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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Today, let’s continuing comparing starting pitcher ERA forecasts as part of our 2023 projection showdown, pitting THE BAT against Steamer. This time, I’ll discuss the next six names on each system’s leaderboard. Today, we’ll jump back to THE BAT’s favorites. If you forgot who their top six bullish names were, here’s a refresher.

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Question: What’s the most important thing to remember when drafting in any fantasy baseball league?

Wise-Guy Answer: The type of beer you have on deck.

Wise-Guy’s Friend’s Answer: Making sure you have snacks that don’t grease up your keyboard!

Serious Answer: Your league’s scoring system.

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Yesterday, I moved on to comparing ERA forecasts as part of my 2023 projection showdown, pitting THE BAT against Steamer, discussing the starting pitcher THE BAT was more bullish on. Now let’s switch on over to Steamer’s favorites.

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Today, I’ll continue the starting pitcher side of the 2023 projection showdown, pitting THE BAT against Steamer projections. This time, we’ll hop on over to ERA. This is probably what we all pay most attention to and it’s likely what keeps a pitcher in his team’s rotation. There are a number of drivers of a pitcher’s ERA, such as skills like strikeout and walk rates, along with luck-influenced metrics like BABIP, LOB%, and HR/FB rate. So let’s begin by reviewing the starting pitchers that THE BAT is more bullish on for ERA compared to Steamer. It’s important to note that THE BAT’s ERA projections are meaningfully higher than Steamer, so you’ll see smaller differences among its favorites, compared with Steamer’s favorites that will be revealed on Thursday.

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Yesterday, I flipped my 2023 projection showdown, pitting THE BAT/THE BAT X projections against Steamer, to the pitching side. I began by comparing strikeout rate between the two systems, discussing those starting pitchers THE BAT is more bullish on. Let’s now find out who Steamer is more bullish on. It’s important to note that Steamer K% forecasts are higher on average than THE BAT’s, so the differences are higher than in yesterday’s article. That’s why relative projections are much more important when valuing players in your fantasy league than absolute projections.

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At last, we have completed our 2023 hitter projection showdown pitting THE BAT X against Steamer in various categories. Let’s now jump over to starting pitcher. I’ll start by comparing K% forecasts. THE BAT X only projects hitters, so I’ve switched over to the original THE BAT system to compare with Steamer. Let’s see which starting pitchers (defined as starting at least 75% of total games appeared in, and also using a minimum of 100 projected innings) THE BAT is more bullish on for strikeout rate.

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