Of course, the concept of “stuff” is very subjective, and my formula is not so much of an attempt to quantify a subjective concept as it is an attempt to measure how well pitchers do things we associate with great stuff. Because I used Pitch f/x data exclusively, the ratings were limited to pitchers from 2007 to the present.

My formula is ((4*O-Zone Swing% *O-Zone Whiff%)+(3*Whiff%)+(5*Zone-Whiff%)+(2*IFFB%)*(FBv/100)*(4))

I will probably tinker with the formula, and will welcome any suggestions with regards to improving it. I have only applied it to starting pitchers. Of course it can be applied to relievers, but their scores run much higher unless some kind of a “relief penalty” is applied. The STUFF ratings for all starting pitches who threw at least 160 innings since 2007 run between 3.4 and 9.7. The following list presents the top 15 career STUFF pitchers since 2007.

1. Rich Harden 9.7. If you’re having trouble remembering just how filthy Harden could be, visit his player page. Harden got swings and misses like no other starter. In 2008 he had an unearthly 48 ERA- and 68 xFIP- despite the fact that injuries had already started to take their toll on his fastball velocity, as it dropped to 91.7, compared to 94.1 the year before. In 141 innings in 2009, he got whiffs on 22.6% of swings on pitches in the zone. Max Scherzer, the 2013 leader in that category, gets whiffs in the zone at an 18.4% clip. When Aroldis Chapman averaged 100 mph on his fastball in 2010, he sat at 21.9%. Unfortunately, a litany of injuries would decimate Harden’s career, and he was recently released by the Twins, an organization known for their disdain for swing and miss stuff.

2. Matt Harvey 9.4. The young right-hander with the dynamic fastball places near the top in all five of the STUFF factors, with only Scherzer, Harden, and Escobar topping his 17.6 Zone-Whiff%. Besides the fastball, Harvey also features a slider, curveball, and changeup. Harvey’s plethora of filthy offerings produces whiffs on over a quarter of his pitches overall. Furthermore, Harvey is one of the rare pitchers who has actually experienced an increase in fastball velocity since his debut season.

3. Yu Darvish 9.2. Darvish uses his assortment of pitches to produce whiffs on over half of swings at pitches he throws outside of the zone, easily the best in the sample. Combine that with a whiff rate of 15.9%  for swings on pitches in the zone and you get an overall whiff rate of 28.6%, also the best in the sample. Pitch f/x credits Darvish with six different pitches, four of which he throws at least 12 percent of the time. Though Darvish averages 92.9 mph on his fastball, he has thrown his slider nearly as often as his four-seamer and two-seamer combined. The unconventional approach has produced five games of 14+ strikeouts in 2013.

4. Kelvim Escobar 8.9. Escobar only had one year of data, but what a year it was. At the age of 31, Escobar’s fastball velocity surged to 94.1, higher than any of the pre-pitch f/x years, and he utilized an excellent changeup to get whiffs on over a third of swings at pitches he threw outside of the zone and a quarter of swings overall. However, in spring training of 2008, Escobar was diagnosed with a shoulder injury that required surgery and except for a 5 inning stint in 2009, he never returned to the majors.

5. Michael Pineda 8.7. Like Escobar, Pineda only has one year of data in the sample due to shoulder surgery. Elite fastball velocity combined with a slider that helped generate swings on a third of the pitches he throws out of the zone and contact on less than sixty percent of those swings earns him this ranking. The big righty also used his height to get one of the highest infield fly rates in the sample. Pineda was placed on the DL shortly after an August 2 rehab start resulted in stiffness in his shoulder, and it appears unlikely that the righthander will pitch again in 2013.

6. Matt Moore 8.6.While Moore’s fastball velocity has dipped steadily since he came into the league in 2011, its overall average is still 93.6. Moore’s ranking is based heavily on his 2012 STUFF rating of 9.3, his 2013 rating has fallen to 7.4. Moore has battled elbow soreness this year, and hopefully this will not be a long-term issue and he can return to the form that generated a dominant 19.0 Zone-Whiff% in 2012.

7. Francisco Liriano 8.6. Liriano’s slider has long been one of the best pitches in the game, and only Darvish can top his whiff rate on pitches outside the zone. Since joining the Pirates, Liriano has been using the slider even more, throwing it on 37.1% of his pitches. Liriano is also throwing his changeup more than he ever has before. While his 13.1 Zone-Whiff% in 2013 is one of the lowest numbers of his career, the offspeed pitches have resulted in a 36.1% chase rate, the highest of his career. It’s anyone’s guess as to how long Liriano’s oft-troubled elbow holds up, but Pirates fans should enjoy the ride while it does.

8. Cole Hamels 8.5. A master of deception, Hamels’ changeup has helped him produce a career whiff-rate of 24.5%. Among pitchers on this list, Hamels 90.9 mph fastball is faster than only fellow changeup artist Johan Santana. However, the 8-9 mph difference between his fastball and changeup produces a 33.8 chase rate, the 5th highest in the sample, and his 37.0 rate in 2013 leads the majors. Hamels has also been very durable, among the top 15 STUFF pitchers, only Justin Verlander has thrown more innings.

9. Stephen Strasburg 8.5. While Strasburg’s fastball velocity has fallen from its pre-Tommy John high of 97.6, his 95.9 average is still tops Felipe Paulino, the next closest in the sample by 0.7 mph. While we will probably not see the pure electricity of the pre-injury Strasburg which produced a 9.5 STUFF rating in 2010, Strasburg still gets whiffs on over 15% of swings on pitches in the zone and 25% overall. If the Nationals’ controversial innings-management plan pays dividends and the 25 year-old can stay healthy, he should be getting whiffs for years to come.

10. Max Scherzer 8.3.  It seems fitting that a noted sabermetrician would obtain a high ranking on a list based on Pitch f/x and batted-ball data. To the misfortune of AL hitters, Scherzer has vastly improved his secondary pitches while maintaining his fastball velocity. Before his trade to the Tigers, Scherzer threw his fastball over two-thirds of the time. With the Tigers, Scherzer’s fastball usage has decreased each year, and his use of secondary pitches, particularly his changeup, has increased. Not surprisingly, this has resulted in higher chase and whiff rates, and his Zone-Whiff%  of 19.9 since 2012 leads the majors.

11. Clayton Kershaw 8.1. Kershaw burst onto the scene in 2008 as a 20 year-old rookie with a 94 mph fastball and 73 mph 12-6 curveball. Since then he has added a slider to make life even more miserable for hitters. Kershaw ranks near the top in all five of the STUFF factors. Kershaw appears to be the odd bird that can use his pitch arsenal as much to suppress BABIP as to generate swings and misses, and this factor probably keeps him from being ranked even higher.

12. Tim Lincecum 8.0. You would be hard-pressed to find a smaller starting pitcher than Lincecum. While that height limits his ability to get infield flies, the dynamic changeup more than compensates for his lack of size. Of the top 15 pitchers, only Darvish and Liriano have higher whiff rates on swings at pitches out of the zone. Lincecum’s fastball velocity has steadily dropped from its high of 94.0 in 2008 to 90.2 in 2013. Since 2011, Lincecum has been throwing a slider more often, and while he has been prone to the longball, he still gets whiffs on a quarter of swings. While Lincecum is no longer the pitcher that won CY Young awards in 2008 and 2009, he is a very intriguing free agent, and at the least, it seems that he could be a dominant reliever.

13. Chris Sale 8.0. The lanky, or perhaps paper-thin lefthander has made a successful transition from the bullpen to the rotation. After experiencing a predictable velocity drop from the move, Sale has actually regained some of that velocity this year, as his fastball has jumped from 91.3 to 92.4. Since moving to the rotation, Sale has added a changeup to go along with his excellent slider. Sale’s herky-jerky sidearm delivery and late movement have helped him generate a 32% chase rate, 5th best among pitchers on this list. While concern’s about Sale’s elbow and durability are certain to persist, Sale is on pace for over 200 innings this year after throwing 192 last year.

