After just updating the appendix last year, The Process has been fully updated for next season and is available in digital (full and appendix) and print versions.
Some of the new highlights from the book are:
For the first time this year, I added an all-star break hitter poll to pair with my pitcher poll. The hitter poll pitted the 10 greatest xwOBA overperformers against the 10 most significant underperformers. I asked you which group would post a higher second half wOBA and which range each group’s wOBA would fall into.
During the all-star break, I once again polled you on which group of 10 starting pitchers would post a lower ERA during the second half, and which ERA range each group’s aggregate would fall into. Let’s now review the results.
Yesterday, I wrote about the replacement level hitters in the NFBC 12-team Online Championship (OC) and the 15-team Main Event (ME). In the comments, Joe Wilkey asked:
There are a couple of good questions here, so it’s time to start working through them. I’ll start with “Is batting average unpredictable?” Yes, with any given player’s actual range being unacceptable for a fantasy manager. Looking back at Steamer Projections (2010 Steamer to 2021), I found the standard deviation for the difference between the projected and actual batting average. Read the rest of this entry »
In most weekly leagues, the ability to add and drop players is gone for this season. Since there are no more moves, I’m going to analyze the most added and dropped players in NFBC’s Main Event and Online Championship with the main goal to create a composite replacement-level player.
For reference, the Online Championship (OC) leagues have 12 teams while the Main Event (ME) has 15. Both of the leagues require 23 starters each week with 7 bench spots (no IL spots). At all times, 360 players will be rostered in an Online league and 450 in a Main Event league. The reason I decided on the two NFBC formats were:
I know at times we may seem a little NFBC centric here at Rotographs. Now, if some other platform had the ability to select a league type and make available all the adds and drops, I’d use them. The NFBC is the only platform that offers this service. Read the rest of this entry »
After more than two days of pouring over pitcher batted ball data, I better write something before I am fired. In the end, I found nothing groundbreaking. Popups and groundballs good… everything else (i.e. line drives and flyballs) is bad. The change I’d recommend going forward is to move to a more granular grading of batted to bins, like barrels, based on outcomes. Read the rest of this entry »
I’ve been trying to get to the root cause of how much can a pitcher limit hard contact. I’ve had some hits and misses, but today I’ll finish going over the possible inputs.
I’ve already investigated the groundball/flyball issue and generate weak contact when hitters make contact with pitches out of the strike zone in this series and previously looked into two-strike counts and found nothing. Here are the five possible causes.
So it’s time to finish the list to see if changing speeds and horizontal movement help limit hard contact.
Recently, I started working through predicting which pitchers limit hard contact and by how much. Today, I dive into if contact out of the strike zone can be predictable and if that contact is weaker than contact in the strike zone. First, I need to go over a couple of concepts
The division of credit for WAR goes with 50% to hitting and 43% to pitching and 7% to fielding. People way smarter than I have determined that split.
Major Note: For simplicity, I’m going to adjust the pitching percentage up to 100% so the fielders are allocated 14% of the credit for what happens when a pitcher is on the mound.
On top of the fielding allocation, not every batter puts the ball in play with the league at an 8.7% BB% and 23.4% K% this season. So now, 68% of all at-bats end with a ball in play with 14% points of that 68% goes to the fielders and 54% (68%-14%) to the pitcher’s batted ball talent. So it works out that 79% (54%/68%) of a pitcher’s batted ball results should be attributed to him. Read the rest of this entry »
Sometimes conclusions to tough questions just don’t sit right, especially when the answer is “We don’t really know.”. How pitchers control batted balls has never had a simple definitive answer. I’m going to give it another shot.
I have some ideas of what might be a cause, but I want to start with a blank slate. What’s got me diving back in is the following table from a recent article of mine.
While a few percentage points of difference may not seem like much, I expected a lot more regression to the mean with my limited sample size. With just the above information, I felt I needed to re-investigate the subject. I know that some of the regression amounts have previously existed, but I wanted to dive in with some fresh eyes and new batted ball data. Read the rest of this entry »
For the second week in a row, I had to voice my amazement that JT Brubaker was cut in the NFBC Main Event. While Brubaker’s results have not been great (4.95 ERA), there are several signs that point to him being closer to a 4.00 ERA pitcher. The stat that sticks out is his 1.9 HR/9. The home runs have him with a 4.98 FIP while his xFIP is a full run lower at 3.98. I wanted to see if I should blindly assume that his home run rate will drop. With the expected drop, will his FIP and ERA regress downward to his xFIP? Also, are there any measurable traits that make a pitcher more home run prone? I ended up with a “maybe” and a solid “no”.
Brubaker isn’t the only pitcher who fits this mold. Bailey Ober has a 2.2 HR/9. His 4.99 ERA is almost identical to his 5.18 while his xFIP is down at 4.12. Another is Yusei Kikuchi (1.6 HR/9, 4.37 FIP, 3.47 xFIP). Adbert Alzolay (2.0 HR/9, 5.03 FIP, 3.89 xFIP). The season is over halfway over and fantasy managers are losing patience. Read the rest of this entry »
