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Pitchers
Friday, February 03, 2012
Same service time, similar in age. Over the last 4 years, 125 starts for one and 124 for the other. 48-43 for one and 44-41 for the other. 3.77 ERA to 3.72 ERA. 12 innings apart. One gave up 6 more HR, the other gave up 15 more 2b+3b. 59 more K, but 25 more walks+hit batters. Even their contracts over their last two years: 9.3MM$ for one and 9.45MM$ for the other.
Danks of course signed that big contract extension which we talked about, with the first year coming in at 8MM$. It’s no surprise that Garza was going to eventually settle at something a bit above that figure. (In the Danks thread, I had figured close to 10MM.) He’s not at the Felix/Verlander/JJ/Weaver level, so he wasn’t going to get 13-14MM$. He’s one level below that, and 9MM$ is right about there.
It’ll be interesting if he ends up signing a 5/65 deal like Danks did to supplant the arb deal.
Thursday, February 02, 2012
Derek’s turn at it.
Friday, January 27, 2012
Dave is arguing that they do not:
On the other hand, strikeout rate has skyrocketed, increasing by 40% since 1982. This would seem to support the idea that relievers can be more effective in shorter stints, and that playing the match-ups can help prevent run scoring. However, there’s a problem with that theory – the strikeout rate of starting pitchers has gone up 41% during the same time frame. While strikeout rate has been raising at the same time that the modern bullpen has been evolving, this seems to be a case where correlation is not causation. If starters are seeing the same rise in strikeout rate, that points to a more fundamental shift among hitters – more sluggers swinging for the fences, the rise in acceptance of the strikeout as just another out among organizations – rather than a specific benefit being given to relievers from their new roles.
It would seem to me that this is evidence that they ARE performing better. While the number of starters per team has remained fairly stable (after all, you have 162 starts per team, and pretty much everyone is on a 5-man or 5-day rotation, and they average 100 pitches per start), the number of relievers have skyrocketed.
What happens when you give more jobs to guys on the bubble? Well, the overall average must go DOWN. If for example the average team in 1982 used 10 different relievers, and the average team in 2002 used 15 different relievers, then those five extra relievers would have been in AAA in 1982. The overall average in talent therefore would have gone down. But, we see the PERFORMANCE has remained relatively stable. This would imply that the good relievers must pitch better in shorter outings.
Let me try to illustrate numerically. I’ll just give some made-up numbers for talent:
Talent Reliever#
150 1
120 2
100 3
90 4
85 5
80 6
75 7
70 8
65 9
60 10
The average of the top 5 relievers on a team would be 109. But the average of the top 10 relievers on a team would be 89.
But, what if we add a new column that shows the performance level for each reliever, if we give them less to do:
Talent Reliever# Performance
150 1 170
120 2 140
100 3 120
90 4 110
85 5 105
80 6 100
75 7 95
70 8 90
65 9 85
60 10 80
Now, the performance level of the top 10 relievers is 109.
So, just as we know that the performance level of pitchers jumps substantially when pitching in relief than when pitching as a starter, it’s very possible that the performance level jumps somewhat when pitching in short relief than long relief.
Sunday, January 22, 2012
Nice idea (though it doesn’t seem he included hit batters).
To be more strict, I’d not only make it no HR, no BB, no HBP, but also so that you have a FIP of 0.00. Since we have to add a constant of 3.2, that would simply mean if this term ends up at -3.2 or lower: 2*K/IP. So, if you throw 6 innings, that means 10 K. If you throw 7 innings, that means 11K. 8 innings means 13 K, and 9 innings means 15 K.
Under those conditions, how many Fielding-Independent Perfect Games have there been?
Monday, January 09, 2012
You have to love the huge amount of effort and great presentation.
So, what correlates best with Year 2 ERA? Basically, K/PA! That’s right, just knowing strikeouts per PA is as good as just about everything else. FIP is a smidge better. So, including BB and HR barely moved the needle. In your first, just barely ahead of FIP, is tERA, which I think is the Stat Corner stat that used batted ball characteristics.
There are a couple of things in there to note:
1. There is a selection bias issue, since you needed to have 162 IP in both samples.
2. There is no reason to have counting stats correlated to rate stats. There is both ER and ERA in the correlation. We don’t need the ER information as a counting stat. We’re saved here, since everyone has close to the same number of innings 162 to 250 or something. But, it’s luck that the results came out as they did.
In terms of what correlates with ERA- in year2 (meaning ERA adjusted for league and park), we have: FIP- in year1 (meaning FIP adjusted for league and park). SIERA did as well as FIP (meaning unadjusted FIP).
