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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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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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.

• Catchers
• First Basemen
• Second Basemen
• Third Basemen
• Shortstops
• Outfielders

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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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:

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?”

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:

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.

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:

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:

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.

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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I’m going to finish up the 2023 projection showdown, pitting THE BAT/THE BAT X forecasts against Steamer projections in the various fantasy categories, with starting pitcher dollar values. This sums up the forecasts in one tidy number and makes it easy to identify which pitchers each system likes better. Today, we’ll start with the starting pitchers that THE BAT is valuing significantly higher than Steamer.

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The second word in the “music to many people’s ears” term, Spring Training, is an important one to consider. Pro ball players are training. They are preparing for the season. What types of things are they working on? Beat writers report out every year that pitchers are tinkering with new grips, different release points, varying arm slots, diets, cleats, the list is endless. This is assumed to be even more evident in pitchers. As they ramp up to game-ready status, what exactly are they ramping up and can it be quantified by a writer with only so much publicly available data at his fingertips? Away we go in answering that question together.

With statcast data available in spring training ballparks, we can access pitch-level data from the good folks at baseball savant. God bless them. There are a few metrics that measure what I would consider pitcher mechanics and here they are:

[‘release_speed’, ‘release_pos_x’, ‘release_pos_z’, ‘effective_speed’, ‘release_spin_rate’, ‘release_extension’,’spin_axis’]

These seven variables are incredibly manageable from a data perspective when compared to some of the more advanced biomechanical data teams and private company analysts are working with today. However, it can be really difficult to notice patterns from game to game just by looking at a spreadsheet:

Yes, you could look at this and make general assumptions. But, what if we want to visualize this? What if we wanted to hyper-analyze this so that the only people who really know what the heck is going on are the ones who are too busy playing the game in hyperspace? Bring in principal component analysis!

I’ve used this technique for a few articles here on FanGraphs. In this case, a principal component is being created based on multi-dimensional data, like the spreadsheet above with numerous columns, to create a new column. It cuts through the data and builds new “axes of variation” to better explain multiple data points. A more simplistic way of explaining this is that it’s taking multiple columns in the spreadsheet and condensing them into one. If we then create two of those new, condensed data columns, or principal components, then we can create a visualization. If this is too much data talk for you, hopefully, it gets better as I bring in the baseball.

Let’s start with the young, yet-to-debut major league pitcher, Grayson Rodriguez. How do the metrics above look, game by game as he ramps up for a season in which he expects to debut? I’ll create two principal components to help summarize a dataset similar to the table above and I’ll plot them on the x and y axis of a scatter plot, like this:

What we see is two variables, principal components one and two, explaining all the variables listed at the top of this article for one Gray-Rod Spring Training game’s worth of fastballs. It’s not very exciting. But, bring in a second game’s worth of fastballs to the visual and the excitement levels go through-the-roof!

…Ok, maybe it’s not that much more exciting. But, at least we can now see a little more of a story starting to develop. Ideally, since these variables are mostly repeatable we should see the blue and red dots sit closer together. What’s up with that game 2 outlier at the top of the second plot? We can compare that pitch with the averages of the other pitches in that game to analyze it further:

It seems that this pitch had a higher release, effective speed, and release spin rate. Is this significant? I really have no idea. It could just be noise. I would love to know if Rodriguez would have noticed any difference after that pitch was thrown. Would he have admitted that he really wanted to get that guy out? Let’s go to the video to see what the situation was:

…Oh, wait. We can’t because MASN doesn’t want to film in sunny Florida. Luckily, we can still look at the savant video-less page here. On a 2-0 count against Spencer Torkelson, maybe Gray-Rod reared back and put a little extra mustard into making sure he didn’t get to 3-0. We’ll likely never know.

How might this compare with a pitcher who is more established? Let’s conduct this same analysis on two of Max Scherzer’s spring outings this season and compare:

Scherzer shows a little tighter spread between all of his pitches and lacks the clear outliers showcased by Rodriguez. The more interesting part to me is that the pitches get closer together from game one to game two. Could that mean anything? Could he be getting ramped up and more consistent, more repeatable?

Now the ultimate question in baseball analytics, how can we actually use this to win? I believe checking in on pitcher components throughout the season may be able to help us identify fatigued players who need rest in order to get their components back into a form that is more in line with areas of succes. This would require measuring the game by game spread or variation of the points. If that number is larger, is that a measurement of inconsistency? If it is lower, does it correlate with success? This analysis really brings up more questions than it answers, as per usual:

What if we changed the colors of the data points in the visualization to reflect individual start game scores?

Are tighter pitches (less spread among single games point locations) better?

What could be done with more data? Can this analysis be applied to biomechanical data?

How does it apply to non-fastballs? Do certain pitchers struggle with repeated motion on certain pitches and not others?

If this post is a thread in that old spring training baseball jersey you pulled out of the closet for your trip to Florida, then let’s start pulling until there’s nothing left and you’ve gotta borrow sunscreen from the shirtless guy next to you. My hope is that with a little more time and research, I’ll be able to utilize this analysis to detect in-season struggles by starting pitchers.

We finish up our starting pitcher ERA projection comparison, as part of the 2023 projection showdown, pitting THE BAT against Steamer. Today, I’ll discuss the next six names down on the list of Steamer ERA favorites compared with THE BAT. This is the list of the six pitchers Steamer was most bullish on compared to THE BAT.

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Let’s shift now from Lucky/Unlucky, where we’ve been for the last three installments, to another gimmicky approach that has proven surprisingly useful over the seasons. We refer to the Quadrinity. Brief history lesson: long ago, Bret Sayre, then of Baseball Prospectus, posited that “the three skills that are most important to the art of pitching [are] getting strikeouts, reducing walks, and keeping the ball on the ground,” and that pitchers who can do those three things, as betokened by their above-average numbers in those categories, are worth the attention of those of us who care about such matters. He called this approach The Holy Trinity. And it made sense to us.

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