## New SIERA, Part Two (of Five): Unlocking Underrated Pitching Skills

Hidden statistics are the bread-and-butter of any good analysis, but most DIPS models rarely go beyond the obvious to find a player’s true value. FIP can take you most of the way by looking at the three true outcomes (home runs, walks and strikeouts) and xFIP adjusts FIP to what it would be with a league-average HR/FB rate. But neither of those systems considers how well pitchers control more volatile statistics — the ones that take up the other 70% of plate appearances. Now, with the new SIERA here at FanGraphs, we’re finally gathering the kernels that’ll help all of us figure out the small things that make good pitchers, well, so good.

A year after its release, SIERA has undergone some important changes, which we’re highlighting this week. I think you’ll like what you see. FanGraphs’ new-and-improved ERA estimation system now uses different proprietary data, takes more interactions and quadratic terms into account when reaching its conclusions, treats starters and relievers differently and adjusts for run environment. In other words, the new SIERA does an even better job analyzing pitching skills.

The run-environment tweak is perhaps the most important part of the SIERA version at FanGraphs. Almost immediately after rolling out the initial version in 2010, run scoring began to decline in baseball. That means the initial SIERA version at Baseball Prospectus is about .25 runs higher than the league-average ERA. At FanGraphs, the constant term will be adjusted yearly for SIERA — as it is in xFIP and FIP — which better approximates the current run environment and gives a more accurate statistical assessment.

The differences between the coefficients in bpSIERA (Baseball Prospectus SIERA) and fgSIERA (FanGraphs SIERA) are summarized in the table below.

Variable | bpSIERA coefficient | fgSIERA coefficient |

(SO/PA) | -16.986 |
-15.518 |

(SO/PA)^2 | 7.653 |
9.146 |

(BB/PA) | 11.434 |
8.648 |

(BB/PA)^2 | - | 27.252 |

(netGB/PA) | -1.858 | -2.298 |

+/-(netGB/PA)^2 | -6.664 |
-4.920 |

(SO/PA)*(BB/PA) | - | -4.036 |

(SO/PA)*(netGB/PA) | 10.130 |
5.155 |

(BB/PA)*(netGB/PA) | -5.195 | 4.546 |

Constant | 6.145 |
5.534 |

Year coefficients (versus 2010) | - | From -0.020 to +0.289 |

% innings as SP | - | 0.367 |

(where netGB=(GB-FB), and where +/-(netGB/PA)^2 is + when GB>FB and – when GB<FB.)

Add the following numbers to the constant term for each year:

Year | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 |

Coefficient | -.020 | +.093 |
+.154 |
+.037 | +.289 |
+.226 |
+.116 |
+.103 |
.000 |

For now, the constant for 2011 is -.210 to mimic the league-average ERA, though this will be updated after the season’s end.

While most of the relationships between skills and run prevention were already accounted for in the original SIERA, many of these make more sense than before. During my research, I’ve discovered several important things about how pitchers keep runs off the scoreboard — such as:

**• Pitchers with more strikeouts have a lower HR/FB ratio.**

Example: Tim Lincecum

Simply put, Lincecum is among the best at preventing fly balls from flying too far. With a career HR/FB of 7.2%, hitters struggle to launch baseballs against him, even when they put them in the air. It’s a pretty simple concept: Pitchers who allow less contact see weaker contact from hitters. Since SIERA only looks at ERAs for high-strikeout-rate pitchers like Lincecum, it gives them credit for the run-prevention effects of the strikeout and for the lower BABIPs and HR/FBs they generate.

**• When low-walk pitchers give up a walk, it doesn’t hurt them as often.**

Example: Tom Glavine

Glavine walked 15% of his opposing batters with first base open — even after netting out intentional walks — but he only walked 6% of hitters the rest of the time. While Glavine might be the most extreme example, most control-wizards’ walks are more often unintentionally intentional. Essentially, better pitchers risk walks when making a mistake is less risky.

**• Pitchers with more strikeouts have lower BABIPs.**

Example: Jered Weaver

Pitchers who allow less contact also see weaker contact from hitters. Overall, the average BABIP for high-strikeout pitchers is lower than that for low-strikeout pitchers.

Yesterday’s article discussed this in detail.

