Yesterday, I discussed the fantasy relevant average fly ball and line drive exit velocity (EV) surgers, which overwhelming fueled a spike in HR/FB rate. Let’s now check in on the other side of the ledger — those hitters whose EV declined precipitously from 2015 to 2016.
We officially have two seasons worth of Statcast data! Mind you, it’s not two full seasons, nor does it include every batted ball. But it’s still highly useful data. Although I continue to work on developing new equations with the data, we could all agree on one thing — harder hit balls are better. This is especially true when considering fly balls and line drives. We care far more about this bucket of batted balls than grounders because I have calculated a correlation of 0.769 between average fly ball and line drive exit velocity (EV) and HR/FB rate. So let’s find out which fantasy relevant hitters enjoyed EV surges from 2015 to 2016 and if those spikes resulted in HR/FB rate increases as well.
Johnny Cueto: Sabermetric Anomaly
I am on a mission, to try to explain why some pitchers don’t fit into normal molds. I created pERA to help explain the advantages pitchers who have a mix of high and low groundball pitches experience. I tackled the Robbie Ray/Michael Pineda group of pitchers who can only throw their fastball for strikes. The next pitcher whose production can’t be explained by the above methods is Johnny Cueto. He just a hard player to figure out and here is why.
Yesterday, I wrote up Rich Hill’s 2016 season, declaring him The Leader of Baseball’s Movementarians. In researching the piece, I dove deep into Baseball Prospectus’ Pitchf/x leaderboards. I wanted to find out what makes Hill’s fastball so dominant, despite its unremarkable velocity, and his curveball so effective, given its unimpressive whiff rate. I’m not an expert in pitching mechanics. I can’t breakdown video or tell you much about grips. For that, talk to Eno. But I found that Hill enjoys two distinct advantages that make his (essentially) two-pitch arsenal play up.
Statcast data is now everywhere and everyone seems to be using it in some form. While detailed pitch information has been available via Pitchf/x, full season batted ball data was missing. Now the batted ball data is leading to some interesting findings, but it’s not a true answer. So far, 12.6% of the batted balls is missing data. I wouldn’t see this as an issue if the missing data was evenly distrusted, but it is biased. I have made a simple correction to the data and now how have available corrected overall data and leaderboards.
I went over the procedure I used to correct the data in this previous article. Here is a quick review of the problem and corrective procedure:
Baseball’s supposed to be a simple game and it is in theory. But the deeper a person digs, the more complex it gets. ERA estimators like FIP, xFIP, kwERA, and SIERA came along to help explain the limits of ERA. The main issue with each metric is how to deal with batted ball data. More specifically, they fail at it. I tried to answer the batted data question with pERA but it only explained some of the differences. Even with all those attempts to fill in the missing data, some differences haven’t been explained. Today, I am going to fill in on missing gap while examining a pitcher’s breaking ball zone rate.
Today’s study will be sponsored by Michael Pineda and Robbie Ray. Each pitcher has posted good strikeout and walk numbers which historically has pointed to good ERA’s. Instead, they get hit around and their ERA’s are quite a bit higher than their estimators. Here are the career stats for the pair.
Yesterday, we were treated to a thrilling blockbuster of a trade, the type that has probably become all too common in keeper leagues, in which a contending team gives up their top prospects, and a rebuilding team “dumps” their star(s) in return. You know by now that Chris Sale is heading to Beantown and will don a Red Sox uniform. Or perhaps you just heard that his socks have changed color, ya know, from white to red. Paul Sporer gave you a quick rundown on how the move is likely to affect Sale’s fantasy value, but I wanted to go through the park factors and get more specific. So let’s compare the relevant park factors and discuss how the park switch may impact his performance.
Hyun Soo Kim signed with the Orioles before last season on a two-year deal. He struggled mightily in spring training and because of the nature of his contract, the major league team was forced to roster him. After just 17 plate appearances in April, he became a decent semi-regular in the lineup. The left-handed hitter got on the strong side of a platoon with Joey Rickard or Nolan Reimold being the other half.
He became a decent fantasy option in daily transaction leagues by posting a .329/.410/.454 in until July 10th when he went on the DL with a hamstring injury. After the injury, he hit only .275/.353/.386. The second half numbers are decent numbers, but not as good as before the injury.
With the new StatCast data, I examined it to determine if there was a drop in Kim’s exit velocity around the time of the injury. Using a 10-day rolling average, the available data may not look like it but the rolling average generally stays around 7 mph of the overall average. Additionally, there was no huge pattern change around the time of the injury.
The problem is that StatCast is not able to collect all the available data as documented at FanGraphs and The Hardball Times. The missing data is normal weak groundballs or high infield popups. I am not going to regurgitate the reasons and the exact details from the previous articles but I am going to take a step forward in accounting for the missing data.
At last, we come to our final outfielder Steamer and I review. Today, I’ll recap my Steamer and I battle over Michael Conforto, which pit my Pod Projection against the Steamer projection system. I was surprised to learn that I was significantly more bullish on Conforto than Steamer was, as I felt he was actually overvalued in fantasy leagues, though mostly due to his expected lineup slot toward the bottom of the Mets order. Let’s see what we expected versus what actually transpired.
About a week ago, I went over the stats I use when examining hitters during the offseason. Today pitchers take center stage. With Pitchf/x and now Trackman publicly available, I find it quite a bit easier to evaluate pitchers and the changes they make. For pitchers, I have one sparkling new main source and one old standby.
Pitchers are so much easier to evaluate compared to hitters. If a pitcher gains a couple ticks on his fastball, we know right away within a couple of pitches. If a hitter can no longer catch up with a 96 mph fastball, it may take a few months to know for sure and even then, we may not be sure why. Here are the tools I use to help find pitchers who have changed for the good or the bad.
Pitch Type Metrics
Ever since helping Eno dive into pitch-type metrics, I’ve determined that I will evaluate pitchers using this type of data. Even though it took a while to hammer everything out, the final results have been extremely promising. We can now determine what each small pitch change will mean for each pitcher and how pitchers can improve their results. The complete write up of the process is available, but here is a quick summary.
