Recently I wrote up Myles Straw, I found out the league was throwing him the most fastballs (60%, league at 49%) and the most pitches in the strike zone (57%, league at 50%). Additionally, slow fastball pitchers weren’t afraid of him with the 13th lowest fastball velocity against. I went one step further and looked to see how far outfielders were playing him and he was one of the shallowest played guys. He was just getting no respect. Read the rest of this entry »
On this past weekend’s On The Wire Podcast, there was a discussion surrounding Esteury Ruiz stolen base numbers and the effect of the minor league pitch clock. The gist of the situation can be taken from this Tucker Davidson tweet thread.
Some Take Aways From the newly installed pitch clock. I’ve made 3 starts with it and each time it’s been a different experience. Overall not a fan. Let’s talk about the good and the bad. (Thread) @jjcoop36 @JeffPassan
— Tucker Davidson (@tucktuck6) April 29, 2022
The rate of stolen bases is up in the minors as soon as they implemented the change. With the numbers up, I wanted to calculate a simple rule to see how much I should expect a hitter’s stolen base rate to change once promoted, especially rabbits like Ruiz.
For the calculations, needed to set some ground rules. I limited my sample to this season’s hitters who stole four or more bases in AAA. Additionally, they had to go on and have 30 MLB plate appearances. In the end, 40 players met the requirements. I know the sample isn’t the biggest, but I’m just looking at how a hitter’s stolen base rates should change.
For the first test, I compared the stolen base attempt rate ((SB+CS)/(1B+BB+HBP)) in AAA and the majors. In AAA, the median stolen base rate among these guys was 19.5% and it dropped to 10.8% in the majors. When comparing the ratio change (MLB rate/AAA rate) it worked out to 53.5%. So a hitter’s stolen base rate should drop about 50%.
The next test was to look at the success rate. In AAA, this group had an 80.2% success rate and it dropped to 72.1% in the majors. A success of 80% would help a team while 72% hurts their chances of winning.
One final test, what was stolen base per plate appearance change. This value is a quick rule of thumb that takes into account attempts, success, and changes in on-base rate. This value works that the MLB rate is 43% of the AAA numbers.
So going back to Ruiz, here are his expected stolen base rates knowing what he did in AAA.
| SB Stats | AAA | MLB Estimate |
|---|---|---|
| SB Rate | 50% | 27% |
| Success Rate | 85% | 77% |
| SB/PA | 16.2% | 7.0% |
| SB/600PA | 97 | 42 |
Here are the stolen base per 600 PA values for our projection systems.
| Projection | SB/600 |
|---|---|
| ZiPS | 35 |
| Steamer | 40 |
| FGDC | 42 |
| THE BAT | 31 |
| THE BAT X | 37 |
His projections are close but just a little lower than the value I found. Maybe I should just follow the projections.
The following nerd talk can be blamed on Ozzie Albies. Currently, he’s on the IL after needing surgery on his broken foot but plans on an August return. While digging through his stats, I was not sold on him being an early-round difference maker. Over the past two seasons, he just had a .255 AVG after it was at .279 in his first four seasons. One obvious change was that he has really started going for flyballs with his Flyball Rate (and Launch Angle (LA)) heading up.
What I wanted to know if Albies has changed his swing for more power and a lower batting average should be expected going forward.
To find the values, I had to do some manipulation of the public StatCast data. I don’t like how it’s currently being provided, so I needed to make a few adjustments.
First, instead of the 95 mph cutoff for the Hard Hit rate (used by BaseballSavant.com), I prefer 102 mph based on this Twitter thread I had with Jon Anderson.
Okay minimum exit velo at 26-30 degrees to reach .500 AVG and 1.500 SLG
2019: 98 mph
2020: 98 mph
2021: 100 mph
2022: 102 mph— Jon Anderson (@JonPgh) May 20, 2022
A batter’s swing path is the point where they create the hardest contact. The theory behind this concept can be found in these two articles. The value is a by-product of the Hard Hit query. Both of the values can be found in this BaseballSavant search.
The key with this information is to find the sweet spot of getting enough air under the ball for line drives and home runs while at the same time not popping up for some easy outs. Here are six graphs that will get us to a simple rule. If you don’t want to be overwhelmed by the graphs and numbers, feel free to jump down to the Conclusions section.
For this step, I going to compare the average hitters’ Isolated Power (ISO) and BABIP using the Hard Hit% and Bat Path. I studied all non-pitchers from 2015 to the present who had 50 batted ball events in a season. I’ll start with ISO since the results are cleaner.
First, here is a simple table of average ISO and for certain Hard Hit% and Bat Path. I tried to limit the number of empty values here.
To no one’s surprise, the higher and hard a ball is hit, the hitter’s ISO get higher
The deal is that on the right side of the table a change is starting to occur. For the weak hitters, their ISO has peaked and is heading down.
The change isn’t 100% clear, looks to start around 22 degrees.
Looking at the information another way, here is the average ISO grouped just by the Swing Path.
The ISO values peak around 20 degrees and then start declining. The key swing path for power seems to be around 20 degrees
Power isn’t the only factor to take into account So to start out again, here are the average BABIPs for a certain HardHit% and Bat Path.
The image is not as clean as the ISO one, but there is a range of high BABIP under 15-degrees Bat Path and over 15% Hard Hit%
Again, here is a look at the limited results from an upper cut bat path.
Again, not the prettiest image, but all the extremely low BABIP values fall in this range.
And finally, one last graph to show the average BABIP at different bat paths
While there is some curvature to the BABIP graph, it’s flatter from -5 to 20 degrees.
