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Thursday 14th Dec 2017

In a recent column, I stated that in a snake draft, Ryan Braun would be my first choice, then Miguel Cabrera. I have received a little flak for that, questioning my wisdom, or perhaps the lack thereof. Some feel that should be reversed while others suggest Mike Trout should be the first guy to be picked. Truth be told, this bandwidth is probably better spent writing about some of the other 747 players that will break camp in April, but since the reasons behind my assertion are even more pertinent than the conclusion, I thought this would be a good topic to bridge us out of football mode and into baseball mode. Plus, it serves as a nice introduction for those new to the site as it will give you an idea of my philosophy and a taste of what you might get if you become a Platinum subscriber.

For now, I’m going to leave Trout out of the discussion. There’ll be plenty of time to talk about the sophomore phenom. Suffice it to say the fact I am willing to consider him with the third pick is testament to the fact I feel he will have a strong season. But, I don’t feel it will be as strong as that of Braun or Cabrera, and that’s without even considering the risk involved.

There are two general reasons why some prefer Cabrera over Braun. The first downplays Braun’s stolen base superiority while the second cites the difference in positions, Braun being an outfielder and Cabrera playing the perceived weaker position of third base. I’ll address each of these and explain why neither makes Cabrera a better choice than Braun.

Before I get into the meat of the discussion, it is important to keep everything in context. There are many different ways this game is played, in terms of formats, rules and strategies. The most important aspect of a discussion of this nature is something I preach ad nauseum, and that is “know thy league.” If according to your format, Cabrera’s stats are potentially more helpful than Braun’s, you’ll get no argument from me if Miggy is your choice. Knowing your league’s dynamics and market treatment trumps any objective valuation and ranking. In other words, if your argument is “I know how my league drafts and even though Braun may be more valuable, I can end up with a better team if I draft Cabrera,” I’ll accept that. Well, first I’ll ask for an explanation and if I buy it, I’ll stand down.

Let’s talk about the notion of Braun’s steals not serving as the primary means of differentiating the duo from a fantasy perspective. From a strict valuation point of view, the fewer the number of teams and the fewer active roster spots, the less impact steals provide. There are some mixed universe leagues with ten teams, three outfield spots and no corner infielder. In this format, the value of Braun’s steals is less as compared to a league with more teams and active roster spots. For the sake of this discussion, let’s assume a more standard setup with five outfielders.

The usual argument is “I can always get steals later.” Yeah, you can. But if you already have them, you don’t have to get them later. Any time you put yourself in a position to HAVE to chase something, you’re not efficiently utilizing all your assets. Restricting the inventory from which you can select hinders your ability to construct a team to maximum potential. By choosing Braun, I will cede that I am probably giving up a few points in average and perhaps a handful of homers and RBI. But I am plus 20-something steals and overall, have a more balanced foundation from which to build. Getting technical, in a vacuum, the extra steals are worth more than the slight difference in HR, RBI and average. Why would you want to knowingly put yourself in a hole?

The answer to that for many is “because Miggy is a third baseman and I’d rather draft him than an outfielder.” The implication is Cabrera plays a perceived scarce position and it is better to get it out of the way early, so as not to be left with dregs later. Unfortunately, this opens the Pandora’s Box labeled scarcity as there are numerous interpretations and connotations of the term. I’ll leave that for another time and just equate scarce with weak – however you want to define it. I won’t even get into the fact that mathematically, the raw value of the same stats from a third baseman are worth the same if they came from an outfielder. I’ll instead address the notion that “Cabrera plus Outfielder X is better than Braun plus Third Baseman Y.” The problem with this is it is not a two-player comparison but rather a total roster comparison. The premise is outfield is deeper than the hot corner, so in a later round, the available outfielders are better than the available third basemen. And maybe they are. But if they are and I took Braun, I’m not going to take a lesser third baseman in that round if that’s all that’s available. At some point, the level of talent is going to be such that everyone is the same player, regardless of position. It’s a fallacy to think at the back end of the player pool that the available outfielders and first basemen are better than the remaining middle infielders and third basemen. In most leagues (context is everything), they’re not. At some point, I’m going to find a third baseman and he’s going to be comparable in value to whoever you picked in the same round. It’s the nature of snake drafts.

Looking at these two points in a more general sense, irrespective of the reason, it is not wise to sacrifice potential, unless you are 100 percent sure you can make up for it and then some. This is the draft dynamics alluded to earlier. If you know your league way undervalues steals, then yeah, maybe you can go with Cabrera and get bargains later without losing ground in power by grabbing speed late. This doesn’t happen in my leagues so my lean is Braun. Getting a bit mathematical, if you assign an auction dollar value to the players and use that to set up your draft rankings, you will find the biggest delta of potential to be at the top. That is, the difference in potential between adjacent players in the first round is greater than that of the second, which is greater than that of the third, etc. The earlier you sacrifice potential for scarcity (statistical or positional) the harder it is going to be to make up the difference.

