Archive for Meta Analysis

Yesterday, The Marlins traded Dee Gordon to the Mariners for three minor league prospects. The trade came out of nowhere since the Mariners already rostered a top second baseman, Robinson Cano. Then it became known that Mariners plan on playing Gordon in centerfield. Having both middle infield and outfield qualifications a couple weeks into the season could move up his average draft position. The question remains though, will Gordon be able to cut in centerfield? Probably.

Gordon’s offensive talent is easy to access. The 29-year-old makes a ton of groundball contact and gets on first base using his speed. Once on base, he uses those legs to steal as many bases as possible. That’s it.

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Spin rate is becoming ever more important to baseball analysis now that we have access to more reliable measurement devices. Namely, Trackman. But there are other technologies as well which are being used by high school, college, and minor league teams. Trackman is the big name, though, since it has been adopted by MLB, NPB and KBO along with many colleges and even a few high schools.

Trackman uses Doppler radar to measure the movement of the ball. I want to paint a picture in your mind of what this may look like, in the eyes of the radar. Remember, we’re trying to track the ball here. Read the rest of this entry »

About a week ago, I compared rankings with some actual ADP. One player which stood out was Rougned Odor. He ranked 47th overall (AVG vs OBP league) and it’s tough to rank a person so high who hit only .204 last season. Steamer projections currently have him back up to a .255 AVG. Acceptable but not great.

Additionally, Odor comes to the plate hacking and rarely walks (4.2% for his career) so almost all of his value comes from his BABIP. If his batted balls don’t fall for hits, he’s not getting on base. Since his value is so BABIP driven, I decided to see what the BABIP bounce-back chances were for low-walk hitters.

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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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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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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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