From the graphs and tables, the key to being productive is to hit the ball as hard as possible (duh) with a swing plane of 20 degrees. To get to elite levels, the Hard Hit% needs to be over 20%
Going back to Albies, here are his Hard Hit% rates and Swing Paths over the years.
| Season | Hard Hit% | Swing Path | ISO | BABIP |
|---|---|---|---|---|
| 2017 | 4% | 15.3 | .171 | .316 |
| 2018 | 7% | 15.7 | .191 | .285 |
| 2019 | 12% | 15.8 | .205 | .325 |
| 2020 | 10% | 22.8 | .195 | .317 |
| 2021 | 13% | 21.1 | .229 | .278 |
| 2022 | 6% | 20.2 | .161 | .266 |
The change in Swing Path obviously occurred between the 2019 and 2020 seasons. The change wasn’t as obvious since his Hard Hit% was in the low teens. When that rate dropped to 6% this season, all his results tanked. Albies swing path is fine as long as he’s hitting the ball hard.
Besides Albies, here are some other hitters with a Swing Path over 22 degrees but a sub-15% Hard Hit%.
| Name | Hard Hit% | Swing Path |
|---|---|---|
| Rosario, Eddie | 3% | 25.5 |
| Arraez, Luis | 3% | 23.6 |
| Marcano, Tucupita | 6% | 22.7 |
| Vogt, Stephen | 6% | 26.0 |
| White, Eli | 11% | 23.9 |
| Phillips, Brett | 13% | 22.1 |
| Muncy, Max | 14% | 24.0 |
| Wade Jr., LaMonte | 15% | 24.4 |
| Belt, Brandon | 15% | 23.9 |
| Luplow, Jordan | 15% | 22.1 |
I’ve had a hard time figuring out Dean Kremer and couldn’t explain his low 2.48 ERA while most of his ERA estimators are in the high 4.00s. I finally got around to looking at his splits with the bases empty and those with runners on base. With the bases empty, Kremer has a .391 BABIP. Once someone gets on base, he has a .167 BABIP. Since he has a .310 overall BABIP, the discrepancy wasn’t obvious. I decided to look into the predictability of BABIP with and without runners and with BABIP-related issues and how much a difference can help predict an ERA regression.
To do the study, I look at starters (>90% games where starts) who at least threw 80 IP in season one and at least 40 IP in the next to help account for survivor bias. Just a reminder that previous studies found it takes forever for a pitcher’s normal BABIP to stabilize (2000 BIP, ~ 4 seasons of 180 IP). Read the rest of this entry »
Over the last two seasons, several highly-touted rookies have struggled with the jump to the majors like Jo Adell, Jarred Kelenic, Joey Bart, and Josh Lowe. I examined strikeout and shift numbers. Also, I wanted to find the major league replacement level OPS where hitters get demoted this season. Usually, it’s just under .650 but I was wondering if it has changed.
I’m not going to put a story around each study. Examples of hitters struggling with the jump to the majors are everywhere. I just want the facts when those stories pop up in the future. Read the rest of this entry »
This past weekend, I was trying to decide how much to lean into Oakland’s nine-game week. I didn’t know if just the regulars would start in all the games or should I focus on some of the lesser bats (there are many of them on Oakland). I figured this question would come up several times during the season, so I decided to get an overall view of who sits and plays.
I’ll apologize upfront for most of the article being a big data dump. If anyone is not interested in the numbers, just jump to “Conclusions” for my take on the information. Read the rest of this entry »
Yesterday, I dove into some of the hitting metrics to determine what has been driving the decline in offense. Today, let’s over to the pitching side and review another set of metrics. Obviously, we use a lot of the same metrics on each side, so I’ll only present and discuss those that weren’t shared yesterday.
We’re about a month into the season (holy cow, seriously, it’s whizzed by so far!), and offense is down at levels we haven’t witnessed in a loooooong time. So I’m going to take a break from my usual player specific leader and laggard boards and review some leaguewide metrics. You may have noticed in your fantasy league standings that pitching ratios are significantly better than we’re used to. In my shallow 12-team mixed league, four teams are sitting pretty with a sub-3.00 ERA! Two teams have a sub-1.00 WHIP (I’m barely above at a 1.0057)…whaaaaaaaaaaat?! Even in my AL-Only Tout Wars, four teams have posted a sub-3.00 ERA, which is just insane! Naturally, these strong pitching results must mean that offense has been missing. Let’s review some of the most basic of metrics to find out what’s driving the decline in offense.
Yesterday, I built a $14 NFBC offense using their average auction values, limiting myself to an entire squad of $1 hitters. Today, let’s now build a $9 pitching staff. I only had 58 pitchers to choose from, but I can guarantee that you will be so jealous of the squad I assembled, you will wish you had done the same in your 15-team league.
Earlier this offseason I examined the accuracy of several projections. I’m going to give my recommendations today on the best way to balance those findings between getting the best results and keeping the process simple. I’m going to focus on playing time and stats for pitchers and hitters. While there could a way to weigh every single stat of every projection, it’s just a waste of time in my opinion. The best answer is to aggregate the best options.
I know some people will want a more in-depth answer while the following will be too much for others. Some projections, like ATC, are already trying to perfect the mix and still fall short of a straight average. The cause for the disconnect is that some of the stand-alone projections are constantly improving. What may be the best projection mix in one season is suboptimal in the next. I’m willing to have 95% of the projection accuracy and instead spend my time looking for information that the projections might have missed. Read the rest of this entry »