The bottom line is I don’t want to have to make up the difference. I want to continue to separate my team from the pack. Ryan Braun gives me the best chance to do just that.

We'e going to do something a little different this time; you have a homework assignment!

Below are five pitching ranking lists, using five different scoring systems, all based on last season's final stats. Your task is to decide which set best reflects how you feel pitchers should be ranked. In other words, if you were to join a new fantasy league, which league would you prefer to join based on these rankings? Please feel free to share your opinion in the comments.

On Friday, I will reveal the scoring system that generated each set and discuss which is my favorite.

And yes, for those wondering, if not assuming, the impetus of this is the craziness that is the closer chaos this season, which actually makes the fact the discussion will occur in the column titled Organized Chaos a bit apropos.


Justin Verlander Justin Verlander Justin Verlander Justin Verlander Justin Verlander
Clayton Kershaw Clayton Kershaw Clayton Kershaw Clayton Kershaw Clayton Kershaw
Roy Halladay Cliff Lee Roy Halladay Roy Halladay Roy Halladay
Cliff Lee Roy Halladay Cliff Lee Cliff Lee Cliff Lee
Jered Weaver Jered Weaver Jered Weaver Jered Weaver Jered Weaver
James Shields James Shields James Shields James Shields James Shields
Ian Kennedy Ian Kennedy Ian Kennedy Ian Kennedy Ian Kennedy
Cole Hamels Cole Hamels Cole Hamels Cole Hamels Cole Hamels
Dan Haren CC Sabathia Dan Haren Dan Haren Dan Haren
CC Sabathia Tim Lincecum CC Sabathia CC Sabathia CC Sabathia
Matt Cain Dan Haren Matt Cain Matt Cain Matt Cain
C.J. Wilson C.J. Wilson C.J. Wilson C.J. Wilson C.J. Wilson
Ricky Romero Josh Beckett Ricky Romero Ricky Romero Ricky Romero
Josh Beckett David Price Josh Beckett Josh Beckett Josh Beckett
Tim Lincecum Matt Cain Tim Lincecum Tim Lincecum Tim Lincecum
Doug Fister Ricky Romero Doug Fister Doug Fister Doug Fister
Tim Hudson Yovani Gallardo Tim Hudson Tim Hudson Tim Hudson
David Price Felix Hernandez David Price David Price Craig Kimbrel
Craig Kimbrel Zack Greinke Tyler Clippard Tyler Clippard David Price
Felix Hernandez Gio Gonzalez Felix Hernandez Felix Hernandez Felix Hernandez
Yovani Gallardo Madison Bumgarner Yovani Gallardo Yovani Gallardo Yovani Gallardo
Madison Bumgarner Doug Fister Jonny Venters Madison Bumgarner Madison Bumgarner
Daniel Hudson Tim Hudson Mike Adams Craig Kimbrel Daniel Hudson
Gio Gonzalez Matt Garza Craig Kimbrel Jonny Venters Gio Gonzalez
Hiroki Kuroda Jon Lester Madison Bumgarner Daniel Hudson Hiroki Kuroda
Jeremy Hellickson Mat Latos Daniel Hudson Gio Gonzalez Jeremy Hellickson
Johnny Cueto Hiroki Kuroda Gio Gonzalez Mike Adams Johnny Cueto
Ryan Vogelsong Daniel Hudson Hiroki Kuroda Hiroki Kuroda Ryan Vogelsong
Tyler Clippard Michael Pineda Jeremy Hellickson Jeremy Hellickson Drew Storen
Ervin Santana Cory Luebke Johnny Cueto Johnny Cueto John Axford
Shaun Marcum Anibal Sanchez Ryan Vogelsong Ryan Vogelsong Ervin Santana
Drew Storen Ryan Vogelsong David Robertson Ervin Santana Shaun Marcum
Zack Greinke Chris Carpenter Drew Storen Shaun Marcum Zack Greinke
Jon Lester Ervin Santana Ervin Santana Zack Greinke Jon Lester
Mike Adams Shaun Marcum Shaun Marcum Jon Lester Chris Carpenter
Jonny Venters Craig Kimbrel John Axford David Robertson Mariano Rivera
John Axford Javier Vazquez Zack Greinke Chris Carpenter Jose Valverde
Chris Carpenter Brandon Beachy Jon Lester Drew Storen Justin Masterson
Justin Masterson Tyler Clippard Sean Marshall Justin Masterson J.J. Putz
Matt Garza Johnny Cueto Chris Carpenter Matt Garza Matt Garza
Kyle Lohse Tommy Hanson Jose Valverde Sean Marshall Kyle Lohse
