Tag Archives: mike fast

You’re Wrong; No, You Are!

As I’ve mentioned before, the comments section of the Hardball Times is a barren wasteland. But Matt Swartz’s latest treatise on being an idiot with a stats software package attracted some controversy, mostly because he’s an idiot with a stats software package. I’ve archived the SABR fight in case the comments disappear as things sometimes do on THT. And I’ve even highlighted the best parts.

Mike Fast:

The community has shown with certainty that there is little difference between pitchers? I would say that my study of HITf/x data indicated exactly the opposite.

And similarly for team defensive efficiency, a large portion of it is due to how hard the team’s pitchers allow the ball to be hit.

Single-year BABIP is a crude measure of pitcher skill, and it’s leading you to conclusions about the game of baseball that are very wrong.

Matt Swartz:

I’m not coming to any wrong conclusions. I don’t know what you think I’m doing with single season BABIP, but it’s not leading myself to wrong conclusions.

There IS little difference relative to the difference between pitchers in strikeout rate, which is why it takes more than a season to stabilize.

What your study showed was that how hard balls are hit is persistent, and that it is correlated with BABIP. It didn’t widen the spread of pitcher BABIP skill levels in the MLB, which is and always has been minimal compared to the spread in strikeout rates.

I find your comment about “leading you to conclusions about the game of baseball that are very wrong” to be fantastically indicative that you haven’t really read and understood this or anything else I’ve written on the topic of pitcher BABIP. If you did, you could certainly understand your own findings better, and you’d know they aren’t contradictory.

The reason that single season BABIP is a crude indicator of pitcher skill is sample size. The variance in an individual’s BABIP skill level due to randomness is going to be about [.21/sqrt(number of batted balls)]. Knowing this, we can actually pin down that about 75% of single season BABIP variance is due to luck for pitchers with >=150 IP. The rest of it comes down to know the other 25%. We know that regressing team BABIP by the same process would yield another 13% of the variance in BABIP, which means that there is 12% for pitching.

Using single season BABIP to understand that 12% will due a pretty poor job. However, using peripherals and running a regression as I have will eliminate a lot of that noise. In fact, you can explain about 10.4% of that 12% by knowing peripherals. What your study likely did is duplicated some of the effort in understanding the first 10.4% (hard hit balls or correlated with peripherals; check your data, I’m sure it’s true) and supplemented a good portion of the remaining 1.6%.

In other words, nothing you found negates anything I’ve found at all. You’ve come up with a way to use propietary data effectively. Unless you have that available, using peripherals does a pretty good job. I can’t even imagine what it is that you disagree with here, or what you think I don’t understand.

MF:

I’m not disputing your statistics. I’m disputing your conclusions about the game of baseball.

“What your study showed was that how hard balls are hit is persistent, and that it is correlated with BABIP. It didn’t widen the spread of pitcher BABIP skill levels in the MLB, which is and always has been minimal compared to the spread in strikeout rates.”

Right. But I did show that BABIP is a poor way to measure pitcher skill. We sorta knew that already, but some people had taken the BABIP findings to mean that pitcher skill was also minimal. I established that that conclusion from the evidence was wrong.

You are correct that strikeout rate picks up some of the hard-hit ball skill that pitchers have. However, it does not pick up nearly all of it.

Moreover, batted ball categories are pretty good at picking up vertical launch angle effects, but they are lousy at picking up how hard the ball is it.

So your regressions are still missing some pretty important data.

Yes, the ways we have found to measure that data so far are proprietary. That doesn’t mean that we shouldn’t learn about the reality of baseball from that data and let that effect how we frame questions, though. I would certainly wonder why BABIP doesn’t better reflect how hard the ball is hit.

I found that almost half of team BABIP was due to how hard the ball was hit. So when you say it’s 12 percent pitching skill, that’s what I’m disputing. You could say that you can only detect that 12 percent of the team BABIP is due to the pitchers, but it’s a leap of logic to say that you’re looking at pitching SKILL there. And HITf/x data indicates in fact that you are not.

Also, I don’t understand why you insist on looking at single-season pitcher/team BABIP to determine that number. It is simpler to calculate, but it’s deceptive. Being rooted to single-season numbers is one of the big failings of modern sabermetrics.

MS:

Which of my conclusions about the game of baseball do you dispute?

You found that how hard a ball is hit is highly correlated. This is a self-contained statistic that is only useful inasmuch as it can teach you about singles, doubles, triples, home runs, outs, and errors. It doesn’t do me any good to know the statistic otherwise, except for how it relates to outcomes that affect games. So BABIP is a logical skill to try to infer from how hard a ball is hit, and your numbers do a nice job of hitting on that.

I think when you say “half of BABIP was due to how hard the ball was hit,” you’re either using same year data or R instead of R^2 or doing both. I’m guessing you’re doing correlations, while I’m doing R^2.

