Archive for Starting Pitchers

Buy or Sell: First Start Wonders

The majority of MLB starting pitchers have still made just one start, but in your Ottoneu leagues (actually, in all your leagues), managers are making decisions about who to speculate on. Is that first great start a sign of the next Kyle Wright breakout? Or another Kyle Gibson (9.51 P/IP in his first start; 3.35 the rest of the way)? With just one start, it is hard to know who you can trust, but if you wait until you are sure, someone else will have grabbed these arms already. Who should you bid on and who should you pass?

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Starting Pitcher Fastball Velocity Decliners — Apr 5, 2023

Yesterday, I reviewed and discussed seven starting pitchers who enjoyed significantly increased fastball velocity during their first start compared to full season 2022 velocity.

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Ottoneu Starting Pitching Drip: April 7–9

Welcome back to the SP Drip. My goal for this bi-weekly column is to comb through the upcoming schedule each week to find a few under-owned pitchers (less than 50% ownership across Ottoneu) who could be used to help you hit your games started cap in head-to-head leagues or to make sure you’re hitting your innings pitched cap in points leagues. Tuesday’s article will cover the weekend (Friday, Saturday, Sunday) and Friday’s article will cover the upcoming week (Monday, Tuesday, Wednesday, Thursday). That way, you’ll have time to start your auctions in time to actually drip these pitchers into your lineup.

Let’s get into it.

Upcoming Schedule:

April 7–9
Home wOBA HR Park Factor Away wOBA
CLE 0.322 101 SEA 0.318
CHC 0.318 98 TEX 0.320
PIT 0.317 95 CHW 0.321
SFG 0.321 90 KCR 0.317
TBR 0.320 94 OAK 0.299
ATL 0.338 98 SDP 0.334
MIL 0.322 103 STL 0.329
COL 0.330 111 WSN 0.311
LAA 0.334 107 TOR 0.335
ARI 0.321 94 LAD 0.333
DET 0.307 93 BOS 0.331
NYM 0.327 97 MIA 0.317
PHI 0.324 106 CIN 0.317
BAL 0.321 95 NYY 0.327
MIN 0.317 96 HOU 0.335
Team wOBA projected via FG Depth Charts

Favorable schedules include the Astros, Dodgers, Giants, Mets, Rays, Red Sox, Royals, and White Sox. You may be able to get away with playing starters from the A’s, Pirates, and Yankees since they’re playing tougher opponents in safer environments.

Lots more teams to avoid this weekend, including the Angels, Blue Jays, Braves, Brewers, Cardinals, Nationals, Padres, Reds, Rockies, and Twins.

Highlighted matchups:

Recommended Starting Pitchers
Pitcher Roster% Opponent Opponent wOBA FIP K-BB%
Mike Clevinger 38.46% PIT 0.317 4.59 13.2%
Nick Pivetta 31.09% DET 0.307 4.40 13.3%
Anthony DeSclafani 16.35% KCR 0.317 4.13 13.1%
Bailey Falter 8.97% CIN 0.317 4.22 16.1%
Brad Keller 6.41% SFG 0.321 4.34 8.7%
Stats projected via FG Depth Charts

It’s a pretty light slate of recommendations this weekend with so many teams playing tough matchups or in poor environments. Nick Pivetta shows up for the second week in a row. Monitor his first start of the season against the Pirates today to make sure the increased velocity on his fastball carries over from spring training.

There are two starters who could be nice pickups in that Giants-Royals series, one for each team. Anthony DeSclafani started on Monday and held the White Sox scoreless over six innings while striking out four. He was an effective starter back in 2021 but injuries limited him to just five starts last year. If he’s fully healthy, he could be a sneaky addition with an excellent home ballpark. Brad Keller made his first start of the season on Sunday against the Twins and looked okay. He only lasted 4.2 innings, walked four, and struck out six. He introduced two new breaking balls to his repertoire this spring, though the command issues could be an issue as he figures out how to locate them properly.

Mike Clevinger handled the Astros capably on Sunday, throwing five shutout innings with eight strikeouts. He limited his pitch mix to just his fastball and slider and the latter generated a 44% whiff rate. That breaking ball lost a ton of effectiveness last year and it’s a big reason why he struggled so much in San Diego. He’s got a nice matchup against the Pirates in Pittsburgh to continue building off that early success.