14. Johan Santana 7.9. Shoulder troubles robbed Santana of some of his fastball velocity, and his average of 90.3 is the slowest among pitchers in the top 15. However, his changeup was devastating. In its heyday in 2007, Santana had a Zone-Whiff rate of 23.2%. While some of Santana’s best years were in the pre-Pitch f/x era, the Mets still got highlights such as a 36.0 chase rate in 2009, and the no-hitter in 2012. Santana’s changeup also had the effect of suppressing BABIP,  as noted by a .276 career mark. Of the top 15, only youngsters Harvey and Moore can top Santana’s 12.9 IFFB%.

15. Justin Verlander 7.9. It took Verlander a couple of years to fine-tune the curveball, but when he did, he started churning out elite swing-and-miss rates. Since 2012, Verlander has been utilizing the changeup more than the curveball, and it too has produced excellent whiff rates. The secondary offerings go along with an average fastball velocity of 94.8 that only the less battle-tested Stephen Strasburg, Matt Harvey, and Felipe Paulino can top. Since 2007, Verlander has thrown over a 100 more innings than Cole Hamels, the next closest person on this list.

Clearly, the list favors younger, less tested pitchers. But I don’t think there’s anything wrong with that. As pitchers age, their velocity declines, and while Felix Hernandez is a better pitcher throwing 92 then when he was a young flamethrower, he probably doesn’t create the same kind of excitement in fans or fear in hitters when he averaged 96 with his fastball.

I also made a list of the worst 15 starting pitchers by STUFF since 2007. I didn’t think it would be worth anyone’s while to go through the list, but suffice it to say that the worst three were Steve Trachsel, Sidney Ponson, and Livan Hernandez. Yeah, I’d say that sounds about right. Aaron Cook of the 1.9 K/9 in 2012 also made the list. The following table is a comparison of the best and worst 15 starting pitchers since 2007 by STUFF rating.

So the best STUFF pitchers seem to have an ability to limit hits on balls in play and overachieve their peripheral stats, while the worst STUFF pitchers allow hits at slightly above the league average and underachieve their peripherals. Some of this is due to infield flies, which was a factor in the STUFF formula. The best 15 had an IFFB% of 11.0, while the worst 15 had an IFFB% of 7.4. But there are other factors involved. Tim Lincecum has a 7.4 IFFB% and a .296 BABIP while Nick Blackburn has a 8.6 IFFB% and a .309 BABIP while the BABIP of their respective teams since 2007 is .297 and .300. Both of these pitchers are well past the stabilization point for BABIP. So it seems that pitchers with dominant STUFF have some control over hits on balls in play outside of IFFB. Of course I cherrypicked an example, and I’m sure there are counterexamples, but the general idea seems good. Great STUFF can have an effect beyond generating swings and misses.

Young players are exciting. They’re fun to watch, fun to talk about, and especially fun to project, and young players that succeed early in their careers are even more exciting. If, over the next few weeks, you find yourself sitting in Progressive Field holding a $4 beer (yes, they’re that cheap) while watching the Indians play a meaningful late-season game for the first time since 2007, mention Danny Salazar to the fans in your section. About the worst thing you’ll hear someone say about him is, “Salazar? Potential front-line arm, but I dunno, maybe he throws too hard?”

I’m just as fascinated with young talent as those title-starved Indians fans drinking their reasonably priced beverages, and one player who’s caught my eye this year is Chris Archer, the 24-year-old, flame-throwing pitching prospect currently shutting down MLB lineups to the tune of a 2.95 ERA over 15 starts this season. When a pitcher with Archer’s level of raw talent shows flashes of that potential brilliance right out of the gate, it’s easy to get carried away and envision him turning into the next Max Scherzer (who Harold Reynolds thinks is the AL Cy Young, hands down), but is that a fair comparison? Are we putting too much emphasis on Archer’s string of early successes?

Unfortunately, we can’t really know the answer to that question until Archer himself is 29 years old and either anchoring the front end of an MLB rotation, filling in at the back end, contributing out of the bullpen, or worse. Fortunately, we can speculate. Even more fortunately, there’s a wealth of data and numbers from which we can speculate.

Using pitch data compiled by FanGraphs and readily available on player pages and custom leaderboards, I looked at every player-season from 2002-2012 for Archer’s closest pitching comparison. I considered factors such as pitcher age and experience, pitch usage rates, velocities, and effectiveness, batted ball distribution, strikeout and walk rates, and even non-pitching factors like height and handedness, which matter for release point and pitch trajectory.

After crunching the numbers, I am officially proclaiming Edwin Jackson the winner.

On the surface, this comparison makes some sense. Back in 2007, Jackson was a 23-year-old pitcher of the same height and weight as Archer is currently listed, and both were getting their first extended major league looks. Jackson was drafted out of high school in the sixth round of the 2001 amateur draft. Archer was drafted out of high school in the fifth round of the 2006 amateur draft.

Both paid their dues in the minors as Jackson compiled a 4.39 ERA over 556 innings in parts of six minor league seasons, whereas Archer was slightly better with a 3.77 ERA in 769.2 innings over parts of eight seasons. Both showed big-time velocity but struggled with control. Jackson’s strikeout rate in the minors was lower than Archer’s, but so was his walk rate. All told, Jackson posted a strikeout-to-walk ratio of 1.91. Archer’s was nearly identical at 1.80.

Pretty similar, huh? Well, it gets a little eerier.

The table above shows Archer’s pitch usage, velocity, and effectiveness over his first 15 starts of 2013 versus what Jackson did during his first full season back in 2007. Right off the bat, we see three-pitch pitchers who featured a fastball and slider prominently while occasionally mixing in a change-up. Both could dial up the heat, and both used the slider as their out pitch.

By now I think we’ve done a pretty good job of establishing just how similar these two pitchers are (though if you’d like, you can check out the deliveries of Jackson here and Archer here), but what does that mean for Archer’s future? Let’s pretend for a moment that he does in fact follow in Jackson’s footsteps. How good has Jackson been?

Well, Jackson’s been about as average as they come. His career ERA is 4.45, and he’s never finished with a single-season ERA better than 3.62. In six-plus seasons since Jackson became a full-time starter in 2007, there have been 50 pitchers that have logged over 1,000 innings (Jackson has tossed 1,295). Of those 50, Jackson’s 4.36 ERA ranks 44th, ahead of only Kevin Correia, Jason Marquis, Barry Zito, Roberto Hernandez/Fausto Carmona, Joe Blanton, and Livan Hernandez. Jackson’s 17.6 WAR over that span is good for 26th, but his WAR/IP drops him down to 35th. His most notable accomplishments are ranking 17th among that group in innings pitched and 11th in games started.

Jackson has been very durable, and there’s something to be said for durability, but if all Archer turns into is a league-average starter best known for taking the mound every fifth day, then Rays fans will long for the days when Archer unexpectedly bolstered Tampa’s rotation and when he showed filthy stuff, a fiery demeanor and, most importantly, promise.

This post originally appeared in slightly different form on my blog: Biotech, Baseball, Big Data, Business, Biology…

Baseball players break down.  Their performances fluctuate.  As a group there are some interesting generalities with respect to how pitching, hitting and fielding change with age.  But the error bars are huge.  There are many things we still don’t know about baseball players, about why one prospect hits the ground running and another flames out.  And we also don’t know if there is any way to know, since the task of putting together the skills needed to play major league baseball may be one of the most complex of the major sports, and understanding complexity is hard.

But it seems worthwhile to give it a try.

The Mystery of the Missing Ligament

Let’s talk about R.A. Dickey for a minute.  Not because he’s a highly interesting human being, although he is.  And not because he’s a knuckleballer, which is fun and interesting due to rarity and the entertaining sight of six foot athletes flailing at baseballs traveling with the flight path of a drunken small-nosed bat.  But rather because he was drafted in 1996 in the 1st round by the Texas Rangers, and only during his physical workup was it discovered that he was missing a key ligament in his arm.  The Ulnar Collateral Ligament (UCL) to be exact.  Without which, it is assumed, a pitcher cannot pitch.