Mat Latos Justin Masterson Justin Masterson Kyle Lohse Mat Latos
Javier Vazquez Jeremy Hellickson Matt Garza Mat Latos Javier Vazquez
Mariano Rivera Ted Lilly Kyle Lohse Alfredo Aceves Alexi Ogando
Alexi Ogando Wandy Rodriguez Mat Latos John Axford Joel Hanrahan
Fernando Salas Brandon Morrow Eric O'Flaherty Javier Vazquez Fernando Salas
J.J. Putz Alexi Ogando Javier Vazquez Alexi Ogando Francisco Cordero
Jose Valverde David Robertson Alfredo Aceves Eric O'Flaherty Michael Pineda
Michael Pineda Vance Worley Alexi Ogando Fernando Salas Brandon McCarthy
Alfredo Aceves Jonny Venters Mariano Rivera Michael Pineda Jonathan Papelbon
Joel Hanrahan Mike Adams J.J. Putz Mariano Rivera Tyler Clippard
Brandon McCarthy Scott Baker Fernando Salas Cory Luebke Jair Jurrjens
Cory Luebke Jaime Garcia Michael Pineda Brandon McCarthy Josh Collmenter
David Robertson Jordan Zimmermann Cory Luebke J.J. Putz Cory Luebke
Francisco Cordero Brandon McCarthy Joel Hanrahan Jose Valverde Heath Bell
Jair Jurrjens Colby Lewis Brandon McCarthy Jair Jurrjens Matt Harrison
Josh Collmenter Derek Holland Jair Jurrjens Josh Collmenter Jordan Zimmermann
Matt Harrison Kyle Lohse Koji Uehara R.A. Dickey Alfredo Aceves
R.A. Dickey Matt Harrison Josh Collmenter Matt Harrison Ted Lilly
Jordan Zimmermann Bud Norris R.A. Dickey Jordan Zimmermann R.A. Dickey
Ted Lilly Koji Uehara Matt Harrison Ted Lilly Mike Adams
Jonathan Papelbon Josh Collmenter Jordan Zimmermann Joel Hanrahan Jonny Venters
Sean Marshall Alfredo Aceves Ted Lilly Jason Motte Vance Worley
Vance Worley Jhoulys Chacin Greg Holland Vance Worley Jaime Garcia
Heath Bell Gavin Floyd Jason Motte Francisco Cordero Wandy Rodriguez
Eric O'Flaherty Max Scherzer Jonathan Papelbon Koji Uehara Tommy Hanson
Jaime Garcia R.A. Dickey Francisco Cordero Greg Holland Ryan Madson
Wandy Rodriguez Greg Holland Francisco Rodriguez Francisco Rodriguez Anibal Sanchez
Tommy Hanson Jair Jurrjens Sergio Romo Jim Johnson Kyle Farnsworth
Anibal Sanchez Sergio Romo Vance Worley Jonathan Papelbon Scott Baker
Francisco Rodriguez Fernando Salas Antonio Bastardo Sergio Romo Mike Leake
Jason Motte Mike Leake Jim Johnson Jaime Garcia Sergio Santos
Scott Baker Jonathan Papelbon Scott Downs Wandy Rodriguez Mark Melancon
Ryan Madson Sean Marshall Heath Bell Tommy Hanson Jordan Walden
Greg Holland Kenley Jansen Jaime Garcia Anibal Sanchez Mark Buehrle
Kyle Farnsworth John Axford Ryan Madson Scott Downs Derek Holland
Jim Johnson Eric O'Flaherty Wandy Rodriguez Antonio Bastardo Colby Lewis
Mike Leake Antonio Bastardo Tommy Hanson Scott Baker Francisco Rodriguez
Koji Uehara Philip Humber Anibal Sanchez Edward Mujica Randy Wolf
Mark Melancon Aaron Harang Edward Mujica Heath Bell Jhoulys Chacin
Mark Buehrle Drew Storen Scott Baker Grant Balfour Brandon League
Derek Holland Erik Bedard Daniel Bard Mike Leake David Robertson
Colby Lewis Vinnie Pestano Rafael Betancourt Ryan Madson Brandon Beachy
Antonio Bastardo Jeff Samardzija David Hernandez Rafael Betancourt Gavin Floyd
Randy Wolf Jason Motte Grant Balfour Daniel Bard Jeff Karstens
Jhoulys Chacin Sergio Santos Mike Leake Jesse Crain Jason Motte
Sergio Santos Edward Mujica Joel Peralta Kyle Farnsworth Neftali Feliz
Sergio Romo Randy Wolf Mark Buehrle Mark Buehrle Sean Marshall
Edward Mujica Chris Capuano Derek Holland Derek Holland Philip Humber
Brandon Beachy Tim Stauffer Colby Lewis Colby Lewis Josh Tomlin
Gavin Floyd Al Alburquerque Jesse Crain Mark Melancon Ivan Nova
Jordan Walden Jesse Crain Randy Wolf David Hernandez Jim Johnson
Jeff Karstens Bartolo Colon Jhoulys Chacin Randy Wolf Greg Holland
Scott Downs Rafael Betancourt Mark Melancon Jhoulys Chacin Guillermo Moscoso
