I have a major love-hate relationship with the STUFFF metrics. After just a few pitches, useful information becomes available to determine if a pitcher has improved or not. On the other hand, the issue I have against STUFFF is the lack of transparency and values change as the dataset increases. With all the STUFFF talk, all I want to know is how changes in it will affect a pitcher’s fantasy-relevant stats. In my first article, I set some ERA baselines for the STUFFF values. The next step is to understand what a change in a STUFFF value has on a pitcher. For example, if I hear their Stuff+ jumps from 90 to 110, why should I care? Is the pitcher’s ERA going to drop by 1.00 or by 0.10 or not at all? I decided to just make a major data dump to have a reference when a STUFFF value does move.
Caution: The following values may or may not be predictive. They could just be descriptive. There is just not enough information (2 years of information) to run any ideal predictive test at this point, especially with STUFFF’s vagueness and everchanging nature.
On a personal level, the All-Star break can be declared a success as I’ve made major improvements to my pitch result evaluator, pERA. I was supposed to do dive into it last season, but I spent most of the time dealing with the league’s new rules so this update got pushed off until now. I planned on adding Ball Percentage (Ball%), Called Strikes (CStr%), and StatCast batted ball information. I felt each add would provide a clearer picture of the pitcher’s pitches. I eventually found out I was double counting the same information with Ball% and CStr% and needed to remove one. Read the rest of this entry »
Keeping track of the machinations of 30 major league bullpens is pretty tricky. In standard leagues, it’s hard enough trying to discern which relievers are earning save opportunities, especially since more and more teams are using a committee approach in the ninth inning. In Ottoneu, with both saves and holds earning points, that search for high leverage relievers becomes even more of a challenge. There are plenty of resources out there — the Roster Resource Closer Depth Chart is one of my favorites — but even the most vigilant fantasy player can’t keep track of everything going on across the majors.
Here are a few relievers who have been seeing high leverage usage over the last two weeks, who are also rostered in less than 60% of all Ottoneu leagues:
Chris Martin is currently the primary setup guy ahead of Kenley Jansen in the Red Sox ‘pen. His strikeout rate isn’t as high as it was last year with the Cubs and Dodgers but his walk rate is still a tidy 2.6%. Nothing has really changed in his profile; his swinging strike rate is right in line with where it was last year and his CSW% is up to a career high 30.7%. I’d expect his strikeout rate to bounce back towards where it was, giving him a bit more ceiling than his surface-level stats would indicate.
I wrote up Josh Sborz the last time I looked at under-rostered relievers and his roster rate barely ticked up from 0.3% to 1.9%! He’s definitely taken hold of the eighth inning duties in the Rangers bullpen ahead of closer Will Smith and his FIP is currently the lowest among this group. He currently has a career-high strikeout rate at 36.8% and his walk rate has come down two points from his career norm. More importantly, he’s only allowed a single home run this year, something that had plagued him in the past. Grant Anderson was called up by the Rangers at the end of May and has already inserted himself into the late inning picture. He dazzled in his debut, throwing 2.2 innings and striking out seven.
Justin Lawrence has taken over closing duties for the Rockies. He’s using his big sweeping slider to earn swings and misses, though his overall strikeout rate is held back by a sinker that’s used to get weak contact on the ground. Still, that’s a benefit for a reliever pitching in Coors Field and he’s only allowed a single home run this year and a 95th percentile barrel rate.
Sam Hentges missed more than a month of the season with a spring shoulder injury but has come back strong and has converted a number of high leverage opportunities for the excellent Guardians bullpen. He’s collected seven holds and has been used for multiple innings a handful of times as well. His command has been uncharacteristically off, though that might just be him still shaking off the rust after his injury. He’s throwing in the zone as often as he was last year, but his chase rate has fallen by nearly eight points.
Lucas Sims missed most of last year and some of this year with a back injury, but he returned in late-April and has taken his place as the primary setup guy in Cincinnati. He really struggled with his command after being activated off the IL, though he’s only walked two batters over his last six outings. Back in 2020, it looked like he had taken a big step forward as a lockdown reliever and the slider that drove that success is still intact. Opposing batters are whiffing 45.7% of the time they offer at his breaking ball, right in line with the whiff rates he ran in 2020 and ‘21.
After bouncing around three different organizations over the last three years, it looks like Chris Devenski has finally rediscovered the changeup that made him one of the best relievers in baseball all the way back in his debut season in 2016. His FIP across the last six seasons has been an ugly 4.27 with a decent 3.71 strikeout-to-walk ratio. This year, he’s throwing his changeup more often than ever, it’s returning a whiff rate close to 40%, and he only walked the first two batters of his season yesterday. He’s taken hold of the eighth inning role in the Angels bullpen.
