There is no need to beat around the bush with an intro full of cliches and examples. Simply does playing in the postseason wear down a player enough to effect their next season’s production?
Simple answer: Not really.
Less simple answer: Some with hitters, not at all with pitchers.
Complex answer: Danger math.
Yesterday, I shared and discussed a plethora of hitting metric trends from 2015 to 2019. More strikeouts, more fly balls, and more home run power are the obvious trends. Now let’s look at how the early 2019 pitcher trends I discussed in mid/late April ended for the season.
In mid-April, I took an early look at hitting trends in a plethora of metrics since 2015. Now that the 2019 season has concluded, let’s now revisit those metrics and see what 2019 ended up bringing us. Did it continue a trend, reverse a recent trend, or begin a new one?
For a while, I’ve been trying to have other people do my research for me with no luck. I figured with all the smart minds in the stock market and other securities, some method would line up. It really didn’t for the fact that everyone could basically buy or sell as much of every available option to everyone. Some hedge funds or Warren Buffett could buy up an entire asset, but basically it was tough for stocks to run out.
I needed to find a limited asset class where one unique asset (house) and several buyers determine its value. That is why I eventually got to looking into real estate. Most of the public literature involves how to get loans and fix up the house. There is surprisingly little on actually determining a property’s value. So far I’ve only gone to the library and the local book stores to find information since most books are completely useless with nothing beyond, buy low, sell high. I’m not wasting my time and effort on ordering a bunch of books.
When I wrote my article on disappointing 2019 hitters, the data diverged from expectations in one subset of hitters. Injured players dominated the list but they also saw a huge drop in their production to the tune of about 100 points of OPS. While the season-to-season OPS values are never exactly one-for-one, a 100 point dropped is huge especially since the league’s overall OPS jumped 30 points last season. I needed to dig in.
It’s going to get a little nerdy as I have to explain how I examined the data. To try to minimize the pre and post-injury production from the investigation, I only looked at players who were placed on the IL in March or April. Little if any of their production should have been before the injury. A second reason was to help project hitters who are dealing with offseason surgeries (e.g. Adalberto Mondesi). So, over the past 10 seasons, 627 hitters met these criteria.
Read the rest of this entry »
A couple of weekends ago at BaseballHQ‘s First Pitch Arizona conference, The Athletic’s Eno Sarris and I talked about hitter metrics most descriptive and/or predictive of power. In Eno’s presentation, he included a quip from analyst Hareeb al-Saq:
“Knowing barrels on top of average EV [exit velocity] tells you a lot. Knowing average EV on top of barrels tells you a little.”
Eno was surprised by this finding — that barrel rate is a more beneficial metric than average EV, or even EV on a certain type of batted ball event (BBE), such as fly balls and line drives. Incidentally, this is something Al Melchior and I researched last year for which we reached the same conclusion: barrels, whether as a percentage of batted ball events or plate appearances, correlate more strongly than average, maximum, or fly ball/line drive EVs did to common power metrics such as home runs per fly ball (HR/FB), isolated power (ISO), or hard-hit rate (Hard%).
However, it made more sense to Eno when I articulated that calculating barrel rate is simply the act of isolating a hitter’s most-optimal batted ball events. In other words, the inclusion of launch angle (LA) adds another explanatory dimension to EV. In my head, it’s like having two separate circles — one for EV, the other for LA, each containing every individual batted ball outcome from the season — and overlapping them. The overlapped portion of the Venn diagram signifies barrels, and it changes in size depending on the quality of the batted ball events.
Note: For my next few articles, I’m going to examine the hitters and pitchers who underperformed and overperformed in 2019. Each article may spawn off others since some areas may need to be explored in more detail. After performing horribly in 2019, I need to take take a hard look at why I missed last season and what I can do to improve.
I’m going to start with the one player class every owner hopes to hit on, breakout hitters. A couple of these cheaply acquired star hitters can help carry a team. It could be a prospect turned uber-prospect (e.g. Pete Alonso) or just a hitter displaying new skills (e.g. Ketel Marte). I’m going to dig into the reason these breakouts were not draft-day targets and look for any common themes.
To get the test subjects, I ran our auction calculator for end-of-season production and then compared the auction dollars to the values created from their ADP. I didn’t want to just use the difference in ranks because the gap from #1 to #15 could be over $10 but the difference between #250 to #300 might just be $1. Using this method, I found 62 hitters who outperformed their value by $10 or more.
Read the rest of this entry »
After doing an overview of the juiced ball and a focus on pitchers, I started down a path to find out which hitters would be most affected by the juiced ball. I didn’t know it was going to be overrun with thorn bushes and misleading signs. The process was nearly impossible for the simple fact that the league’s hitting profile changed. Besides even considering the ball, batters were hitting more flyball, hitting the ball harder, and pulling it over short corner fences. I tried to find one answer but ended finding another.
One key to this analysis is that I wanted to keep it simple. I didn’t want to pump the data into some neural network for a more “correct” answer where I’d not sure of the factors in play and how each one was weighted. I wanted some clarity.
One set of factors I initially used was the StatCast information but I didn’t use it for the final analysis because it didn’t add any accuracy. Groundball rate is almost a perfect proxy for Launch Angle. Home run per batted ball is basically a Barrel. Also, StatCast data has only been available since 2015 when the juiced ball started. There is no baseline data for the deadened ball period.
Read the rest of this entry »
… but not by much for teams I just owned …
My 2019 fantasy season did not live up to my standards with most of the struggles being self-inflicted. Here are some overarching themes I spotted with each league plus some additional points at the end.
I ran out of money in almost every league and spent too much FAAB on worthless assets. Looking back over the leagues, the root cause was chasing week-only plays. From my work writing “The Process”, I found out how valuable it is to grind out each week. Additionally, I ran the weekly FAAB projections here so I knew around what it would take to get each. Initially, I got the players and but dropped them a week or two later for better options with little to show for the FAAB spent
I need to set a FAAB limit for chasing week-only plays and just accept it’s fine to miss out on a few players. A week’s advantage is worth the same in week 1 or the final week. The rest of my FAAB can be used for chasing long term improvements. Some players may straddle the long-term and weekly play so the FAAB may come from both the weekly and long term pools. I need to have a plan and stick to it.
Read the rest of this entry »
I’ve screwed up. A lot. A few years back, I created a pitching metric called pERA which took each individual pitch’s results (swinging-strike and groundball rate) and combined them into one metric. The problem was that I wrote the article in 2016 and used a formula I created back in 2015 with 2002-2015 data. The juiced ball arrived and I never adjusted the formula for the change. Oopsy.
I felt a little sick when it finally dawned on me that the formula needed updating and ma initial findings are available in this Twitter thread.
I've invested too much time this morning looking into GB%, ERA, and ERA estimators. Two conclusions:
1. Happy Fun Ball has everything messed up. Don't assume any previous assumptions are true.
2. Groundball pitchers are ruling the day.1/6
— Jeff Zimmerman (@jeffwzimmerman) August 16, 2019
It finally dawned on me that the formula I was using to evaluate pitchers was off. With strikeouts and walks being equal, groundball pitchers outperform flyball pitchers. With a deadened ball, a high flyball rate meant most flyballs would go for easy outs. Not any more. Now those flyballs go for home runs. It’s time for a little math to show the change.
Read the rest of this entry »