When it comes down to it, it’s extremely difficult to do better than FIP, no matter how hard you try.
Friday, December 30, 2011
Yes, according to Jaffe, and yes according to rWAR, though fWAR has Morris at 57 wins and Radke at 46 wins.
Trying to come up with the Fangraphs number fast, we have Radke at 90% of runs allowed and Morris at 97% (or so). Morris has 3800 IP to Radke’s 2400 IP.
So, (1.25 - .90) x 4.5 x 2400/9 / 10 = 42.
And (1.25 - .97) x 4.5 x 3800/9 / 10 = 53.
That basically explains Fangraphs’ position.
(The 1.25 is the replacement level, the 4.5 is RA9 for league, the /9 is to turn innings into games and /10 is to convert runs to wins.)
What you, as the reader, has to do is decide:
1. What’s the replacement level? If you really like Morris, you’re going to put that 1.25 up to 1.30 or 1.35 or something
2. What is the performance level? If you like Morris, you’ll figure out how to put that 97% of league average down to 95% or something, and you’ll bring Radke up from 90% to 92% or something.
Then you plug in the numbers and ACCEPT THE RESULTS.
Oh, and of course, you have to figure out how to weight the various post-season starts, given Morris extra credit for his Twins post-season and much less credit for his Jays post-season. (And of course, apply the same process to Smoltz, Schilling, and Hershiser.)
Thursday, December 29, 2011
This blogger is looking for a comparable to Daniel Bard.
Friday, December 23, 2011
By , 04:01 AM
If I told you that there was a 25 year old starting pitcher who threw at least 100 IP this year, how would you answer the following questions:
1) If he throws at least 50 IP next year as a starter, is he likely (better than 50% chance) to have improved or declined in performance, as measured by something like RA per 9 or component ERA?
2) Same question but rather than likely to improve or decline, is his average performance next year going to be better or worse? One is a median and the other is a mean. For example, if he was 4.00 in year I and then had a 60% chance of being 3.50 next year and a 40% chance of being 6.00, the the answer to the first question is, “He improved,” and the answer to the second question is, “His average performance was a decline.”
Questions 3 and 4 are the same as 1 and 2, but the pitcher (he is still a starter) has no minimum number of IP in either Year I or Year II.
Thursday, December 15, 2011
Matt gives us his take.
I’d like to point out two things:
1. SolvingDIPS (pdf) has gotten to similar conclusions in terms of the split between pitching and fielding as it relates to BABIP.
2. The conversion from BABIP to runs is usually ignored in these DIPS talks. A GB pitcher gives up more hits, but fewer % of extrabase hits, but also gets more DP. Overall, and you may find this hard to believe, in terms of RUNS per batted ball in play, GB and FB are the same! (Excludes Pops, Liners, HR.)
So, even if you find a BABIP skill, it’s irrelevant to know that. What we really want is his RUNS skill on batted balls.
And in that case, Voros’s initial claim is much more realistic.
Friday, December 09, 2011
Matt follows up John Mayne’s article from almost two years ago, with his article.
I’m not really surprised by the results. However, I’d like to correct Matt’s interpretation when he says things like:
Not only do pitchers who throw faster succeed more often, but they improve more as well.
Let’s go back to what ERA and component ERA (like FIP) tells us: it is an INFERENCE of their true skill, based on the OBSERVATIONS available. That’s all these things are. If all I know is his K/PA, then I can infer how good he is, with a certain confidence interval. If I know his BB and HR and BABIP, then I can infer better.
So, if you have two guys who have an identical K, BB, HR, BABIP rates, but one also happens to throw 95mph and the other throws 90mph, then we can INFER that the harder thrower guy has less good luck in his ERA than the 90mph guy. It’s more real for the harder-thrower than the softer-thrower.
The job of the saberist is simply to figure out: how much more real. That’s the only job of the saberist, to infer as best he can, given the observations at hand.
Now, if the softer-thrower actually locates his pitches better, then that’s another variable to consider.
But, the important point is this: ERA is simply an observation, and observation that has a certain amount of good luck and bad luck.
It’s more obvious with things like steals per SB attempt. If one guy can run from 1B to 3B in 6.2 seconds, and another guy can run that in 8.2 seconds, and both guys have an 80% SB success rate, guess which guy got luckier? Now, obviously, you want to measure things like jump and reading-the-pitcher, etc. But, given a large enough group of players, say 20 in each, those kinds of things will likely cancel out. So, you’d bet on the faster guys to have a better success rate (since this will presume “all other things equal").
