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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A few days ago, Travis Sawchik ask me to help find some comps for the Shohei Ohtani using a 2016 Davenport translation. The list of potential hitters with similar 2018 Steamer projections was impressive (Charlie Blackmon, George Springer, Mookie Betts, Carlos Correa, Yasiel Puig, and Aaron Judge). Additionally, I found pitchers who had similar 2018 projections to his 2016 translation but the list wasn’t as impressive (Jimmy Nelson, James Paxton, Jon Gray, Luis Castillo, Luke Weaver). Thanks to Dan “The Man” Szymborski, a 2018 projection now exists and results will be a little disappointing.

First, from what I heard from most fantasy websites, Ohtani’s will be two separate draftable players. Ohtani the pitcher and Ohtani the hitter. No site, that I know of, has yet to combine the two. If they did, they will likely have to count all the hitting stats accumulated by all pitchers. I hope this doesn’t ever happen.

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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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Every year we have a number of players who make their debut towards the end of the season, wildly exceed expectations, and leave us wondering what the future may hold. Last year we had Gary Sanchez. This year, Rhys Hoskins.

Hoskins hit the ground running. I mean, how many guys reach double digit homers before they reach double digit singles? I could probably look it up, I’m not going to. I don’t want to know. Hoskins did it, and that’s good enough for me.

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This past weekend, I was in Phoenix for Baseball HQ’s First Pitch forum. It’s an intensive few days of catching up with old friends and focusing on the upcoming fantasy baseball season. There was an underlying theme of the weekend, the fantasy baseball game is being forced to change. Some game facets have experienced some massive adjustments. The following are some of the meta-trends which have quickly popped up over the past few seasons.

Home runs are way up

A few days ago I wrote the following incorrect statement about Carlos Martinez for a 2018 player preview.

His 1.19 HR/9 will likely drop back below the league average.

It was pointed out to me, his home run rate was below league average. I was for sure it was not near 1.20 but I was wrong. Here are the recent league-wide HR/9 values.

2014: 0.86
2015: 1.02
2016: 1.17
2017: 1.27

I remember when the HR/9 hovered around 1.0. Not anymore. Some other pitching stats are feeling the effects of the jump like ERA, but the root cause is more home runs.

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I have spent the past few years creating a family of stats that I’ve called xStats. These stats use Statcast batted ball metrics to analyze each player, which I then manipulate and export in a manner I hope is useful for fans and analysts.

Exit Velocity and launch angle data are good, and I include those, but they aren’t yet intuitive for more baseball fans so I have set forth to display my data in terms of numbers that are more relatable. Namely the standard slash line numbers. I have expected batting average, on base percentage, slugging percentage, batting average on balls in play, and weighted on base average. For pitchers I have bbFIP, which is an ERA scalar. Today, though I’m only going to be looking at batters.

These stats are available, but they don’t help much unless you know how well they are working. To that end, I have created the following table, which compares the regular, standard slash line to the xStats slash line. Read the rest of this entry »

Happy Game 7 Day!

About a year ago I released my (2017) Top 50 Fantasy Prospect rankings using the Prospect Scorecard to weight a variety of important variables in the context of fantasy baseball.  Today I’m publishing an (early) expanded list of the Top 100 Fantasy Prospects for 2018 for both Ottoneu’s FanGraphs Points leagues (where wOBA is a key measure on offense) and Roto leagues (5 x 5).

A few quick notes before we begin:

• Since “Cost” is league-dependent (auction salary, keeper round, etc.), I’ve ignored it here for simplicity by keeping it constant for every prospect listed. Feel free to use the Scorecard to make changes that reflect true player costs for your league, which will impact these rankings.
• These rankings below are intended to represent the 100 most valuable prospects for fantasy leagues (depending on scoring format).
• It’s quite possible I’m missing an obvious player that should be ranked, so let me know in the comments.  We can discuss the specific rationale for player rankings in the comments, too.  Player ages are current ages.
• For a lot more prospect resources, check out the Ottoneu community.

Here are the (early) 2018 Top 100 prospects for the linear-weights-based FanGraphs Points scoring format (a good proxy for those in OBP leagues):

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Most fantasy owners expect drafted players to under and over perform some amount. When a player overperforms, the owner looks and feels great because they “knew” a breakout was coming. Owners hope they didn’t pick too many players on the other end of the spectrum. The underperformers are the ones who drag down a team and owners find as escape goats for a bad season. I’m going to start laying the groundwork to determine a hitter’s disappointment chances.

The first major step in finding a disappointing hitter is to define what is a disappointment. After owning too many fantasy teams over the years, I’ve had my share of disappointments (e.g. Brandon Webb in a 2009 first round) and feel they are just part of the game. This ambivalence doesn’t mean I shouldn’t know the breakout chances. Even small changes in the odds can make a major difference after rostering 20 or more players.

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