With Ben Joyce sidelined with an elbow injury, another young relief arm for the Angels has stepped into high leverage opportunities in his place. José Soriano was called up in early-June and has picked up holds in three of his first four appearances in the big leagues. The flamethrowing righty had been a starting prospect in the past but command issues forced him into the bullpen for Los Angeles. He’s currently unrostered in Ottoneu.
Welcome to the first automated installment of fastball velocity risers and fallers. For reference, here are a few articles that explain both the process and the importance of increased or decreased velocity when predicting future success:
This article won’t take the place of my weekly RotoGraphs article and will not have much analysis. Instead, it will only provide data tables for your own analysis.
Quick Note: The data for this article is through games played on May 30th.
Relievers
Relievers only qualify to be placed in the table below if they have three appearances in the last 25 days. Though the time range is 25 days, the calculation only includes the three most recent appearances. In addition, I have isolated the table to relievers who have displayed an average change of .60 or greater in either direction (increase vs. decrease).
* Among all starting pitchers with three appearances in the last 25 days.
**>= .60 Average Change
Starters
Starters only qualify to be placed in the table below if they have three appearances in the last 25 days and threw in at least the first inning in each of those appearances. The 25-day range should be wide enough to include three consecutive starts, but I may alter that time period in the future. Like in the above relievers table, I have isolated the table to starters who have displayed an average change of .60 or greater in either direction (increase vs. decrease). One final note, I do not remove pitchers who were recently injured. I think it’s advantageous to see how a pitcher’s velocity changed prior to injury. In today’s post, Julio Urías is a good example.
After rethinking what my Friday column looks like last week, I think I’ve settled on what this Tuesday column will cover for the foreseeable future. Instead of focusing on making recommendations for starters each week (which I’ll be doing anyway in my Friday SP Planner), I’ll cover under-rostered pitchers more broadly. I’ll switch off between covering starters and relievers each week with the goal of finding pitchers who are performing well and deserve a second look.
Keeping track of the machinations of 30 major league bullpens is pretty tricky. In standard leagues, it’s hard enough trying to discern which relievers are earning save opportunities, especially since more and more teams are using a committee approach in the ninth inning. In Ottoneu, with both saves and holds earning points, that search for high leverage relievers becomes even more of a challenge. There are plenty of resources out there — the Roster Resource Closer Depth Chart is one of my favorites — but even the most vigilant fantasy player can’t keep track of everything going on across the majors.
The first thing I do when looking for under-rostered relievers is look at Leverage Index, first at the seasonal averages and then the change in Leverage Index over the last two weeks. Generally, teams know what they’re doing when deploying their relievers, and they’ll use their best pitchers in the most high leverage situations. When the bullpen hierarchy shifts, LI is pretty quick to pick up on those changes, though it’s obviously limited by the number of high leverage situations a team sees.
Here’s a list of relievers who have seen the highest positive change in Leverage Index over the last two weeks, who are also rostered in less than 60% of all Ottoneu leagues:
It’s tough to own any Rockies reliever, not only because they pitch half their games in Coors Field, but because the team sees fewer high leverage situations than a more competitive team would. Pierce Johnson is definitely seeing all the save situations in Colorado, though his peripherals aren’t anything special with both his walk rate and his HR/FB rate approaching 15%.
Zach Jackson was just placed on the IL with an elbow injury and there really isn’t anyone in Oakland’s bullpen that’s worth rostering because they’re seeing even fewer high leverage situations than the Rockies are.
The Rangers bullpen situation could be an interesting one to speculate on. Jonathan Hernández was holding the eighth inning role, setting up for Will Smith the closer, but he has allowed eight runs in his last three appearances. He’s probably pitched his way out of the high leverage calculus for now, opening an opportunity for the other two Texas relievers listed above. Josh Sborz had collected holds in three straight appearances before allowing five runs to score across his last two outings. He might have the best peripherals of the bunch, but his footing seems to be unstable after his last two meltdowns. Brock Burke doesn’t have the high strikeout rate you’d expect from a high leverage reliever, though it was 27.4% last year. He might be next in line for the eighth inning based on the struggles of the pitchers who were ahead of him on the depth chart.
There are a few interesting names in the Royals bullpen, though they suffer from the same problem as the A’s and the Rockies. Taylor Clarke seems to have the seventh inning role locked down ahead of Aroldis Chapman and Scott Barlow. He’s increased his strikeout rate by more than seven points behind a new emphasis on his breaking balls. Kansas City seems to have given up on using Carlos Hernández as a starter and have transitioned his extremely hard fastball to the bullpen where it’s already elite velocity and ride will only play up. He’s responded with a strikeout rate north of 30% and a couple of high leverage appearances. Of course, the Royals have used him as a short-stint opener in his last two outings so maybe they’re not ready to put him in a back-end role yet.
