Alas, we have finally reached our final 2017 preseason article recap! Welllll, this one shouldn’t have been the last one, but no one wants to read a recap of my David Dahl Pod Projection, right? So we wrap things up by reviewing my Pod Projection for curveball aficionado Lance McCullers, who was coming off around 200 innings of 3.22 ERA ball supported by strong skills over his first two seasons. Health was a question mark, but there was no doubting his talent. Let’s remind ourselves what I forecasted for his 2017 performance and how he actually performed.

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In my last article, I examined the potential value differences between the top-50 rank players and their average draft position (ADP). Today, I will examine the next 50. While the first list contained quite a few players moving up, today’s list is a little more balanced with over and undervalued players.

One of the biggest takeaways from the first article was the extra replacement value catchers receive in a 2-catcher format. To simply explain the idea, I will turn to Joe Bryant who goes through a fitting example but with football.

The league’s bottom catchers are so bad so any catcher who can hit has good value. Evan Gattis being ranked #17 got most of the scrutiny in the rankings. As was pointed out, the projection may be high on the plate appearances but the process was still sound. Here is how Gattis compares to the last catcher ranked (Yan Gomes) and Francisco Lindor compared with the last middle infielder (Kolten Wong).

Yan Gomes is such a sink, especially with a total of 55 Runs+RBIs. It’s imperative to understand and value catchers correctly for each league formats. It’s a potentially huge advantage for those owners who spend the time. Read the rest of this entry »

Today let’s continue recapping one of my 2017 Pod Projections, this time heading to Milwaukee to discuss Keon Broxton. Coming off an intriguing half-season in 2016 that featured an exciting blend of power and speed, along with some clear flaws, he was a popular sleeper for 2017 and one whose projections people couldn’t really settle on. So what was I projecting and how did that compare to his actual results? Let’s find out.

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“Long ago, Ben Graham taught me that ‘Price is what you pay; value is what you get.’ Whether we’re talking about socks or stocks,

… or fantasy baseball players

I like buying quality merchandise when it is marked down.” –Warren Buffett

Collecting as much value (talented players) from as little possible resources (draft picks or auction dollars) is the key to starting off a winning fantasy season. From now until each draft, owners should be trying to calculate player values and the possible range of outcomes. With these value ranges in mind, owners can use their draft resources to get the best deals. It’s time to start finding those deals.

To find the bargains, player values first need to be calculated. To create the values, I will use the average final standings from the 32 leagues in the 2017 NFBC Main Event (15 team, 5×5 roto with AVG).

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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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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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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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Breakouts and busts. If there was a set procedure for finding both, it would have been found years ago and incorporated into projections. For now, all we have is the overall chances of either happening. Over the past few weeks, I’ve been trying to put a simple value on these chances. I’ve completed the underperforming calculations and will now finish the overperforming metric. Additionally, I will compare both metrics to get an overall idea of the projection’s volatility.

In my last article, I found the breakout thresholds for plate appearances (222 PA) and wOBA (.040) and won’t change these values. Besides these two values, I determined who had both thresholds crossed and when both were partially achieved. The overperformance needed to increase near to the threshold values.

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About a week ago, I finished creating some simple stats for the chance a hitter underperforms. Now it’s time to find the overperformers. These are the potential breakout guys every owner hopes to hit on and help carry their team to a championship.

To start with, a breakout needs to have some set baseline values. I went to Twitter to help find a baseline value to use. I’ll start with a playing time boost.

A hitter is projected as a bench bat to start the season. How many additional PA over projection (200 PA) would you consider him to break out?

Assume as .310 wOBA hitter (ex. Alex Gordon, Kevin Pillar, Andrelton Simmons).

— Jeff Zimmerman (@jeffwzimmerman) November 13, 2017

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A few days ago, Jake Leech asked me if Zack Cozart’s 2017 improved plate discipline would stick into 2018.

https://twitter.com/Stroke_19/status/931525718667943936

Cozart saw quite a bit of improvement with his K%-BB% dropping by 6% points.

Note: I like using K%-B% to get an overall value for a hitters plate discipline. Earlier this year, I investigated what early season stats point to a true breakout. K%-BB%, along with launch angle (FB%), were the two key factors to focus on.

The Steamer projection has his K%-BB% regressing closer to his 2016 values than the ones from 2017. This is how projections work with previous season stats having some weight along with some regression.

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