Philip Humber Francisco Rodriguez Kyle Farnsworth Brandon Beachy Aaron Harang
Josh Tomlin Jeff Karstens Joaquin Benoit Gavin Floyd Brian Wilson
Ivan Nova Ubaldo Jimenez Brandon Beachy Jeff Karstens Chris Perez
Brandon League Jeff Niemann Gavin Floyd Joel Peralta Joe Saunders
Rafael Betancourt Edwin Jackson Jeff Karstens Joaquin Benoit Tim Stauffer
Guillermo Moscoso Josh Johnson Sergio Santos Sergio Santos Antonio Bastardo
Aaron Harang Ivan Nova Tony Sipp Philip Humber Eric O'Flaherty
Joe Saunders Mark Melancon Vinnie Pestano Josh Tomlin Koji Uehara
Tim Stauffer Mariano Rivera Philip Humber Ivan Nova Edward Mujica
Grant Balfour Chris Sale Josh Tomlin Vinnie Pestano Sergio Romo
Neftali Feliz Guillermo Moscoso Ivan Nova Jordan Walden Carlos Marmol
David Hernandez J.J. Putz Chris Sale Guillermo Moscoso Max Scherzer
Jesse Crain Grant Balfour Guillermo Moscoso Aaron Harang Bruce Chen
Joel Peralta David Hernandez Aaron Harang Joe Saunders Bud Norris
Brian Wilson Mark Buehrle Brandon League Tim Stauffer Freddy Garcia
Chris Perez Daniel Bard Jeff Samardzija Jeff Samardzija Rafael Betancourt
Daniel Bard Kyle Farnsworth Joe Saunders Tony Sipp Scott Downs
Jeff Samardzija Joel Hanrahan Tim Stauffer Chris Sale Joakim Soria
Vinnie Pestano Casey Janssen Jordan Walden Brandon League Jeff Niemann
Chris Sale Josh Tomlin Chris Perez Neftali Feliz Leo Nunez
Joaquin Benoit Joel Peralta Neftali Feliz Joe Smith Joel Peralta
Max Scherzer Freddy Garcia Brian Wilson Matt Belisle David Hernandez
Bruce Chen Jim Johnson Joe Smith Brian Wilson Josh Johnson
Bud Norris Jonathon Niese Glen Perkins Chris Perez Brandon Morrow
Freddy Garcia Jose Valverde Max Scherzer Max Scherzer Jeff Samardzija
Tony Sipp Ryan Madson Bruce Chen Bruce Chen Kyle Kendrick
Carlos Marmol Ryan Dempster Bud Norris Bud Norris Edwin Jackson
Jeff Niemann Joaquin Benoit Kenley Jansen Glen Perkins Jesse Crain
Matt Belisle Bruce Chen Ramon Ramirez Freddy Garcia Grant Balfour
Joe Smith Octavio Dotel Freddy Garcia Casey Janssen Chris Sale
Casey Janssen Scott Downs Casey Janssen Darren Oliver Bartolo Colon
Josh Johnson Glen Perkins Darren Oliver Kenley Jansen Casey Janssen
Brandon Morrow Matt Belisle Matt Belisle Jeff Niemann Javy Guerra
Kyle Kendrick Chad Billingsley Jeff Niemann Ramon Ramirez Erik Bedard
Edwin Jackson Ramon Ramirez Bill Bray Josh Johnson Kenley Jansen
Kenley Jansen Jordan Walden Josh Johnson Brandon Morrow Huston Street
Joakim Soria Tony Sipp Al Alburquerque Kyle Kendrick Vinnie Pestano
Ramon Ramirez Sam LeCure Brandon Morrow Bill Bray Matt Belisle
Bartolo Colon John Danks Octavio Dotel Edwin Jackson Al Alburquerque
Glen Perkins Homer Bailey Kyle Kendrick Al Alburquerque Roy Oswalt
Leo Nunez Carlos Marmol Edwin Jackson Carlos Marmol Joe Smith
Al Alburquerque Francisco Cordero Javier Lopez Jeremy Affeldt Ramon Ramirez
Darren Oliver Aroldis Chapman Jeremy Affeldt Bartolo Colon Paul Maholm
Erik Bedard Joe Saunders Bartolo Colon Kameron Loe Jason Vargas
Javy Guerra Trevor Cahill Chad Qualls Chad Qualls Joaquin Benoit
Octavio Dotel Roy Oswalt Santiago Casilla Octavio Dotel Daniel Bard
Huston Street Marco Estrada Carlos Marmol Jose Veras Santiago Casilla
Roy Oswalt Ernesto Frieri Erik Bedard Javier Lopez Tony Sipp
Jeremy Affeldt Joe Smith Jose Veras Joakim Soria Octavio Dotel
Santiago Casilla Brett Myers Huston Street Erik Bedard Andrew Bailey
Paul Maholm Mike Dunn Javy Guerra Santiago Casilla Chris Capuano
Bill Bray Chris Narveson Leo Nunez Leo Nunez Glen Perkins
Jason Vargas Darren Oliver LaTroy Hawkins Roy Oswalt Darren Oliver
Kameron Loe Aaron Crow Joakim Soria Cory Wade Kyle McClellan
Kyle McClellan Jason Vargas Cory Wade Paul Maholm Trevor Cahill
Cory Wade Tom Gorzelanny Kameron Loe Javy Guerra Jeremy Affeldt



