Archive for Statcast

Maximum exit velocity (max EV) measures a player’s hardest-hit ball, typically measured within a single season and compared against other players. Our Mike Podhorzer has documented its leaders and laggards. Rob Arthur, one of baseball’s best public analysts and whom I admire greatly, wrote intelligently on the importance of max EV as a projection-buster back in 2018. Max Freeze (real name) blends extremely hard hits (114+ mph) with launch angle to look for possible power breakouts ahead of 2020.

It has been established (by Al Melchior and me, in fact) that max EV, while an effective indicator, is not the or even a superior indicator of hitter power.

That’s not to say max EV is useless, by any means. It is altogether a different breed of metric than, say, barrel rate (Barrel%, either per plate appearance [PA] or per batted ball event [BBE]) or average exit velocity (EV), both to which fantasy baseball analysts refer much more often. The latter two, and many others, are rate metrics that need large sample sizes to become reliable — or, in common parlance, to “stabilize.” (More on that here, from our former and beloved Eno Sarris.)

Meanwhile, max EV is not a rate or average but a singular data point. It can happen at any moment in time — including the very first batted ball of a hitter’s season. This makes it an intriguing addition to the ol’ tool belt insofar as it could become “reliable” (not necessarily in the statistical sense) much sooner than would barrels or EV. Potentially, we could use max EV loosely as a leading indicator of where a hitter’s barrel rate, average EV, or even weighted on-base average on contact (wOBAcon) might eventually settle.

So: what are the merits of max EV?

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Introduction

This is the fourth article in my wPDI vs. CSW series. You can catch up by reading the first three articles – on called strikes, whiffs and residuals.

Here is a quick summary of some of the basics of wPDI & CSW from this series:

Last year, I developed the Weighted Plate Discipline Index (wPDI) framework, whereby all pitches can be classified into six different outcomes as follows:

Each outcome is then assigned a weight, or an index. A% through F% are the percent of pitches thrown in each outcome. The general formula for wPDI, the Weighted Plate Discipline Index is given as:

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

wPDI can generate an all-in-one sortable metric used to evaluate pitchers. The plate discipline framework may be tailored to mimic (or to correlate to) various measures of deception or effectiveness.

In the first three articles of this series, we developed indices for wPDI to approximate the PitcherList metric, CSW. The Called Strikes + Whiffs (CSW) statistic was featured in last year’s FSWA Research Article of the Year by Alex Fast, and is defined as:

Called Strikes + Whiffs
Total Pitches

We separately tacked the called strikes and whiffs components, and landed on the following wPDI equation to represent CSW: Read the rest of this entry »

Introduction

This is the third article in my series – wPDI & CSW. You can catch up by reading the first two articles – on called strikes and whiffs – found here and here.

Here is a quick recap of what we have covered so far:

In this series, we are looking at the PitcherList metric, CSW and how it relates to my plate discipline framework, wPDI. Last year’s FSWA Research Article of the Year by Alex Fast featured CSW, which is defined as:

Called Strikes + Whiffs
Total Pitches

With the Weighted Plate Discipline Index (wPDI) framework, all pitches are classified into six different outcomes as follows:

Each outcome is then assigned a weight, or an index. A% through F% are the percent of pitches thrown in each outcome. The general formula for wPDI, the Weighted Plate Discipline Index is given as:

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

Read the rest of this entry »

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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Introduction

Last year’s FSWA Research Article of the Year, CSW Rate: An Intro to an Important New Metric, was awarded to Alex Fast of PitcherList. In his article, Alex presents the pitching statistic, CSW – a metric which was originally coined and created by Nick Pollack in 2018. As cited in the author’s article summary, CSW is more predictive than Swinging Strike Rate (SwStr%), and is more descriptive than Whiff Rate (Whiff%).

The short and simple formula for CSW is defined as follows:

Called Strikes + Whiffs
Total Pitches

I enjoy elegant formulae. Sure – wOBA, wRC+ and the like are extraordinary metrics in their own right, but they are not the simplest to jot down. CSW is plain, simple, easy to understand, and nicely predictive.

Coincidentally, and unknowing of CSW, I came up with the concept of wPDI back in 2018. I then published my first works of the plate discipline framework on April 2, 2019. The original article was entitled Introducing: Weighted Plate Discipline Index (wPDI) for Pitchers, and can be found here.

What jumped out to me immediately upon reading Fasts’s article – was that the two metrics have something very in common. CSW and wPDI both share the very same denominator – Total Pitches. The base of both of our metrics are identical. Both utilize the very same sample size, both stabilize just as quickly, and both describe baseball through the very same lens – the pitch.

As a quick reminder of how wPDI works, every pitch can be classified into 6 possible pitching outcomes.

