Chance Favors the Prepared Mind

Taking BABIP to the Next Level

Written by Todd Zola

Thursday, 10 May 2012 02:06

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:

	ALL	HARD	WEAK
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.

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Last Updated on Thursday, 10 May 2012 06:45

Improving the Way we Look at K/9

Chance Favors the Prepared Mind
Written by Todd Zola
Tuesday, 01 May 2012 10:57
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. Comments (1)
Last Updated on Tuesday, 01 May 2012 11:31

A Little Help, Please?

Chance Favors the Prepared Mind
Written by Todd Zola
Tuesday, 24 April 2012 00:49
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 drop This e-mail 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 e-mail address is being protected from spambots. You need JavaScript enabled to view it . Comments (5)
Last Updated on Tuesday, 24 April 2012 08:50

Troy Tulowitzki and Scarcity

Chance Favors the Prepared Mind
Written by Todd Zola
Wednesday, 11 April 2012 00:00
Earlier this week, I polled my fellow Tout Warriors and asked that they define the term scarcity as it pertains to fantasy baseball and then discuss how they account for it in their drafting strategy. The results can be found in this week’s edition of Lord Zola’s Fantasy Baseball Round Table, posted HERE. What follows is my take on the subject. The purpose of this exercise was to illustrate how a single concept can be looked upon in so many ways. At the end of the day, no definition is right or wrong, though as will soon be elucidated, I do take issue with the means some choose to account for their perception of scarcity. As I alluded to in my wrap-up to the Round Table, my first thought when it comes to scarcity is ensuring there are sufficient players in the draft-worthy pool to legally fill your league’s rosters. The size of the draft-worthy pool is exactly as necessary to fill everyone’s opening day active roster. In a 12-team league with 14 active hitters, the hitting draft-worthy pool contains 168 batter priced at $1 or more. The idea being if the entire player pool is ranked blindly, irrespective of position, there may not be enough players at some positions in the draft worthy-pool. According to my perception of the term, these positions are scarce and a pricing adjustment needs to be enacted. This adjustment is bottom-up, with the lowest ranked player at each position being assigned a value of $1 with everyone else scaled up in proportion to the expected percentage contribution. The draft-worthy pool now contains ample players at each position to legally fill everyone’s roster. As I suspected, there is a plethora of uses of the term "scarcity." Two that I sense existed were indeed confirmed in the Round Table. The first has to do with the gap in talent between the top tier and the rest of the players in a positional pool while the second considers the relative talent in the positional pool as a whole. Obviously, I have always been aware of these scenarios, but I have never deemed it necessary to adjust my pricing because of it. I have always felt these were issues to be dealt with strategically. For what it is worth, I am not the only person with this conundrum as I recall fellow Tout Jeff Erickson from Rotowire blogging about this very topic. As I have been thinking about where I want to take this next, I have concluded I can take it in any of a number of directions. But since I only have another 1000 or so words before I lose your attention today, I will focus on one particular conundrum I have and save the rest for what will hopefully be some follow-up discussion or future essays. My problem is I have a bunch of thoughts jumbled up in my head, and I am having a difficult time making sense of it all. Individually, each make sense to me, but collectively, I see some contradictions. To wit: I understand player expected performance is best thought of as a range and thus the associated value we then place on a player is best thought of as a range. I believe that it is reasonable that regardless of this range, we can still say Player A is ranked higher than Player B who is ranked higher than Player C. I realize the big picture objective is to assimilate as much potential on your roster as possible, which at times means drafting a lower ranked or paying more for a lesser valued player – usually of the scarce variety. I get that the draft or auction is only part of the process, there is still 26 weeks’ worth of roster management ahead. Here’s the deal. I have no issue with the talent drop-off in some positions being thought of as a form of scarcity. If you want to play semantics, it can be said that the talent at the top of a pool is scarce. Where I take issue is the manner some choose to deal with this gap and that is to overdraft or overpay for players at those positions. To use a specific example apropos to this season’s player pool, I question drafting Troy Tulowitzki in the top-five or paying the same amount as Miguel Cabrera, Ryan Braun, Matt Kemp or Albert Pujols in an auction. My reasoning is as follows. No one that champions taking Tulowitzki that high does so saying they feel the Rockies’ shortstop will produce a raw stat line akin to Pujols, Cabrera, et al, but rather citing the huge talent gap before you get to the next tier of shortstops. The idea is the edge you have at the position over your competitors leads to an overall roster edge, which is the point I dispute. Given that value or rank is a range and we don’t know what will happen, overdrafting or overpaying is still an inefficient use of assets. I believe that while you will indeed have the best shortstop in the league, arguably by far, this does not aid in constructing the best roster, at least at the conclusion of the draft or auction, which ultimately may be the point I am missing….. MAYBE. We’ll get to that in a moment. The best way to explain this is to pretend we are not drafting dynamic potential and instead we are drafting fixed, static numbers. By taking Tulowitzki that early, you are leaving stats on the table that you WILL NOT be able to make up for later. I’m sorry, but in a static environment, you won’t. Your shortstop is better than my shortstop, but my roster is better than your roster because the difference between the player I get in the first round and my shortstop exceeds Tulowitzki and the player you get the same time I take my shortstop. The rest of the rounds can be a complete wash – I win because my first round guy has better stats than Tulo and my shortstop has the same stats as the guy you took when I took my shortstop. Now let’s remove the fixed stats constraint. As I explained, I understand all we are dealing with is potential, but I also explained that this potential can be ranked and I will leave the draft or auction with more POTENTIAL than the person that drafted Tulowitzki in the top few picks or paid very top dollar for him in an auction. However, I am not willing to take the next step and proclaim that my potential TO WIN is any greater, though I obviously believe it is. Here is the point where this is just the beginning of the process kicks in. I am willing to cede that by the end of the season, because you have the best shortstop, you can POTENTIALLY have a better roster than mine if you use the next 26 weeks to gain an edge at some of the other positions. That is, if it ends up that all the other positions end up equal, you win because you have a better shortstop. If this is your reason for taking Tulo, I can accept that. I just don’t like your odds of being able to pull this off. One of the philosophies I have when assembling a team is there are some positions you draft an edge, there are some you work hard to gain an edge and there are some you just hope to break even. Actually, it is not just a positional consideration, but categorical as well. Depending on the depth and format of the league, I may choose to use the long season to embellish a light category such as steals or saves. Examples of gaining an edge are considering spots like your corner, middle, utility and fifth outfielder to be fungible, and funnel players through those spots until you settle on a consistent, reliable performer, superior to the player you drafted or bought in the end game. The same principle can be applied to the back end of your pitching staff and closer, you work the waiver wire in an effort to upgrade those spots. At the beginning of a snake draft, depending on my pick number and the picks previous to my selection, I will decide what positions I intend to draft an edge and which I hope to break even. Similarly, in an auction, after the first handful of nominations, based on the prices, I will make the same decision. But here’s where I likely differ in philosophy from those proponents of the drop-off form of scarcity – my decision will involve not paying more than market value to accomplish both goals. That is, in order to gain my edge, I will not overpay or overdraft the player. I am simply not willing to leave potential on the table in the name of scarcity. I am completely comfortable that if I am patient, a player will be available to me at that position, at market value, sometime during the draft or auction. As I suggested, there are a bunch of other big picture considerations. What position is most likely to produce undrafted useful players as the season progresses? What stats are most likely to be available? This all influences the intrinsic value of a player to your roster and may lead to a slight overpayment or overdrafting of a player to avoid the spots where the replacements are thin. This could even lead to tweaking the initial rank or value of each player to account for the fact that by season’s end, the number of players that contribute value exceed that of the number assigned value in the draft-worthy pool. But, and here is the key – it is going to take an awfully persuasive argument to convince me there are factors I have omitted that result in it being better to start the year with a roster with less potential value in an effort to end the season with more realized value. It is my contention that dealing with the drop-off form of scarcity by overdrafting or overpaying results in a roster with lesser value. Anything you say to convince why your team will be better than mine can all be 100% true. But I will still be able to say, “Yeah, but your team could have even been BETTER had you not drafted Tulowitski first overall.” Add new comment
Last Updated on Wednesday, 11 April 2012 08:51