**• Pitchers with more strikeouts get more ground balls in double-play situations.**

Example: Cole Hamels

Hamels has a career 48.1% ground ball rate in double play situations, but just a 42.6% ground ball rate the rest of the time. On average, this effect is only slight, but it’s notable. Situational pitching is a skill when you control the strike zone.

**• Relief pitchers have lower BABIPs and HR/FB.**

Examples: Billy Wagner and Tom Gordon

Billy Wagner had a career BABIP of .261, a number almost impossible for a starter to match. Wagner got one inning at a time, which meant he could unleash what he had without conserving energy.

But perhaps the best example is Tom Gordon, a pitcher who both started and relieved during his 21-year major-league career. As a starter, Gordon had a .300 BABIP. As a reliever, it was .275.

Knowing that relievers consistently allow lower BABIPs and HR/FBs, SIERA would add 0.37 to a pitcher’s ERA if he starts in all his innings, versus if he relieves.

**• The more base runners a pitcher allows, the higher the percentage of them will score.**

Example: Me

Put me on the mound, and I’ll load the bases. And how many hits that I give up will be run-scorers? That’s pretty obvious.

Now put nine of me at the plate and figure out how often I’d find myself in scoring position? Again, pretty obvious.

Considering these examples of lopsided competition, the pitcher who constantly has runners on base is going to benefit more from a strikeout. The pitcher who rarely has runners on base will suffer less from occasional contact.

The new SIERA includes a coefficient on strikeout-rate squared, which allows a strikeout’s effect to be different for pitchers with higher and lower K rates. Simply put, a pitcher won’t decrease his SIERA as much when his strikeout rate goes from 24% to 25% as he will when his strikeout rate goes from 14% to 15%.

Similarly, a walk with a man on first base puts a runner in scoring position, while a walk with the bases empty does not. SIERA allows a walk to have the snowball effect of a real-life situation. A jump in walk rate from 4% to 5% wouldn’t increase a pitcher’s SIERA score as much as a jump from 14% to 15%. This walk-rate-squared term also is an addition to the new SIERA.

By incorporating an interaction term, SIERA allows the importance of ground-ball rate to vary with strikeout rate. Pitchers who strike out fewer hitters need ground balls more often to generate double plays, and fly balls hurt them more often.

On the other hand, pitchers who K enough batters to keep the bases empty aren’t hurt as much by an occasional home run. For example, the average home run in 2010 knocked in 0.6 extra runners — but home runs against Johan Santana only scored 0.4 extra runners. This home-run mitigation is what I call the “Johan Santana effect.”

**• The more walks and singles that a pitcher allows, the more often a ground ball will induce a double-play.**

In SIERA’s previous version, the coefficient of walk rate multiplied by ground-ball rate was negative. This time, though, it’s slightly positive. On one hand, ground balls after walks can lead to double plays, erasing potential base runners — and pushing the coefficient towards the negative. But walks after ground-ball singles put runners in scoring position — pushing the coefficient towards the positive.

Overall, both effects are in play, so I let their combined effects shine through by including the small, positive coefficient. For most pitchers, this will only affect the hundredths digit on their SIERA.

**• Ground balls become hits more often than fly balls.**

BABIP on ground balls last year was .233; on fly balls, BABIP was .137. SIERA assumes higher BABIPs for pitchers with more ground balls. This explains why Lincecum had higher BABIPs than some other fireballers who were discussed in yesterday’s article.

**• The more ground balls a pitcher allows, the easier they are to field.**

Example: Brandon Webb

The infield defense behind Brandon Webb has been very average in his career, but his BABIP on ground balls is not the league-average .233. During his career, it’s been .205. Webb’s sinker is heavy, and it has a tendency to chop off the bottom of the bat. Nearly every pitcher with Webb-like ground-ball skills allows fewer grounders to go for hits. Fausto Carmona, Roy Halladay, Derek Lowe, Tim Hudson, Chien-Ming Wang and Jake Westbrook all rack up ground outs. This is why the coefficient on net ground-ball-rate squared is actually negative.

In other words, a pitcher who increases in ground-ball rate from 45% to 50% will not help his SIERA as much as a pitcher who increases his ground-ball rate from 55% to 60%, because even though both are giving up fewer home runs by increasing their ground-ball rates, the latter pitcher is getting more outs on those extra ground balls.