One of the most commonly asked questions when I venture out into the public at various fantasy baseball events such as Ron Shandler’s First Pitch Forums, Tout Wars, the NFBC and the FSTA Conferences is "What will the next big strategy be?", or if "There ever will be any more advances in strategy? "My answer is most strategy comes from gaining an edge by understanding the player pool better than your opponents. We are presently in between phases. Companies like Baseball Info Solutions revolutionized data collection (specifically batted ball data) and a bunch of really smart people then worked with the data to a point where we are all sick and tired of hearing a player was lucky or unlucky because his BABIP or HR/FB is out of whack due to as small sample size. I don’t mean to belittle this sort of analysis, heck, include me with those that discussed it ad nauseum. But I think we are ready for the next wave of data and the ensuing analysis (I’ll take care of the fantasy application, thank you). This is coming in the form of individual pitch data, improved measuring of defense and how it relates to pitching along with further enhancements to batted ball data. It is one thing to take the next step with respect to fine tuning expected player performance but another to know what to do with it in terms of game theory. So the answer to the question is “yes, new strategies will evolve, but not until we take player analysis to the next level.” And trust me; there are a whole lot of really smart people out there endeavoring this right now.

What I thought I would do for the next few weeks is survey the landscape and share some of the current data I find to be the most useful to the fantasy game. Don’t worry, we won’t forget about BABIP and all that good stuff, but there is some fairly new data available to the mainstream that can be used to help improve our fantasy game playing.

After all that build-up, today we are going to start with a rather simple concept that does not even involve anything new. That said, the topic is rather important and something I admit I have been a bit remiss on discussing over the years.

Everyone is familiar with the metrics K/9 and BB/9, commonly known as strikeout, and walk rates. An alternative, and to some a better means of looking at whiffs and walks is K/BF and BB/BF, where BF is the numbers of batters faced, though some will use plate appearances since it is more readily available. These stats are often referred to as K% and BB%.

The difference may be slight, but it is relevant. Pitchers that face more hitters have more opportunities to rack up a strikeout, so in a vacuum, a higher K% is more impressive than a higher K/9. That said, most of the time pitcher’s are ranked similarly with respect to K% and K/9. Only those that give up or prevent an inordinate number of runners have their K/9 and K% out of sync.

There are two applications of this principle that immediately come to mind – pre-season player projection and in-season evaluation of performance. We’ll spend a little bandwidth on each.

While everyone does projections slightly differently, I like to work off of innings pitched, using historical K/9 to determine the number of strikeouts then using batted ball data to determine what happens the rest of the time. If a pitcher’s BABIP (told ya we wouldn’t forget about it) was exceedingly high or low for any of the seasons used to gauge future K/9, that computation could be a little off. It is going to take a little bit of work to the projection engine, but I feel adjusting to work off of K/BF (or K/PA) will render a better foundation for the pitcher’s expected performance. A like argument can be made for using BB/BF in lieu of BB/9 and HR/BF and not HR/9. The other repercussion of this is many expected ERA formulas utilize K/9, BB/9 and HR/9 as inputs. I am going to have to look into the manner I determine ERA and tweak it to use the batters faced metrics as the data feed.