Here are 10 more relievers rostered in less than 60% of all Ottoneu leagues who have been performing well over the last few weeks and are seeing high leverage opportunities in their respective bullpens.
After making four starts at the beginning of the season, Nick Martinez has transitioned to the bullpen and has been thriving in a high leverage role for San Diego. His strikeout-minus-walk rate out of the pen is a fantastic 25.4% and he’s only being held back by the lack of hold opportunities for the scuffling Padres.
It looks like Miguel Castro is at least getting some consideration in a closer committee in Arizona. He earned two saves last week before getting inserted into the game in the seventh inning as the Phillies were threatening in a two-run game yesterday. He allowed an inherited runner to score but escaped the jam to earn a hold. He’s cut his walk rate by nearly three points while maintaining his strikeout rate this year.
The White Sox bullpen has been in flux all season long as they’ve tried to cover for the loss of Liam Hendriks. Joe Kelly might have emerged as the best option for Chicago; he’s struck out nearly 40% of the batters he’s faced this year. Hendriks is on the mend and working through his rehab assignment right now which means Kelly might settle in as a seventh or eighth inning option for the White Sox once their closer returns.
John Brebbia and Danny Coulombe are in weird positions in their respective bullpens. The former has been used as an opener a number of times this year, including yesterday ahead of Sean Manaea, but is seeing regular work in the seventh inning when he’s working out of the pen. His strikeout rate is 35.9% which is up among the league leaders and a huge improvement over what he was posting earlier in his career. Coulombe has also been striking out gaudy amounts of batters this year, but it’s been really hard to stand out in a very good Baltimore bullpen. His 28.2% strikeout-minus-walk rate sits behind only Yennier Cano and Félix Baustita in the Orioles ‘pen and it seems like it’s inevitable he’ll eventually start seeing more and more high-leverage opportunities if he continues to pitch this well.
How much WHIP changed in the two “Stuff” models was almost too good to be true. In both cases, the walk rate increased as a pitcher’s stuff got better, but the hit suppression was so large that the WHIP declined.
Well I was wrong about the hit suppression. I went back and found no link to BABIP. The difference was because WHIP is on an innings denominator and a strikeout removes the chance for a Hit and Walk. An out comes down to the random chance of a batted ball. I know it’s confusing so here is an example assuming a pitcher with a 9 K/9, 3 BB/9, and .300 BABIP and throws 6 IP/GS. Read the rest of this entry »
Welcome to the first automated installment of fastball velocity risers and fallers. For reference, here are a few articles that explain both the process and the importance of increased or decreased velocity when predicting future success:
This article won’t take the place of my weekly RotoGraphs article and will not have much analysis. Instead, it will only provide data tables for your own analysis.
Relievers
Relievers only qualify to be placed in the table below if they have three appearances in the last 25 days. Though the time range is 25 days, the calculation only includes the three most recent appearances. In addition, I have isolated the table to relievers who have displayed an average change of .60 or greater in either direction (increase vs. decrease).
* Among all starting pitchers with three appearances in the last 25 days.
**>= .60 Average Change
Starters
Starters only qualify to be placed in the table below if they have three appearances in the last 25 days and threw in at least the first inning in each of those appearances. The 25-day range should be wide enough to include three consecutive starts, but I may alter that time period in the future. Like in the above relievers table, I have isolated the table to starters who have displayed an average change of .60 or greater in either direction (increase vs. decrease). One final note, I do not remove pitchers who were recently injured. I think it’s advantageous to see how a pitcher’s velocity changed prior to injury. In today’s post, Drew Rasmussen is a good example.
I recently joined a men’s 30+ hardball league because, apparently, I love the bitter taste of failure. I felt like a superstar when I left my local sporting goods store with a scroll of a receipt and some fresh gear that would certainly make me, at least, look like I know what I’m doing out there. In my first at-bat, I struck out swinging, missing on a changeup by a country mile. This pitcher knew what he was doing.
He saw me whiff on a heater, threw it again and again as I timed it up and fouled off a few, changed pace and sent me back to the dugout, head down and red-faced. It was hard to time up. I’m serious. Now, replace that 75 MPH heater coming from a former D3 college pitcher with a 102 MPH heater coming from a man they call “The Mountain” and I would have certainly been found in the fetal position on the right side of the batter’s box.
