Introducing Fielding Dependent Pitching

A few minutes ago, David Appelman announced the launch of several new stats here on the site, and since they hit on a topic of frequent discussion, I wanted to go into a bit more depth on our thought process behind their creation and what we see as their role in the evaluation of pitching.

Over the years, FanGraphs hasn’t been shy about promoting the concept of DIPS, which showed that most of the variance in a pitcher’s abilities can be viewed through the prism of walks, strikeouts, and home runs. We often cite a pitcher’s FIP — Fielding Independent Pitching, if you’re into proper names – when talking about his performance, and for most Major League pitchers, FIP works really well as an evaluator of their contribution to run prevention.

However, because FIP only focuses on walks, strikeouts, and home runs, it does not include all aspects of run prevention. Specifically, it takes no stance on two aspects of the game that do have a significant impact on a pitcher’s total number of runs allowed – the results of batted balls that are not home runs and the effects of sequencing of the various events. Because the spread in talent among Major League pitchers is not as large in these areas as the spread is in the components of FIP, ignoring these two areas doesn’t have a drastic result on the evaluation of most pitchers. However, there is certainly a subset of Major League pitchers who do accumulate (or fritter away) value through their performance in these two categories.

So, today, we’re introducing a set of metrics designed to help quantify the affects of run prevention that are not so easily isolated as the result of a pitcher’s actions. Because these metrics essentially serve to capture the value that FIP does not, we’re calling the sum of these metrics Fielding Dependent Pitching.

The idea for FDP was to quantify the remaining aspects of run prevention that are not measured by walks, strikeouts, and home runs. With a FIP-based WAR, we have a metric that tells us how many wins a pitcher added through success in those three key areas. What we did not have was a metric that gave us the wins added through either hit prevention or runner stranding. With FDP, we wanted to be able to break down the remaining aspects into those two categories, so that we could identify exactly where a team’s run prevention — with a specific pitcher on the mound — was coming from.

To do that, we simply worked backwards. First, we calculated the total WAR that a pitcher would receive credit for if he was only evaluated by his runs allowed, and we assumed that he had 100 percent responsibility for every variable that influenced run scoring. That stat is now on the site, and is called “RA9-Wins”. If you do not want to consider any impact of fielding on run prevention, and solely want to evaluate a pitcher by what actually happened when he was pitching (accounting for park and league adjustments, at least), then this is the metric for you,

However, for those of you who want to look at a pitcher’s contribution to run prevention in a more detailed way, we are also adding the two components of FDP to give you a better view of just how a pitcher is going about preventing runs.

To do this, we decided to calculate the linear weight value of singles and doubles — the difference between a double and a triple is almost certainly not due to something the pitcher did, and thus the value of advancing that extra base was not included — the same way that we calculate the value of walks, strikeouts, and home runs, so that we could quantify the wins added or lost that can be credited to a pitcher’s results on balls in play. Regardless of how you want to apportion the credit for those results, it is helpful to know what the value of those turning those hits into outs (or vice versa) actually is. Just as with WAR, these numbers are park adjusted and then converted into a number of wins added. These are on the site as BIP-wins, and can be thought of as the amount of wins a pitcher saved through his hit prevention, or lack thereof in many cases.

Once we knew the win value of a pitcher’s hit prevention, the remainder of his FDP could essentially be described as runner stranding. Now, this is not one particular skill, as there are many ways to skin this cat, but represents the value added through various skills that all essentially lead to the same result – not allowing baserunners to cross home plate. Some pitchers achieve this through effective control of the running game, picking off runners and refusing to let them advance through stolen bases. Other pitchers do this by simply altering the way they pitch with men on base, increasing the amount of only-semi-harmful walks they allow in order to reduce the amount of very-harmful home runs they allow. And still others simply seem to excel (or fail) at pitching out of the stretch relative to their peers, and demonstrating significant differences in their performance with the bases empty and with men on base.

No matter how they get there, however, the result can be measured by taking the remainder of a pitcher’s FDP that is not measured by his context-neutral hit prevention. This is called LOB-wins on the site, and serves as the value of wins added through all the miscellaneous ways a pitcher can strand runners.

BIP-wins and LOB-wins can be thought of as the components of Fielding Dependent Pitching, and represent the part of keeping runs off the board that aren’t measured by FIP. By definition, the sum of a pitcher’s FIP-wins and FDP-wins will equal his RA9-wins, so you can essentially see total run prevention through this basic formula:


So, that’s the somewhat boring explanation part of the introduction. Now, let’s get to the fun stuff and actually play with the data.

