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Consistency Factor

Hey everyone, I'm a regular poster at the Mets SB Nation site, Amazin Avenue.  I put this little case study together and thought you guys might be interested since there's some pretty interesting info in here; basically I developed a stat that I feel is pretty useful. So enjoy.

In honor of Oliver Perez week, I figured I would try to wrap up this case study on consistency.  For anybody who missed it, I took a shot at quantifying start to start consistency, or what I'm calling Consistency Factor (CONF), for pitchers a few weeks back in this post.  It was enlightening but the results just didn't seem quite right.  So I've made some tweaks, basically utilizing Bill James' Game Score to evaluate each game started instead of WPA.

A brief refresher for those who missed the original post or forgot how it worked:  Basically I evaluated a pitcher's starts individually (using Game Score) and then took a Standard Deviation of those 30-something starts.  The higher that #, the higher the range a pitcher regularly pitches within thus the lower his consistency.  So lower = better.  However, this # does NOT relate to how effective a pitcher is.  A pitcher can have the worst CONF but still be very good, all it means is that he doesn't pitch consistently from start to start.

I compiled the CONF #'s from 2008 as well as the last 3 seasons totaled for every qualifying NL starter.  However, before we dive into all of those #'s, here's a nice visual breakdown of this idea using, who else, Oliver Perez:

3489168434_14ca7f0d65_medium3488452639_7e886d4458_m_medium

So as you can see, each point represents a start measured in Gamescore (high=good low=bad), the gray area represents 1 Standard Deviation away (in both directions) from Oliver's Mean (which happened to be a Gamescore of 51.65).  In english, the blue points are each start, the gray represents the average range where he usually pitches and the red baseline is the level of his average start in 2008.

Now let's take a look at another pitcher for some perspective.  How about Tim Lincecum:3488354863_a35bab4dcd_medium3489266308_427661e42c_m_medium

First of all, Lincecum's Mean or average start is obviously higher (Gamescore: 62.06), the guy did win the Cy Young.  (For reference, a Gamescore of about 50 represents Replacement Level)  More importantly for us, Lincecum's starts are packed much more densely within his average range than Oliver's.  Only a handful of starts fall outside of the gray area whereas Perez has many more fall outside.  As a result, Lincecum's Average Range is smaller, which represents less variation and higher consistency.

As far as the most important figure that we can draw from these graphs, its that Standard Deviation I mentioned that makes up the gray Average Range.  This is the figure that represents Consistency Factor.  For Lincecum it's 13.18.  For Perez, it's 17.08.  The league average is around 16.

Star-divide

So now that we can kind of visualize where these #'s come from, lets look at the results.  I grouped all NL starters (who pitched at least half a season in '08) by team and I've got all the teams here so lets look at them by division, East first of course:

3486856907_6e4587f0db_medium  3486857025_c63a14eee8_medium 3487673772_047251fb6e_medium  3486857075_7dd06ee0e8_medium

3487673710_5f188d841a_medium 

Well there are a few interesting things we can see here:

  • We already knew that Johan was awesome, now we know hes consistently awesome.
  • Apparently Maine & Redding are very consistent as well, although Perez, Livan & Pelfrey are not.
  • However, Oliver Perez: not that inconsistent... at least not as much as many of us would think.
  • Brett Myers on the other hand... ugh.
  • However, Cole's about average and guy's like Moyer & Blanton are really only valuable because of their consistency.
  • And damn, we all know Josh Johnson is going to be great but hes already pretty damn good (though to be fair this was based on only half a season of starts).
  • Another surprise, Daniel Cabrera wasn't bad at all in '08.

Onto the NL Central:

3487673552_1b7a5cc8e0_medium3487673802_de340d02ee_medium

3486856959_507451394d_medium3487673538_b07f301cd9_medium

3486856835_0042d12604_medium3486857141_3657cb1703_medium

  • The Cubs have a remarkably consistent staff...
  • ...except for Carlos Zambrano who is officially THE least consistent starter in the NL.
  • Surprisingly consistent seasons from the 2 young Reds rookies especially Volquez, not so much from their 2 vets though.
  • As usual Paul Maholm is quietly very good.
  • And a shockingly consistent season by Todd Wellemeyer and that came in only his first season in the rotation.

