addition the tables below include Ottoneu eligibility (5 games started/10 games played in the prior year). Players are ranked at their most valuable position, and the hierarchy we are using is C/SS/2B/3B/OF/1B (with 3B and OF being a coin toss in terms of replacement level, we chose to include 3B/OF eligibles at 3B).

Prior Rankings: C/1B/3B, 2B/SS, OF

Key:
Split – Difference from highest to lowest $ value
Otto.– Average price across Ottoneu FGpts leagues
AVG. – Average $ value from the four of us
(+/-) – Difference between Ottoneu average price and our average $ value

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We’re going position by position this week and next with our initial roll out of rankings. We will update these in March based on Spring Training activity and injuries.

We’re using Yahoo! eligibility requirements which is 5 starts or 10 appearances. These rankings assume the standard 5×5 categories and a re-draft league. If we forgot someone, please let us know in the comments and we’ll make sure he’s added for the updates. If you have questions for a specific ranker on something he did, let us know in the comments. We can also be reached via Twitter:

• Paul Sporer
• Jeff Zimmerman
• Mike Podhorzer
• Brad Johnson
• Justin Mason
• Al Melchior

There will be differences, sharp differences, within the rankings. The rankers have different philosophies when it comes to ranking, some of which you’re no doubt familiar with through previous iterations. Of course the idea that we’d all think the same would be silly because then what would be the point of including multiple rankers?! Think someone should be higher or lower? Make a case. Let us know why you think that. The chart is sortable. If a ranker didn’t rank someone that the others did, he was given that ranker’s last rank +1.

Key:

• AVG– just the average of the seven ranking sets
• AVG– the average minus the high and low rankings
• SPLIT– the difference between the high and low rankings

Previous Editions:

• OF
• SP
• 1B
• SS
• 3B
• 2B
• C

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This is not a “sleeper” list. Read the rest of this entry »

Earlier this month, we took at a look at starting pitcher replacement levels for 2017. Today, we will continue this process for relief pitchers using the same methodology. Please refer back to that post as a primer on how I put my replacement levels together, though I’ll recap some of the methodology here.

2017 Replacement Levels: SP

There are two ways that replacement levels can be defined. This is either as a specific point per game (P/G) or point per inning total (P/IP) or as the nth player ranked at a position. For example, I could say, replacement level for RP is about 6.67 P/IP, or I could say that replacement level for RP is about the 70th RP.

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Earlier this week, I discussed my latest plan for my holds league. Since chasing both saves and holds usually comes at the expense of hitting categories, I’m going to focus on holds early in the season then pivot to saves around the mid-way point. The most efficient way to accomplish this is to draft setup men who will eventually matriculate to closer. Preferably cheap setup men (unlike Nate Jones).

To that end, volatile bullpens are my friend. But it’s not enough to say “that bullpen is unsteady.” The Padres have a shaky bullpen with as many as four relievers competing for the closer job. However, how many save and hold opportunities do you expect that rotation and offense to produce? Not many. Those starters might be historically bad…

So we want a synthesis between opportunities and bullpen volatility. Here is a division-by-division review of the teams I’ll be monitoring closely.

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This is our last position in the ADP previews, though I’m breaking it up into two parts (top 20 here and then the rest). Relief is especially interesting on the fantasy landscape because fantasy managers have such varied approaches. Some swear by locking up a superstar in the early rounds.

Hell, some swear by double-tapping studs if they have a pick on one of the ends, especially in a format like the NFBC that bars trading. Others gladly draft the crusty, unspectacular guys who just rack up saves regardless of their skills and ratios. Then there’s the group that refuses to expend many resources on closers and focus either bottom of the barrel, closers-in-waiting, or in most cases both.

I lean toward the high end. I have no issues spending an early pick on a superstar closer because of how much they can help in strikeouts and even the ratios. Nabbing someone who can pop a triple-digit strikeout total allows you some leeway on your starters, too. Like I said earlier, today we’ll focus on the first 20 closers (and some of their handcuffs) and then I’ll dive in on the low-end closers and hidden gems to cap off the series.

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Strange things happen in holds leagues. The extra pressure to roster relievers often leads to inefficiencies on offense. Meanwhile, the bar is set very high for pitching rates since many owners try to start three closers and three setup men. In my experience, chasing both reliever categories usually comes at the cost of mediocre offensive performance. Today, let’s discuss my latest scheme for having my cake and eating it too (ooh, more cake!)

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