In the last 10 years, here are the top five and bottom pitchers in total FDP.

Johan Santana: +11.4 wins
Tim Hudson: +10.9 wins
Ryan Franklin: +10.4 wins
Matt Cain: +9.2 wins
Jered Weaver: +9.2 wins

Derek Lowe: -10.0 wins
Mark Hendrickson: -8.5 wins
Ricky Nolasco: -8.3 wins
Jeremy Bonderman: -8.2 wins
Sidney Ponson: -7.1 wins

These names are probably familiar to you if you’ve had any kind of discussion about the validity of FIP in the last few years. The four big names in the top five are the most often cited as pitchers who FIP underrates, and FDP shows just how large the gap is between their FIP-wins and their RA9-wins. Meanwhile, the guys on the bottom of the list are notorious underachievers, each of whom has been derided for failing to live up to their expected potential. As you can see, FDP returns the results you might expect if you were to look at the biggest FIP outliers of the last decade.

However, this is also an example of why breaking FDP down into BIP-wins and LOB-wins is useful, as we can present this same list, just showing where those wins added or lost came from.

Pitcher BIP-wins LOB-wins
Johan Santana 10.7 0.7
Tim Hudson 8.3 2.6
Ryan Franklin 5.0 5.4
Matt Cain 11.1 (1.9)
Jered Weaver 7.5 1.6

Santana and Cain’s extra value has all come entirely through hit prevention. Santana’s stranded just about as many runners as you’d expect from a low-FIP/low-BABIP pitcher, while Cain has actually stranded fewer runners than you’d expect based on his context-neutral stats. Hudson and Weaver both accumulated value in both areas, but got the majority of their value through hit prevention, while Franklin actually got more of his value through runner stranding, though his RA9 is also significant impacted by hit prevention.

And now for the laggards:

Pitcher BIP-wins LOB-wins
Derek Lowe (4.9) (5.2)
Mark Hendrickson (4.6) (3.9)
Ricky Nolasco (4.1) (4.2)
Jeremy Bonderman (3.1) (5.0)
Sidney Ponosn (6.9) (0.2)

Here, we see a more even split, with all five pitchers being negative in both areas. However, my suspicion is that being bad at both hit prevention and runner stranding is necessary to show up on an FDP leaderboard, because pitchers who truly awful at one or the other are likely weeded out before they ever make the Major Leagues, or at least spend significant time pitching for a big league club. That’s why the tails are higher at the positive end of the spectrum, as those pitchers success is keeping them in the big leagues longer and giving them more opportunities, while those who fail spectacularly at one of the two aspects of FDP simply don’t last long enough to show up on a list of most value lost over a ten year period.

Things get more fun if we look at even larger periods of time, however. If we expand the filters to cover the last 50 years, we find examples of guys where FDP tells quite an interesting story. For instance, Jim Palmer — with his career 2.86 ERA and 3.50 FIP — accumulated an incredible +27.8 wins through hit prevention and +15.5 wins through runner stranding. His +43.2 FDP-wins are, by far, the most of any pitcher in the modern era. Perhaps the most recent example of a similar type of pitcher is Tom Glavine, and he’s at +26.1 FDP-wins. Palmer is the biggest FIP outlier in the part of baseball history that at least resembles the game that is played today.

However, he’s not the leader in either BIP-wins or LOB-wins over the last 50 years. The pitcher who got the most value from hit prevention? Charlie Hough, which shouldn’t be surprising given the research that has been done on knuckleballers as the strongest exception to the DIPS theory.

Perhaps most interestingly, however, is the career of Nolan Ryan, who demonstrates how the two aspects of FDP don’t really go hand-in-hand in many cases. Ryan posted a career FIP- of 84 and a .265 BABIP, which should have resulted in dominating results that made him among the best run preventers in baseball. It didn’t, though, because Nolan Ryan was atrocious at stranding runners (relative to his own established levels, anyway), posting -30.2 LOB-wins over his career. Certainly, that is inflated to some degree simply through longevity, but there is no mistaking the fact that Ryan consistently posted higher ERAs than FIPs, even though his BABIP was also below average.