Bailey Falter is a bit more of a risk since Citizen’s Bank Park is a very hitter friendly venue. The Reds offense isn’t good but the combination of opponent and ballpark still isn’t ideal. Falter threw 5.1 innings in his first start of the year, holding the Rangers to two runs on seven hits, striking out three.

Recap: March 30–April 2

Drip Retrospective
Pitcher IP PTS P/IP
Aaron Civale 7 49.6 7.09
Spencer Turnbull 2.1 -9.5 -4.09
Matthew Boyd N/A
Kyle Muller 5 29.6 5.92
Nick Martinez 7 27.9 3.99
Michael Wacha 6 11.5 1.92
Kyle Gibson 5 21.4 4.28
Seth Lugo 7 43.1 6.16
Marco Gonzales 5 5.1 1.02
Jhony Brito 5 40.8 8.16
Totals 49.1 219.5 4.45

As a way to keep myself accountable and just because I’m curious, I’ll be showing the results of my recommendations throughout the season.

Civale, Muller, Lugo, and Brito were pretty clear wins while Turnbull, Wacha, and Gonzales big losses. Looking back, it was probably too early to recommend Turnbull before he had even made a regular season start after his injury. Since these recommendations ran a week ago, Civale has cleared the 50% owned mark which is probably warranted. His stuff is pretty good and he was the recipient of some pretty bad luck last year. It was surprising to see seven strong innings from Lugo in his return to the rotation after spending so much of his career in the bullpen. If he continues to provide that kind of bulk with solid ratios, he needs to be owned in a lot more leagues.


Starting Pitcher Fastball Velocity Increasers — Apr 4, 2023

While it’s far, far too early to evaluate the majority of statistics generated during the season so far, there are a few that already provide meaningful information. One of those data points is pitcher velocity. All else being equal, a faster fastball could work wonders for a pitcher’s performance, while a slower fastball could either signal an injury or a decline in results. It’s never too early to check in on starting pitcher fastball velocities, even after just one start. So let’s identify the starting pitchers with the biggest jumps in fastball velocity versus 2022.

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Ottoneu Starting Pitching Drip: April 3–6

Welcome back to the SP Drip. My goal for this bi-weekly column is to comb through the upcoming schedule each week to find a few under-owned pitchers (less than 50% ownership across Ottoneu) who could be used to help you hit your games started cap in head-to-head leagues or to make sure you’re hitting your innings pitched cap in points leagues. Tuesday’s article will cover the weekend (Friday, Saturday, Sunday) and Friday’s article will cover the upcoming week (Monday, Tuesday, Wednesday, Thursday). That way, you’ll have time to start your auctions in time to actually drip these pitchers into your lineup.

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Ottoneu Starting Pitching Drip: March 30–April 2

Welcome to what I hope will become a regular bi-weekly column this season. Streaming starting pitchers is a popular and effective strategy in fantasy baseball but the benefits are largely lost in a dynasty format like Ottoneu. The 48-hour in-season auctions make streaming in this format an exercise in foresight and planning while the deep rosters make finding starting pitching on the waiver wire tougher than in other, shallower formats. But finding ways to fill your innings pitched or games started cap is a real concern for many teams, especially considering the rate of attrition for pitchers in the modern era. In Ottoneu, you can’t really stream, but you can drip.

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4 Breakout Starting Pitchers for 2023

Kamil Krzaczynski-USA TODAY Sports

As I did in the 1B piece, I’ll ask that we not get too hung up on the actual phrasing of “breakout”. These are guys I like above their market price and have them easily outperforming their draft cost.

Kyle Bradish | BAL

My Projection: 3.74 ERA, 1.19 WHIP, 175 Ks, 10 W in 166 IP

Bradish is getting some spring buzz in different pockets of the fantasy world, but remains remarkably affordable at the draft table as the 81st SP off the board in Main Event drafts so far. The 26-year-old righty is looking to build off a strong second half (3.73 FIP in 71 IP), including an absolute gem against Houston in late-September (1 out shy of a Complete Game with 10 Ks and 0 BB). He will need to trim his home run rate (1.3) which should certainly be possible in the revamped Camden Yards that is now a pitcher-friendly park and one major key will be continue reliance on his slider over the fastball. He was using it 36% of the time in his final 8 starts, up 10 points from his first 15, and shaving fastball usage is addition by subtraction. I’m not getting hung up on Bradish’s ugly spring ERA (8.74), but rather focusing on the 14 Ks and 3 BB in 11.3 innings. If he can trim down his implosion starts (8 last year), there is substantial potential here.