And, hard-throwers are more likely to perform better than soft-throwers, all other things equal.
Wednesday, December 07, 2011
I liked this chart here:
count fip
0-2 3.88
1-2 3.90
2-2 3.92
0-1 3.92
1-1 3.94
0-0 3.94
3-2 3.95
2-1 3.96
1-0 3.96
2-0 3.99
3-1 4.00
3-0 4.01
So, the quality of pitchers that find themselves in the 3-0 count is a FIP of 4.01. And those who find themselves in 0-2 counts have a FIP of 3.88.
It’s clear we expected some bias, and in the direction that we see. Now, we know the extent of this bias, which is great stuff from Josh.
Tuesday, December 06, 2011
By , 10:10 PM
I don’t get where this universal love for Buehrle comes from. I listen to MLB radio on XM every day and they constantly talk about the fact that these are the two premier FA starters. Many of the talking heads prefer Buehrle to Wilson and speak of Wilson as a relative unknown (in terms of what you are going to get).
Are you kidding me?
Wilson has been over a half run better in FIP over the last 2 years and most of his projections are even better than that, compared to Buehrle. I have Wilson almost a run better than Buehrle. James, .6. ZIPS has Buehrle as a league average starter in 2012, which I think is generous. And of course because CJ’s post-season as a starter is a little suspect in a grand total of 52 IP, he is considered “questionable” - which is a joke.
To me, this is an example of how the baseball community is still so far behind the sabermetric community in terms of evaluating players (not to mention all I hear all day on the radio is BA, HR, and RBI as a “measure” of a position player’s value and the first thing that comes out of all the talking heads’ mouths when they speak of pitchers is, “He won X games this year,” or he is a 12 or 13 game winner type of pitcher...").
Jeff Zimmerman makes the case.
Jason gives us some great data.
Our wonderful stats team pulled a report showing instances of a pitcher throwing at least 750 more pitches than the season before and found 1112 instances of pitchers that qualified since 1988, which was the first season that full pitch count data was available on the pull. As a whole, there was an average of 865 pitches thrown in the base season with 2205 thrown in the following season—an increase of 61 percent. The breakdown of the metrics for each season are represented in the table below.
And he shows that the FIP for the group in year 1 goes from 3.53 to 4.19 in year 2.
Except… there’s no control group. If you had a group of pitchers that had a 3.53 FIP in one year, you’d expect to see a higher FIP in the next year. That’s because a low FIP would imply more good luck than bad luck. And since luck is not persistent, then some of that good luck will go away.
Let’s try to figure out what we’d expect using regression. Jason noted 865 pitches, which would imply about 234 plate appearances. For a FIP regression, we’d probably want to add about 300 PA. So, we’d regress about 55%. The league FIP is probably around 4.4 for this time period. So, regressing 3.53 55% toward 4.4 gives us an estimate of 4.00 true talent FIP.
Jason is showing 4.19, so it’s still above what we’d expect.
So, there might be somethign to it, but we’d really need to look at it more carefully. I also didn’t see any minimum threshold settings either. So a pitcher that threw 20 innings one year and 120 innings another year would seem to qualify for the study.
I also didn’t see any control for reliever-starter, where you’d naturally get a jump, on the change of role alone.
Anyway, good start, but we need more.
Wednesday, November 30, 2011
Two articles, both dated yesterday, both referencing the Rule of 17, and both using Aaron Crow.
Tuesday, November 29, 2011
By , 02:23 AM
On XM radio’s MLB channel the other day, the talking heads were discussing Papelbon (right after his signing). One of them asked the question, “Do you think that Papelbon’s best days are behind him?”
Here’s a news flash, and an important statistical concept for you newbies:
For any overall pitching stat, like ERA, FIP, or ERC (component ERA), if a pitcher has been above average for that stat in the past, his better days are always behind him, regardless of his age or experience, assuming that we know nothing else about him. Of course when I say “always,” I mean that our projection for him going forward is always going to be worse than his past performance, using a weighted average of his last 3 years (say, 1, 2, 3 weights) to represent his past performance.
If you don’t believe me, I challenge you to give me any parameters that you think would defy that proclamation, and we’ll test it using historical data.
Monday, November 21, 2011
My first indication is when I saw this:
PVORP PWARP WARP NAME
58.3 6.2 6.7 Justin Verlander
56.9 6.1 7.3 Clayton Kershaw
53.4 5.7 5.5 Jered Weaver
50.5 5.4 6.2 Cliff Lee
45.4 4.8 4.8 Roy Halladay
Their PWARP and PVORP correspond. But the WARP is completely different. At first I thought it was an AL/NL thing, but then look at Lee/Halladay. I then went to Felix’s page, and at the top it shows him with 3.2 WARP, but in the stat line it shows him with 2.8 WARP. So, the 2011 calculation seems to have changed, and is not consistent on the player pages.