While Ryan is strange in the magnitude of his inability to prevent runners from scoring, he is representative of the lack of correlation between the two components of FDP. The correlation of BIP-wins and LOB-wins (scaled to IP, so as to account for differences in innings pitched) for all pitchers with at least 50 IP in 2012 is -0.008. In other words, there is no correlation. Of the top 10 pitchers in BIP-wins this year, only two (Jason Vargas and Kyle Lohse) also have positive LOB-wins. Clayton Kershaw is essentially acting as the current-day Nolan Ryan, and has been so ineffective stranding runners that it nearly cancels out all the value added through hit prevention, so that his FIP and ERA nearly match despite the fact that he has a .256 BABIP.

This lack of correlation holds up over longer periods of time, too, so this isn’t a sample size issue. The components of FDP are measuring two things that are quite different, and very few pitchers stray from the norm in both. In fact, those longer periods of time actually show just how effective FIP is as a measurement of pitching skill. In looking at all 3,951 pitchers who have thrown at least 100 innings in the majors since 1963, the correlation between the FIP-based WAR and RA9-wins is .96. For most pitchers with long careers, a WAR based on FIP and a WAR based on runs allowed is going to bring you to the same conclusion.

However, most pitchers is not all pitchers, and for pitchers like Jim Palmer — or, nowadays, pitchers like Jered Weaver, Tim Hudson, and Matt Cain — FDP helps us put a number on the mental adjustment we’ve been making to help compensate for the fact that FIP does not measure a part of run prevention that they have contributed to in a meaningful way.

Through adding FDP-wins (and its components, BIP-wins and LOB-wins) and RA-9 wins to the site, we hope that we’re now presenting a more comprehensive picture of how runs are saved when a pitcher is on the mound. It is still definitively true that runs are mostly saved by limiting walks and home runs and keeping batters from making contact, but FDP fills in the gap between FIP and runs allowed, and gives us a clearer picture of the impact of various performances in the things that aren’t captured in FIP.

In a subsequent post — this one is already over 2,100 words, straining the limits of the word introduction — that will be up in a few hours, we’ll discuss the role that FDP will play in our pitcher WAR and how we hope the addition of these new metrics will help us better reflect the value that different types of pitchers produce. I’m also hosting my regularly scheduled noon chat today, and will make it FDP-centric, answering as many questions about these stats as you guys are interested in asking.

For now, though, we hope you enjoy the new tools that are now available, and enjoy perusing the leaderboards and learning new and interesting things that you may not have known before, like how Nolan Ryan was a really good pitcher, but could have been so much better had he performed well with men on base.

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Dave is a co-founder of and contributes to the Wall Street Journal.

48 Responses to “Introducing Fielding Dependent Pitching”

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  1. Ruki Motomiya says:

    Begin applause now.

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    • Balthazar says:

      FDP is an outstanding addition to the analytic repertoire, and I’m ecstatic to see it advanced to cover some of the blind spots in FIP and its like. I’ll add, Dave, that your exposition and choice of examples for this post are of exemplary clarity; this post will be a classic in the Fangraphs archives.

      FIP patently captures the _most important_ things a pitcher does in aggregate. BBs are bad; Ks are good. Producing them (or not) are primary skills. Changes in the rate of production correlate strongly with a pitcher’s results, over time and hence in aggregate. Situational pitching is a considerable part of performance, however, with real impact on runs, and so on wins. What kind of contact a pitcher yields beyond G/F also matters greatly to game and career outcome. FIP has been somewhat deaf to those qualities, but FDP looks to get the sense of them better.

      Dave: “Other pitchers do this by simply altering the way they pitch with men on base, increasing the amount of only-semi-harmful walks they allow in order to reduce the amount of very-harmful home runs they allow.” And by getting the groundball with men on base. FDP surely speaks to many kinds of outcomes, but above all we are likely getting a window on groundballs with men on base. Producing these is a _skill_, but GB% alone doesn’t fix sufficiently who can do this. Even flyball pitchers can delierately work for the GB in a game situation for instance. And it isn’t just a matter of ‘pitchers pitching better,’ since with men on base the batters to a (varying) degree may offer at pitches that they might not otherwise, something a pitcher can exploit. With men on base, a GB can matter _more_ than a K, which only nets one out when several may be needed. Keeping the ball in the infield generally _with men on base_ hasn’t been broken out well, but we may get the windo on it here. At last.