In season, using batters faced may be a better indicator of how a hurler is performing relative to his history, especially in small samples where base runners allowed, either good or bad have yet to normalize. Actually, as I further think about what I plan on writing as a write it, something else I have long considered important is about to come into play (more on that in a moment).

The idea behind the difference in K/9 and K% with a low (or high) BABIP is as follows: Let’s say a pitcher normally sports a K/9 of 7.0 with a BABIP of .300, basically a league average guy. If his BABIP is low, he is getting more outs than normal in places he normally would not get outs. This should result in a K/9 LOWER than his usual standards. Conversely, if the BABIP is high, then the pitcher needs to find a way to make up for those lost outs, with one opportunity being the chance to face, and therefore punch out the next batter.

Here’s where the proverbial wrench gets tossed into the analysis and actually speaks towards the areas where data collection and analysis can be improved. I am convinced that every pitcher is actually two pitchers – windup guy and stretch guy. I believe most hurlers will exhibit different skills working from the windup versus the stretch. While this is just a hypothesis, it is my contention that one reason some pitchers always seem to over or under pitch their peripherals is the difference between windup and stretch guy. Those more effective from the stretch are more likely to outperform their peripherals; at least that is what my intuition tells me. I have something beyond anecdotal, but not quite conclusive data to demonstrate this as unfortunately, windup and stretch data is not specifically archived. That said, at least for starting pitchers, some assumptions can be made. With no one on, the pitcher works out of the windup. With runners on base but not on third, they work from the stretch. When there are runners on third things get hairy as some starters will go from the windup with a man on third, second and third or with the bases juiced. Using some common sense to delineate windup from stretch, it can be shown that globally, pitchers have superior skills when working from the windup. These include a higher strikeout rate with lower walk and home run rates. Additionally, their BABIP is lower from the windup, but that’s a rant for another day.

Tying this together, it was surmised above that a low BABIP may lead to a lower than usual K/9 since more outs are recorded on balls in play, requiring fewer strikeouts. If my windup versus stretch theory is true, then a lower BABIP results in more work from the windup and MORE strikeouts, perhaps negating the loss from a lower BABIP.

The take home lesson is there is more to K/9 than meets the eye, but there are could be more to K% as well. As with anything, no single metric tells the whole story. But there are going to be cases where K% is more representative than K/9 and be it in site columns, Platinum content or on the message forums, whenever I inclined to discuss K/9, or even BB/9 and HR/9, I can going to take a step back and consider if K%, BB% or HR% may be a better indicator.

Last week, we began a series on some of the new-fangled statistics or new means of looking at standard stats. Today, we are going to take old friend BABIP to the next level.

I wonder if Voros McCracken knew the monster he created when he first introduced DIPS theory to the baseball populace. Originally, the primary conclusion was pitchers have limited control over the fate of a batted ball in play. Be it Pedro Martinez or Pedro Feliciano, when round bat met round ball, there was about a 30% chance a ball in play would be a hit.

Next, the concept was applied to hitters and it was observed that hitters develop their own baseline BABIP.

Soon thereafter, improvement in data collection expanded to include classifying batted balls as line drives, fly balls and grounders, which helped explain why batters had differing BABIP’s. Line drives result in hits most frequently, about 71% of the time. Ground balls follow with around 24% becoming hits. Fly balls bring up the rear, landing safely only 15% or so. So hitters producing more line drives carry a higher BABIP. If two hitters had similar line drive rates, the one hitting more grounders sported a slightly higher BABIP.

With respect to pitchers, correlation studies suggested that line drive rate was random but they influenced whether a guy hit the ball on the ground or in the air. Thus, ground ball hurlers generally carry a higher BABIP than fly-ballers.

The focus of today’s story is the next iteration of batted ball data, and that is the further classification of the three hit types into soft, medium and hard, in terms of the force the ball was struck. It should also be noted that until now, the bulk of data collection of this nature has been subjective. The next major advancement, which is actually a work in progress, is electronically capturing the speed and trajectory of a batted ball which will eventually render classification an objective endeavor.

In the name of full disclosure, I only have data classified as hard and soft for my personal use. I will present some analysis using the information and will reference some more detailed studies incorporating medium hit data.

Let’s cut right to the chase. While this may appear to be right from the Mr. Obvious archives (long time reader, first time poster, if you get that give me a “right on” in the comments), the BABIP of hard hit balls is far higher than that of soft hits. Furthermore, some hitters historically hit balls harder than others, which really helps explain the different BABIP baselines, beyond solely line drive rates.