Catch(er) Me If You Can

Chance Favors the Prepared Mind

Written by Todd Zola

Tuesday, 27 March 2012 02:05

This past Sunday, I had the privilege of representing Mastersball in the National League Tout Wars auction. The entire weekend is one of my favorites of the year, featuring the gathering at Foley’s on Friday night, the annual meal at Virgil’s and of course the auctions. This year, we were the guests of SiriusXM and held the festivities right in their studios, which was pretty neat unto itself.

My strategy was pretty simple as I decided to channel one of the original Mastersball credos: Draft for Value, Trade for Balance. Much of my recent focus has been on the National Fantasy Baseball Championship, where it is more important to draft in a balanced manner since there is no trading. But contrary to what some may contend, it is possible to consummate deals in industry leagues. I just need to practice what I preach when others complain that it is really hard to trade in their league – I tell them give the other person a reason to deal with you.

There is this belief that since it is hard to trade in a league like Tout Wars, one should not draft a team predicated on having to make a deal or two to win. I’ve never been one to believe in that sort of thing and find if you approach a negotiation in a respectful and courteous nature, it is often pretty easy to hammer out a symbiotic swap. I decided that if the auction dictated it, I was going to put that belief to the test.

So the plan was to find the soft spots in the auction and buy players in an agnostic manner, being blind to the names, positions and specific statistical contributions. I would simply accrue as much potential as I could, then spend the next twenty-six weeks managing the potential into rotisserie points. If I ended up strong at a position or in a category, I would parlay that excess into help elsewhere, just like I have advised my tens of followers over the years.

I would know within the first ten or so nominations how I would approach the bidding. If the room was overpaying, according to my numbers, for the top tier talent, I was going to exhibit excruciating patience and not get involved until that inflation turned to deflation and then I would just buy, regardless of position and contribution. If the first few players were not bid up to my value, I would be more than happy to give them a home. My gut expected the former and my gut was right.