      A non-analytic aspect of FDP I particularly like is that it correlates with ‘visual inspection’ of performance. For Tom Glavine, for instance, FIP and hence WAR have never particularly put a shine on him, but as you highlight Dave, FDP isolates just why he was so valued by the teams and towns he pitched for. And the counter-example of Nolan Ryan is telling too. The knock on him until his late career excellence was that his results never matched his stuff. He walked many; he didn’t control the running game at all; there was some knock on him that he watched his stats more than the scoreboard or the standings for much of his career. He spent a lot of time pitching with men in scoring position, and didn’t shut own opponents’ scoring nearly as much as his K rates might imply. We see that in what FDP presents. And this stat allows those who, for whatever reason, want to compare the results of Ryan Franklin to Brandon Morrow to have a meaningful conversation about performance impossible in terms of FIP.

      A good step forward.

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  2. chuckb says:

    Very glad to see this. Thanks to you and the Dark Overlord.

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  3. tz says:


    Nicely done. I like having these kind of metrics to help explain the outliers comparing FIP to actual run prevention.

    The only thing I’m not clear about is whether the impact of defense falls into the BIP wins or bleeds into LOB wins. If the BIP wins are based on linear weights of singles vs expected and (2B+3B) vs expected, it look like the impact of errors vs. expected flow into the LOB wins.

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    • Dave Cameron says:

      Defense certainly impacts both areas, as I note in the Jordan Zimmerman example in the follow-up post about FDP and pitcher WAR.

      To answer your question, though, LOB-wins is essentially a catch-all that quantifies the remainder of run prevention that isn’t captured by FIP or context-neutral hit prevention, so yes, reaching base on errors would be part of LOB-wins.

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  4. Tyler says:

    When do you think Steve will have the glossary updated?

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  5. Matt Hunter says:

    Thank you Dave. This is a great compromise, and I applaud you and the FanGraphs team for being innovative and flexible. I’m looking forward to seeing how these changes are implemented and how they affect pitching evaluation.

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  6. Tom says:


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  7. Mike Green says:

    Excellent job. It would be helpful for the site to have a “user’s guide” for pitcher statistics with links, as I now see the statistics here quoted regularly in the mainstream media without a full understanding of their meaning.

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  8. craigtyle says:

    Fantastic stuff! One question — Is a pitcher’s propensity to allow batters to reach base on error included in the hit prevention metric?

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    • Matthias says:


      I think Dave answered this above when he said:

      “…LOB-wins is essentially a catch-all that quantifies the remainder of run prevention that isn’t captured by FIP or context-neutral hit prevention, so yes, reaching base on errors would be part of LOB-wins.”

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  9. Scott Clarkson says:

    This is excellent! I have been hoping something like this would be put together although I would have expected it to be based on batted ball data or swing/discipline data rather than hit/XBH and strand rates. However since FIP is about results (K/BB/HR) more than components of those results (GB%,swSTR%,HR/FB etc.) this makes sense.

    I’d like to see a SIERA implementation that factors in the batted ball profiles along with FDP input that could possibly be the ERA estimator/pitcher skill evaluator to beat them all.

    I’d also like to see if there is a correlation between O-swing%/O-contact% and FDP success, especially on bip.

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    • Dave Cameron says:

      These metrics are more about allocating past runs into specific bins than about projecting future runs allowed, but there is definitely a lot of room for adding various pitch-based components into a projection to determine whether we can get a better estimate of how much control of FDP a pitcher actually has.

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      • Scott Clarkson says:

        Thanks for the reply Dave. I was curious as to the predictive vs. descriptive properties of the new metric. I’d love to see some studies about whether FDP can be used in any sort of predictive way or if we could come up w “xFDP”.

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  10. Jack says:

    Looks like Matt Cain finally won.

    +12 Vote -1 Vote +1

  11. Well-Beered Englishman says:

    My company hasn’t been hesitant about promoting dips, either.

    +7 Vote -1 Vote +1

  12. Joeskil says:

    Awesome, just awesome. Finally a crack in the wall surrounding this subject.
    The gap between FIP and explaining exactly what goes on just got smaller.

    Vote -1 Vote +1

  13. Kevin says:

    This is why I love your website.

    That said, however, I fear that this still oversimplifies the ability of pitchers to mitigate good contact with the ball through good sequences (some of the credit on that belongs to catchers), ‘late’ movement, high velocity, and generally great ‘stuff’. We’ve all watched a dominant pitcher throw a game where the contact he induces tend to be weak ground balls or lazy fly balls, which, unless they are placed in exactly the right spots on the field, are likely to lead to outs. Similarly, poorly thrown pitches can get hit hard on the ground and be more likely to find holes, lined into the outfield, driven to the gaps, or pounded over the walls. While I understand that it’s difficult to isolate for this and that statistics can only do so much of a job to achieve this, I’d love to see some of the more sophisticated technology used for PitchFX used to read the speed of the ball off the bat, consider how far from a fielder’s starting position it was hit, and factor the time it takes for a ball to travel from the plate out to a fielder into the outcomes of a play to be able to identify the extent to which the outcome was caused by the pitcher, the batter, and plain old luck.