As alluded to above, this data is further refined by the inclusion of medium hit balls. Since I don’t have access to these numbers for the purpose of my own research, I am only comfortable referencing some work that I have heard discussed in public arenas. This part may not be as intuitive, but of the three classifications, medium hit balls exhibit the lowest BABIP. The original study was published by Patrick Davitt of Baseball HQ (a subscription service, sorry no link available), but I have heard and read similar analysis.  This is just personal conjecture, but the results make sense. A softly hit grounder is more likely to be beaten out than one of the medium hit variety. A softly hit fly ball requires a longer run to be caught than a medium one, which might explain its BABIP being higher than a medium fly ball.

What I do have is data for hard and soft hit balls, which is presented in the table below for the 2011 and 2010 campaigns:

2011 Line Drive 0.714 0.730 0.683
2011 Ground Ball 0.238 0.576 0.185
2011 Fly Ball 0.139 0.404 0.045
2010 Line Drive 0.708 0.723 0.691
2010 Ground Ball 0.239 0.552 0.187
2010 Fly Ball 0.158 0.426 0.062

As suggested, refinement of BABIP is possible by using the three classifications of batted balls. But this does not really address what component of BABIP is in fact a player’s skill. The reason this is important is skill should not be regressed, only that which is out of control of the player should be regressed, be it a hitter or pitcher.

Thinking about this anecdotally, what if a pitcher had the skill to induce weak contact? This makes intuitive sense from the perspective that if a pitcher has the ability to make a batter swing and miss, he should have the ability to induce weaker contact. So now, instead of regressing a pitcher’s BABIP in total, or even using the three hit classifications, if only the percentage of hard hit balls allowed by each pitcher were regressed, since intuitively there is less control of the fate of a hard hit ball, then a more precise BABIP for each pitcher is possible.

Moving onto hitters, the same line of thinking holds true, but it is even more important to refine what is regressed and what is not since the BABIP variance around the norm for hitters far exceeds that of hurlers. Let’s first think in general terms. A faster player will beat out more weak and medium hit grounders than slower players and a hard hit grounder has as good a chance to get through regardless of who hit it. But is that really the case? Don’t infielders cheat in when a speedy guy is up, in an effort to cut down on infield hits? This may reduce some infield hits but also allow for more medium and hard balls to sneak through. And, don’t infielders play back with a slow runner up, increasing their range?

A similar thought process can be applied to balls hit in the air. Intuitively, the outfield cheats in when a non-power hitter is at the plate. This could result in fewer soft and medium fly balls (and line drives) being caught. On the other hand, more hard hit balls in the air may fall, especially those hit over the drawn in outfield. When a power hitter is up, he may have a greater percentage of weak and medium hit flies and liners land safely, since the outfielders are playing deeper. Of course, this reduces the number of hard hit fly balls that may land.

Putting this all together, it is clearly apparent to me why each hitter hovers around his own BABIP, and not simply because of their line drive, fly ball and ground ball distribution. For a speedy hitter, their BABIP on ground balls could be in part skill whereas it is completely luck on the part of a lumbering power guy. That is, what is a skill for a speedster may not be for a power hitter, and vice versa.

All this sounds well and good, but there are still two huge issues before any significant results can be determined. First, sufficient data breaking down each hit type needs to be recorded to eliminate randomness from the equation, significant referring to the number of years. Second, presently, there is too much subjectivity with respect to the classicization of batted balls. But as mentioned, data is being collected electronically. The bad news is this electronic collection is fairly recent. Subjective batted ball data has been collected for about 10 years, the hard/medium/soft label for only a couple. The good news is the added precision of the electronically collected data will reduce the sample necessary to render significant results, as some of the need for a large sample was to wash out collection bias.

For those curious, I utilized the above BABIP data in my projection engine in an effort to refine what is regressed, but most of the incorporation was global in nature. That is, Juan Pierre received the same treatment as David Ortiz.

Actually, that brings up a third problem with this intended study and that is the improvement in assessing how defense, specifically player positioning, plays a role in all of this. But that’s a story for another day, as the improvements in defensive metrics will be covered as a standalone topic in this series.


It was over ten years ago. Jason Grey and I were enjoying a sandwich from Schlotzsky's before taking in that day’s Arizona Fall League contest. The now Tampa Ray scout offered some advice I adhere to this day, “put yourself in the position of a site reader and write what you would want to read.”

Sounds simple, right? But when you think about it, Jason was implying there is a body of information available elsewhere and he was challenging me to come up with something fresh, something not available elsewhere. I’d like to think that over the years, I have been ahead of the game when it comes to fantasy baseball game theory. And with your help, I would like to stay that way.

I am about to describe a body of data to which I have access. I will then suggest what I feel would be useful means to analyze said data. My request is that you supply additional suggestions in the comments section and perhaps we can discuss their feasibility. The end result will be proprietary advances in game theory available to you, the Mastersball reader.

