Archive for Meta Analysis

In an MLB.com article from earlier this month, Adam Berry investigates the secrets of the Rays’ pitching staff. First, read the article because it contains so many great pieces of information. I wish MLB.com did more pieces like this one.

And the guts of the article comes down to this quote:

The Rays don’t employ a one-size-fits-all philosophy, Snyder said, besides imploring pitchers to throw strikes and get to a two-strike count as quickly as possible. Beyond that, their approach is player-centered: They recognize what each pitcher does best, which they can typically tell by identifying what each does most consistently, and revolve everything around that.

Two-strike counts are important? In previous work, I’ve noticed pitchers who are behind in the count generate weaker contact along with those who have a diversified pitch mix. It’s time to dive into the effects of two strikes counts and determine if they can help pitchers limit hard contact.
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Our MLB DFS lineups don’t start and end with pitching. My first five-figure tournament score came on a night where Collin McHugh scored negative points, I think–or maybe it was, like, six points. Extremely flukey, as I made the big money because Justin Turner hit three HRs for me at nearly no ownership. I’m not saying to put pitcher every night or even every now and then. I’m just stressing that each and every slate does not rest upon our pitching.

The pitcher position is so vital because it’s the slot where we can get the most accurate projection in an extremely volatile wing of DFS.

Our pitching isn’t just a source of fantasy points. The price tags on pitchers make it so they shape they dictate the freedoms and restrictions of building our lineups. Before reading this article, it’s highly suggested that you read my article, “DFS Pitching Primer,”so the concepts discussed here make more sense.

That we’re not selecting the best players to win tournaments. We’re constructing the lineups which carry the most leverage without sacrificing many projected fantasy points.
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On Monday, I shared the names of eight pitchers whose Pod Projected ERA is significantly lower than Steamer. Today, let’s flip to the ERA downside names. Remember that in aggregate, Pod ERA projections are lower than Steamer, so the gap between ERA forecasts below are a lot smaller than on the upside list. Since it’s really relative projections and calculated dollar values that matter (we care how the projections compare to the player pool, not whether the pitcher is projected for a 3.00 ERA vs a 14.00 ERA), try to ignore the small degree Pod’s ERA is higher than Steamer and remember these are the largest outliers, so if put on the same ERA scale, the difference would be greater.

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Yesterday, I compared my Pod Projections in the stolen base category to Steamer and identified five hitters I am forecasting for a meaningfully higher stolen base total. Today, let’s now look at the hitters I’m projecting for fewer stolen bases than Steamer. I’ll only highlight the fantasy relevant names as there are a number projected for limited playing time that aren’t worth discussing.

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Today, I continue my Pod Projections vs Steamer battle, this time moving along to stolen bases. Similarly to the way I compared our home run forecasts, I’m going to calculate a PA/SB rate first and then extrapolate that projection over 650 plate appearances so we’re only comparing stolen base projections and playing time forecasts don’t factor in.

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Yesterday, I compared my Pod Projections to the Steamer projections to identify the hitters with home run upside. I calculated each hitter’s AB/HR rate and then extrapolated it over 600 at-bats. At that point, I compared how many home runs each system is forecasting, given a 600 at-bat projection. Today, I’ll share the names of hitters Pod is projecting for significantly fewer home runs than Steamer.

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Every year, I pit my Pod Projections against the Steamer projections in various categories. Today I’m going to continue the annual smackdowns by calculating AB/HR rates and then extrapolating them over 600 at-bats. At that point, I’ll compare how many home runs each system is forecasting, given a 600 at-bat projection. I’ll start by sharing the names of hitters Pod is projecting for significantly more home runs than Steamer. Many of these players figure to be part-timers, so consider them sleepers in deeper leagues.

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As part of my preseason prep, I watched a Kris Bubic start from last season. During it, Rex Hudler, who is never short on opinions, brought up an interesting point. The more pitches each batter sees, the quicker the batter becomes with the pitcher repertoire, and the more likely the batter gets a hit. At first, I thought someone else was speaking, but no, the concept warranted further investigation. It’s the same theory behind the times-through-the-order penalty but the new effect could be felt depending on how many pitches a pitcher throws per hitter and depth of arsenal for the pitcher. That idea started me down a wormhole that led to many questions and one subpar answer, but there seems to be at least one nugget of wisdom in Rex Hudler’s head.

First off, with less than a month before the season starts, it’s not an ideal time to start a study that could take weeks to iron out. I barely have enough time to report news, velocity readings, and draft my own teams. The following “answers” are not set in stone and there are so many more questions to investigate. I could either shelve the ideas for months or just make a snippet available and let others run with the ideas while I grind through the fantasy season. I’m giving others the chance to refine the ideas before I come back to them.
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Yesterday, I assembled a 14-player offense from the hitters who weren’t bought in NFBC auctions since Feb 1. Today, let’s flip over to the pitching side.

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The inspiration for this study came from this blurb in a recent Mining the News.

In the past, the general rule is that a higher fastball velocity leads to more strikeouts. Once spin data became available, the link between more spin and strikeouts was quickly found.

And they can go in separate directions. After going way too deep down this rabbit hole, I came out with a simple formula.

Before diving in, a few items need to be cleared up. First, other factors besides velocity and spin can affect a fastball’s effectiveness such as pitch location, deceptiveness out of the hand, and how often the batter has seen the pitch. The key with this study is to have a simple formula to start evaluating fastball changes for the accessible stats.

Second, I tried to use Bauer Units (simply spin/velocity) to determine how a fastball performance would change. While there was some correlation between Bauer Units and pitch performance, it became unnecessary noise. Seperating out spin and velocity was a better approach. Bauer Units are more helpful with pitch design than pitch evaluation.

Now, to the analysis. I found all the pitchers from 2015 to 2020 who threw 200 four-seamer fastballs in matched seasons (n=1134). Sinkers were not included because the goal with them is to remove spin and get the ball down through the zone.

After looking at several possibilities, I ended up with the following three rules of thumb for determining a change in a fastball’s swinging-strike rate. The r-squares aren’t high for any of the values (~.1 to .2), but that’s expected with so many inputs into a fastball’s performance.

Note: Remember that spin and velocity are related, don’t combine the first two formulas. Use the third one.

Spin: 100 rpm change * 1.0% SwStr%
Velocity: 1 mph change * 0.9% SwStr%
Spin & Velocity: 1 mph change * 0.78% + 100 rpm * 0.5%

Going back to the Paddack example (+0.2 mph, -60 rpm, -2.0 SwStr%), here are the expected changes.

Spin only: (-60/100) *1.0% = -0.6%
Velocity only: 0.2 * 0.9% = +0.2%
Spin & Velocity: 0.2 * 0.78 + (-60/100) * 0.5% = -0.14%

The loss of spin outweighed the velocity loss but the entire decline in swinging-strike rate can’t be explained with just the two factors. Paddack and the team identified those other causes and hope to correct them.

That’s it for today. I believe Mining the News will be more important than going over a few more examples. The formula will be useful once the regular season rolls around and spin and velocity data are available on every pitch.