    This seems like a great step in the right direction to identify which pitchers have successfully mitigated runs through making it easier on their defenses, but taking it to the next level seems like the only way to truly identify a pitcher’s abilities.

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    • Dave Cameron says:

      There’s no question that having access to the kind of information you’re talking about would be very helpful. However, right now, there is no publicly available source of speed-of-bat data for most contacted balls, and the tools that are measuring such things (HITF/x, Trackman, etc…) are made exclusively available to MLB clubs.

      Eventually, some of that data might become public, and we would jump at the chance to do the kinds of analysis you are talking about. For now, though, we hope that we can continue to do the best we can with the data we do have.

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  14. Carlos Collazo says:

    I’m excited

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  15. Interesting article. I wish we had some pitching in Cleveland, heck I wish we had a baseball team that could last the season in Cleveland….

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  16. Snowblind says:

    So, what is the different in FDP and ERA, in layman’s terms (i.e. something that might ever have a snowball’s chance in hell of making it to a game broadcast or mainstream publication)? The way FIP has been talked up and used over the years, it seemed like ERA pretty much *is* “fielding dependent pitching”.

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    • Tangotiger says:

      Basically, you split ERA (or more accurately RA9, runs per 9 innings, that includes earned and unearned runs) into FIP and FDP.

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  17. I’m slightly confused since hit prevention sounds a lot like what SIERA already accomplishes while still keeping the defense out of it. In a sense, it predicts BABIP. Why would we look at the results of balls in play when SIERA is already pretty good at this? The value of stranding runners may be one that is not encapsulated by SIERA, so I understand that, just not this BIP data.

    Or were you wary of using the somewhat unreliable batted ball data to calculate WAR?

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  18. illtakebothdakotas says:

    “introducing fielding dependant pitching”

    this is a new and interesting way of saying

    “watching Jason Vargas pitch against anyone the twins roll out”

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  19. Jason B says:

    Sidney Ponson also scores very high in other areas as well. His 9.27 GDP (gravy dependent pitching) figure is the second highest in the last decade, nestled comfortably between CC Sabathia (8.97) and Rich Garces (an amazing 10.18). Hideki Irabu scores very highly as well (7.74) despite his abbreviated career.

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  20. jim says:

    no surprise at all about palmer, with that defense behind him

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    • Matthew Cornwell says:

      After extracting the defensive runs saved by his fielders, Palmer’s FDP is still one of the best all-time.

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  21. Thomas says:

    Did fangraphs drop some sort of weird subconscious hint that this was going to happen? Because I had this idea last week and was going to ask Dave if it was any good in today’s chat. Maybe I can read Dave Cameron’s mind…

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  22. illtakebothdakotas says:

    Answer: “now introducing fielding dependant pitching”
    Question: “what should the twins public address announcer say if he forgets a pitchers name”

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  23. illtakebothdakotas says:

    Answer: “now introducing fielding dependant pitching”
    Question: “what should the twins public address announcer say if he forgets a pitchers name”

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  24. Nathan Nathan says:

    Palmer is the biggest FIP outlier in the part of baseball history that at least resembles the game that is played today.

    Exactly what you’d expect when you consider who was behind him, notably Brooks Robinson, Mark Belanger, Bobby Grich, Cal Ripken, Jr., Davey Johnson and Luis Aparicio.

    Using Fangraph’s Fielding Runs data and approximating a win as 10 runs it looks like those players alone had about 53.3 fielding wins. The other players in that time period that I looked at, Boog Powell and Frank Robinson, were close to zero wins fielding. I’ll make a rough correction and assert that in that run environment, a win cost about 9 runs, so I’ll credit them for about 59.2 fielding wins.

    Palmer pitched about 14% of the time across that time period. From those players alone, pretending Palmer pitched the same number of innings every season and smearing the fielders’ performances across those innings, those players alone should account for about 8.3 fielding wins behind Palmer. That’s about a third of Palmer’s 28 Balls In Play Wins.