This is probably the most important non-linear term included in SIERA. In the article linked above, I showed that all of the pitchers who had consecutive 60% ground-ball-rate seasons had below average BABIPs on ground balls.

**• Pitchers who have higher fly ball rates allow fewer home runs per fly ball.**

Example: Matt Cain

With a career 45% fly ball rate, Matt Cain is among the best at keeping his fly balls in the yard. He gives up mostly infield flies and shallow outfield flies, which is why his career HR/FB is just 6.8%. SIERA assumes that pitchers who allow more fly balls have below-average HR/FB rates, and that’s exactly what happens.

**• Run scoring has dropped in the past few years.**

SIERA now has a term to represent the run environment, net of peripheral statistics. A pitcher with the same peripherals in 2006 and 2010 will have an ERA that is .29 runs higher.

For the researcher, SIERA’s discoveries give a blueprint to better analyze pitching. For the average fan, SIERA factors in each of the pitching tendencies highlighted here, and spits out a neutralized ERA version of that accounts for all of them.

Eyeballing a pitcher’s strikeout rate, walk rate and ground-ball rate will give you a pretty good sense of how well a pitcher has pitched. But juggling the interplay between all of the effects hasn’t been possible. In a way, SIERA frees your mind.

Fans now can look at pitchers like Cain, Glavine, Lincecum and Weaver and get the shorthand explanation behind their successes. For the rest of the pitchers, SIERA makes you think about their performances in ways that other DIPS metrics have traditionally ignored.

The next parts of this series will:

1. Discuss pitchers with large differences in their xFIPs and SIERAs and explain what they teach us about pitching.

2. Test SIERA against different ERA estimators.

3. Discuss some attempted changes to SIERA that didn’t work.

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Excellent work, look forward to the rest of it.

“Pitchers who have higher fly ball rates allow fewer home runs per fly ball.”

I imagine that ground-ball machines like Tim Hudson have higher than average HR/FB rates because if they don’t keep the ball down on a certain pitch, it’s likely to go a long way. That’s why his xFIP and SIERA easily beat his FIP in 2010.

Yeah, the conventional wisdom is that when a ground-ball pitcher misses up, it goes into the seats (often as a line drive); when a fly-ball pitcher misses up, it becomes a pop-up (and thus an out).

In yesterday’s article when you introduced the idea that a high K rate correlates to a lower BABIP and HR/FB, I didn’t see any mention of SwStr% or BB% as an additional factor. A simple thought experiment leads me to believe (as you’ve shown) that a pitcher’s ability to control the ball and induce swings and misses correlates with his ability to induce weak contact and to limit HR on FB. My question is, instead of using K rate, would you gain a more accurate projection of BABIP and HR/FB if you used the components that drive K rate, and not K rate itself?

I kind of rushed this post, could’ve made it more clear:

Instead of deducing or projecting BABIP and HR/FB from K rate, why not use more granular composite factors, things that make up K rate, to get a more accurate projection?

I’ve found that things like swinging strike rate are useful in small sample sizes if you want to deduce strikeout rate (so they’re perfect for analyzing the first half performances of rookies for example, or checking if a pitcher is injured or something), but they stop telling you more than strikeout rate itself tells you directly as you get more data on a pitcher. Strikeout rate predicts strikeout rate better than swinging strike rate predicts strikeout rate, once you have a year or two of data under your belt.

Interesting. That I can definitely believe – that previous K rate is a better of predictor of K rate when your sample is big enough. But I still think the essence of what we are trying to capture – the ability to induce the batter to put the NON sweetspot of the bat on the ball, or if connected on the sweetspot, a propensity for the contact point to be a high/low miss radially on the barrel (grounders/popups) – is deeply connected to: making the batter swing and miss, and controlling the baseball. Now, maybe K rate really is the best, most practical summary of those two skills (control and whiff), it’s obviously a very good one, but I just have the feeling that these more granular components can be measured with the tools we currently have that would allow us more insight on BABIP and HR/FB.

I agree, but I’d imagine that the K rate data is easier to obtain and work with than the components that drive it. Furthermore, you have to make sacrifices at some point by limiting your number of parameters or else your fit becomes meaningless.