And finally the NL West:

3486857201_e99335ac43_medium  3487673614_c9c6c20f4c_medium 3487673756_a4d87dbaef_medium3486856983_d1e825a973_medium 3489207694_8c8337c4f0_medium

  • 2 more young guys with surprisingly consistent seasons in LA, not so much for Hiroki Kuroda.
  • What's the only difference between the Giants rotation and a roulette wheel? Tim Lincecum.
  • SI product Jason Marquis, very nice.
  • Surprisingly high from Webb, even more from Garland who is known for his consistency.
  • And a perfect example of what I said earlier about consistency not always equaling performance, Kevin Correia the NL's most consistent crappy pitcher (Gamescore Mean: 43.63...ugh); looking at these #'s, it's amazing he is still in a Major League rotation today.

And last but not least, here are the rankings of the best and worst performers of 2008:

3489207734_c2617d3847_medium3487673504_57f0838950_medium  3487224987_9215d124a1_medium 3488041060_ed4b280245_medium

  • Redding on the Top 5 (3 Year) list, I didn't expect that
  • And John Garland on the Bottom 5 (3 Year) list, I bet people would have had him pegged as super consistent not the other way around...
  • Another supposed "Mr. Consistency", Derek Lowe finds himself on the Bottom 5 (3 Year) list
  • And yes, Oliver would have been on that list too but I gave him a little home-cooking if you will by excluding that bizarre stretch in '06; basically I'm just evaluating him in his time with the Mets.
      Star-divide

So I hope this has been enlightening.  At the very least I figured it would break up the interminable dross that we've witnessed in the FanPosts recently.  Just as last time, I apologize for the high level mathematics and such but thats the breaks.

I set out to disprove the myth of Oliver Perez as "Mr. Inconsistency" and I think I've done that.  That title definitely goes to Carlos Zambrano.  Perez is definitely on the higher end but even if I included all of the #'s, he's not as bad as the media portrays.  I think a lot of that mindset derives from how he can be so inconsistent in-game or inning to inning rather than game to game, which definitely is no myth.  That would be another interesting case study but thats another story for another day.  As far as what this means to his overall performance, not too much because he's obviously been terrible this year anyway.  But at least he's been consistently terrible...

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Interesting stuff

and it definitely showed Oliver Perez isn’t that inconsistent from start-to-start.

But the larger question is, what does it mean? Is it better to be consistent than not? Why?

Also, since your measure is basically standard deviation, are you sure that the samples are normally distributed? I wouldn’t be surprised if many pitchers were skewed one way or the other.

Again, good work, and I’d like to see more.

by Dan Turkenkopf on May 3, 2009 3:24 PM EDT reply actions   0 recs

Consistency -- I have thought way too much into this is the past (Damn you Jeff Suppan).

First of all, nice work. It is a tough subject to tackle and here is my sad attempt using my new stat of PSV.

I was thinking of re-tackling the subject and maybe this is the time. From what I have read and understand, the following needs to be added to the discussion.
1. Some way of measuring consistency. Game score may be it, but need to get a good measure before moving on. What changes did you incorporate? I don’t like per quality start (mainly because pitching a 6 inning game giving up 3 runs is better than a 9 inning pitched game giving up 4) or Ron Shangler’s Consistency ranking is that they are either one way or another. Doesn’t measure how close someone is to next level.

2. Once a way to measure has been established, you need to come up with 3 values, average of the values, consistency of the values (Standard deviation is pretty good) and some value to measure times above and below (I am thinking averaging values above or % above and below).

3. Finally, could look into outcome of games (league and stadium adjusted) in which that fit into various buckets/scores considering the teams offense. Wins my not be a great measure, but that is the goal in baseball is to win more than your opponent. I always figure a decent pitcher that is consistent (Suppan and Wakefield) could do good at a place with a good offense. They could limit the damage until enough runs are scored to win. A poor offensive team might want some like Zambrano where he is inconsistent from start to start, but when he is good, he gives a team good chance to win.

If you want any help, let me know.

by Jeff Zimmerman (TucsonRoyal) on May 3, 2009 5:20 PM EDT up reply actions   0 recs

+1

an article articulating the thinking behind that position: http://www.hardballtimes.com/main/article/same-old-same-old/

by robcast23 on May 3, 2009 11:24 PM EDT up reply actions   0 recs

The problem with that article is they don't separate the numbers above and below.

