The Auction Calculator is now loaded with the 2017 end-of-season data along with some 2018 projections. I’m sure the preceding comment will be sufficient to keep many readers busy for a while. I’m glad some came back. While its output can lead down several different discussion paths, I’m going to analyze what I consider to be the third starting pitcher tier. I feel many 2018 leagues will be won or lost by navigating this minefield.

So far this offseason, fantasy owners have placed four starters (Kluber, Kershaw, Sale, and Scherzer) in the top tier, After those four, I believe there are a dozen or so pitchers who would make acceptable aces, especially if they can be doubled up with another pitcher from this tier.

Read the rest of this entry »

This was supposed to be a quick paragraph on Max Fried but it turned into a borderline Quick Look. I was doing an article on pitchers who saw their ERA balloon because of starts at Coors. His non-Colorado ERA dropped stood at 3.09 vs 3.81 which made him seem like a borderline ace. I kept digging and found additional encouraging information. Here are some of my thoughts on my first 2018 deeper sleeper.

First, here’s how industry sources graded him including his pERA grades from his short MLB stint.

Some definite disparities exist. I will examine each pitch with a video from his September 9th start against the Marlins.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

Yesterday, the Justin Mason posted the ADP from four of the slowest drafts containing industry experts and myself. One thought I had after a handful of rounds was the lack pitching available and how the good were the available hitters. I decided to go back and examine draft results from last year and these draft to see if pitching was being taken early. While it wasn’t, some other information could be extracted.

Read the rest of this entry »

Last offseason, I created an individual pitch metric, pERA, which gives each pitch an ERA and prospect grade based on its ground ball nature and swing-and-miss capability. With the 2017 season over, I’ve compiled the final 2017 values. This year, I’ve added in exit velocity (EV) grades for each pitch.

The process I used for creating pERA is in the article linked above but here is a quick rundown.

• The key change is to give each pitch an ERA value (pERA) based on the pitch’s swinging strike and groundball rate. All the values are based on the average values for starting pitchers. Closers will have higher grades because their stuff plays better coming out of the bullpen.
• The pitcher’s control is determined from their walk rate which is separate from the pitch grades.
• I’ve put each pitch on the 20-80 scale with 50 being average, 80 great, and 20 horrible. For starters, target pitchers with three average or better pitches. For relievers, they just need two pitches.

Read the rest of this entry »

Last week, I examined pitchers whose strikeout per nine (K/9) increased while the strikeout per plate appearance (K%) dropped. This article focuses on the pitchers who saw their strikeout rates go in the opposite directions. Besides the strikeout divergers, I’m going to include the walk rate divergers since both player sets aren’t long.

Read the rest of this entry »

The season seems to never end for fantasy baseball writers. Once the regular season is over, it’s time to begin writing player previews for the next season. Pitchers who’ve had their strikeout (K% and K/9) and walk rates change in different directions spin me for a loop. Now, I query these schizophrenic pitchers to start the preseason previews. I’ll give a quick look at some of these pitchers. I’ll start with those pitchers who’ve seen their K% (strikeout per batter faced) drop while K/9 (strikeouts per nine innings) increase.

Two reasons exist for why the rates diverge. The key for both is increasing the number of plate appearances per innings. More plate appearances lead to their K% dropping if the strikeouts remain constant per inning. The other factor is how many hits a pitcher allows (basically BABIP). If a pitcher had good luck on balls in play and recorded more outs, they could quickly get through an inning and thereby raise their K%. Once the BABIP normalizes, the K% will drop.
Read the rest of this entry »

Last week I laid out my plans for combining prospect grades and “scoutable” traits to help project major league performance. Finally, I’m able to output projections with encouraging results. Just by using traits people can scout with their eyes, I created a set of projections which competes with Steamer projections. Additionally, it helps point to the traits people should look for in prospects.

Previously, I tried to use just the five traits prospects get graded on (Hit, Power, Speed, Field, and Arm) to come up with a player’s value. I found the Speed and Power grades useful but came to the following conclusion on the Hit grade:

Basically, the Hit tool is a useless component to determine hitter value as it’s currently being distributed.

The more I thought about the Hit tool, the more I concluded that it’s trying to evaluate too much information (examples of different Hit tool definitions).

For these projections, I matched up the traits hitters display with common stats. To start with, here are the core traits I decided to utilize:
Read the rest of this entry »

Last offseason I committed to finding if there was any information to be gleaned from prospect grades. Sometimes the grades were useful. Other times not at all. While I made some conclusions, many are still unanswered. Over the next few weeks, I going to try to find those answers.

I’m heading down the path with an unknown timetable or conclusion. My goal is to take scoutable hitter traits and come up with a usable projection system. For inputs, I will use the standard five 20-80 scouting traits of Bat, Power, Speed, Defense, and Arm. Using just these factors last year, I found an OK estimate of a player’s projection.

Read the rest of this entry »