I’d like to see some analysis of the following:

AJ Burnett’s ERA is almost 2 runs LOWER when he walks 3 batters than when he walks less than 3. Discuss.

His curveball is nastier but more wild? So he gives up less hits but a few more walks?

What else is going on here? Does that mean his stuff is nastier and, therefore, he’s also striking out more when he walks more?

It’s called selecting a cutoff after the fact, a.k.a. “noise.”

bizarre, but like evo says, that sounds like noise

You can’t explain that.

Ball gets thrown, ball gets hit.

“Pitchers who allow less contact see weaker contact from hitters. ”

Less homeruns != weaker contact.

They also allow lower BABIP though.

It’s interesting that for a fair number of pitchers who outpitch their peripherals, like Jair Jurrjens, SIERA actually makes them look WORSE than xFIP does.

Matt, you really confused me with this sentence: “Since SIERA only looks at ERAs for high-strikeout-rate pitchers like Lincecum, it gives them credit for the run-prevention effects of the strikeout and for the lower BABIPs and HR/FBs they generate.”

Are you saying that SIERA actually factors in raw ERA numbers for certain pitchers? That doesn’t make sense at all to me.

Your first point applies to John Lannan as well, and as you suggested many others as well.

Not sure what it all means though.

Not on an individual level, but the formula is based on best fit of over 3,000 pitchers, but since pitchers with strikeout rates have (on average) lower ERAs than they would with league average BABIPs, the group of high-strikeout pitchers get good SIERAs.

There’s a mixture of guys who outpitch their peripherals. Jurrjens does better with xFIP, Wakefield does better with SIERA, Cain does a tiny bit better with SIERA. It all depends on the pitcher.

I’m wondering what some people might say about using Lincecum and Cain to demonstrate points about HR/FB…

Yes, exactly. This is precisely what I wondered. Is Matt controlling for park effects?

I like the idea that SIERA is pursuing, but I have to take issue with assuming that the correlations are linear or quadratic. For example, in the extreme case where a pitcher has a SO/PA = 1, his ERA would be 0. Rather than having a good SO/PA ratio contribute “negative” ERA and subtract from a constant, a SO/PA ratio of less than 1 should add to ERA. A more meaningful fit would obey distinct boundary conditions.

That being said, it’s likely that the current method is a good approximation of the “true” correlation equations for common ratios. I sincerely hope for the best for SIERA and I look forward to seeing how it stacks up against the other ERA estimators.

Yeah, any of these estimators will fall apart at the boundary. FIP, xFIP, and SIERA all are negative if you strike out 100% of batters faced. In case it wasn’t clear, strikeout rate still decreases ERA at the extremes, just by less when strikeouts are already super high and there are fewer runners on base.

Yes, but from your model it appears that a strikeout rate of .9 is actually better than a strikeout rate of 1. I understand that these ratios are nothing like anything we ever see and that the model behaves appropriately in the common range. I excuse xFIP because it’s purpose is to be extremely simple to calculate. If you’re going to toss simplicity aside and go completely for accuracy, the model should be meaningful over the whole spectrum of possible values.

To be clear, I’m not saying that’s what SIERA should do – you obviously have to take shortcuts at some point. My point is that a choice other than a quadratic fit (perhaps something logarithmic) would have more meaning. I would hope that this extra meaning would imply better predictive powers.

Still, this is good work and I’m looking forward to parts 3-5.

Ah, yeah, that’s true. If you have BB=0 & GB=FB, then SIERA would technically start going up at 85% Ks. But anything above 85% Ks and SIERA is negative anyway, so like you said, minor issue but fair point.

Yeah, this was bothering me as well. Do you have a theoretical or empirical basis for using a power of two in your non-linear components, rather than say 1.8 or 2.2? The Pythagorean RS/RA equation is known to be better approximated by something other than squares, but people use a power of two to keep a simple calculation simple. That’s clearly not a consideration in SIERA, so a suspiciously simple integral power seems open to questioning.

The goal is to make the effect non-linear; in other words, to make it such that ground balls can have different effects for pitchers who throw sinkers for example. A quadratic effect simplifies it to the extent that it makes the derivative linear. I’m not sure there is much value in messing with the exponent.

“Pitchers who have higher fly ball rates allow fewer home runs per fly ball.”