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A Pitch Mechanics Consistency Data Experiment Part II

On July 17th of the 2022 season in Minnesota, Dylan Cease dealt. He threw seven innings, only gave up one hit, and recorded eight strikeouts. His showing left a game score of 83. It wouldn’t be his highest game score of the year (94), in fact, it wouldn’t even be his second-highest (90), but it was a great outing nonetheless. I’m going to use this game as way of continuing my analysis from last week on what we can measure from a pitching mechanics standpoint using statcast pitch-level data. Like in last week’s post, I took the following variables from Cease fastballs on that great start, July 17th:

‘release_pos_x’, ‘release_pos_z’, ‘release_spin_rate’, ‘release_extension’, ‘spin_axis’

I then conducted a principal component analysis in order to bring these five columns of data down into two. That allows me to then plot the data points on a scatter plot like so:

Cease 7/17/23 PCA Scatter Plot

The graph above shows two principal components of all of Cease’s fastballs thrown on July 17th. I am interested in understanding if the spread, or variance, of these data points, relates in any way to performance. A helpful suggestion from FanGraphs member, “couthcommander” came in last week’s post:

“[C]an you…change the point-character shape based on inning?”

Cease 7/17/23 PCA Scatter Plot By Inning

I chose a slightly different route and changed the color of the points based on the inning. I was expecting to see the darker points (later innings) on the outer edges of the scatter plot and lighter points (earlier innings) tighter around the center, but it’s hard to notice much of a pattern from this one game. Let’s visualize it in a different way. Rather than directly plotting the two principal components as X and Y, I calculated the variance of each by inning and compared the two components:

PCA 1 and 2 Variance by Inning Bar Chart

Click to enlarge

 

The first principal component shows higher variance as the game goes on through the fourth inning, but then comes back down for the fifth and seventh. A similar pattern is shown in the second component but only through inning two. The variance in PCA2 jumped in inning five but came back down in inning seven. No fastballs were thrown in inning 6.

It’s important to remind ourselves of what we’re actually looking at here. PCA1 finds a new axis of variation in this multi-dimensional dataset. Imagine a straight line being drawn through a multi-dimensional scatter plot. This new “principal component” does its best job of explaining as much of the variability in the dataset as possible. By that logic, PCA1 is just a little more informative than PCA2. The bar chart is telling us that as the game increased, that component become more variable through the fourth and then stabilized in the fifth. But remember, this is only explaining the following:

‘release_pos_x’, ‘release_pos_z’, ‘release_spin_rate’, ‘release_extension’, ‘spin_axis’

So the question is, does it matter? Does the variance of a component measure of these five features correlate with success? We can look at the components of Cease’s start before and after the great July 17th start.

 

–July 12th @ CLE: Game Score 66–
PCA1 = 3.3
PCA2 = 0.3

–July 17th @ MIN: Game Score 83–
PCA1 = 1.8
PCA2 = 0.3

–July 24th VS CLE: Game Score 63–
PCA1 = 2.1
PCA2 = 0.2

Variance = STD(PCA)^^2 x 10,000

 

While this is in no way conclusive evidence, it’s a start. The variance of PCA1 was lowest on July 17th. The next step in this analysis, as always, is to bring in more data! I will work towards answering the question, does a low variance PCA1 or PCA2 correlate with better performance? If it does, fantasy managers could use this information, if it is tracked and made available, to determine hot spots in a season where pitchers are locked-in. Thanks for participating in this data journey with me. We’ll see where it takes us.

 

 

 


Ottoneu: Prospect Pitchers That Might Be Worth Rostering for 2024

ZiPs 2024 gives us some insight as to how prospects will perform if and when they make it to the big leagues. If we can get a general sense of how a player will perform with projections, we can get a general sense of how much they should be valued. To call this process an oversimplification is to look up at the sun and say, “Bright!” Yes, it is an oversimplification, that’s a given. First, we’re trying to predict not only the future performance of a player who hasn’t actually done it yet. Next, we’re trying to determine how much that performance will be worth without any real context. Where will they play? Who will be on their team? Are they as mentally strong as they are physically strong? Finally, we’re assuming they’ll be healthy.