Well, except  that he did.  This shouldn’t be under-emphasized.  Pitching without a UCL is thought to be akin to trying to play tailback for the Seahawks without an Anterior Cruciate Ligament (ACL) in your knee.  And yet he pitched and pitched well for years without a UCL.  RA Dickey got his UCL replaced and then knocked around the major and minor leagues for several years, eventually learned how to throw a knuckleball, and now has pitched successfully in the majors for several years more.

A story like this illustrates two points.  One, we may be making assumptions that aren’t always supported by the data—for example, that the UCL is required for pitching.  And two, you can learn a lot just by looking and measuring.

Measure by Measure

What should be measured and how?  I think an area to look into might be the tools being developed now to support self-measurement.  The quantified-self movement has gained enough prominence that magazines like Newsweek are running profiles.  For people in the movement, the motivation for participation stems from a desire to better understand themselves; to have data that will give them a data-driven view of what is going on in their bodies and minds.  The goals are often better health, losing weight, tracking mood, athletic prowess, increasing the levels of good indicators and decreasing the levels of the bad.

One of the distinguishing elements of how this is being done is granularity.  Apps on a smartphone, portable electronic devices, and logging tools can capture data in intervals ranging from several times a day up to a more or less continuous stream.  Even tests and procedures that might normally be performed once a year at an annual physical become fair game for more frequent monitoring, as long as you have the money to pay for the testing.  The open question is whether collecting all of this data will reveal new insights.  Or, to put it graphically, if you tested a metric infrequently, and got this graph:

Would the result of more frequent testing look like this?

Or like this?

This example is borrowed from the site of Ginger IO, a company that is developing tools for continual measurements of health related metrics, among other things.

Where baseball comes in to this is I believe MLB teams are continually in a search for new ways to gain an advantage in building a quality team.  You know, that extra 2%.  A baseball team has vast resources, and those resources are focused on getting the most out of the several hundred baseball players that comprise the major and minor league talent of the team.  There are trainers, and doctors, and team dieticians, and masseuses, and coaches.  What would it take to add an additional technological and analytical group dedicated to gathering data on the players and seeing whether any of this information provides additional retrospective or prospective insight into individual performance?

Here is where an enterprising team could probably reach out to a couple of different groups for help in setting this up.  One would be device and software manufacturers who are building tools in this space.  I’ve written before about EmotionSense and have also learned recently about GingerIO (HT to @Dshaywitz).  Another highly interested party would be the nearest medical school and those researchers looking into patient reported outcome (PRO) techniques and patient monitoring efforts.  If an MLB team doesn’t already have its own high-powered statistical analysis group (or even if it does), it could reach out to suppliers of software tools for analyzing large scale datasets and finding patterns, like Ayasdi or Google.

I could also see a viable group for a partnership being other professional sports teams.  Many MLB teams are in the same city as NFL, NBA, NHL, and/or MLS franchises.  To spread the investment costs as well as providing control groups for each other, it would be useful to collaborate with these other franchises to learn more about the effect of sports training in general.

A speculative area for data collection and analysis could be in genomics, transcriptomics and proteomics.  Michael Snyder of Stanford University has been demonstrating for some years now how a program of monitoring personal molecular information about one’s health, along with other more conventional measures, provides new insights into health and disease.

The metrics should also include the conventional.  Going back to the example of R.A. Dickey, wouldn’t it be useful to perform elbow and shoulder scans for every player on major and minor league rosters on at least a yearly basis?  So often in sports you hear the term “typical wear and tear” when describing an elbow or shoulder or knee.  My question is, how do you know it’s typical?  Until you have a large, well-defined baseline that you follow for years under the rigorous conditions that baseball players are subjected to, how can you know what real wear and tear is?  And if you did know, wouldn’t that help you in making decisions about training and protecting your own players, to say nothing of evaluating free agents?  One of the truisms of baseball is that every team knows more about their own players than anyone else, leading to information asymmetry in trading and signing.  It seems an imperative for each team to reduce or reverse that asymmetry if at all possible.

An additional area that personal monitoring could help in is understanding on-field performance.  I’ve already touched on how MLB could use various kinds of GPS and positioning sensors to more accurately measure defense, for example, so I won’t elaborate further except to point out Chip Kelly is bringing this approach to the Philadelphia Eagles, and it will be interesting to see if we get reports on the effectiveness of using GPS to monitor his NFL players’ movements.

Biological passports

Another benefit of building a baseline for different kinds of metrics in your team would be helping to detect the possibility of doping.  This seems to be in the news right now for some reason, so let me just say that if a team began collecting, analyzing and storing biological samples on a regular basis, this would help in detecting those who are taking performance-enhancing substances.  This isn’t a new idea; the World Anti-Doping Agency is advocating this approach already.  However, I think MLB could take it to a high level of rigor and quality.  Would this have to be negotiated?  Sure.  But there is probably no better time than now to see if such an agreement can be forged between the union and the MLB owners.

Essentially, by taking samples from enough players over time, as well as healthy, age and ethnicity-matched volunteers as a control group, an MLB team could build up a comprehensive profile of what normal is with respect to the known indicators of performance enhancement such as hemocrit levels, not just as an average, but on an individual basis.  With this kind of data, a rapid, unusual change in specific metabolites could provide grounds for more intensive investigation.  When athletes come up with a positive test, a standard argument has been that he or she always has had an unusually high level of the tested substance.  Well, you know, the only way to know that for sure is to have a record dating back years that demonstrates outlier status or not for that athlete and that test.  Continual sampling is almost certain to deter many would-be attempts to use performance enhancing substances.

This would be invasive.  No doubt about it.  Which is why there should also be stringent controls on data and better maintenance of privacy than we’ve seen so far in the Biogenesis saga.  However, there is also probably no better time to negotiate these kinds of tests as baseball strives to clean its image again.

Too much data?

Of course, collecting all this data provides no guarantee of actually finding out something specifically useful and actionable for any given MLB team.  As Nate Silver has pointed out many times in his columns and book, given enough data you can find a correlation for almost anything.  However one thing is certain: you can’t find new things when you don’t look, and trying to apply concepts of the quantified self to MLB teams will lead to a whole lot of cross-discipline interactions and innovative thinking, which a forward-looking team might be able to parlay into the next big market inefficiency in baseball.

Thanks to an atrocious month of May, the 2013 Milwaukee Brewers were abruptly transformed from a fringe contender into a rebuilding baseball club.

Most people agree that the Brewers need to build a new core, but what does that mean? Many teams have young players in the midst of an above-average season, but that doesn’t necessarily translate to sustainable success for the roster as a whole. And the opinions expressed about so-called core players are usually subjective and not expressed in a way that allows direct comparisons between teams.

We could really use a metric to compare the rosters of teams who are developing potentially sustainable talent with those who aren’t. My effort to do this is called Core Wins, which summarizes the extent to which a team’s success is being driven from players most likely to constitute core talent, as opposed to players on their way out the door, probably in decline, or both.

To do this, we need define what it means to be a core player, and specifically the factors by which we evaluate a core player’s respective contributions to the team.

The Core Player

In my view, core players do three things: (1) contribute significantly to their team’s success, (2) do so while under extended team control, and (3) do so at or before they reach their peak ages of likely productivity. Each of those attributes needs to be mathematically summarized to reduce these contributions to a measurable value.

The first factor is the easiest: a core player is expected to contribute, and to do so above what could be found in an entry-level minor-league call-up. A major league player’s ability to do so over the course of a season is commonly summarized in some version of the wins above replacement (WAR) metric, which attempts to combine the player’s batting, fielding, and if applicable, pitching contributions. A counting statistic also fits our needs best, since we are looking for aggregate contributions over the course of a single season. So, we’ll use WAR, as calculated by Fangraphs (fWAR).