I wonder if this is because any pitcher with a high fly ball rate and a high HR/FB ratio would not be in the Major Leagues in the first place.

if you think about it, it’s the logical corollary to “The more ground balls a pitcher allows, the easier they are to field.”

basically, for pitchers who are at the extremes — either extremely good at getting batters to hit above the ball (GB%) or extremely good at getting them to hit below the ball (FB%) — the “distribution” of their outcomes will skew to the “easy outs”, compared to pitchers with skills closer to the median.

it makes total sense to me — an extreme flyball pitcher will not only induce MORE fly balls, but more WEAK fly balls. And IMHO it’s one of the biggest failings of more linear estimators like xFIP which throw out a chunk of data…. they are going to work very well as a predictive tool for the center of the distribution, but start to break down more severely with outlier types.

Probably the case.

I’m guessing that those who have a high FB rate also have a disproprtionately high IFFB rate. That is, many of their additional FBs are infield fly balls which are turned into outs 99.9% of the time. This, in turn, will have an effect on their HR/FB rate since IFFBs become HRs 0% of the time.

Re: relievers and their HR/FB rates— how about Carlos Marmol. Guy has given up 5 home runs since the start of 2009, and only ~2.7% of his flyballs have left the park.

I have a question for anyone that wants to help out. I feel like SwStr% should be exactly the same as 100% minus Contact%. Because wouldn’t the number of pitches that a batter doesnt make contact on be the same as the number of pitches that a batter swings and misses? This is just something that i cannot seem to sort out in my head. Thanks to anyone that answers this.

SwStr% = strikes swung and missed / strikes

Contact% = strikes swing and hit / strikes swung at

They have different denominators. In other words, SwStr% includes strikes looking.

Thank you. That makes it much easier… SwStr% is basically what percentage of strikes were due to swing and misses. And contact% is what percentage of swings that result in contact.

The way I sorted it out in my head is

SwStr% is “pitches in the strike zone that are actually swung at”

Contact% is “pitches swung at that are actually hit”

fakdaddy – correct

joser – not quite

When you say, “pitches in the strike zone that are actually sung at”, you’re describing Z-Swing%

Matt — How does SIERA handle pitchers who split innings between the bullpen and rotation? Corey Leubke is an obvious example for this season.

This is done by %, so Corey Leubke would get .367*(24 IP as SP)/(63 total IP) added.

Hey Matt- What’s the justification for suddenly adding in BB^2 and SO/BB^2? They weren’t statistically significant in the BP version, what’s the p-values on those terms now? And what’s the theoretical justification as well now?

BB^2 had a p-value of about .06 or something IIRC. BB*SO wasn’t significant, but the sample size would limit it. They both came from the fact that I realized what was generating most of the non-linearity was the fact that base-runners are more damaging when you have more of them on base. So high K means less damage from an additional BB. More BB means another BB is more damaging.

Increasing the R^2 should increase the out of sample results as well. But it totally sounds like your #’s are pretty close to normal, seems like you’re doing it right anyways, your results are coming in better than QERA and XFIP. I’m loving your series, really glad someone’s going through the process and explaining the steps. Thanks for the amazing efforts!

Matt-

Have you tested any of the independent variables or even the dependent (SIERA itself) to see if they’re normally distributed? Have you thought about increasing the accuracy by either modeling a possible skew stable model to increase R^2 further, or non-parametrically with kernel regression or a neural network? Even at a sample of 3000, you would have ample sample size to test non parametrically, with increased R^2, although no specific coefficients obviously…

Thanks for the suggestion. Most baseball statistics are normally distributed if you limit the IP or PA restriction enough. ERA is pretty much normally distributed over 40 innings, enough that this would be a go.

I only have some exposure to non-parametric regression, but I’m curious what about this data makes you think kernel regression or anything non-parametric would be necessary. I figure that ERA should be monotonic with respect to each of GB, BB, and SO, but I admit that I don’t know enough about non-parametric regression to know. I do know that QERA by Nate Silver effectively assumes a log-normal distribution of ERA in its modeling and is less accurate. That’s not non-parametric, but maybe in the direction of what you’re thinking?