    Of course, the contributions of Robinson, Belanger and Grich in particular are being underestimated by smearing Palmer’s innings across his career instead of measuring out innings and contributions on a year by year basis.

    Taking 1975 as an example, Palmer pitched 323 innings, which we’ll call 22% of the season. Together, just Robinson, Grich and Belanger were worth about 7.6 fielding wins and so just those three players ought to account for 1.7 of Palmer’s 3.3 Balls In Play Wins.

    It’s nice that at, in this case at least, the, well, ballpark numbers add up like they should.

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    • GoToWarMissAgnes says:

      You forgot about this guy:

      I’d love to see an article on the O’s defense of 1965-1975. They were the Murderer’s Row of fielding. I know defensive runs saved is a broad stroke and imperfect, but the O’s had three guys ranked in the top 12 all-time in fielding runs on the same team at the same time, plus an elite 2B in Grich. It’s like having Ruth, Bonds, and Musial in the same lineup. There’s a reason four guys won 20+ in 1971.

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  25. Tonyc says:

    From the chat:

    “…Johnny Cueto doesn’t give up HR with men on base, which leads to LOB-Wins…But not giving up HR with men on base *could* be a result of luck as well. ”

    “We’re not trying to factor out luck, we’re trying to factor out defense. Luck is inevitable, and we don’t take a hit away from a hitter just because he squibbed a ball in between three defenders and got a bloop single. ”

    Wouldn’t that answer suggest that RE24 and not wRAA should be used in WAR?

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  26. Scott says:


    Have you guys looked at the year-to-year correlation of an individual player’s FDP stats?

    Phrased another way, do these stats contribute to separating luck from skill?

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    • Tangotiger says:

      year-to-year correlation among pitchers that switched teams is going to be fairly low.

      It’s going to be a little higher for pitchers who don’t switch teams, because it’ll have the bias of the same-fielders and/or same-park.

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  27. Tom says:

    How are park adjustments done?

    Is there any issue in parks which have divergent run and HR factors (which I think is an issue with FIP).

    For example a park like Fenway which has a high run environment (which will “help” a pitcher when the adjustment is made to WAR for FIP-WAR) also has a low HR factor (which “helps” the FIP before the adjustment is done)

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  28. Eric says:

    Dave, you wrote that “In looking at all 3,951 pitchers who have thrown at least 100 innings in the majors since 1963, the correlation between the FIP-based WAR and RA9-wins is .96.”

    What is the correlation between FIP-based WAR and just FDP?

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  29. philcastle says:

    For what it’s worth … Palmer NEVER gave up a grand slam … which says something about situational pitching and run prevention

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  30. bcp33bosox says:

    Tits article!

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  31. Paul Clarke says:

    To do this, we decided to calculate the linear weight value of singles and doubles — the difference between a double and a triple is almost certainly not due to something the pitcher did,

    For extra-base hits, shouldn’t you use a weighted average of the linear weight values of doubles and triples? E.g. if hitters generally hit four times as many doubles as triples the value should be (4 * w2B + w3B)/5. Otherwise you’re going to underestimate the value of preventing extra-base hits.

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    • Tangotiger says:

      You are correct that the run value should be the league-average blend of the two.

      For example, when I use wOBA, it’s 1.25ish for 2B, 1.60ish for 3B, but 1.3 for 2B+3B. You’d take about 10 parts 2B and 1 part 3B.

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    • Dave Cameron says:

      Yes, that’s how it’s being calculated.

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      • Paul Clarke says:

        Ah, from this and David Appelman’s article it sounded like you were just using the value of a double. Thanks for the reply (Tango too).

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  32. AsupporteroftheAs says:

    I did a quick scan, and a couple interesting things:

    Catfish Hunter is off the charts here compared to most other pitchers I could think of (didn’t check Palmer, though). That would seem to be expected.

    Tommy John had a slight positive impact. Which one would expect – he tended to make a lot of errors himself, which probably cancelled out some of the other good work he did stranding runners, controlling the running game, getting double-plays, etc.

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  33. Nathaniel says:

    Dave, I’m trying to replicate RA9-WAR and can’t quite make it work. I know you’re supposed to use runs allowed in place of adjusted FIP as the basis for RA9-WAR—but are you also supposed to use runs allowed when calculating the pitcher’s self-created run environment (as described here Or is this considered a constant (i.e., the “RtW” referenced in BIP-wins) for the pitcher across the board after it’s calculated from his FIP? Why or why not?

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