The second factor, team control, is more complicated. Player control comes in two primary forms: (1) players under club control due to the terms of baseball’s collective bargaining agreement, and (2) players who have signed freely-negotiated contracts. The collective bargaining agreement keeps players under club control for at least six major league years. Free agent contracts range from one-year stop-gaps to those lasting a decade or longer. Most ballclubs are a collection of young players under sustained club control, long-term (and typically expensive) free agents, and stopgap players on value contracts. But teams with a sustainable core should be drawing significant production from players who will actually be around in future years. If too much production is coming from departing or declining players, the club is asking for trouble.

The third factor — player age — is less significant, but still important. Younger players are cheaper than older players, and thus easier to afford and keep around. Younger players are less frequently injured, meaning they will be in the lineup more often. Younger players who have not yet reached their peak production age will also probably continue to improve, whereas players beyond their peak age will probably decline.

However, age can be overemphasized. The primary advantage of youth— extended club control — is already being considered. Moreover, mature players signed to long-term contracts tend to be some of the most valuable players in the game — Joey Votto, Felix Hernandez, and their peers. And while prospects are important, most ballclubs would strongly prefer Joey Votto over a 22-year old prospect who may, but probably won’t, someday turn into Joey Votto. So while age matters, it is not as important as control.

So to summarize: we need to weigh player value, but do it in a way that primarily emphasizes team control while still placing some value on a player’s age.

Method

Player Contributions

All WAR figures were drawn from Fangraphs. The figures for batting fWAR (which incorporates fielding) and pitching fWAR were combined into one spreadsheet for each team year. When a player generated values for both batting (plus fielding) and pitching WAR, those values were summed, including the effect of any negative values. Once a net value was obtained for all players on a team roster for the year, all zero or net negative WAR values were disregarded.

Player Control Index

Player control numbers were drawn primarily from Cot’s Contracts, and cross-checked with Baseball Reference, other sources, and common sense as needed. Cot’s provides individual player contract data from 2009 onward, so only data from 2009 through 2012 was used. Control years were weighted identically, regardless of whether they arose from the CBA or a free agent contract. A player subject to a club option was considered to be under club control for that year. The author’s best estimate of remaining club control was necessary in a few cases when contract details were unclear, but not surprisingly, most of those players were fringe contributors that would not constitute core talent anyway.

A player was assigned one control year if his contract expired after the current season, two control years if his contract expired after the following season, and so on. For practical reasons — including the frequent shuffling from the minors experienced by young players, and the oft-diminishing returns of the longest contracts — the maximum number of control years considered for a player was 5. A Control Index was then calculated for each player in each roster year, with the number of control years as numerator, and an assigned denominator of 2 — for the minimum years that would constitute extended organizational control. So, for example, a player with an expiring contract would have a Control Index of 0.5 (1 season left divided by 2), and a typical player in their final pre-arbitration year would have a Control Index of 2.0 (4 seasons of control divided by 2). The maximum Control Index is 2.5.

Age Index

A player’s “baseball age” — their age on July 1 of a given season — was drawn from Fangraphs. An Age Index was then calculated for each player using an assigned value for a typical peak performance age as the numerator and the player’s baseball age for each season as the denominator. There has been some debate on the overall peak performance age for ball players, but, taking a strong hint from one of my reviewers, I used 27. To give some sense of the value range, the Age Index in 2012 for Mike Trout would have been 1.35 (27/20) and for Livan Hernandez would have been 0.73 (27/37).

Determining Core Win Value

In my formula, Core Win value is a weighting exercise. To calculate a player’s Core Win value to a roster, I multiplied the player’s net fWAR for each season by the Control Index and the Age Index. The Control Index has a greater range (0.5 to 2.5) and thus a greater potential weight than the Age Index, which seems appropriate for the reasons stated above. The combined effect of these indices means young prospects that produce at a level of 2 fWAR or higher are weighted the most heavily. This makes sense: players who promptly adjust to the difficulty of the major leagues, yet still have years of probable improvement ahead of them, all while under extended team control, are those most likely to constitute a sustainable core of talent for the ballclub.

Discussion

Now that we have a formula for Core Win Value, we need to decide what it means to have a winning core. That cut-off is ultimately in the eye of the beholder, but I looked to the gold standard: the Tampa Bay Rays. The Rays are widely acclaimed for their ability to acquire and maintain control of young talent, often through early buy-outs of free agent years, combined with club options that retain team flexibility. This has been particularly true over the years covered by this study: 2009 through 2012.

To provide some contrast with the Rays, we will also consider the roster construction during that same time period of the New York Mets and the Oakland Athletics.

The Gold Standard: The Rays

Not surprisingly, the Core Wins formula likes the Rays very much. Indeed, three characteristics of the Rays between 2009 and 2012 suggest a working definition of a team with a strong, sustainable core: (1) the Rays consistently feature five or more players producing a Core Win Value of 5 or higher per season, which is my working definition of a “Core Player”; (2) they have accomplished this feat in multiple consecutive years (all four years I studied, in fact) and (3) at least two of these Core Players were usually pitchers.

Let’s start with 2009. For ease of viewing, in each of these tables, I’ve bolded wins figures for potential Core Players (five or more Core Wins). I’ve also italicized the names of pitchers who cross the Core Wins threshold, to distinguish them from position players.

2009 Tampa Bay Rays

In 2009, the Rays won 84 games, featuring seven players that delivered 5 Core Wins or more. This depth, plus MVP-level performances from Evan Longoria and Ben Zobrist, prepared the Rays for the eventual departure of Carl Crawford, whose dwindling team control was removing him from the team’s core. Note that the team’s two best pitchers in 2009, James Shields and Matt Garza, were both under team control for 5 more years. David Price generated only 1.3 fWAR in 2009, and thus barely missed the Core Wins cut, but he was on the upswing.

2010 Tampa Bay Rays

In 2010, the Rays maintained 7 players at a Core Win level of 5 or more, culminating in 96 team wins and a first-place finish in the AL East. Only one pitcher (David Price) made the Core Win cut-off of 5 this time, but James Shields just missed it. Matt Garza regressed a bit (and was promptly traded to the Cubs for more prospects, without any negative effect). Carl Crawford, despite an MVP-level year of 7.4 fWAR, is discounted out of the team core by the Core Wins formula, due to his team control ending that year.

2011 Tampa Bay Rays

2011 featured more of the same. Carl Crawford was gone, but the Rays did not miss him, as the formula anticipated. Six Rays met the Core Win threshold, two of them pitchers (Price, Shields). Superstar contributions by Zobrist and Longoria, combined with ascending contributions from four others — including Price and Shields — resulted in a highly-successful season from Tampa Bay’s controlled talent, and others. The Rays won 91 games and made a wild-card playoff appearance.

2012 Tampa Bay Rays

By 2012, the Rays had developed an astonishing eight players that crossed our Core Win threshold. An incredible five of these players — over half the team’s core, under our formula — were starting pitchers with at least four years of team control remaining. This means that the Rays’ entire starting rotation was under long-term control. Despite a hamstring injury that kept him out for over three months, Evan Longoria still contributed 2.5 fWAR to the effort, and his new contract provided the team with the long-term control to keep him in the team’s core. The 2012 Rays won 90 games: not enough for even a wildcard in the American League that year, but a terrific season nonetheless.

Before the 2013 season, the Rays dealt James Shields to Kansas City for the bat of Wil Meyers and other prospects. As of the publication of this article, Fangraphs projects them to win 93 games in 2013, on a payroll of only $62 million. In sum, the Rays have been, and continue to be, the prototypical team that demonstrates what it means to have a sustainable core of controlled talent.

By Stark Contrast, the New York Mets

The Mets have been bad for years, and the Core Wins formula identifies major flaws in roster construction as a possible culprit.

2009 New York Mets

Dreadful: there is no other way to describe the 2009 Mets. That year, the Mets spent $140 million for 70 team wins, generating only three Core Players under our formula. Even those players gave only ok performances. From a Core Wins perspective, this roster was terrible. One of the three players to meet the Core Wins threshold, and the only starting pitcher — Johan Santana — is heading past his probable prime.