Is is 5 semi good games and 1 horrible game (5 and 1 record)

or

3 great games and 3 horrible games (3 and 3 record)

As the article shows, that sometime it is better to be consistent, if you are a better pitcher. I would also like to see more empirical numbers, than the theoretical ones that Glasko has to use in his article (e.g. pitcher going 9 innings over 30 starts for 270 innings in a season – like that’s going to happen anytime soon).

by Jeff Zimmerman (TucsonRoyal) on May 4, 2009 1:04 AM EDT up reply actions   0 recs

This is phenomenal. Bumped to the front page.

Yes, there could be improvements, but this is an 8 of of 10 whereas we mostly see 4 out of 10s when it comes to pitcher consistency. I’d love to see more of the data presented in interesting ways.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 3, 2009 8:39 PM EDT reply actions   0 recs

thanks

i’m glad you guys liked it. i experimented with a couple formats before i decided on the current one, kind of because i liked the relative simplicity of the calculation. in the end it kind of just felt most accurate/least convoluted. but in addition to tucson’s improvements, feel free to specifically point out where you’d go with it because i’m definitely open to some constructive criticism.

by robcast23 on May 3, 2009 11:39 PM EDT up reply actions   0 recs

Some ideas:

Long lists of pitchers with 25+ (or whatever) starts ranked by consistency factor. Group players by different skills (K/9, FB%, etc) and see if there’s a connection in consistency. With all the pitch data available, maybe group pitchers by percentage of fastballs thrown, or percentage of curveballs thrown. Or lefties versus righties?

This may need a bit of park tweaking, too, as going from Coors to away parks on a regular basis will make pitchers look inconsistent.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 4, 2009 8:07 AM EDT up reply actions   0 recs

Consistent vs. Good

Why do you repeatedly use the word “good” as a synonym of “most consistent,” wspecially when you say that it is not a true representative of performance, Correia, etc.? The lists at the end you call the “best and worst” performers. You also do it multiple times during the analysis and it really takes away and confuses me.

I think the numbers start out interesting but the rest of the article is mediocre as well as much of the analysis, but the leaderboards are cool. If you take some of Tucson’s advice I think this would be really cool. Also, are you going to do the AL?

by gingerbreadmann on May 3, 2009 9:36 PM EDT reply actions   0 recs

apologies

its hard not to fall into old habits of writing i.e. good, bad, etc. however like i did mention a couple of times, when i say good and bad it must be taken in the context of the article.

as far as the AL, i had only planned on doing it if people liked the stat. the response so far has been pretty positive so when i get a good chunk of time i’ll start to put together a database for the AL next. however, i may start to make some tweaks based on the excellent suggestions i’ve received here before i go ahead with it.

oh and here was a little extra tidbit i’d thrown in for the guys over at amazin avenue:

by robcast23 on May 3, 2009 11:32 PM EDT up reply actions   0 recs

no need to apologize

I get what you’re saying, thanks.

by gingerbreadmann on May 5, 2009 10:34 PM EDT up reply actions   0 recs

When I finished reading this, I thought...

“Somewhere, the FJM guys are simultaneously having aneurysms and don’t know why…”

Seriously though, this was highly entertaining, and I think using Game Score makes this easy to approach.

by jwiscarson on May 4, 2009 10:48 AM EDT reply actions   0 recs

Does fangraphs have...

WPA available, per game, in an easy to D/L way for alot of players/years at once?

I grabbed WPA, per game, for Santana and Perez for 2008 and got the following:
….. AVG SD
JS .120 .207
OP .013 .249

Perhaps then take AVG/SD to put quality and consistancy together?

Johan comes out at .579 and Perez at .053… granted, this would assume that being consistant is better than not, which isn’t necessarily the case [would you rather get 4ER, 6IP every game from a 6.00 ERA pitcher, or 4IP-6ER half the time and 8IP-2ER the other half?]

by erosen on May 4, 2009 11:51 AM EDT reply actions   0 recs

WPA is probably not the best because of the differences in values for the same game pitched

For example Zack Greinke pitched 2 CG Shoutout wins

One had 10Ks, 7 hits and 0 BB
The other had 1Ks 3 hits and 1BB

He got credit for .776 WPA for the first game and .235 for the second.

by Jeff Zimmerman (TucsonRoyal) on May 4, 2009 12:21 PM EDT up reply actions   0 recs

+1

exactly. i had actually started this whole process using WPA and about 10 minutes into creating the database realized that it wasn’t going to work.

by robcast23 on May 5, 2009 3:45 PM EDT up reply actions   0 recs

If we're trying to improve on Bill James' game score, I'd go with park-adjusted RAR, based on FIP and xIP (or tRA and xO).

