On Thursday, I shared and discussed some of the early 2019 hitting trends. Today, let’s flip over to the pitching side. In an effort not to double up on the metrics already discussed last week, I’ll focus mostly on pitch mix and velocities.
It’s been fascinating to watch baseball evolve and how various metrics have trended over the years. While it’s still a tiny sample size for players, it’s definitely not for the entire league as a whole. So let’s observe and discuss the major hitting trends several weeks into the season and how it impacts fantasy valuations and your teams.
Yesterday, I compared batter HR/FB rates to my xHR/FB rates to identify nine hitters who posted xHR/FB rates significantly higher than their actual marks, suggesting upside this season if they are able to maintain those underlying skills. Today, I’ll check in on those hitters on the opposite end of the spectrum, with a list of six batters who overperformed their xHR/FB rates most. If the underlying skills driving the xHR/FB rates stick in 2019, these hitters are at serious risk of HR/FB rate regression.
News broke on Friday that Francisco Lindor would be out seven to nine weeks with a strained calf. The reaction to the injury was swift. In NFBC’s Online Championship before the injury news, he had a 4.8 ADP in ten contests. In the two contests over the weekend, his ADP dropped to 16.5 after Jose Altuve, Javier Baez, Alex Bregman, and Trevor Story. So, is the drop deserved or did they drop him too far or not far enough? I’m going to fully breakdown how much I would change his value in a sample league.
With so many league variations, I need to pick a lane with his value adjustment. I going to take a 15-team roto league using AVG. For this league, I’ll use the Standing Gains Points (formulas in my book) as a shortcut to help determine his value and use couple tools to help speed along the process.
Injuries analysis is becoming a pain in the butt. First, MLB goes and changes the days missed from 15 to 10 thereby nullifying several projection models. And now they’ve gone and renamed the Disabled List the Injury List. I think of naming it the BDL (Broke D__k List). I’ve been waiting for a few more seasons of DL data to create a new formula which will become obsolete with the 12-day DL but why wait. I have two seasons of 10-day IL information to create a few comparisons, especially for pitchers.
While I’ve historically collected the data, Ryan Brock jumped in and completed the 2018 season. I’m not sure if I would have gotten to pulling it together because when I normally do it (post-season), I was finishing my book. I can’t thank Ryan enough.
Yesterday, I discussed 10 hitters with big HR/FB rates whose marks were actually validated by their xHR/FB rates. Comparing HR/FB rate to xHR/FB rate helps guide my 2019 Pod Projections. Now let’s flip it and check on the hitters who posted surprisingly low HR/FB rates, but that were actually validated by low xHR/FB rates.
Yesterday, I identified and discussed five hitters who made surprise appearances near the top of the average fly ball distance leaderboard. Today, I’ll talk bottom dwellers. Let’s find out which surprising hitters found themselves bringing up the rear. Like I did with the leaders, I won’t include the hitter if he also appeared on the surprise barrels per true fly ball laggard list as well.
The amount of stats available to analysts today is overwhelming. At least it’s getting that way with me. I’d prefer everything to be available to investigate an idea. But no one has the time to go over every single stat to see if a player has changed for the better or worse. I’m going to eliminate all but 10 stats to focus on those few that matter the most.
The key behind my analysis is to find if a pitcher or hitter has changed. A few dozen projection sets exist to set a talent baseline but once the season starts, most people want to throw them out (some even before the season) if a hitter is batting .150 or a pitcher has a 6.00 ERA. Players are human and creatures of habit so most won’t change. But some do and being able to focus on these few can help to find values.
About a week ago, I shared the surprising hitters who finished amid the top tier in barrels per true fly ball rate, a metric I created that acts as one of the primary components of my xHR/FB rate equation. Another major component of the equation is average fly ball distance (Avg FB Dist), which isn’t typically discussed, as it’s not on the default Statcast leaderboard. So let’s find out which surprising hitters, who hit at least 30 fly balls, finished near the top in the metric. I also decided not to include any hitters that also appeared on the Brls/TFB surprise leaders list.
The 2018 season opened my eyes to the risk of young players losing playing time because of massive platoon splits. Usually, it’s the established major league veterans like Matt Adams who get platooned. Manual Margot was such a player who usually led off against lefties but was dropped in the batting order or even to the bench against righties. Those lost plate appearances can add up over the course of a season whittling away a hitter’s value. Today, I’m going focus the most talented hitters who are most like to be platooned.
For Tanner’s and my book, The Process, Steamer projections were kind enough to include every hitter’s projected OPS versus right and left-handed pitchers. From the book’s research, we found teams have historically started platooning hitters when one side of the platoon’s OPS drops below .650 and definitely at .600. Here are some the hitters who may end up in a platoon situation if their team decides to go that route. Besides the risk of sitting out games, some teams move these players up and down the lineup based platoon differences. I’m likely going to give some of these hitters a playing time adjustment.
Note: The projected platoon OPS are league and ballpark neutral and may seem off compared to the current projected OPS.