2010 New York Mets

The results for the Mets weren’t much better in 2010 — 79 wins — but their roster at least improved. Six players made Core Player-type contributions, and two of those players were starting pitchers. If these performances proved to be sustainable over multiple years, or at least into 2011, the Mets had some reason for optimism.

2011 New York Mets

But it didn’t work out. In 2011, the Mets were right back to a pathetic three Core Player performances, with only one starting pitcher among them. In fact, the Mets’s strongest core performance in 2011 came from 2.8-win Daniel Murphy. Not good. Ike Davis promptly regressed out of the core, David Wright fought injuries, and Johann Santana didn’t play all year, which is why Core Wins discounts the value of aging players. Although Jose Reyes provided a superstar WAR of 5.8 and a batting title, as a departing free agent, that performance provided no ongoing value to the team, and the Core Wins formula discounts it accordingly. It all amounted to 77 wins, and low expectations for the following season.

2012 New York Mets

Validating this expectation, the 2012 Mets did even worse, winning only 74 games. Only three players could pass the Core Wins threshold, and one of their best players — R.A. Dickey — could not even quality as a Core Player, despite 4.4 fWAR. The Core Wins formula discounts the going-forward value of 37-year-old performances, and Dickey’s 2013 performance with the Blue Jays has validated that skepticism.

But, the Mets get enough bad news, so let’s focus on some positive aspects. In 2012, David Wright performed at an MVP level. And while the Mets had only four Core Win players in 2011, two of them are starting pitchers, which is an important positive from our study of the Rays. In fact, one starter, Jon Niese, was signed to an early long-term contract a very Rays thing to do, putting a competent starter under extended team control. Matt Harvey also looks to be a championship-caliber ace, and remains under maximum team control.

So far, 2013 is not being kind to the Mets either — Fangraphs currently projects them to finish with 76 wins — but there are hints that things may soon be looking up, particularly if their farm system can continue to develop strong rotation talent, as many project that it will.

Trending in the Right Direction: The Oakland Athletics

Finally, let’s conclude with what turns out to be a Goldilocks example: the team that like the Mets, tried and failed to improve their core, but stuck with it and seems to have gotten the hang of it lately: the Oakland Athletics.

2009 Oakland Athletics

In terms of roster-building, the 2009 Athletics took a fairly solid approach: they ended up with six potential Core Players, and three of them are starting pitchers. All these players offered at least five years of team control. However, the 2009 Athletics also underscore that just because your wins are coming from the right place does not mean you are getting enough of them. The best performance in this group is still only 3.9 fWAR — good, not great. The 2009 Athletics won only 74 games, although at least they didn’t have to pay Mets prices to get there.

2010 Oakland Athletics

In 2010, the Athletics were better. Leveraging some of the previous year’s young talent, they ended up 81-81. There were six core-type player performances, and four of them pitchers: ordinarily, a good thing. But notably, there was not a significant amount of improvement from 2009’s core contributors. In fact, the strongest core contributors in 2010, Daric Barton and Cliff Pennington, were marginal contributors the year before, raising the possibility of fluke performances. And, only two core performances came from position players, which didn’t leave much room for error going forward in the scoring department. So, the 2010 Athletics showed hints of a developing core, but a fragile one.

2011 Oakland Athletics

And indeed it was. The Athletics rotation was devastated by injuries in 2011: Dallas Braden needed shoulder surgery, and Brett Anderson needed Tommy John surgery. That would be a tough blow for any team, but particularly for Oakland, which did not have much behind them. What was left of the rotation (and roster) collapsed to three core-type players. The two core bats of consequence in 2010, Daric Barton and Cliff Pennington, immediately regressed and revealed themselves to be one-year wonders. The only developing bat remaining was an average, but unspectacular debut by Jemile Weeks, whose own performance later proved unsustainable.

Although two out of the three core players were starting pitchers, there was little to support it. Brandon McCarthy actually had a very good year (4.5 fWAR), but since he was completing a 1-year-deal at the time, he offered the A’s no core value.

Things looked bleak. Fortunately, the A’s stuck to their guns and kept developing young talent. Then, 2012 happened.

2012 Oakland Athletics

2012 found the Athletics again having restocked their core, this time with a balance of bats and pitching talent. Five core players are represented, and their values are not all projection, either: Josh Reddick produced 4.5 fWAR, Jarrod Parker generated 3.4 fWAR, and two other controlled players produced close to 3 fWAR. Two core players are starting pitchers. Furthermore, in 2012, the A’s finally enjoyed a little luck. They outplayed their Pythagorean expectation by a few wins, got 2+ win performances from non-core starters on short-term deals — Brandon McCarthy and Bartolo Colon — and ended up with 94 wins and an AL West title, on top of what appeared to be developing core.

If you thought that the Athletics were finally getting the hang of this roster-building thing, you may be right. The Athletics have spent much of 2013 on top of the AL West, and Fangraphs currently projects them to finish with 91 wins — on a budget of $62 million. A very Rays-like experience all around, which corresponds with quality roster construction.

Conclusion

The Core Wins metric profiles the extent to which team performances are being delivered by so-called Core Players, and also tracks the progression of players in and out of the club’s core over time. Even herculean performances by impending free agents (see Carl Crawford, 2010) tend to wash out of the metric, while young players who initially impress, but fail to sustain (see Ike Davis, 2011) also fall out of the measured core, despite their built-in advantages of youth and team control. As such, Core Wins strikes me as useful and if nothing else, an improvement over the prevailing practice of eyeballing the roster and cherry-picking performances by younger players.

Because it is based on WAR (a counting statistic), Core Wins is primarily backward-looking. But, the general method can also be used prospectively. For example, if you input projections from your preferred player projection system, you could forecast the extent to which your team is likely to get future contributions from sustainable sources — a useful thing to know when deciding between trades, farm system call-ups, or free agent signings. Similarly, if you want to focus on particular positions of concern — (third base, starting rotation) — or skill sets (batter OBP, pitcher FIP) — you can adjust the Age Index to account for the peak performance ages corresponding with those particular positions or skills. Those analyses can be retrospective or prospective.

Of course, superior roster construction does not guarantee superior performance, as the Oakland A’s can attest. Previously healthy players can be felled by injury, and promising talents too often fail to sustain early achievements. But in general, developing Core Players makes good sense, and certainly seems to be delivering results for the league’s most efficient ballclubs. So if your favorite team seems incapable of stacking success, you might check to see how good of a job the front office has been doing in generating Core Wins.

Special thanks to Paul Noonan and Tom Tango, who both offered helpful comments on the general direction of this article. All errors are entirely my own, including some table pasting errors in the original version. Thanks to Andrew Yuskaitis for pointing those out. They have now been corrected.

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

Last Friday against the Rockies, Matt Wieters had a plate appearance that perfectly epitomized his 2013 season. Coming to the plate in the bottom of the 3rd, with the Orioles up 2-0, two outs in the inning, and the bases loaded, Wieters worked Juan Nicasio for an eight-pitch full count; on the ninth pitch of the at-bat, Wieters hit a perfect, textbook line drive…right to DJ LeMahieu at second, for the third out of the inning.

While watching this game with my father, I was forced to restrain him from destroying the flatscreen upon which this atrocity had been viewed. My level of outrage was not nearly at that of my progenitor’s, however, for I–being more statistically inclined than him–knew that Wieters had been rather unlucky on batted balls this season; after another lineout in Saturday’s game, and two more on Tuesday against the Diamondbacks², Wieters now has a .596 BABIP on line drives, “good” for 170th out of 183 qualified players. At this late in the season, a player’s numbers start to level off to what they’ll be at season’s end, and despite the reassurances of experts, Wieters has not ceased to be unlucky.