Where xIP is like McCracken’s original DIPS IP: if a pitcher gets unlucky in the BABIP department, more of those hitters “should” have been outs. So not only does he get unfairly docked for ERA, he’s accumulating fewer IP for facing a certain number of hitters.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 4, 2009 8:38 PM EDT reply actions   0 recs

Wouldn't adjusting based on BABIP make everyone more consistent

Since you’re removing what I would assume is one of the biggest causes of inconsistent performances?

I’d recommend something based on batted ball type more than FIP – maybe DIPS 3.0?

by Dan Turkenkopf on May 4, 2009 9:00 PM EDT up reply actions   0 recs

Hadn't thought of that, but it's a good point too

More that a large portion of what we call inconsistency may be masked when we strip out the influence of BABIP.

My guess is the variation from start to start is less captured in HR, SO and BB than in what happens when the ball is in play (note: I haven’t checked this at all). Some of that is variation in defense (which we probably don’t want to “blame” the pitcher for), but some is variation in pitching performance.

If we were to break down a pitcher’s performance to use the TTO stats as well as batted ball stats, then we detect the games when a pitcher is giving up more line drives than usual and mark his consistency accordingly.

by Dan Turkenkopf on May 5, 2009 7:43 AM EDT up reply actions   0 recs

I think we're just choosing to measure different things.

One is to measure the consistency of a pitcher’s outings by results. Another is to measure the consistency of his outings based on his performance. Similar yet different. Both have their uses.

Heck, with pitch f/x, we could measure consistency of pitch breaks from start to start. And maybe command somewhat if we can fudge measures for that.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 5, 2009 11:25 AM EDT up reply actions   0 recs

A DIPS-Based Pitcher Game Score Equivalent to Support Neutral Win Probability

since posting this study around the sb nation blogs, i’ve received a few suggestions about using kevin harlow’s DIPS-based gamescore equivelant.

i’ve been keeping up with some of your related suggestions down below and it looks like he addressed many of the same issues that you guys had brought up. now it looks pretty intuitive to me but you guys are much more adept at this type of analysis and than i so let me know, is this a suitable alternative to gamescore? at this point i’m basically looking for a more accurate statistic before i start building the AL database.

by robcast23 on May 5, 2009 3:46 PM EDT up reply actions   0 recs

yeah

i felt that way too, now if only it were as readily available as standard gamescores…

by robcast23 on May 5, 2009 5:38 PM EDT up reply actions   0 recs

Do you have database skills capable of handling the lahman database or something like it?

If so, any of these calculations would be quite easy. And while I’m no expert, setting up something basic is not all that difficult with the proper tutorials (ahem, Colin’s over at statspeak.net)

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 6, 2009 11:38 AM EDT up reply actions   0 recs

Harlows game score is very easy to calculate

you could probably do it with Excel.

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 4:59 AM EDT up reply actions   0 recs

Yeah, solid idea.

The only thing I’m not a fan of is using actual IP. If you allow a .500 BABIP, you’re facing way more hitters per inning than you “should”. IP needs to be more of an expected IP, which is something like (BFP – BB – HR – K) * (1 – lgBABIP) + K, all divided by 3.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 5, 2009 9:21 PM EDT up reply actions   0 recs

Agreed

If you’re going to adjust the results, you need to adjust the IPs as well.

by Dan Turkenkopf on May 5, 2009 9:31 PM EDT up reply actions   0 recs

GS DIPS seems pretty good

robcast — can you send me an email wydiyd at hotmail dot com

I have looked at GS, consistency and game outcome and have come up with some “interesting” results.

by Jeff Zimmerman (TucsonRoyal) on May 5, 2009 4:10 PM EDT up reply actions   0 recs

That does seem like a good way to handle game score

Although I’m still somewhat unsure whether it’s valuable to apply a strong version of DIPS on a per game basis.

My intuition is that a large portion of the difference between a pitcher being on and a pitcher being off will be reflected in the quality of the batted balls he gives up.

So a pitcher on an off day will give up more line drives than he would on a good day. That won’t show up in a standard DIPS/FIP approach, or in GS DIPS, but will show up in runs.