But if it’s just same year data, you’re including luck in terms of how hard a ball was hit (of course pitchers will deviate around their true talent rate in this category as well). That doesn’t measure skill. That measures outcomes.

My regressions are not intended to be the end-all summary of a pitcher’s true BABIP skill. They pick up about 80% of the possible variance that could exist in BABIP skills.

Since this seems to be a point of contention—how much variance in true BABIP skill there is to find—I’ll prove to you that R=0.5 or even R^2=.25 is insane for one season of data.

Take all pitchers with 150 IP or more in a single season from 2003-2011. They average 592 BIP. There true BABIP skill is about .30, give or take, so the variance in luck HAS to be .21/592 for the average pitcher in this group. It’s impossible binomially for that not to be true. That’s a random variance if .000354. The actual variance in BABIP for that same group is .000457. That means randomness HAS to explain 77% (last time I got 75% but same diff)! I don’t know how much you think is team defense, but you’re it’s not 0%. If you look at how much variance is explainable by defense seriously, it’s about 13%. That’s just regressing the data.

So my original 12% number is the maximum explainable by differences between pitchers. That’s not what my regressioun found. That was 10.4%. Obviously give or take here or there, but you get the point. Most of it is explained by peripherals.

And just because you’re saying I’m looking at single-season numbers to prove that point, that has nothing to do with the implications of that 12%. The 12% means the standard deviation is pitcher skill is about .007 of BABIP. It can’t be much greater than that, and it has nothing to do with choosing a single season. The same analysis on careers or half seasons or whatever would give you about the same conclusion. I look at single-season because it’s the easiest to run these tests on quickly.

So what exactly do you think are my wrong conclusions? Where in that description of variance will you determine that BABIP skill level has a higher spread than about .007, and where as about .005 or .006 can be explained by a regression on peripherals, tell me what’s wrong here. If you want to say there is value in the last .001 or .002, great, keep at it. It may only be attainable with propietary data, and good for you if you can use it to your advantage. But nothing that I have found here is wrong.

And there it ends, without even a snide remark from Fast on Twitter. I feel like Matt Swartz took Nate Silver’s Baseball Prospectus columns a little too much to heart.

I Wish Dave Allen Was Never Born

What the hell is this crap?

Look at that mess. What the hell is it? Apparently, it has something to do with Ryan Theriot’s swings, but you can’t be sure.

I know what it really is, though. It’s just another in a long parade of terrible heat maps created by people who shouldn’t be allowed to use R. (I mean, really? It’s impossible Ryan Theriot is at his best on swing two feet past the outside edge of the zone.)

Ever since Hephaestus axed open Zeus’ head and Dave Allen sprung forth, the SABR community has enjoyed lots of neat charts. And as was bound to happen, the man co-opted Allen’s ideas and now anyone can simply copy and paste a heat map into existence on their computer. An understanding of how to smooth and regress is not required.

Carl Craword 2010 Strikeouts, from Baseball Prospectus

This blood splatter analysis is originally from an article by Mike Fast at Baseball Prospectus about how BIS blows. That article seems to be gone (Bed Jedlovec and his Beatdown International Syndicate probably got to Steven Goldman), but discussion–and the charts–live on at The Book blog.

Brett Gardner's something plotted by something, from Fan Graphs

The usually-not-crappy Jeff Zimmerman posted this, along with a mind-boggling number of other terrible heat maps on Fan Graphs.

Ryan Howard's fly ball distance on soft stuff thrown by right-handed pitchers... sure

We can thank TruMedia for this. As you can clearly see here, Ryan Howard’s fly ball distance against soft-throwing righties… is… well, the trend is obvious. Pitchers have been tie-dying the zone in an effort to contain Howard’s mammoth raw power.

Mark Buehrle's fastballs to righties, from Fan Graphs

Even FanGraphs has gotten in on the bonanza. But they’ve taken it to the next level: create your own heat maps. Shown here is a typically-incomprehensible one from Mark Buehrle’s page. And when this feature was announced at the end of January, the readers went nuts:

  • “I have such a huge boner for this.” –The Nicker
  • “Heat maps rock my world. FanGraphs rocks my world.” –shibboleth
  • “This is cool. I’m just starting to understand pitch fx so what do these maps mean?” –MauerPower

I’ve saved the best for last.

Curtis Granderson's "old" swing, from Pending Pinstripes Curtis Granderson's "new" swing, from Pending Pinstripes

These come from Pending Pinstripes, and I think the article is about how Curtis Granderson’s new swing is making everyone throw up? I’m not sure.

Sabermetrics got along well without heat maps for a long time. I know it seems that pitch location data is just begging for them and they’re just so darn easy to make, but cool it, people. It’s played.