Which got me thinking…

Would there be a way to measure how lucky or unlucky a player has been as a whole? Not just for one individual stat, but for an entire stat line, over the course of a whole season? After exhaustive Google searches returned nothing, I decided to take matters into my own hands. Using my rudimentary statistical knowledge, and the findings of Mike Podhorzer–who created equations for xK% and xBB%–and Jeff Zimmerman–who devised an xBABIP equation–I created a basic equation to determine how lucky a player has been 0verall³. Because I have absolutely no idea how linear weights and all that shit works, I kept it simple:

bFI = 100*((xK%–K%) + (BB%–xBB%) + (BABIP–xBABIP))

I call it the Basic Fortune Index; I would’ve called it the Luck Index, but I didn’t want to confuse it with Leverage Index. Basically, I took the difference between each player’s xK% and K%, BB% and xBB%, and BABIP and xBABIP, added them together, and then multiplied it by 100 for shits and giggles. Since a lucky hitter would have a lower K% than expected (as opposed to a higher BB% and BABIP than expected), I took the difference from xK% to K%, instead of the other way around. A positive bFI would indicate a lucky player, and a  negative value would indicate an unlucky player. Also, due to time constraints, I was only able to compile stats for the AL.

On to the leaderboards⁴!

Wieters ended up 70th out of the 85 players, as his xBABIP wasn’t as high as I thought it would’ve been.

After compiling this table, I noticed a trend (one that has been noticed by others before me): the “lucky” players were mainly good players, whereas the “unlucky” players were mainly bad offensive players. I then matched each player’s wRC+ up with their bFI, and made a table of the result⁵:

Apparently, the correlation was not as strong as  I had initially hoped (thanks, Dunn and Ibanez!), as the .53746 R Squared implies.

In the end, it’s probably not a very good statistic–more of a Pseudometric–which, to be fair, is why I named it the Basic Fortune Index. Like most everything I post here, there really wasn’t a point to this whole thing. In addition, it’s fairly likely that, if this is actually published, someone will be so kind as to inform me that there is already a better stat out there for determining the luck of a hitter, and that–despite the disclaimer–I should care about this. If, however, this is an original idea, I invite those more statistically knowledgeable than myself to expound upon it (assuming, of course, I receive all the credit).

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¹How should that be capitalized?

²I refuse to use their nickname, and usage of it by anyone else should be considered cause for legal euthanasia.

³I wanted to use HR/FB%, but since Parts 6 and 7 of this series were never released, I was forced to go without.

⁴All stats are as of Tuesday, August 20th.

⁵I tried to put in the graph, but couldn’t figure out how.

I’m a big fan of Bill Simmons’ BS Report podcast. Some of my favorite parts are when Bill talks about trade possibilities between teams. It’s always fun to try and step into a general manager’s shoes and imagine what they can and can’t do to improve their teams. During one of these shows, Jonah Keri was on, and he and Bill were doing a pretty good job of breaking down the options that some MLB teams had in the coming years. It seemed like Jonah had a great command of the restrictions on some of these teams and even what the free agent market is going to look like at various points in the future. I found myself trying to picture and organize all this information in my head. I was inspired to map all this out in a big visualization.

Also, I just wanted to find out how screwed my beloved Phillies are in the coming years.

The image below is a link to the visualization:

The first thing you can do is to click the arrows or use the left and right arrow keys to scroll through past and future years. I collected data back to 1998, when the Baltimore Orioles led the league in payroll with players like Mike Mussina and Rafael Palmeiro. Scrolling back to the present day shows a lot of story lines: how the Yankees expanded their payroll way faster than the rest of the league in the early 2000s, fire sales of the Marlins in 2006 and to a lesser extent in 2013, and the Dodgers’ rapid leapfrog to post the absolute largest payroll this year.

When you scroll to future years, the 2013 payroll hangs around as a ghost image to provide a rough benchmark of what you might expect the team to eventually pay. The solid bars drop down to show the contracts that the teams are currently obligated to pay in that particular year. Here, you can clearly see the Dodgers and Angels leading the league in earmarked money over the next few seasons. Going all the way to 2023 shows that the Reds have actually signed the longest contract so far.

Clicking on a team in that upper chart will show a time series of that team’s payrolls over the years broken out by player. For example, clicking on the Reds shows large green boxes way out into the future. Clicking on any of those boxes will show you that first baseman Joey Votto can expect to be paid $25M to play baseball in the year 2023. Each color in these bottom charts corresponds to a position.

There are some caveats here. I grabbed the data from Baseball Reference who gets their data from Cot’s Baseball Contracts. As far as I can tell, the data is not updated very regularly because I know of a couple contract extensions that have not made it onto their pages yet. Those contracts won’t be displayed here.

Also, when a player misses a whole season to injury, that player’s salary doesn’t show up on the Baseball Reference page. I took care to add the biggest instances of these missed seasons back into the data by hand, but I’m sure I didn’t get them all. There’s also the question of whether those salaries really should be here. I believe most teams take out insurance policies on players and thus they aren’t responsible for paying injured players. Since I have no details about that sort of thing, I just tried to include all the missed seasons I could find.

Lastly, teams sometimes agree to pay part of a player’s salary when they trade them away to another team. A good recent example of that is the Cubs paying most of Alfonso Soriano’s salary while he plays for the Yankees. The Baseball Reference site has good information about these arrangements in the current and future years. But the site does not have information about past arrangements. Again, I took care of a couple of the biggest discrepancies by hand (hello Mike Hampton!), but I’m sure there are lots still in there.

Despite those couple issues, I believe this chart does a great job of showing a snapshot of the MLB economy. I learned a lot just clicking around the whole thing while building it. I think it’s a great indication that you’re building something interesting if you constantly get distracted playing with the thing instead of working on it.

The 2014 Rangers have an interesting predicament.  The same predicament they currently have, but it will be more pronounced, more necessary to solve in the off-season.

They have two shortstops and a second baseman.  One shortstop, Elvis Andrus, is locked up for a long, long time.  And the other shortstop, Jurickson Profar, is most likely going to move over to second base permanently, giving the Rangers what should be  a very good and young middle infield, for many years.

I’m assuming the Rangers keep Profar at 2b, rather than move him to the outfield or trade him for another top prospect.  It would mean Ian Kinsler either must change positions, or more logically, be traded to a team who will value him more highly since he can man second base for said team.

Kinsler has been on a decline the past few years, whether it’s due to injury or diminishing skills.  Or perhaps a combination of both.  For example: The league average wOBA in the American League this season is .318.  Over the past two seasons, Kinsler’s wOBA’s have been .327 and .330, respectively.  The .330 has been over the course of 97 games in 2013, so he has some room to improve upon that.  But there is only so much he can do with only a month and a half of the season remaining.

Best option for 2014?  Trade Ian Kinsler.  There are certainly obstacles.  He is going to turn 32 next season.  He, as I mentioned, isn’t hitting like he used to hit, as just two years ago, he posted a 7-WAR season with a .364 wOBA.  He is guaranteed four more seasons, and $62 million on his current contract (including the 2018 option which has a $5 million buyout).  So most teams will be wary of committing that kind of money to a player who is past his prime, and probably past the point of “good” nowadays.  Above-average, maybe.  But I can’t see Kinsler being worth much more than 3 wins in a season moving forward, and he might be worth even less than that.

There is one team that could use a 2B next season though, and has a fairly new obsession with throwing around money: the Los Angeles Dodgers.  Mark Ellis has a $1 million buyout on his 2014 option and is going to be turning 37 next summer.  There is no doubt that Ian Kinsler will be an upgrade at 2B for the Dodgers over Ellis (And at $5 million, Ellis might even be worth a utility role).  If the Dodgers don’t bring home a championship this season after spending an absurd amount of money in 2013 (and beyond), there will be even more pressure to win next year.

In comes the potential acceptance of either the remaining Ian Kinsler money or most of it, without having to give up much.  Maybe a prospect with some upside.  But they definitely won’t have to surrender a bonafide prospect of any kind.