Seems like a good place to invest some more time to study.

by Dan Turkenkopf on May 5, 2009 6:10 PM EDT up reply actions   0 recs

I agree with you that it is a mistake to use FIP on a game-to-game basis

as pitchers have much more control of their BABIP in a small sample size. tRA would probably be the best metric to use as it takes into account batted ball types. Then to account for innings, you could just use something like Statcorner’s pRAA, which is lgTRA * xOuts/27 – xRuns. FanGraphs includes batted ball types in their gamelogs, so if you had the updated linear weight and out values of each of the events, it would be pretty easy to calculate.

Also, I suppose that you could use park factors for each start, although they probably wouldn’t make that much of a difference in such a small sample size.

If someone could get me the updated run and out values of each event (K, BB, GB, FB, etc.), I would gladly throw up a FanPost with some gamelogs and stuff using tRA.

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 4:58 AM EDT up reply actions   0 recs

I either misunderstand what you're saying...

or I totally disagree:

pitchers have much more control of their BABIP in a small sample size

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 9:53 AM EDT up reply actions   0 recs

I'm saying that pitchers

will have off days (when they give up a ton of line drives and off the wall doubles), and good days (when they rarely give up hard hit balls). In that case, the xBABIP would vary depending on how hittable the pitcher is that, instead of staying around .300.

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 10:02 AM EDT up reply actions   0 recs

I agree that having different stuff will affect BABIP true skill, as well as K, BB true skill.

But if we can’t really detect BABIP skill over a full season, how do we know a pitcher’s batted ball profile is a result of his true skill that day, and not just a bunch of noise. LD% is pretty quirky over a full season, much less one day.

I’m going to batted ball run values for you. At some point. Probably from Stat Corner’s methodology page.

And I’d love to see a DIPS 3.0-like stat that properly regresses each batted ball and event (K, BB) rate the proper amount given the sample size of each. (Which might be exactly what tRA* is)

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 11:14 AM EDT up reply actions   0 recs

...
But if we can’t really detect BABIP skill over a full season, how do we know a pitcher’s batted ball profile is a result of his true skill that day, and not just a bunch of noise

The point isn’t to estimate a player true skill, just to see how he pitched that day. If a player strikes out 6 and doesn’t walk anyone, but gives up 10 line drives, than I would say he had a bad day, even though the K:BB ratio is most likely more indicative of his true talent level that his LD rate.

Statcorner has run and out values on their tRA primer. They are for 2008, but do you think they will do?

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 2:33 PM EDT up reply actions   0 recs

yeah, 2008 values will do.

Do you think one start’s LD% represents a pitcher’s true skill for that one start?

I don’t. It’s pretty similar to using actual event results. If that’s what you’re going for, then it works.

http://statcorner.com/tRAabout.html for those who are interested. Let’s see if I can’t get Harry to post leader boards using those numbers instead of actual results.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 3:18 PM EDT up reply actions   0 recs

How would you suggest regressing the tRA components for each start?

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 4:40 PM EDT up reply actions   0 recs

A lot if you're looking for talent, not value.

I’d be more of a fan of using UZR data for something like that.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 4:52 PM EDT up reply actions   0 recs

That seems like it would be an intesting way to look at a pitcher's talent

Pitchers who pitch really well most of the time, but have a couple of terrible starts that obscure their overall numbers, are probably more valuable than guys who are consistently average. If you had enough data to show that one or the other is a repeatable skill, it could end up being a useful way to evaluate pitchers.

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 15, 2009 5:34 PM EDT up reply actions   0 recs

Wait a week or two

I have something in the pipeline. The work is done (fun part). Now I need to write it up for the public (I’d rather pull glass out of my foot).

by Jeff Zimmerman (TucsonRoyal) on May 15, 2009 5:48 PM EDT up reply actions   0 recs

Jeff, let me know the basics of what you're working on...

I might be working on something similar, actually, and would like not to have to figure out the programming I’m about to attempt…

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 10:25 PM EDT up reply actions   0 recs

Eric Seidman emailed me to point out that he wrote a related article at BPro.

Link

He used BPro’s flakiness stat based on support neutral value added, and also took a look at some peripherals and pitch f/x data. Interesting read.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 8, 2009 5:37 PM EDT reply actions   0 recs

oh

never heard of Flake. basically the same idea i had, but using SNVA instead of Gsc. And here I thought i had an original idea…

by robcast23 on May 12, 2009 9:25 AM EDT up reply actions   0 recs

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