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Ball_Tracking
Friday, May 25, 2012
Terrific article! Max looks at the change in fastball speed, based on the 24 base-out state, count, and quality of batter.
First, he finds that the pitchers throw harder with more runners on base, up to 1mph more, even though with runners on base, they are not going with the full windup. Either they are counting on adrenaline, or, they are sacrificing something (movement? location?) in these cases. That would be a good one for part 2 for Max, to see what the horizontal and vertical movement numbers change, as well as pitch distribution in the strike zone (or, for something quick, % of pitches that are called balls). It seems he controlled for count as well, based on his first note.
He also shows that the 2-strike count get close to 1mph more speed. Again, is he reaching back, or is he sacrificing movement and/or location for it? Location will be hard to determine, because we won’t have a baseline to compare against, since we expect him to throw on the edges on 2-strike pitches (even on 3-2, though not as much).
Finally, he shows it by hitter, and the big boppers are getting thrown to faster, while pitchers-as-batters are getting off a bit easy. Again, is it adrenaline, movement, or location that is the trade?
Anyway, love this stuff!
Thursday, May 24, 2012
Jason documents the ten pitchers who have lost the most velocity.
If you want to improve the study, you should:
1. Forget the AL/NL distinction
2. Show the pitcher’s RA9 and FIP in 2011 and so far in 2012
3. Show the change in speed for the non-fastballs
4. Show the change in frequency of the fastball (and pitches in the fastball family)
5. Give us the totals for the 10 pitchers
Tuesday, May 22, 2012
Cool piece by Troy, showing the percentage of called pitches were deemed strike, according to the umpire, and the percentage of called pitches were tracked as strikes, according to PITCHf/x. At the league average, both are 45.4%.
. YEAR Zone % Zone % Pitch f/x
2009 51.70% 50.80%
2010 47.80% 50.90%
2011 48.30% 52.90%
2012 43.50% 51.00%
Thursday, May 17, 2012
Whether by bunting, or going the other way, it all works.
Wednesday, May 16, 2012
Wonderful article by Ben, on the Molina v Lawrie episode (though Molina was invisible to everyone except Ben).
This writer gives us some examples of what happened following changes in pitch selection frequencies. Obviously, it is both anecdotal, and shows no specific correlation=causation effect.
But this is one of those areas of PITCHf/x that could potentially lead to dramatic results.
Friday, May 11, 2012
Max gives us some good data:
Pitchers deliver the ball over 0.7 miles per hour faster (relative to an 0-0 pitch) when they need one more strike to eliminate the batter, throw around 0.2 mph slower when grooving a 3-0 pitch, and throw roughly 0.2 mph harder on every other count.
...
I expected to see that pitchers throw harder in bases-empty situations because they can pitch from the windup; however, that does not seem to be the case, as they apparently reach for a little extra gas (+0.2 mph) when they are in a tight spot. Maybe the extra effort pitchers make with men on is enough to make up the difference between the full windup delivery and the set position, and then some.
...
However, while the month table, like the one we showed at the beginning of the article, indicates a strong (and expected) relationship between temperature and pitch speed, the one with times of the day illustrates something different, as there is an increase in speed in the coolest parts of the day.
Note that the identity of pitchers is already considered, so the fact that flame-throwing relievers are more likely to be on the mound at later hours is also an already neutralized factor in the model.
He also shows alot more, like the longer you pitch, the slower you pitch during the game. It’s not noticeable, about a quarter to half mph per pitch, but it’s there. If let’s say we treat 1 runs per 9IP as the result of losing 2-4 mph, then you’d lose 0.1 runs per 9IP due to fatigue (on the speed of the pitch). Add in (presumably) another bit of loss due to fatigue for movement, for pitch location, and familiarity of seeing the same batter, you end up with the effect we’ve seen, each time through the order. The interesting part is trying to quantify all these components that we know likely have an impact. That’s usually our job, to measure things, rather than to find things.
Thursday, May 10, 2012
This writer is suggesting so:
And the scout remarked that Posey’s at-bats are dropping off in quality in the later innings. The numbers bear it out. In the first three innings, Posey is a .361 hitter. He has struck out five times in 36 at-bats. In the fourth inning through the ninth, Posey is a .250 hitter. He has struck out 20 times in 64 at-bats. And if you take just the seventh through the ninth innings, Posey has 13 strikeouts in 30 at-bats, while drawing just one walk.
Two points:
1. If the scout says what he did WITHOUT looking at the outcomes, that Posey’s swings “look” slow, etc, then that’s one thing. If the scout however CONSULTS the outcome numbers, then that’s a completely different thing.
2. 5/36 in one instance and then 13/30 in another? Meaningless by itself. Again, combined with the scout’s view (but without the scout seeing the outcomes), then it could be modestly valuable.