The Rangers COULD decide to just move Kinsler to 1B or a corner outfield spot.  But a .330-ish wOBA at first base would be below the league average at the position.  And even though .330 would be a little above average in left or right field, he would be learning a new position.  That might not go well.  There is a not-miniscule chance Ian Kinsler is a below-average player in 2014 if he is moved off of 2B, especially if it is to the outfield.

The Rangers would probably be just as good bringing back David Murphy as one of the outfielders, rather than moving Ian Kinsler out there.  Murphy is a solid defender, and even though he’s been terrible at the plate in 2013, he should be very cheap next year and regress back closer to his normal offensive numbers.

The other outfield spot could be solved with a platoon, potentially a minor leaguer, depending on who is ready (if anyone), a stop-gap, maybe even Nelson Cruz.  Although, knowing that Cruz was just suspended, I would simply let him walk.

They can solve their outfield situation in a better manner than using Ian Kinsler to fill one of the two voids.

And they can find a 1B for a year that’ll hit like Kinsler probably will in 2014.

Overall, the best bet for the Rangers is to move on from Kinsler, assuming there is a team that wants or needs a 2B badly enough.

‘Pitching to contact’ and ‘throwing ground balls’ are classic baseball buzzwords. Twins pitching coach Rick Anderson has essentially built a career around this philosophy. It seems like every time a young pitching phenom arrives and starts striking hitters out, people start talking about how he needs to pitch to contact. The strategy has been around since this guy played, and while Kirk Rueter pitched in his last game in 2005, Kevin Correia is still hanging around and Jeremy Guthrie signed a three-year deal last offseason. And, lest we forget, Aaron Sele got a Hall of Fame vote. To take a more in-depth look at the merits of pitching to contact I grouped all 394 starting pitchers from 2002 onward (the batted ball era) who had thrown 200 or more innings, and organized them by Contact% into eight groups. The following spreadsheet details the results of my study. Groups 1-4 are classified as contact pitchers, while groups 5-8 are strikeout pitchers.

Of the Group 1 pitchers, only 5 had an xFIP- better than the league average, and only 6 had an ERA- better than league average.  Two of these were posted by aging control artists Rick Reed and David Wells, who had success on the strength of their walk rates of 4.0% and 3.7%, respectively. Chien-Ming Wang rode his 59.5 GB% to a 98 xFIP- and 99 ERA-. Overall, Nate Cornejo was more typical of the group than these three. xFIP- went down with decreasing contact, and except for a small blip between groups 2 and 3 (both contact groups), so did ERA-.

There is a strong connection here between fastball velocity and contact rates, but there is also a strong connection between fastball usage and contact rates. Group 1 had both the slowest average fastballs and the highest use of fastballs. As anyone watching Gerrit Cole and the Pirates can tell, contact rate has almost as much to do with fastball usage as fastball velocity.

Though the contact pitchers had lower walk rates than the strikeout groups, their strikeout rates were far below average. The separation between strikeout and walk rates was better for the strikeout pitchers, with an average separation of 11.3, compared to 6.7 for the contact pitchers. In terms of K/BB, the strikeout pitchers posted a 2.4 K/BB, and the contact pitchers were at 1.9 K/BB. The old adage that groundball pitchers prevent home runs did not bear out. While the contact pitchers had a groundball rate of 43.7% compared to 42.7% for the strikeout pitchers, the contact pitchers had a HR% of 2.8, and the strikeout pitchers had a HR% of 2.6. Home runs are connected to contact.

The contact pitchers also slightly underachieved their peripherals. The ERA- for the contact groups was an average of 4.5 points higher than their xFIP-, while the ERA- for the strikeout groups was on average less than 1 point higher. The contact pitchers had an average BABIP of .298 compared to the .291 for the strikeout pitchers. High strikeout pitchers can often sustain slightly lower BABIP than their counterparts.

The connection between contact and efficiency is slight. The difference in Pitches/IP was the biggest between group 1 and group 5. The difference of 0.6 Pitches/IP translates to only 120 pitches per 200 IP. While the pitch count and innings limit debate has overtaken the nature of starting pitching, pitching to contact does not seem to be the answer. Teams and pitching coaches that are advocating pitching to contact as a means to pitch longer in games are essentially sacrificing a lot of quality for a tiny amount of quantity. And with 12 or 13 man pitching staffs being the rule of the day, this strategy seems absurd.

Despite mounting evidence that pitching to contact is a futile strategy, teams keep encouraging their young pitchers to stash away their strikeout stuff in the name of efficiency. Young pitchers Nathan Eovaldi and Gerrit Cole currently own the 3rd and 4th fastest fastballs among starting pitchers. Both of them, and Cole in particular, posted very high strikeout rates in the minor leagues. Yet both of them own strikeout rates well below the NL average, and Cole and Eovaldi’s respective xFIP- rates of 99 and 101 are decidedly average.  I know, almost anybody with a good fastball can rack up a lot of strikeouts in the minors, and Eovaldi in particular has a limited repertoire that may keep him from reaching his potential. But shouldn’t young pitchers focus on developing strikeout pitches rather than trying to get ground balls? After all, fastball velocity peaks early and Cole and Eovaldi will probably have a tougher time getting outs on contact when they aren’t throwing 96. While Mike Pelfrey has carved out a decent career for himself, I’m sure most teams hope for more out of their top pitching prospects.

Earlier in the month I had an idea. It all stemmed from the idea of quantifying the un-quantifiable. I was going to record grit.

A lot of times we hear about how gritty a player is, but it’s tossed around with no real proof. Sure Nick Punto dives into first a lot, but is that really more gritty than stupid? Is a guy like David Eckstein really the grittiest of all gritty players, or can it be a guy we don’t really notice?

To figure all of this out I, along with some help, wrote a formula. The formula is imperfect, because of a lack of reliable sources for things like headfirst slides and broken-up double plays, but it tries and does its job. The formula is as follows:

(((InfH+1stS3+(.5*CS+SB2+1.5*SB3+3*SBH))(2*P/PA+.5*Foul/S%))/(HR+1)+(.1*PA/Seasons)+PitchingAppearances

Where InfH stands for Infield Hits and 1stS3 means first to third on a single, we have found a way to see a players GRIT (Game Rating In Testosterone.) All this stat is designed to show is who works harder to score a run for their team, it doesn’t show you who is better or worse, but it does show who tries.

Using this formula my small team of experts has found David Eckstein to have a career GRIT of 172.16, which is very impressive over a 10-year career, but it’s no Juan Pierre, who has amassed a career GRIT of, wait for it, 1582.

We also found the difference between Martin Prado and Justin Upton, who was the subject of criticism from Diamondbacks GM Kevin Towers who said he wasn’t gritty enough prior to trading him for Prado. We found out that Kevin Towers may have been wrong.

Using their numbers the formula says that Prado has put together a GRIT of 57.93 in his career, where Upton has a GRIT of 68.65, despite playing in one less season. So, Kevin Towers, you may need to rethink your strategy.

Also invented was TeamGRIT, a stat that uses numerous numbers to calculate how hard a team works for each run.

A disclaimer here before I list the GRITs: I am not trying to say that some teams work harder than others, nor am I saying that a high GRIT is more or less valuable than a low GRIT, all these numbers illustrate is that some teams are more comfortable with power numbers to win games, while others are more inclined to small ball.