I’m not sure how ERA or SIERA is actually distributed, so I don’t know how much more effective any nonparametric model might be. Although with a large enough sample size, you could probably increase R^2, although you would lose confidence intervals and coefficients for the specific variables- NP is essentially a black box, where you’d just get the output.

If you run the SIERA results through a residuals histogram, that will give you a sense if your results are distributed normally across the regression (basically bell curve) or not. If they’re not, then you may get different effective results at farther ends of the spectrum. In which case, a nonparametric model might help.

Even if that’s not the case, non parametric essentially goes with the grain of the wood, as it were, so with a large enough sample size (which it sounds like you may have) you might still get an increased R^2 over trying to superimpose a normal distribution (which it may be close to, but not exact)… You’re doing some excellent work, I was just curious if you’d thought to try it out that way… your inputs seem great, this is just a possible tweak in the modeling, which you may not need…

Okay. I just checked and SIERA, ERA, and (SIERA-ERA) are all pretty much normally distributed with at least 40 IP. Definitely a very sharp unimodal distrubtion, albeit not completely symmetric I don’t think. I guess since I’m not really trying to max out R^2 directly as much as develop something that would work out of sample (but in a similar run environment), it’s probably not worth the investment of learning non-parametric estimation, but I do appreciate the out-of-the-box idea.

“Pitchers with more strikeouts have lower BABIPs.”

While statistically this may appear true, I have to wonder if GB% has a higher correlation with BABIP then K%. Strikeout pitchers tend to allow more fly balls, which tend to produce lower BABIP’s, while low strikeout guys tend to be more groundball guys, which produce higher BABIP’s. I wonder if it’s really fair to give a guy a BABIP benefit for K%, as opposed to giving him a BABIP bonus for having a lower groundball rate (which may be more accurate).

The point is that for a given GB%, the higher the K%, the lower the BABIP.

“Pitchers who have higher fly ball rates allow fewer home runs per fly ball.”

Would it be more accurate to treat OFFB, and IFFB as seperate entities? and use HR/OFFB? A high flyball pitcher who posts lowish IFFB rates (which means more fly balls in the outfield, and thus more home runs/fly ball) shouldn’t be getting the same benefit that a pitcher who regularly posts high IFFB rates should.

Makes sense to me. See ( http://www.hardballtimes.com/main/fantasy/article/introducing-hr-offb-park-factors/ ) for more on this.

Yes, that would do better if I was trying to model same-year ERA, but what I’m trying to pick up with the net ground ball term ((GB-FB)/PA) is just a general angle off the bat skill tendency. Some pitchers are better at inducing IFFB/FB but those pitchers are often the ones that get higher FB% in general, so in general I’m trying to pick up a skill level as shown by angle off the bat.

Slash – the reason high K relates to BABIP is because it suggests a level of stuff (ability to move the ball off the barrel of the bat) to induce a large number of weak-average strength groundballs (which are easier to defend).

A guy like Halladay makes his living off of being able to allow guys to hit the ball, but taking away nearly all their power.

Guys that don`t strike batters out and have a lot of ground balls against run the risk of the Josh Towers effect (being able to make good pitches which induce ground balls but having them all go through because the batters are still getting solid wood on the ball).

loving this series

Kinda wonder about Glavine in terms of BABIP too. He was a moderate GB pitcher, but still had a .285 BABIP on a team (not league) with a .291 BABIP.

He gave up a higher % of LD than an average MLB pitcher, but his BABIP on GB was much, much better than average…what you would expect from an extreme groundballer.

With lots of LD, few Ks and a good, but not great GB rate, he should have had a terrible BABIP, but he still saved 70-80 hits compared to his mates.

I wonder how many “exceptions” there are to these guidelines. Enough to go back to the drawing board or too few to worry about changing the construct.

I did some research on Glavine. He threw a ton of change-ups and most of them low and away..both things that tend to coincide with weak contact. Of course he gave up his fair share of FB’s too, which is surprising. In fact, despite having a good career GB rate, there were a few years in which he gave up more FBs than GBs.

But the biggest clues seems to be in his splits: Glavine’s BABIP was about the same as his mates with nobody on, with batters leading off innings, when ahead in a game, when the game was a blow-out, etc. All the times in which it makes sense to go right after hitters.