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Playing Time Leaders

Mookie Betts leads the league with 21 plate appearances, but here are some available hitters who have been racking up the playing time.

• David Fletcher (18 PA): He leads the league in hits (8) and is hitting (.533/.556/.667), so he’s not moving off the top spot for a while. Also, it helps that he’s qualified at four positions (SS/OF/2B/3B)
• Niko Goodrum (18 PA): He’s leading off, hitting OK (.250/.333/.563), and qualified at three positions (2B/SS/OF). He should at least be a bench bat in all formats.
• Shed Long Jr. (18 PA), Evan White (18 PA), J.P. Crawford (17 PA), and Kyle Lewis (18): The Mariners offense has been turning itself over and all the hitters have benefited. There are some interesting extreme small samples happening with 13 of 18 Kyle Lewis’s plate appearances ending with a strikeout, walk, or home run. Also, Crawford leads the league in walks (5) and triples.
• Freddy Galvis (17 PA): The Reds middle-infield options are drying up. Galvis is being forced into everyday at-bats with a .929 OPS to start the season.

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About every month, podcast mate Rob Silver pours out his undying love and affection for the Rockies Charlie Blackmon. I’m not as much of Blackmon fan but this comment got me thinking.

To play devil’s advocate:

his barrel% went up.

His exit velocity went up.

His xBA and xSLG went up.

Hard hit% went up.

K% went down.

Prior to last year, he hit 28 HRs with 105 runs in his last 161 road games.

It’s possible last year’s road stats were just noise.

— Rob Silver (@RobSilver) May 10, 2020

Blackmon definitely hit the ball harder last year but so did everyone else with MLB’s juiced ball. Even with noisy data, Rob was right and Blackmon exceeded expectations.
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Earlier this week, I examined the batted data on four hitters and I’m diving into four more today. My goal is to see if their breakout or struggles stemmed from normal aging or swing or approach change. Sometimes the change is obvious and other times, it’s murky.

Currently, I’m using five StatCast data points per month:

• Average Launch Angle
• Average Exit Velocity
• Max Exit Velocity
• Hard Hit Launch Angle: The average launch angle for all batted balls hit over 98 mph.
• Average Hard Hit Difference: The difference between the HHLA and the angle for the sub-98 mph hits. From yesterday’s research, hitters start to see a production decline at a 0 AHHD and it accelerates around -4.4 AHHD. Basically, the batter is trying to get too much loft and his batted balls are going for weak flyouts.

I’m plotting the best-fit curves using the LOWESS (LOcally WEighted Scatter-plot Smoother) method. The curves use the nearest data points to create a best-fit line. Additionally, I’ve weighted the curve by the monthly batted balls. These values are represented by the dot size in each graph.
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Yesterday, I introduced Hard Hit Launch Angle (HHLA) and Average Hard Hit Difference (AHHD) after reading a report from Driveline Baseball. After working my way through much of the boring but necessary background information, I’m now going to dive into some players to help explain some of their changes in production. In several cases, nothing was obvious with previous stats, but the two new measures helped a ton to explain some changes. Here is an examination of four hitters who broke out or busted last season.

For the analysis, I’m debuting new comparison graphs. They are monthly StatCast data is plotted against:

• Average Launch Angle
• Average Exit Velocity
• Max Exit Velocity
• Hard Hit Launch Angle: The average launch angle for all batted balls hit over 98 mph.
• Average Hard Hit Difference: The difference between the HHLA and the angle for the sub-98 mph hits. From yesterday’s research, hitters start to see a production decline at a 0 AHHD and it accelerates around -4.4 AHHD. Basically, the batter is trying to get too much loft and his batted balls are going for weak flyouts.

I’m plotting the best-fit curves using the LOWESS (LOcally WEighted Scatter-plot Smoother) method. The curves use the nearest data points to create a best-fit line. Additionally, I’ve weighted the curve by the monthly batted balls. These values are represented by the dot size in each graph.

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I had no idea who Dylan Moser was. In all respects to Dylan and his family, I still don’t. When I saw an article about him come through my feed, I was interested in how Tanner Stokey described Driveline Baseball’s evaluation of Moser. While Driveline has its own advocates and critics, it pushes the research bounds so I wanted to see what they considered important about Mr. Moser. Immediately, I saw this little nugget.

The “Average Hit Angle of Hard Hit Balls” caught my eye and I’ve been investigating its implications ever since.

Determining and finding the effectiveness of a hitter’s launch angle spread has been investigated several times in the past. Andrew Perpetua pointed out the importance of High Line Drives (a close cousin to Barrels) and how too much of an uppercut can hurt a player’s production. Alex Chamberlain and Brock Hammit both found a link between the standard deviation in launch angle and increased production.
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