This oversimplified process can only give us a sense of who might perform like a big leaguer in 2024 and since I’m writing from a FanGraphs points scoring system viewpoint, we can make comparisons with other, more established pitchers. Here’s a reminder of my process. First, I find prospect pitchers yet to debut using The Board. Next, I bring in the ZiPs 2024 projections for the players on that list. Not all of them have projections. After that, I convert their projected stats into FanGraphs Ottoneu points. Finally, I throw the prospects and their projected points into Justin Vibber’s Surplus Calculator output for 2023 and make comparisons. The result tells me how these pitchers will perform in 2024 if they are in a pool of 2023 projected players. The dollar value given assumes that next year’s player pool will be much like this year’s player pool. Here’s an example:

Player Comparison and Value Creation
Name IP rPTS rPTS/IP Dollars
Brandon Pfaadt 153.0 738.0 4.82 $5-$8
Jordan Montgomery 157.3 735.7 4.93 $8
*Yellow=Estimated value

Pfaadt is already grabbing the attention of Ottoneu players as his current FanGraphs points average salary is $4, or $3 Median. Will he increase in value by the end of 2024? ZiPs likes his chances and you can compare his projected points total for 2024 with this year’s Jordan Montgomery. If you pay over the average now, let’s say $6, and this projection comes to fruition, you’ll have a good chance of generating value in 2024. There is, however, another scenario where ZiPs is off the mark and he only brings in $4 in 2024. In that case, you’ll be overpaying. Here are the rest of the 2024 ZiPs projected prospect pitchers and what their value could be at the end of the 2024 season:

Projected Prospect Value for 2024
Name IP rPTS PTS/IP Value
Kodai Senga 142.0 688.2 4.8 $13-15
Brandon Pfaadt 153.0 738.0 4.8 $5-8
Tanner Bibee 115.0 466.0 4.1 $3-5
Grayson Rodriguez 121.7 567.4 4.7 $3-$5
Ricky Tiedemann 112.0 513.0 4.6 $3-$5
Robert Gasser 120.0 511.4 4.3 $3-$5
Gavin Stone 108.0 464.0 4.3 $3-$5
Kyle Harrison 112.0 520.7 4.6 $3-$4
Taj Bradley 120.3 528.8 4.4 $2-5
Gavin Williams 110.3 457.1 4.1 $2-$3
Andrew Painter 112.7 451.2 4.0 $2-$3
Daniel Espino 104.3 446.6 4.3 $2-$3
Bobby Miller 105.3 421.1 4.0 $2-$3
Mick Abel 105.0 371.0 3.5 $1-$2
Owen White 104.0 438.1 4.2 $1
Ben Joyce 56.3 275.9 4.9 $1
*Ottoneu FanGraphs Points Leagues
**Estimates generated by comparing players with similar projections to Justin Vibber’s Auction Calculator values

Let’s compare these estimated 2024 values with some current (2023) average/median Ottoneu salaries:

Current FanGraphs Points Leagues Avg./Med.:

Kodai Senga – Average: $15 / Median: $15
Grayson Rodriguez – Average: $4 / Median: $6
Taj Bradley – Average: $3 / Median: $3
Kyle Harrison – Average: $3 / Median: $3
Ricky Tiedemann – Average: $3 / Median: $3
Robert Gasser – Average: $2 / Median: $3
Tanner Bibee – Average: $2 / Median: $1
Gavin Stone – Average: $2 / Median: $2

This is just one way of trying to look into an uncertain future; mashing a bunch of different spreadsheets together and then estimating a value. Is it worth doing, or would you rather just pay a few dollars now to see what happens later? I think this analysis helps us do both. Remember that the goal is to identify future value and not current value. It allows us to prospect on players because we like them or we believe in them or we saw them at a AA game and were impressed. But, it also allows us to put some kind of filter on how we are rostering and for how much. Are you rostering Taj Bradley for $7 because he was bumped up during arbitration, or you got him in a rebuild trade deal when someone else realized his salary was too high? It may be time to re-examine that hold because, by this analysis at least, he won’t reach that value in 2024. Everyone has a strategy and this is just one approach, but it’s utilizing analytical tools and projections from smarter people than myself to provide insight and that can’t be a bad thing.


2023 Projection Showdown — THE BAT vs Steamer Starting Pitcher Projected $ Value, Part 2

Yesterday, I compared starting pitcher projected dollar value as part of the 2023 projection showdown, pitting THE BAT/THE BAT X forecasts against Steamer projections in the various fantasy categories, identifying the pitchers THE BAT was more bullish on. Today, we finish this series by identifying and discussing which pitchers Steamer is a bigger fan of compared to THE BAT.

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