The formula used is

(((InfH+1.5*BuntHits)+1stS3+2ndDH(.5*CS+SB2+1.5*SB3+3*SBH)(Pitches/PA+.5*Fouls/Strike%)+(GIDPinduced+OFAssists))/(HR+.5*HRA))+(.1*PA/GamesPlayed)

The following are the AL leaders prior to games played on August 7th 2013

Royals – 90.57 (9th in wins)

Indians – 74.77 (6th in wins)

Red Sox – 73.92 (1st in wins)

A’s – 70.57 (5th in wins)

Blue Jays – 61.73 (10th in wins)

Rangers – 56.52 (4th in wins)

Astros – 55.70 (15th in wins)

White Sox – 51.62 (14th in wins)

Rays – 51.10 (2nd in wins)

Angels – 48.98 (12th in wins)

Twins – 46.97 (13th in wins)

Yankees – 45.59 (8th in wins)

Orioles – 40.49 (7th in wins)

Tigers – 30.30 (3rd in wins)

Mariners – 25.90 (11th in wins)

The most interesting numbers to me are those of the Royals and the Tigers. On opposite ends of the spectrum, one is a team that absolutely crushes the ball, everything that comes their way, the Tigers hit it, and they’re fine with it. They don’t feel the need to manufacture runs the way that the Royals do. The Royals seem to grind more to score their runs. More than any other team in the league by a large margin. They, like the Astros at 55 GRITs, are doing everything in their power to score more runs. It doesn’t always work, but there’s something to be said about a team that works to get extra runs and extra outs. If anything, they’re less comfortable with a lead than the Tigers. That isn’t to say the Tigers get lazy, just that they tend to not have to try so much.

In the NL there appears to be a negative correlation between GRIT and wins; I assure you, this is just a coincidence.

NL leaders prior to games played on August 7th 2013

Pirates – 80.83 (2nd in wins)

Rockies – 77.08 (8th in wins)

Marlins – 76.31 (15th in wins)

Brewers – 73.57 (14th in wins)

Mets – 67.33 (11th in wins)

Giants – 64.21 (12th in wins)

Padres – 62.53 (9th in wins)

Phillies – 57.06 (10th in wins)

Dodgers – 51.83 (4th in wins)

Cardinals – 47.67 (3rd in wins)

Nationals – 45.03 (7th in wins)

Cubs – 44.79 (13th in wins)

Diamondbacks – 42.38 (6th in wins)

Reds – 39.99 (5th in wins)

Braves – 31.12 (1st in wins)

The only thing these numbers definitively tell us is that there is a lot more GRIT in the American League, which is a deviation from the stereotype of hard-hitting AL clubs. The longball is less important in the American League, whereas manufacturing runs is a lot more emphasized. In the National League one team stands out from the pack: The Pirates.

They have a GRIT of 80.83 while also being in 2nd place, they are the only team in the top 5 of wins who is also in the top 5 of GRIT. The Pirates also hit a fair amount of home runs, but that’s not enough for them. They aren’t comfortable with just a lead. They want more of a lead. They try their damnedest to score more runs than anyone else by any means necessary. Is this because they spent so many years as a losing team? Possibly, but that’s just a theory.

As I said before, these numbers are not proof that any team is better than another, nor are they proof than any player is better than another, just that some teams and players are GRITtier than others.

So there you have it, your introduction to GRIT.

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

I feel as though this will be the article where my disclaimer is put to full use, as this seems to be a comparison that is an easy one to make, but no one (at least from what I can tell) is making it. Andy Pettitte and Mark Buehrle have very similar career ERA’s (3.88 and 3.85, respectively); they were both late-round draft picks, as I covered earlier, and…well, that’s basically where the similarities end.

Buehrle is obviously famous for his durability, having never gone on the DL, and while Pettitte has had some durability issues recently, he’s been pretty durable for his career, with 10 seasons of 200 innings in his first 14 years in the majors. Obviously, Pettitte has considerably more innings pitched (3255.1 to 2829.1), as he’s existed for five more years.

One key difference between the two is peripherals. Pettitte’s career K% is a solid¹ 17.4%, whereas Buehrle’s is, well, a less solid 13.8%. While Buehrle also has better career control than Pettitte (5.5% to 7.3% BB%), Pettitte is a little more groundball inclined (48.6% to 45.4% GB%).

Put it all together, and Pettitte has a career xFIP² of 3.70, whereas Buehrle’s sits at 4.21, a 51-point difference that would suggest that these two men are not very similar. Their respective WAR values (4.1 WAR/200 IP for Pettitte, 3.3 for Buehrle) also works to support this conclusion. However, this is FanGraphs WAR, based off of FIP; looking at their Baseball-Reference WAR–i.e. runs-allowed WAR–it would appear that Buehrle is better than Pettitte (3.8 to 3.6)³.

While we’re on the subject, let’s see some other pitchers in that general vicinity of career rWAR/200 innings, that I may or may not have picked selectively to further my argument⁴.

Nolan Ryan–3.0

Ted Lyons–3.2

Gaylord Perry–3.4

Steve Carlton–3.5

Phil Niekro–3.6

John Clarkson–3.7

Bert Blyleven–3.8

Fergie Jenkins–3.8

Are several of these pitchers people from the days of yore whom you’ve never heard of? Yes. Are they all Hall of Famers? Also yes.

So why is Pettitte considered to to have a strong case for the Hall of Fame, while Buehrle is borderline at best? It all comes back to that key pitcher stat: wins. Because, as the article cites, Pettitte is part of an elite group: only 46 pitchers have 250 career wins, and 32 of them are in the Hall⁵. Buehrle, meanwhile, is toiling away with a meager 182 wins, only 157th all-time.

Obviously, wins are a completely meaningless statistic, and Pettitte having that many career wins is almost entirely circumstantial. The above article mentions that Pettitte played on playoff teams for 14 of his first 17 seasons, compared to only two for Buehrle’s first 13 seasons, and, of course, Pettitte has played most of his career with one of the best closers of all time, whereas Buehrle played much of his career with a guy who partakes in, uh, unusual fowl ingestion techniques.

There’s also the fact that Pettitte played most of his career in New York, the attention pimp⁶ of cities; while Chicago is one of the larger cities in the U.S., its media shrivels up and dies in comparison to the Big Apple’s. How much this contributed to Pettitte’s alleged divaism–and confirmed indecisiveness–will never be known; what we do know is that Buehrle is humble about himself and his achievements, probably more so than Pettitte.

In many ways, the situation with Pettitte and Buehrle mirrors that of NFL linebackers Ray Lewis and London Fletcher; both Lewis and Fletcher have very similar career stats, but the former is a surefire Hall of Famer, while the latter has more of an outside shot. Some have theorized that the reason for Lewis’s increased fame are twofold: first, that he came from a high-profile school (Miami) as a high-round draft pick (26th in the first round), as opposed to a low-profile school (John Carroll) as an undrafted free agent; obviously, since both Pettitte and Buehrle are both very low-round draft picks (22nd and 38th, respectively) from very low-profile schools (San Jacinto and Jefferson, respectively), this is obviously irrelevant.

And the second reason for Lewis being more popular than Fletcher? Well, this. In short, what Mr. Easterbrook’s theory states is that Lewis–and possibly, by connection, all similarly-inclined athletes–act the way they act in order to promote their own fame, and build up a case for the Hall of Fame. This could easily be applied to to Pettitte and Buehrle; the former is considerably more self-promoting, while the latter is much more willing to give his teammates credit.

So, while this may have been a largely pointless article, the main message remains clear–two pitchers are very similar in most respects, instead of their reputation, and that reputation may have a lasting effect on their immortality. Why are men judged by their reputations instead of their accomplishments? Now there’s a question worth answering.⁷

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¹Remember, this was mainly accrued during the steroid era, when that level was (roughly) average.

²Please note that xFIP only goes back to 2002, and Buehrle’s and Pettitte’s careers (and their career ERAs cited above) go back to 2000 and 1995, respectively.

³If aggregate WAR values are more your thing: Buehrle has nearly 20 fewer career wins than Pettitte by fWAR (47.3 to 67.0) but is less than five wins worse than him by rWAR (54.0 to 58.5).

⁴Twain was right.

⁵Of the 14 that are not, 8 are still eligible or have not yet become eligible: Pettitte, Greg Maddux, Roger Clemens, Tom Glavine, Randy Johnson, Mike Mussina, Jamie Moyer, and Jack Morris.

⁶I.e. one that makes attention whores out of the famous.

⁷Believe me when I say I did not intend that to sound as deep as it did.