I knew right off the bat it was going to be awhile before I built up my roster as the prices were consistently $3-$6 above what my little black attached to each player. I group players in tiers and almost all of the first several players went for a cost equivalent to a tier higher than I had them.

When using this extreme spread the risk tactic, it is imperative you time your entrance into the foray so you don’t end up overpaying for mediocrity but also don’t leave money on the table. Using my positional value tiers in conjunction with assigning a price I wanted to pay for each the 23 roster spots, I mapped out the plan. My sense was that I would be able to clean up in the outfield and possibly middle infield, but I was nervous about first base and starting pitcher, so if I had to go the extra buck there, I would.

The next step was just waiting, only getting involved with closers, because it is tradition in NL Tout Wars for one of the participants to nominate a closer at their turn each time early in the draft, so the way to deal with it is let the first couple of guys go to set the bar, then jump in the middle, before the “last good closer” realization kicks in.

Something else I noticed that while every other top player was going for a premium, Brian McCann went a bit undervalued. To see if this was going to be a trend, my next turn, I put Buster Posey out to bid and sure enough, he also went a few bucks under what I expected. At that point, I made the conscious decision to take advantage of this and nab two catchers I happen to like if they came in under price, Miguel Montero and Wilson Ramos. I would put them up for bid if necessary, as I expected that at some point, the catcher prices would rise. Sure enough, I got both of them a few bucks below what I would have paid.

A little while later, Jonathan Lucroy went for a reasonable price and while I did not regret buying Montero and Ramos, I shook my head, sort of bummed prices were still cheap since I hoped that prices would start inching up over value. Then it hit me – this year in Tout Wars we instituted a new rule, converting an outfielder into a swingman position, which is a second utility able to be filled by any hitter or pitcher. If the next catcher came in under value, why not use one of my two utility spots and draft for value then trade for balance? So when Carlos Ruiz stopped below value, yours truly was happy to put him at my utility spot. Lawr, who was manning the official roster tracker casually glanced at me as he was typing the name, I gave him subtle head nod and he sort of reciprocated with a head nod if his own.

A little while later, Nick Hundley was making the rounds. I had already decided that if another decent catcher also came in under value, I would take him. So while Hundley may not be the fantasy equivalent of Orlando Cepeda as the first official designated hitter, he will forever hold the distinction of being NL Tout’s first swingman.

My thinking was straightforward. During the auction, the market value of catchers was a couple of bucks less than what I had them priced at. My valuation system accounts for positional pricing, adding a couple bucks to catchers. So what I was doing was breaking even on the two utility spots, effectively paying what it should cost for a non-catcher with the same stats to fill the spot. So even if I am not able to parlay one of the receivers into help elsewhere, at worst I break even at utility.

The Cliff Note’s version on the rest of the team is I indeed ended up paying a little extra for Carlos Lee at first and Jordan Zimmermann to be my staff anchor. My first closer was Huston Street, who is a bit of a health risk, so I opted to buy another full-time closer in Jason Motte, another possible application of drafting value as I liked the price of both relative to other closers. I am fine with my middle infield, getting Marcos Scutaro at what I considered to be a huge discount while picking up Jose Altuve and Zack Cozart, a couple of youngsters I like at what may seem like high prices, but they were in fact spot on with what I expected. Where I made my hay was the outfield, getting four guys all well under my projected cost.

The pitching staff has several holes, with three starters that may not start in the majors and a dinged up Jonny Venters. But remember, there is the swingman position that I can use to occasionally start a pitcher to help make up for lost innings before I deal a receiver for a hurler.

And finally, the purchase of Hundley cost me a reasonable third baseman, as I would not have minded needing to fill the corner infield spot with a part-timer. As it stands now, I may have two dead spots with Jerry Hairston and Brett Wallace joining Carlos Lee at the corner. That said, the prices of the end game third baseman were absolutely through the roof, so I may have actually ended up ahead with Hundley and Wallace as opposed to what I would have put at third and utility.

I certainly did not win the league at the table, not even close. But I do feel as though I have sufficient assets to work with to put the squad in the thick of things, and we’ll see what happens.

Below is the squad with what I paid. All the teams can be found HERE. I am more than happy to address all questions and criticisms in the comments section.

C: Miguel Montero (18), Wilson Ramos (13)

1B/3B: Carlos Lee (21), Jerry Hairston (2), Brett Wallace (2)

2B/SS: Jose Altuve (17), Zack Cozart (13), Marcos Scutaro (14)

OF: JD Martinez (13), Jason Kubel (16), Angel Pagan (17), Allen Craig (17)

UT: Nick Hundley (10)

SW: Carlos Ruiz (9)

SP: Jordan Zimmermann (17), Shaun Marcum (13), Dustin Moseley (2), Erik Surkamp (2), Marco Estrada (1), Julio Teheran (2)

RP: Huston Street (15), Jason Motte (17), Jonny Venters (7)

RES: Jimmy Paredes, Jeff Karstens, Mike Fontenot, Steve Cishek

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Last Updated on Tuesday, 27 March 2012 10:10

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