Archive for Hitters

“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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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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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 »