For those who don’t know, I started in this industry with a focus on streaming pitchers. By using various statistics for both pitchers and their opponents I have been providing advice to followers on who to stream in their leagues. What does streaming pitchers mean? Simply put it means grabbing a pitcher off your waiver wire for one start. You choose that pitcher based on matchup and skill hoping they provide you with decent ratios and then dump them back into the free-agent pool.

With that said it is important to know which pitchers are worth streaming in certain matchups. These pitchers typically aren’t good enough to keep on your team but could prove useful in the right matchup. This is important to know because this is a key factor in streaming successfully. For instance, no matter who Wade LeBlanc’s opponent is, you never want to take a chance on him. There is no strikeout potential, no ratio potential, and in his last three starts he has a 10.13 ERA. 

For this piece I wanted to keep it under 10% in terms of rostered percentage. This way it ensures most if not all of the names below are available in your league. 

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On last October 3rd, I examined how the effects of the Happy Fun Ball could mess with ERA estimator assumptions. I was self-serving in that I wanted to see how the variables in my own ERA estimator (pERA) changed*. Once I had the new constants, I created the valuations, and Randy Dobnak came in with an estimated sub-3.00 ERA ahead of starters such as Carlos Carrasco, Blake Snell, and Shane Bieber. The rankings were there for the public to admire and they were completely ignored throughout draft season.

I probably would have ignored them also if it weren’t for Spencer Turnbull. At the end of the 2018 season, Turnbull had a 6.06 ERA and was on no one’s radar for 2019. But I had his pERA at 2.31 better than both Justin Verlander and Chris Sale. I completely blew off the rankings and paid for it. From the beginning of the season until a shoulder injury in late June, Turnbull had a 2.97 ERA, 9.2 K/9, and 1.29 WHIP. And I had him rostered on no teams.
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Yesterday, I listed and discussed the top 20 starting pitcher SIERA leaders. Since the entire season represents a small sample size, you should be far more inclined to focus on a pitcher’s SIERA, driven by his underlying skills, than the ratios that actually count in your fantasy leagues, ERA and WHIP. The underlying metrics fueling SIERA stabilize far quicker and account for skills pitchers have more control over. It therefore makes for a significantly better rest of season projection, even though SIERA is meant to be backwards looking, not forward looking. With that said, let’s check in on the bottom 20 pitchers in SIERA and discuss the interesting names. As a reminder, just because a pitcher finds his name here doesn’t mean you should drop him or trade him away, as the pitcher could improve his skills, pushing his SIERA down into more attractive territory.

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Even though we are about halfway through the season, the league leader in innings pitched sits at just 40.2 innings. That’s far too small a sample size to find much meaning in traditional surface statistics such as ERA and WHIP (plus LD%, HR/FB, BABIP, etc). The results that feed into ERA take significantly longer to stabilize, so it makes more sense to focus on the underlying skills that pitchers have more control over. Luckily, we have a metric that takes all those underlying skills, throws them into a blender, and spits out a skills-based ERA. Of course, I’m describing SIERA, and it’s what I exclusively look at early in the season to forecast future performance, as it’s far better than ERA itself. So with that said, let’s take a gander at the top 20 qualified starting pitchers in SIERA and discuss any surprises.

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SwStr% is a simple metric that is calculated by taking swings and misses and dividing it by total pitches. Why is SwStr% important? Simply put, if a pitcher can produce a bunch of swings and misses it means his strikeout rate should be high. The more strikeouts the better, because if you look at an elite pitcher in baseball you will see a high strikeout rate. It is well known that SwStr% correlates well with a pitchers strikeout rate. Want to know if a player’s K% is over or underperforming? Check out their SwStr%. The rule of thumb (although it isn’t exact) is to double a pitchers SwStr% and their K% should be around that number. Keep in mind some pitchers will be outliers if they consistently rely on called strikes, like Aaron Nola.

Let’s take a look at the SwStr% leaders so far this season.
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With a mid-to-high 90s fastball that has touched as high as 101 MPH and an elite splitter that has generated a SwStk% over 20% every season of his career, a lot has been expected of Kevin Gausman. You figured that with a two-pitch foundation like that, he would be racking up the strikeouts and rank as one of the better pitchers in baseball on an annual basis. And while he’s posted a couple of seasons of sub-4.00 ERAs, the underlying skills just haven’t been super impressive, and he sports a career ERA of 4.31. That’s perfectly serviceable, especially in a hitter friendly home park for most of his career in the American League. Sure enough, his career ERA- stands at an almost perfectly league average 101. So he’s been fine, but not what fantasy owners hoped for.

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This is the second article of my series – wPDI vs. CSW. For those new to either metric, I will quickly catch you up. [The opening article can be found here.]

In last year’s FSWA Research Article of the Year, CSW Rate: An Intro to an Important New Metric, Alex Fast of PitcherList examines his site’s pitching statistic, CSW. The short and simple formula for CSW is defined as follows:

Called Strikes + Whiffs
Total Pitches

Independently, I came up with the concept of Weighted Plate Discipline Index (wPDI). With wPDI, we ask just three questions, or three binary events for every pitch:

1. Was the ball thrown in the strike zone?
2. Was the ball swung on?
3. Did the batter make contact with the ball?

Every pitch can then be classified into 6 possible pitching outcomes based on the above. The definition of each outcome is as follows:

Each outcome is then assigned a weight, or an index. The formula for wPDI, the Weighted Plate Discipline Index is then given as:

wPDI = Index_A * A% + Index_B * B% + Index_C * C% + Index_D * D% + Index_E * E% + Index_F * F%

A% through F% are the percent of pitches thrown in each outcome, and the indexes are linear multipliers to obtain the aggregated, sortable metric.

What CSW has most in common with wPDI, is that it shares the same denominator – Total Pitches. That being the case, we can attempt to use the wPDI framework to express the PitcherList metric. CSW is rooted in Baseball Savant data, while wPDI is fed by FanGraphs figures. By exploring the similarities and differences between the metrics, we can also uncover some great nuggets of understanding.

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Again, I’m diving into some lowly-owned starters who have changed their pitch mix since last season. A couple seem to have potential.

Daniel Mengden (3% Owned at CBS)

While he’s better known for his 80-grade mustache, he’s trying to mix up his pitches to become useful. He’s reorganized a bunch of pitches that have helped but other parts of his game are dragging him down.
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Yesterday, I identified and discussed the starting pitcher leaders in Z-Contact%, which is in-zone contact rate. Today, let’s look at the laggards in the metric. I’ll stick with the pitchers who have posted a rate of at least 90%.

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It’s up for debate which one metric best describes a pitcher’s level of dominance. One of those metrics that doesn’t get as much press is Z-Contact%, which is defined as “percentage of times a batter makes contact with the ball when swinging at pitches thrown inside the strike zone”. In fewer words, it’s simply in-zone contact rate. Since all else being equal, a pitch thrown inside the strike zone is easier to make contact with then pitches thrown outside the zone, then one measure of absolute dominance is how often a pitcher generates a swing and miss on pitches thrown inside the zone. If a pitcher’s strikes can’t be hit, how are batters going to hit their balls (unintentional comedy scale: 10/10)?! So let’s look at and discuss the early starting pitcher Z-Contact% leaders. All these pitchers have posted marks below 80% versus a league average of 84.6%.

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