And even in his best seasons, he’s at only 3.4 and 2.9 WARP. Heck, his Cy season of 2010 is only at 2.6 WARP. That got me to going to the 2010 report, his Cy Young year, and he comes in at #43. Remember that his PECOTA forecast enterting 2011 had him with 7.0 WARP, with a 10th perecentile of 5.8.
I can only presume that Felix has been awarded one mother of a park adjustment.
While I am not a big looking at home/road splits at the seasonal level, I do like to look at them for longer careers. Felix has started 101 games at home and 104 on the road. His RA9 at home is 3.49, and on the road it’s 3.82. That means he gives up 9.5% more runs on the road than at home. However, the average H/R split is to give up 4.7 runs on the road and 4.3 runs at home, or a 9.2% split. So, Felix’s personal H/R split matches the league-wide H/R split.
If it’s not a home/road adjustment, then I’d like to hear what the rationale in the changing interpretation of Felix’s outcome numbers, from superstar, to above average.
Friday, November 11, 2011
Papelbon is 3 months younger. In the last three years, Madson has thrown 2 more games (196 to 194), but thrown 8 fewer innings (191 to 199). Papelbon has allowed 10 fewer nonHR, but 2 more HR. Excluding IBB, Madson has allowed 19 fewer walks, but thrown 35 fewer Ks. Madson has 10 fewer runs allowed. They both make hitters chase pitches outside the strike zone around the same.
Basically, it would be hard to find two more similarly-outcomed players. The Phillies are paying for the more certainty Papelbon provides based on 2005-2008, compared to Madson’s 2004-2008.
And that, really, is the difference. That, and intangibles.
Wednesday, October 19, 2011
By , 10:15 PM
Jaime Garcia. He was interviewed on MLB or XM radio. They asked him about his extreme home/road splits. He essentially said this:
Of course I like pitching at home. The crowd, I sleep in my own bed. But I really have no idea why I’ve been so much better at home. All pitchers have bad and good starts. I just happened to have had a lot of my bad starts on the road. I don’t care where I pitch. I’m not going to do anything differently.
An aspiring saberist or just a smart guy? He’ll never get a color commentator job, though, with intelligent insight like that!
Tuesday, October 18, 2011
By , 08:56 PM
In light of the new research presented on this blog which suggests that when starters pitch the 9th, the score tends to be lopsided in favor of the pitching team and wOBA tends to be lower than expected given the true talent of the pitchers and batters (and other things that affect offense), I have recalculated the “times through the order” wOBA for both day and night games, with indoor games not in the sample, removing all 9th inning data.
In The Book, this is what we presented:
Times through the order expected actual
1 .353 .345
2 .353 .354
3 .354 .362
4 .353 .354
As you can see, the more a starting pitcher faces the lineup the better those batters do, due to familiarity or pitcher tiring, or both (or some other reason or reasons). However, the 4th time through the order, the trend seems to stop and batters actually perform the same as the second time through the order. This seems to make no sense.
We have speculated two things that might be causing the 4th time through the order depression: One, 2/3 of all games are at night and it is colder the 4th time through the order. Two, and more recently, the 4th time through the order sometimes happens in the 9th inning, and as we have just found, 9th inning wOBA versus starters gets depressed because the score is usually lopsided in favor of the pitching team.
So I reran the numbers separately for day and night games (and ignored indoor games), and I also ignored the 9th innings. The wOBA is adjusted for the pool of pitchers and batters in each bucket. The first row is 1st time through the order in the 1st inning only. The second row is 1st time through the order in all other innings. We see a real depression in the first inning. Although the data is for home and road teams combined, it is actually the road team batting that is heavily depressed in the first inning for some reason. Either home team pitchers are already used to the mound, the road batting team starts out “cold” or there is some other reason or reasons.
Night games
1 (1st inn) .339
1 (other inn) .341
2 .352
3 .359
4 .350
So, again, we see a depression the 4th time even though we are not using 9th inning data.
Day games
1 (1st inn) .330
1 (other inn) .341
2 .349
3 .357
4 .367
Here we see a large jump from the 3th to the 4th time. It does appear that either temperature or pitcher tiring during the day (but not so much at night), or perhaps shadow issues during the day, greatly affect the “time through the order” penalty…
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