“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).

Read the rest of this entry »

Pitching blisters were an afterthought just two years ago but the reported instances have jumped the past two seasons. Detailed accounts were written by Eno Sarris here at FanGraphs and Ben Lindbergh at the Ringer.

Throwing a curveball may be to blame according to Sarris:

But we can’t dismiss that chart completely. The players who have gone down with blister problems have thrown curves 14.9% of the time, far above the 10-11% baseball as a whole averaged over that timeframe. The players who ended up on the list more than once averaged 18.9% curveballs. Enough to say there’s some smoke here.

Read the rest of this entry »

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.
Read the rest of this entry »

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.

Read the rest of this entry »

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

Read the rest of this entry »

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.

Read the rest of this entry »

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.

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.

Read the rest of this entry »

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.

Read the rest of this entry »

Unless league rules require it or an owner plays in an NL West-only league, owners don’t use their starters in Colorado. This stance may be a little extreme but the league’s aces should be the only started used in Colorado. Pitching in Colorado adds one run to pitcher’s ERA. Owners know this but visiting team must send out a starter to get sacrificed. I’m going to go over some of the pitchers affected the most by being the sacrificial lamb.

This past season, I owned Jeff Samardzija in a couple of leagues. His 4.42 ERA was just above league average (4.36 ERA). The effect on my team wasn’t that much though. There is no way I was going to start him in Coors and I missed out on his 1.94 WHIP and 11.91 ERA over 11.1 IP. With the starts removed, his ERA dropped to 3.98. While not a great ERA, it represented the true effect it had on my team.

Read the rest of this entry »