His BABIP was far, far better when he had little run support, had runners in scoring position, was behind, and when the game was close. These were all situations in which his BB rate famously and drastically increased too. Not a coincidence.

Seems pretty obvious that he expanded his zone in these situations, causing batters to make weaker contact. Glavine’s BABIP seems very situational based, which explains why he had a ho-hum BABIP in normal situations and “only” 5-6 points better than his mates overall..

I love finding the mysteries behind the outliers.

Maddux’s BABIP compared to mates, on the other hand, just seems to be a case of an extreme GBer who’s BABIP on GB was much, much better than expected from a GB pitcher.

I don’t like the use of examples, because single examples are meaningless. I guess you are trying to use them to explain what you are saying, which is ok, but they are not evidence to support the claims.

I am trying to find an exception and figure out how he beat the “system.” I am not disagreeing with the overall concepts or “claims” of anyone.

Am I misinterpreting this, or is it a typo?

• Pitchers who have higher fly ball rates allow fewer home runs per fly ball.

Example: Matt Cain

With a career 45% fly ball rate, Matt Cain is among the best at keeping his fly balls in the yard. He gives up mostly infield flies and shallow outfield flies, which is why his career HR/FB is just 6.8%. SIERA assumes that pitchers who allow more fly balls have below-average HR/FB rates, and that’s exactly what happens.

I thought average HR/FB was 10%? Cain is a FB pitcher, and his rate is 6.8%. Should it read “SIERA assumes that pitchrs who allow more fly balls have ABOVE-AVERAGE HR/FB rates, and that’s exactly what happens”?

As I was typing that I realized you might mean below average in number, not in rank.

HELP??! haha

Yeah, I meant low/good rates for HR/FB. Thanks for clarifying.

Yea, I figured that’s what you meant after some thought. Thank YOU for clarifying.

“Almost immediately after rolling out the initial version in 2010, run scoring began to decline in baseball.”

So you guys caused the drop in scoring by teaching teams how to improve pitching. Thanks alot. But seriously, very interesting work

LOL, and thanks :)

For murdering the offense in baseball, I hope you get arrested by banditos who say things like “We don’t have to show you no stinkin’ badges!” So I will not “treasure” SIERA.

To think that I endorsed Eric’s book. You guys should be sentenced to play Strat-o-matic with nothing but dead ball era teams for the rest of your natural lives. Have fun matching Mordecai Brown against Christy Mathewson. Enjoy all the popouts and GBAs.

Several previous posters have mentioned IFFB’s. I’d like to bring them up again but suggest a different approach. No one has seriously questioned that IFFB’s should be included in FB’s. I’d like to propose that they are not merely FB’s that are extremely easy to field but that they actually have more in common with strikeouts than they do with OFFB’s. Certainly that’s true in so far as the result is concerned. I would argue that a ball hit almost straight up in the air reflects as much of a failure on the hitter’s part as a strikeout does. So why not treat IFFB’s as K’s and keep them out of your FB data entirely? Then the HR/FB stat would be much more meaningful. More importantly, BABIP* would become less GB/FB dependent because, just as every GB has a chance to become a hit, so too would every FB. In short, every ball would really be “in play”, excluding the virtually automatic outs.

See my response above to slash12 on the same topic. I’ll pose a question back– what is the goal of doing an ERA Estimator in the xFIP/SIERA mold instead of FIP or just using regular ERA? If you don’t include HR as a variable, you shouldn’t include IFFB either. They’re just the two extremes on the “fieldability of fly balls” spectrum.

I really like all these articles matt, but did you find that flyball pitchers might have had a survivor bias though? What I mean by this is that if a pitcher had a really high flyball rate, to remain as a major league viable pitcher wouldn’t they have to have a lower than average hr/fb rate and most likely higher than average k rate a la matt cain to remain in the major leagues? because if not, their era’s could jump from a little bit above league average to probably replacement level, no? also could you see this effect also maybe play out in groundball pitchers as well… thank you for your time

While I am in general a big fan of linear regression, I look at some of the variables used to arrive at SIERA, and I cannot help but think there must be a great deal of co-linearity, that is variables that are correlated at .7 or higher though there is no hard and fast rule, in your variables. Typically, this is viewed as problematic for linear regression, since it essentially uses the same variable twice.

Did you check for this problem? If so, what were the results?