For example, let’s say that the scout says that Posey’s got great mechanics, and he expect him to not K alot, that he should K say 3/36 times, and he ends up K-ing 5/36. We might therefore conclude his true talent in early innings is say 3.2/36 K rate.
And if the scout says that Posey has a lousy swing in the late innings, that he looks like he’d K 10/30, and he ends up K-ing 13/30, then we might say his true talent K rate in late innings is 10.3/30 or something.
But, without the scout’s observation, we are forced to conclude that Posey’s true talent K rate in early and late innings is virtually identical.
Again, that’s IF (and a big IF) the scout’s observation is INDEPENDENT of the outcomes. Which is going to be pretty hard to do, since the scout will get biased by the fact that Posey struck out.
Which is why HITf/x will be so powerful, that we’ll be able to tell if Posey is swinging harder than normal, earlier/later than normal (if Sportvision can start the clock on the bat earlier on the swing), etc.
Wednesday, May 09, 2012
Good stuff from Jeff, whereby he controls for the lack of year-to-year change in fastball, and notices that there is no change year-to-year in FIP.
Monday, April 30, 2012
Here’s the news, for those who love Dan and Harry.
Saturday, April 28, 2012
I’d be interested to hear who the Giants have to mining and analyzing the data, and what they’ve been able to find.
Friday, April 27, 2012
garik does a great job of bringing to light Darvish’s pitches, including a fastball with extra bite.
Wednesday, April 18, 2012
Verlander not only dials it up in the ninth inning, but he also throws the heat more than usual. Basically, he pitches more like an ace reliever, than a starting pitcher.
Note selection biases when looking at league totals especially. And, possibly even for the pitchers themselves, as they may have been “hotter” than usual in the games they went deep.
Friday, April 13, 2012
Cool, and kudos to whoever did this!
Wednesday, April 11, 2012
Image below courtesy of BrooksBaseball.
That’s 4 bad strike calls, and maybe as many as 8 bad strike calls. But maybe five bad ball calls too, and more depending how wide you make the unofficial strike zone. So, you’d have to do the plus/minus on all his pitches to get the balance of whether Strasburg benefited from a Livan strike zone or not.
That’s another suggestion for you Dan and Harry, to give us a plus/minus on the strike calls on a per game basis (i.e., UZR-style). This way, we can look at a game and see if a pitcher benefited or not from the calls, and by how much.
I only saw the highlights. Did those low ball calls come on curve balls and/or against tall hitters? Who was the umpire? I wouldn’t blame him if that’s the case, because his curve balls are impossible to call.
Tuesday, April 03, 2012
Yowza!
One suggestion: convert columns 2 through 8 into percentages. A person can always multiply any of those rates by column 1 to get the counts. As it stands, it’s too difficult to read the chart.
Also, the last two columns have no scale, so we have no idea of what they mean without knowing the whole league. Converting them into percentiles will do wonders.
Just a couple of friendly suggestions to improve presentation.
Tuesday, March 27, 2012
”Excellent."—Mr. Burns
Friday, March 09, 2012
A nice PITCHf/x tracer.
With 1000 pitches, one SD on the sinkers is .01. However, as noted in the article, it would be much better to control for the men on base scenario (as well as the handedness advantage or not). And of course, the identity of the pitcher (on the possibility that sinkerball pitchers are visited more often by the pitching coach).
Anyway, I love the idea, and it’s just a matter of refining the baseline.
Monday, March 05, 2012
There are easy ways to reduce a run environment without too much effort: adjust the ball, adjust the bat, and adjust the strike zone. None of us would even be aware. David notes:
Year Called Strike Rate on Pitches Taken in Zone
2008 74.5
2009 76.3
2010 78.9
2011 79.1
The difference between 2008’s called strike rate on in-zone pitches taken and 2011’s rate amounted to an extra 5,732 called strikes on hitters
This is an enormous change!
I don’t know why David gave the low run values per pitch that he did, which I won’t quote because either they are terribly wrong, or I can’t read. (Both are possible.)
A switch between a called ball to a called strike is close to 0.15 runs, depending on exactly what you are measuring. So, those 5732 called pitches that toggled is worth about 859 runs. And since there are 81 162x30 games, that means it’s an average of 0.35 0.17 runs per game.
He noted that the runs scored in 2008 was 4.65 and it was 4.28 in 2011, which is a difference of 0.37 runs per game.
There you go… no need to look at steroids or anything else. The umpires are calling strikes alot more on pitches that the batter takes. Just like that, you get 0.35 fewer runs per game.
Friday, February 10, 2012
Max gives us a component-by-component analysis of Jose Molina. Bottom-line: if you believe he controls the strike zone, then he’s a great catcher.
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