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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It’s probably been awhile since you’ve read a traditional “buy low, sell high” article.  In today’s golden age of baseball analytics where complex physics and statistics can be boiled down to a few simple indicators accessed instantly using one hand, it’s not very often that we (readers, fans, fantasy players) find ourselves in possession of knowledge before the masses.  For example, try “selling” Avisail Garcia and his recent .375 wOBA around your league without getting some type of response that includes “yeah, but he had a .392 BABIP”.

Thankfully, despite all the data available at our fingertips, the one ingredient that will always play a critical role in the mixture of value is the human element of perception, which can swing wildly in different directions depending who you’re dealing with.  Today I’d like to isolate a few players who’s perception may be suppressing their actual value a little more than it should be, which may represent a buying opportunity for savvy fantasy owners prepping for 2018.  The good news is you don’t have to stand in line to land these deals, but you will still need to get them early.

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Yesterday, I reviewed my Pod vs Steamer projections series with the stolen base upside guys, those hitters whose Pod Projection in stolen bases was well above the Steamer extrapolated projection. Today, I’ll finish the series on the offensive side with the stolen base downside list, the guys I projected to steal far fewer bases than Steamer. Let’s see how they performed.

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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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Let’s continue our recaps of my Pod vs Steamer projections series, this time with stolen bases. As a reminder, I compared my 2017 Pod Projection stolen base forecast to the Steamer projected, extrapolated over the same number of plate appearances I had projected. This group is composed of those whose Pod Projected stolen base total most exceeded the Steamer projection. Let’s see how they did.

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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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Before taking a short vacation, I reviewed the first preseason Pod vs Steamer Projections series posts, focused on home run upside. That article discussed the hitters whose home run Pod Projection was significantly greater than his Steamer projection. Today, I’ll recap the players I identified as possessing significant home run downside compared with Steamer. With the record setting home run total, this shall be interesting.

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Major league batters are generally shifting towards a fly ball approach. The idea is to hit more balls in the air. Not necessarily fly balls, in fact there are those who wish to only hit line drives. When I say in the air, I mean ‘not on the ground.’ You want the ball to leave the infield before it bounces, ideally. Preferably this happens at a very high speed.

Duh, no kidding, right? Well, yeah. Obviously hitting the ball out of the infield is the goal for just about everyone. The goal isn’t the key, we’re talking about the approach used to actualize the goal. Read the rest of this entry »

I’ve finally compiled the 2017 Disabled List (DL) information. The main change from the last few seasons is the transition from the 15-day DL to 10-day DL and the subsequent increase in DL trips. With the total trips up, the number of days lost is down which makes it tough to draw any major conclusions. It’s time to dive into the numbers.

First off, I collected the information from MLB.com’s transaction list. I like to use this list because it is easy to go back and check. I waded through it and it wasn’t pretty. It took me twice as long to compile the data compared to previous seasons. I would just like to give a big thank you to ProSportsTransactions.com for having most of the missing data.

With my venting out of the way, here is how the days missed for pitchers and hitters compare over the previous 4 seasons.

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A few weeks ago, I began the process of determining an underperformance metric. In the article, I laid out the groundwork determining the drop off in plate appearances (PA) and production (wOBA). With these thresholds, I created several metrics, each with its own advantages and disadvantages. I’m not setting the values into stone yet but I’m getting closer to a solution. I’ve found a few value I like better than others.

In the original article, I found fantasy owners considered a drop in 220 PA from 600 PA (37% drop) and of 0.035 wOBA from .350 wOBA (10%) to be the thresholds. I didn’t mess with these two values. Besides the pair, I wanted to know when both occurred. Additionally, from a discussion in the comments, I found when either PA or wOBA thresholds where met and when both dropped close to, but not over, the thresholds. This value (called Minor Drop) I found to provide the most overall value.

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