MGL Engages in Severe Avoidance of Sound Sabermetric Principles

To bring you up to speed, Colin Wyers tried to set the record straight on parallax as it relates to the strike zone and TV broadcasts. He made the fair point that camera angles and zoom are deceiving; the most reliable arbiter of ball/strike calls might just be the home plate umpire. Though I, for one, welcome our new PITCHf/x overlords. Anyway, a controversial call to Lance Berkman in Game 2 of the ALDS might have been a fair one after all.

But MGL was having none of that.

I say poppycock! I claim I can call most pitches almost as well as the pitch f/x graphics you see on TV. How can I do that even with all those camera problems that Colin talks about? Well, when you watch thousand [sic] of games and you get feedback from umpires, batters, pitchers, AND, most importantly, the “pitchtrax” graphics on TV over the last 5 or 10 years, you somehow mentally can make all the necessary adjustments, the same way that a batter can figure out whether a pitch is going to be a ball or strike (Jeff Francouer and Pablo Sandoval excepted of course) in less than 1/2 a second. In other words, for every pitch you see, you have seen that same pitch in the same visual location hundreds of times, and you have also seen what the umpire calls it, the reaction of the players, and many times, the exact location according to the TV strike zone graphic. You can reach into your memory bank, and call the pitch pretty much as well as the average umpire, the average player, the pitchtrax graphic, etc.

Bully for poppycock, but MGL was basically saying, “Screw your science and computers. I know what I’m doing.” This argument by experience–without a whiff of evidence–just reeks of the mythical old-school Baseball Man.

Fortunately, Mike Fast was there to call out MGL.

MGL and Dave [Smyth], it’s easy for you guys to claim that when you don’t have to offer any proof.  I’m extremely skeptical of your claims.

Later…

Put another way, what percentage of pitches do you call correctly, and how did you determine that?

And after some MGL flimflammery,

This thread is is [sic] pointless. It’s just like all of MGL’s favorite announcers and managers.

Kind of a non-sequitur, but still, that’s got to sting. And it did, as MGL replied,

Thanks Mike. That is a real nice thing to say…

If you can’t take the heat, don’t make a summary judgment in a thread on The The Book–Playing the Percentages in Baseball Blog.

This Week in SABR War

The Tangettes are revolting. Over on The Inside The Book The Book — Playing the Percentages in Baseball Blog (which reminds me of the Official Stephen A. Smith My Blog), you can witness the uprising in comment form against the God-King Tangotiger.

For those too squeamish for uncensored carnage, Tango said something about Stephen Strasburg and how he (that being Tango) is always right. And then we get to the comments. Here are highlight selections, in chronological order.

Mike Fast:

I don’t know what lesson, if any, I’d take from such a small sample, but it certainly would not be the lesson you [Tangotiger] are proposing.

Ken:

I don’t see how you can beat your chest on this topic, if anything I would expect you [Tangotiger] to post a “my bad”

Mike Fast:

Could your [Tangotiger's] rule of thumb still be right, despite Strasburg’s performance? I suppose it could be. But to try to use his performance as proof that you were right is involving some major arm-twisting and severe avoidance of sound sabermetric principles.

Tangotiger:

This is how it works guys. That’s why the Tom Seaver Rule is needed.

David Gassko (with an instant 2010 SABR Comment of the Year candidate):

Tom,

No, no, no, no, and once more, no. You CANNOT say that Strasburg was lucky, because we are not having this argument ex-post. The question of how Strasburg would do came up before he had ever thrown a major league pitch—therefore, there is NO reason to “correct” bias in his numbers. That would be like regressing to the mean twice. If a pitcher posts a 2.00 ERA in a season, maybe his likeliest true talent projection is 3.00. If a pitcher posts a 3.00 ERA, maybe his likeliest true talent is 3.75. But if a pitcher posts a 2.00 ERA, it does not follow that his likeliest true talent is 3.75. Which is what you are currently trying to argue. Strasburg was a pre-selected subject. Therefore, there is no reason to expect bias in his numbers. What happened happened. Oliver was right. You were wrong. End of story.

Tangotiger:

End of story.

You can say all the rest, but don’t say that.

Nick Steiner:

I agree with many of the points you [Tangotiger] make, but this is incredibly disingenuous.

Tangotiger:

And I’m saying that we observed 75% keeps the conversation open. Telling me “end of story” is the same thing as telling me to shut up. I’m talking, and I’ll keep talking, thanks.

Jeremy Greenhouse:

Tango, this doesn’t feel right. I think that you should take a few steps back from this argument and start running some numbers. Brian’s projection of Strasburg is something he should take pride in, and it seems like you’re summarily dismissing his work without evidence of your own.

And Tangotiger gets the last word (for now):

I was fair then in that thread, and I was fair in this thread.