Archive for November, 2017

Using Curveball Spin to Predict Blisters

Pitching blisters were an afterthought just two years ago but the reported instances have jumped the past two seasons. Detailed accounts were written by Eno Sarris here at FanGraphs and Ben Lindbergh at the Ringer.

Throwing a curveball may be to blame according to Sarris:

But we can’t dismiss that chart completely. The players who have gone down with blister problems have thrown curves 14.9% of the time, far above the 10-11% baseball as a whole averaged over that timeframe. The players who ended up on the list more than once averaged 18.9% curveballs. Enough to say there’s some smoke here.

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Never Rebuild

Never rebuild – that’s my mantra. As a hard and fast rule, it’s a little too rigid to work in reality. However, as a rule of thumb, it’s a useful code of conduct. I have six leagues that can be described as a dynasty format. Occasionally, I do rebuild. Rosters break. We’re here to talk about those scenarios as well as why I believe rebuilding is for suckers.

Perhaps I should begin with a caveat. Avoiding rebuilds works for me. It’s a battle tested strategy that maps to my strengths and weaknesses as a fantasy player. Not every owner is like me. It’s possible that you should rebuild because it better fits your personal approach.

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Jake Arrieta’s Weak Contact and Strong Defenses

Jake Arrieta may be the most important free agent pitcher this winter. He’s not the best pitcher available. At least by Dave Cameron’s criteria, that would be Yu Darvish. But he’s been good enough to likely earn a $100 million contract, and he’s shown signs of both brilliance and potential decline over the last two seasons such that a $100 million contract feels like it will be either $50 million too expensive or $50 million too cheap and nowhere in between.

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Reviewing 2017 Pod Projections: Trea Turner

Moving along on our recaps of my 2017 Pod Projections, we stumble upon Trea Turner, who delivered a fantasy half-season back in 2016 that made him the talk of the town heading into 2017 drafts. He was so darn good, he was generally a first round pick. How much, if any, regression was I projecting and how did that compare to his actual results? Let’s find out.

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My NFBC Team from AFL

If you have read my work or listened to the podcast, you know I’m obsessed with my Arizona Fall League trip every year. It’s just the best. One of my favorite things about that trip is the NFBC Draft & Hold league we start there. We do the first 23 rounds live and then the other 27 online starting in January. It’s a little bit of a reach-fest. I think part of it is the draft and hold aspect, part of it is recency bias of the season just ending, and part of it is just getting the oohs & ahhs (or playful curse outs) from another participant.

I’ve been doing this draft since 2012 and I think I’ve had an early pick just one. I regularly draft from one of the last three spots in the 15-team league and this year was no different as I nabbed spot 15. For those unaware, it’s a Kentucky Derby Style draft slotting process where they draw the names out of the hat and then you can pick your spot among those available. I can’t recall exactly when my name was pulled, but 15 was available and that was exactly what I wanted. I had done a mock in mid-October and was struck by how deep the first round was so I felt more than comfortable getting the wheel.

Another reason I love pick 15 is because I get the first pick in the January restart. After a few months of Hot Stove activity, there are usually a handful of players who have improved their status beyond a round 24 pick, making them great values at this slot. Of course, this year they cancelled the Hot Stove so there might not be any of those gem picks available to me.

Let’s take a look at my first 23.

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Fixing My Fantasy Weakness: Hitter Evaluation

I was recently asked the following question by Werthless.

Jeff, what are you trying to accomplish here? Are you trying to estimate the volatility in an individual player’s projection? That’s an interesting question, and directly related to the risk of the player. Are you trying to do better than Steamer at predicting performance? That’s a big endeavor. Are you trying to predict injuries? Might be better to do that directly. Are you trying to better estimate number of plate appearances by estimating job security? Might be better to do that directly.

Then, you can combine the models to perhaps better quantify a player’s risk of meeting preseason performance objectives. You can apply your model onto a different year’s data to see how well your predictions match reality (ie. Do the higher risk players actually underperform more often than lower risk players).

I do have a plan I’m implementing but it wasn’t known to my readers. Sorry. I want to understand which hitter traits to concentrate on. If they don’t exist, I created some.

For a few season’s now, my hitters have steadily outperformed my pitchers. In my three main leagues, here are the pitching-hitting splits from this past season.
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Chalk Manny Machado’s Poor 2017 Up to Bad Luck

In almost any way you look at it, Manny Machado’s 2017 campaign did not go well. He finished 12th out of 22 third base qualifiers in WAR and 19th out of that same group in Off rating. He ended the year with sizable decreases in just about every respect, ultimately finishing with a park-adjusted offense figure of just 102, painting him as a barely above average offensive player. That’s obviously not the Manny Machado that we’ve become accustomed to, and it’s certainly not the one that we should expect moving forward, as much of that production, or lack thereof, could be attributed to bad luck above all.

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How Efficient Were 2017 Ottoneu Auctions?

Imagine it’s March of 2017 and you’re getting ready for your ottoneu auction in a first year league. Obviously with the benefit of hindsight we know you should target the breakouts of ’17 like Aaron Judge, Luis Severino, and Cody Bellinger, but is there a macro level strategy that you could have employed to take advantage of the inefficiencies of the auction market? In this post I’m going to examine the average auction values for 30 first year FanGraphs points leagues from ’17, compared to the real value those players provided (based on my retrospective ’17 dollar values).

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Reviewing 2017 Pod Projections: Kyle Hendricks

It’s time to recap some of my 2017 Pod Projections! This preseason, I begun the series with one of 2016’s most surprising pitchers, Kyle Hendricks. We all figured that even backed by the historically strong Cubs defense, he was quite a bit fortunate en route to a sub-3.00 ERA. But how much regression was I projecting and how did that compare to his actual results? Let’s find out.

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Post-Hype Prospects, Part 2

In part 1, I looked at the first five of my post-hype prospects. They aren’t necessarily ranked in order of preference in that piece, but they grouped together as the top tier from this group. I’ve got a second group of seven guys who essentially make up another tier that is a cut or two below the first five. To recap how I’m choosing these guys, here’s what I said in the first part:

I’m looking at younger players with medium-to-high prospect pedigree that they haven’t cashed in yet. They’ve had some major league time (usually 300+ PA, but I didn’t have a specific set of statistical parameters) with maybe a spurt of success, but there’s a real shot for much more, especially if the playing time is there. Best of all, they won’t cost much at the draft table. Examples would include the Tim Beckham and Chris Taylor types. Beckham, of course, was a former #1 overall who was written off in some circles before he could legally drink. Meanwhile, Taylor never had the hype of a Beckham, but was firmly Top 10 in the Seattle org. for 2013 lists and might’ve charted in some Top 100s had he not expired his rookie status in 2014.

Without further ado…

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