The vast majority of this data emanates from the National Fantasy Baseball Championship (NFBC). Some can be culled from Tout Wars and LABR. Here is what is available for every single on these leagues:

  • results from the original drafts
  • daily standings including category totals
  • weekly free agent pickups
  • Additionally, I have Excel programs that can easily calculate standings and category totals with a simple copy and paste of the necessary data.

    The bulk of the NFBC leagues are 15-team and 12-team mixed snake drafts. There are also a handful of 15-team mixed auctions as well as a 12-team AL only, and a 13-team NL only auction. These leagues all utilize a 7-man reserve list and allow free movement of players between active and reserve every Monday as well as Friday moves just for hitters. There is no trading in the NFBC but there is a FAAB period every Sunday evening.

    There is another subset of the NFBC consisting of what were termed Slow Draft Leagues. These are 15-team leagues but the catch is instead of employing a 30-man roster with weekly FAAB, you draft 50 players and hold them the entire season. The weekly moves are identical to the regular leagues, expect of course, there is no FAAB bidding.

    The reason I am asking for suggestions now is some may require early season data, so I want to make sure I capture it before it is too late. That said, there are in the neighborhood of 150 leagues, so studies requiring the capture of data anything more than once every couple of weeks are not practical. I am still at the copy and paste into Excel stage. Once I get the data into Excel I can do pretty much whatever I want with it efficiently, but it is still manual labor for me to get it into Excel. So, if any of you are adept writing those spider things that gather data, please don’t hesitate to dropThis email address is being protected from spambots. You need JavaScript enabled to view it. a note.

    With that as a backdrop, here are some studies I plan on undertaking, with game theory implications in mind. I realize only so much can be gleaned from 12 and 15 team mixed leagues, and that the results may not transcend into deeper single leagues, particularly leagues with trading and especially those of the keeper variety. I also realize there will be trends and situations unique to this season. For an example, look no further than the chaos associated with closers and the saves category thus far in 2012. But hopefully, some of the results will be actionable. Hey, at the very least, we should be able to produce a kick-ass NFBC Draft Guide for 2013.

    The first study I want to conduct involves comparing teams’ standings as if they made no moves all season to the final standings. The idea here is to determine the relative importance of the draft versus in-season managing – or if there even is a discernible difference between the two. To be completely forthright, I want to consider these results in terms of the viability of “target drafting”, the process by which you draft your team with the intent of accruing present category targets.

    Another area I wish to investigate is to examine if there are any consistent features with respect to the original construct of winning teams. Obviously, there are infinite ways to put together a winning team. However, it stands to reason a certain strategy may have had more success than others. For instance, where did the winning teams draft starting pitching? Where did the draft their closers? Were they stronger in any one area coming out of the draft? Again, there is not going to be a one-size-fits-all answer to this. But, if enough champions display similar characteristics, some strategies could be considered to have a better chance of being successful as compared to others.

    Similarly, an analogous study can be done on the worst teams in the league to elucidate the weaker strategies. Just to reiterate, care will be taken not to draw any conclusions that were obviously influenced by events unique to the current season.

    Something else I am curious about is just how poorly a team can do over a defined period and still have a chance to finish in the money. I know we keep saying it is early - not to look at the standings yet. Well, at least don’t lose any sleep over your early plight. But I want to know what the worst month (or whatever) a team can have and still win. This is not in an effort to identify when it is time to pack it in and start prepping for fantasy football, but rather to allow for an objective evaluation and perhaps rethinking present strategy.

    While this may take a little more work and provide rather anecdotal data, it would be interesting to see how the champions approached FAAB bidding. Granted, again there will not be a completely common means, but if the majority of winners broached bidding in a like manner, a lesson or two can be learned.

    Since the NFBC has a goodly number of auctions and snake draft leagues playing under the same rules, it could be interesting to observe any differences between the dynamics of the two formats. If I had to venture a guess, intuitively the categorical spread in auctions will be broader. That is, the leader in each category will be higher in auction leagues. At this point, I’m not sure if anything actionable will be elucidated, but that’s why you do the study.

    Speaking of auctions, it will be intriguing to learn if any particular strategy demonstrated better or worse success than others. As opposed to drafts, auctions really lend themselves to diverse roster construction.

    While I am sure other ideas will come to me, I would like to open the floor to you for more suggestions. I suppose the most important thing to decide immediately is how often I archive the standings and category totals. Presently, I am thinking of doing it the first day of each month as well as the dates corresponding to one quarter, one-half and three quarters of the season. Anything more would be too labor intensive, unless someone out there can help me automate the data collection. If so, you know where to find This email address is being protected from spambots. You need JavaScript enabled to view it..

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