Professional hitters, however, are used to this kind of thing. But, even they struggle. It’s all relative I suppose. Let’s take a look at a hitter, White Sox rookie Oscar Colás, trying to time up Félix Bautista on April 16th:
Colás quickly fouled one off and got the bat on the ball. But, prior to that, he took a ball up and in. The foul ball was his second look at Bautista’s four-seamer and waiting for pitch number three, he sat at 1-1. At this point, Colás saw two fastballs and it couldn’t have hurt to see one more. He took another ball, again the four-seamer, and got ahead in the count, 2-1. Three four-seamers down, Colás must have been feeling like he had Bautista timed. But, wait, doesn’t Bautista have a devastating splitter? That’s what the scouting reports said at least. Maybe that’s coming next? Nope:
Another heater and Colas barely got a hold it, but he was given another opportunity to time it up. Now at 2-2, he was thrown yet another heater and put it in play:
So when oh when does Bautista throw his splitter? At this point in the inning, Bautista threw six straight four-seam fastballs. He had his splitter ready and waiting, but the next batter, Seby Zavala didn’t get to see one. Instead, he was thrown one four-seamer and whiffed, then another that he put in play for a base hit. That means the four-seam count now came to eight in a row and hitters were catching on.
Early this season Orioles broadcasters have continually mentioned that Bautista, who got a late start to spring training due to trouble with his knee and shoulder, just hasn’t yet found the splitter. Stuff+ has given us a new way to look at whether a pitcher has or does not have a certain pitch. Let’s take a look at Bautista’s game-by-game splitter Stuff+ prior to this April 16th outing:
He may not have fully had it in his first few appearances, but the pitch was trending up. For context, the league average Stuff+ on a splitter among all relievers in 2023 currently sits at 103. There were only two games in this early time span where Bautista was below that mark. Prior to this April 16th appearance “The Mountain” started to find his groove with back-to-back appearances above 140. Coming off of two appearances with the splitter working and he hadn’t yet thrown it to a single batter in this game in question. But, just like a brilliant closer does, he waited for the perfect time.
Lenyn Sosa came to bat with two outs, having seen his teammates time up fastballs up in the zone, ready to attack. After a first pitch called strike on a four-seamer, perhaps Sosa was lulled into thinking it was just a fastball kind of day for Bautista. Wrong. The next pitch thrown to Sosa was a totally spiked splitter. In all honesty, it was spiked so hard that Sosa may have not have even identified it as a splitter. The next one, however, was gold:
Bautista then capped off his performance with a swinging strike on an unhittable splitter:
Bringing in the rest of Bautista’s appearances this season (last night’s (5/4) data hasn’t come in yet), we can see that he reached a peak in this April 16th game and in his next appearance on the 18th, but then came back down to earth a bit. What happened on the 29th? Four splitters that looked good, but certainly don’t look 322 Stuff+ good:
Splitter #1 was a non-competitive pitch. Splitter #2 earned a swinging strike, but it was left up in the zone and seems like it could have been sent for a ride. Splitter #3 was a big miss. Splitter #4 was a really good pitch and an even better take. All together it is unclear why these four splitters read at obscure/outlier levels, but perhaps there’s something going wrong in the data. Regardless, and what does seem clear, is there’s some potential for monitoring individual, put away pitches prior to matchups for both fantasy and real-life players. There’s a lot here that needs to be worked out, mostly creating a rolling average chart, quality checking game-by-game Stuff+ measures, and monitoring game-by-game Stuff+ to see if there’s any connection, not from a performance standpoint, but from a usage standpoint. I would like to answer the question, does an individual pitch’s Stuff+ measurement in the game prior, lead to increased usage in the following? For now, Bautista seems to be finding a devastating pitch and we’ll have to see how he utilizes it going forward.
Using baseball-savant data and some Python code, I have written a script that will loop through a pitcher’s three most recent appearances and flag any pitcher who has shown an increase in their fastball velocity. In raw form, it looks something like this:
Looking at the table above we can see that each of these three pitchers increased their fastball (“FF” in savant data) consistently over their last three appearances. Don’t believe me? You can check my work with Savant visualizations:
While I wrote more specifically about the merits of paying close attention to game-by-game fastball increases, quoting many other studies and great pieces along the way, I won’t be writing about it again here. Instead, I’ll simply show you a list of the starters and relievers who have increased game-by-game average velocity on their four-seamers and hope that you can take it from there. Sure, you could scroll through stacks of player pages to find players who have increased velocity until the cows come home, or you could write some code that will detect those increases and flag those players for you. I chose the second way. If you find it useful, I’ll do it on a more regular basis. That’s it. This post is more about the data than the words:
