Valuing players with your E.Y.E.S.

I have come to love the auction draft fantasy format. Tired of watching my targeted sleepers and studs go a few picks before my turn, sick of being entirely helpless and at the mercy of my fantasy provider’s value rankings, I opted to work the free market in recent seasons.

The glory of auction is that every owner has a chance at every player. The auction format and bidding market equalize the stress of not having a top five pick in your league’s snake draft, precluding you from one of the upper echelon elite players. Auction is not without its own stresses, however. The free market operates efficiently only when its participants are informed. Like the seller of an album of baseball cards who does not understand the value of a 1963 Topps Pete Rose card, an uninformed bidder is substantially less likely to profit on the open market.

Being informed requires more than knowing raw stats and having a player projection. Believing that Jay Bruce will hit .280 next season with 30 home runs, five to 10 stolen bases, and 85 runs and RBI is nice, but unless you know the league average player output, Bruce’s projection is meaningless. Player valuation requires some appreciation of relative category weights and scarcity. To deal with this issue and weight player values, I have a methodology, which I explain below. I have also included a pricing guide applying this methodology to Oliver’s 2011 preseason player projections as of Feb. 1.

First, three points that will inevitably arise:

My methodology, like any pricing guide, has an inherent limitation: the quality of the projection system. My methodology weights relative stat production, but if the projections are weak, then the pricing guide will inevitably be weak. For the purpose of this article, I used Brian Cartwright’s Oliver projection system from The Hardball Times to create a pricing guide. Oliver has proven to be a very reliable system, on par with ZiPS (not released in full yet) and CHONE (now defunct). You can access Oliver’s statistical projections (and more) by subscribing to THT Forecasts.

Second, for the purposes of simplification, I analyze all hitters as utility players. Some people like to adjust their numbers to account for position, but I’d rather just index the top 20 players by position after I have my Z-Score totals and know how much actual value, unbiased by position, each player is going to provide.

Third, there are better and more accurate methodologies out there. One is readily available via THT Forecasts. Another system is one created by Tom Tango a few years back. I know that Zach Sanders (of Fangraphs) and some others are working on something very similar as well. What I present to you today is merely what I do on my own, which I hardly proclaim to be unique. I am certain that someone out there probably uses, and invented before me, this same methodology to value players.

In honor of Steve Phillips, who values baseball players with his eyes and heart, I have dubbed this system of dollar valuation the Expected Year’s Evaluation Statistic, or E.Y.E.S. for short.

Step 1: Determining the size of the player pool

Determining the size of your expected active player pool (those players who will be on some player’s team as either a starter or bench player) is essential because only a limited set of major league talent gets used in fantasy. This is true even of AL- and NL- only formats. Because not every player gets drafted, valuing players is a two-tiered process. First, you separate potential active roster talent from “the rest.” Then, you evaluate the players in the pool. This will be explained in further detail below.

Before we can determine a potential player pool, we must determine an approximate depth for the pool. Let the number of teams in your league equal X. Let the total number of drafted hitters per team equal Y, and the total number of drafted pitchers per team equal Z. For the purposes of this analysis, I am going to use a 12-team league with one of each active infield position player (C, 1B, 2B, SS, 3B), one corner infielder (CI), one middle infielder (MI), five outfielders (OF), and one utility player (UTIL), for a total of 13 hitters per team. I am also going to use nine generic pitching slots. There is also the matter of the bench. I usually play in leagues with five bench spots, which will be spit three to two between hitters and pitchers. This gives us a grand total of 192 batters and 132 pitchers, for a total of 324 players.

Of course, the player pool is quite subjective and often much deeper than a consensus 324 players. In step 2, I deal with this problem, but 192 hitters and 132 pitchers will be our starting point.

Step 2: Calculating preliminary Z-Scores

A Z-Score sounds much more complex than it really is. Okay, maybe not, but Excel (or Open Office) makes Z-Score calculations easy. Simply put, a Z-Score measures how many standard deviations from the mean (either positively or negatively) a given statistic is. For our purposes, players with high Z-Scores will help you in a given statistical category. Players with a Z-Score of 0 will have a neutral effect. Players with Z-Scores below 0 will hurt you in a category. The greater (or lower) a Z-Score, the more of an impact, for better or worse, a given player will have for your fantasy team in a calculated category.

To fill out a player pool, I first calculate the Z-Scores for every player for each of the hitting and pitching categories. For hitters, I use only a pool of players expected to accrue a minimum of 400 plate appearances. I am sure there are a few fantasy-valuable players out there who will come to the plate fewer than 400 times this season, but they are few, so I have ignored them for this demonstration. Per Oliver’s 2011 projections, the pool of hitters who are expected to have 400-plus plate appearances is 436 players deep. Among these 436, the mean batting average is .265, the mean home run total is 14.0, the mean stolen base total is 8.1, the mean runs total is 62.6 and the mean RBI total is 61.0. The standard deviations for these respective categories are .019, 7.9, 7.8, 11.6 and 17.1.

To calculate any given category’s Z-Score for a player, you simply take the difference of that player’s stat against the mean for that stat and divide it by the standard deviation. For instance, Albert Pujols is projected by Oliver to hit 43 home runs. To calculate Pujols’ home run Z-Score (labeled Z-HR in my charts), we take the home run mean(14) and standard deviation (7.9) and use the following formula: (43-14)/7.9. If you plug that into your calculator, you will find that Pujols’ Z-HR is 3.67.

Now do this for every player for every statistic, and when you are done, sum up each player’s cumulative Z-Score. Then repeat this process for pitchers, using wins, saves, ERA, WHIP and strikeouts. I also like to use K/9 for the purposes of evaluating pitchers.

By now, you have probably wondered how I plan to value rate stats. A .300 hitter is not nearly as valuable as a .290 hitter if the .300 hitter is getting two-thirds the playing time of the .290 hitter. To deal with this problem, I determined the average at-bat total for all players expected to accrue 400 or more plate appearances (468.8) and I multiplied the batting average Z-Score by the player’s actual at-bats total divided by the league average at-bats total. This adjusts the Z-Scores for batting average to reflect playing time. I do something similar with innings pitched for pitchers.

Step 3: Distillation

Once we have a series of player Z-Score sums for batters and pitchers, we need to select the “cream of the crop” to represent the potential player pool. If you recall above, we determined that, at least for our example, our league would use 324 active players (192 batters and 132 pitchers). Accordingly, I begin by selecting the 192 batters and 132 pitchers with the highest Z-Score sums. These players should represent our best “all-around players” for drafting.

This is not the end of step 3, however. Fantasy teams are dynamically comprised and owners often draft one or two category guys to fill holes and to stream. To account for this, I then rank the residual player pool by categorical Z-Score. I then pull out any player with a Z-Score of 1.0 or higher in any fantasy category. I also add any remaining players who I think are “interesting” to my player pool, even if their categorical Z-Score is less than 1.0, as $1 buys to keep an eye out for. Not even the best projection systems gets every player right, and this element of player selection requires personal judgment. For instance, Oliver is incredibly bearish on Aaron Hill, who I like for 2011. His Z-Score sum is not within the top 192 and he does not have a Z-Score of 1.0 or greater in any single category. Nonetheless, I added him to my player pool.

Doing this, I ended up with 235 hitters and 166 pitchers, for a grand total of 401 players. This seems reasonable.

Step 4: Calculating primary Z-Scores

Now that we have our pool of 401 players, we need to recalculate our Z-Scores to reflect the draft pool talent. If you want remotely reliable numbers for draft day, it is pointless to value player X against undrafted players. These 401 player represent the best of the fantasy crop, and accordingly, the means and standard deviations in each category between them will change. Among the 235 hitters in our example sample, for instance, the mean batting average jumps up to .272 (from .265), the mean home run total jumps to 17.2 (from 14.0), the mean stolen base total jumps to 10.6 (from 8.1), the mean runs total changes to 70.4 (from 62.6), and the RBI total bumps up to 69.1 (from 61.0). The standard deviations also change to 0.018, 8.8, 9.3, and 18.0, respectively. The average expected at-bat total also rises from 468.8 to 497.2.

Re-calculating and re-summing each player’s Z-Score, we are left with the expected relative value weights of each player.

Step 5: Calculating dollar values

Once we have relatively weighted Z-Score sums for each player, we now need to determine each player’s dollar value.

To calculate dollar values, we must determine the total amount of money in our fictional economy. Simply put, we need to determine how much money exists to be split among the players with positive Z-Scores (all players who are ultimately assigned Z-Scores below $1 will have their dollar values rounded up to $1). Using the standard $260/team budget, applied to our 12-team fictional league, we find an economy with $3,120 in it. In real life, you could barely buy a pimped-out MacBook pro with that money, but here you can buy CC Sabathia!. Alas, I digress.

Take this $3,120 total and divide it by the total Z-Score sum across all hitters and pitchers. The Z-Score sum from which to divide the economy value by should not include any players with negative Z-Scores; ignore these players for the sake of Z-Score valuation. Doing this will give you a rough dollar value estimate per Z-Score. Take this dollar value and then apply it to each player’s Z-Score to get your estimated dollar value for that player.

Keep in mind that the minimum bid for any player is $1. Certain players in our draft pool, particularly the “one category” players (hitters or pitchers with a Z-Score of 1.0 or greater in only a single category), have Z-Scores below 1.0. Other players probably have Z-Scores that, when multiplied by our Z-Score dollar value, have Z-Scores under $1. Because these players will actually cost you at least $1, all players with dollar values under $1 are rounded to $1.

And there you have it. That is how you can calculate dollar values (EYES) for auction on your own. Use your EYES (not your heart) on draft day! Empower yourself with information. Of course, you could also do none of this analysis, save yourself some time, and purchase a subscription to the substantially more accurate THT Forecasts, which has its own built-in pricing guide for Oliver. I guarantee you those numbers are much better than mine.

Print Friendly
 Share on Facebook0Tweet about this on Twitter0Share on Google+0Share on Reddit0Email this to someone
« Previous: Take it to the top
Next: Curveball command »

Comments

  1. Nate said...

    One problem I always run into when evaluating players by the Z-score totals that I put together is that it doesn’t provide quick insight into how to build a balanced team across multiple categories. 

    I wonder if building a ‘Z-score’ system where rather than using the mean you relate the scores to the production needed to win a category.  This would be a useful tool during drafts.  As you draft you could see how much far away from being projected to win each category you are and draft accordingly. 

    It basically tells you the same thing on a different scale and the ‘category win’ scale might not even change the numbers significantly, but I still wonder if it’d be a more effective or easier to use tool.

    Itd be nice to have a tool to drag and drop players with Z-score data attached into team slots for easier tracking.  What I mean is easier than me copying and pasting lines from my excel sheets.

  2. Dub Boys said...

    Good stuff. I do indeed do things in a similar fashion. One thing differently that I do is that for batting avg., I calculate what I call xBA, which is AB*(BA-median BA). I think this accounts well for the impact a BA will have over a certain number of AB’s. Can do the same with OPS if you are in an OPS league, but I use PA’s instead and for ERA and WHIP I use innings as the multiplier. I also use a variation of k/9 in my Z-S rankings. I use k/9*k as a value. This normalizes the impact high k relievers have in your Z-S. If you go off of a straight k/9 in a league that uses K’s as a counting stat, relievers would be massively overvalued (hello Carlos Marmol!)….that is all

  3. Bob said...

    Look like alot of work, great job Jeffery. I’m curious, what are your $ values for:

    McLouth, R Zimmerman, S castro, L morrison, and C Lee.
    Thanks!

  4. Jeffrey Gross said...

    I have zimmerman at $28, but his value is probably closer to 24.

    Castro comes out negative in value OVERALL (not by position) because of his low RBI/HR expectations. However, this is because there is no positional adjustment.

    Carlos lee also has a barely negative value, for some reason, so I have $1. it should be closer to 5-10.

    Morrison: $9

  5. RotoChampMike said...

    Good stuff, Jeffrey.  We use some of the same methodologies, except for the ratio categories where we calculate the same way as Dub Boys.

    I’d like to compare your valuations using our pricing model with your Oliver projections (and vice versa using our projections with your valuation system).  If interested, shoot me an email.

    I’d be most curious about Stanton and Francisco.  What line do you give them for 2011? 

    Do you plan on showing your positional adjusted rankings?  I think this is where a valuation system really gains power.

  6. Jeffrey Gross said...

    RotoChampMike,

    I did initially plan to release my Oliver-derived values, but there are two issues regarding it:
    1) THT Forecasts has a much more accurate valuation system which uses oliver to customize prize guides based on individual league needs.
    2) Oliver is a pay-for-service and it is their discretion to publicly release any information en mass

    I can give the lines for the following, however
    Carlos Lee: .271, 21 HR, 3 SB, 83 RBI, 72 R
    Juan Francisco: .283, 34 HR, 2 SB, 110 RBI, 84 R (assumes 633 PA)
    Mike Stanton: .283, 41 HR, 108 RBI, 81 R, 4 SB

    3) I agree with you, but I do not think Brian wants me to release Oliver’s index by position. If you want to check out my own personal rankings, check back in about one week. I have an article with my UPDATED positional rankings (old rankings can currently be accessed on THT fantasy) with a comparison to Oliver’s top rated players by position. Essentially, what you are looking for in part, done via THT Forecasts, can be found next week (Oliver’s updated stat lines will not be listed, however—I highly recommend purchasing THT Forecasts (its really inexpensive) for the roto values)

  7. Will Hatheway said...

    Aaron Hill actually inspired me to look into how projection systems work, since I like him and most systems don’t… learned quite a bit.

    How rigorous is lastplayerpicked.com? Seems okay (though oddly low value for Lester, among others).

  8. Jeffrey Gross said...

    My preliminary top 20 Z-Score totals using Oliver (without positional adjustments, in order):

    Pujols, Albert
    Cabrera, Miguel
    Ramirez, Hanley
    Braun, Ryan
    Stanton, Mike
    Gonzalez, Adrian
    Holliday, Matt
    Fielder, Prince
    Wright, David
    Cruz, Nelson
    Sandoval, Pablo
    Votto, Joey
    Gonzalez, Carlos
    Howard, Ryan
    Francisco, Juan
    Kemp, Matt
    Crawford, Carl
    Teixeira, Mark
    Longoria, Evan
    Choo, Shin-Soo

  9. Jeffrey Gross said...

    My preliminary top 20 Z-Score totals (pitchers) using Oliver (without positional adjustments, in order):

    Lincecum, Tim
    Hernandez, Felix
    Halladay, Roy
    Wainwright, Adam
    Haren, Dan
    Lee, Cliff
    Lewis, Colby
    Sabathia, CC
    Jimenez, Ubaldo
    Hanson, Tommy
    Kershaw, Clayton
    Verlander, Justin
    Carpenter, Chris
    Latos, Mat
    Greinke, Zack
    Oswalt, Roy
    Johnson, Josh
    Lester, Jon
    Weaver, Jered
    Scherzer, Max

  10. batpig said...

    I take a slightly different approach, I used to just use Z-scores because it was quick and easy, but now I’m using a value vs. REPLACEMENT level metric vs. the average player.

    I also start with positional value and then later on join them together in a pool.

    So first I sort players into pools of position eligibility, do a quick-and-dirty Z-score total to sort the wheat from the chaff… then eliminate the guys who are certain to not be on anyone’s roster… which leaves about 25-30 potential players per position. 

    Then, it’s easy to eyeball the pool and use the bottom 10-15 guys to calculate a “replacement level”. 

    To me, it’s essential to know how much value a player will provide beyond the FREE TALENT that will be readily available in the late rounds of the draft or the waiver wire.

  11. Jeffrey Gross said...

    Batpig,

    that’s an interesting approach. it certainly can be done if you use the top 192 z-Score hitters, for example, but my problem with that approach is that the #150-200 hitters picked are super subjective; sometimes its nto about composite Z-score, but rather a need for SB, even if not the most valuable guy

  12. AJ said...

    Do you ever consider adjusting valuations for categories that are easier/more difficult to predict?  Since HR are easier to predict than AVG, for example, does it make any sense to put more weight on HR than AVG?

  13. Jeffrey Gross said...

    Perhaps, but i prefer to go with the expectation and pray for that. I tend to be conservative on AVG projections anyways

  14. Wynn Bowman said...

    Great article, thank you.  Do you know what categories THT Forcasts has.  My league has 7×7 and uses quality starts. 

    I am not as good with the statistics as you, but I usually download projections for the upcoming year in excel, then plug in players that I want and/or I think will go for less than full value in our auction, and compare the totals of my “expected” team against league average totals and point value for each stat in our league standings for the last three years.  This gives me an idea of how I will finish in each stat.  Then I make a list of “interchangable guys” to make sure I don’t get tied into any one guy.  We play in a daily lineup league, so I tend to try to over estimate with my starters in ratio stats (BA, OBP) because I can usually make up in the counting stats by making sure I have a full lineup everyday.  This allows me to pick up low BA and OBP guys with good matchups (who tend to be on the waiver wire) to put in each day.

    Thanks again.

  15. batpig said...

    @ Jeffrey,

    - On your first point, I don’t think it’s a big deal.  So what if you do/don’t include a couple of guys in the sample when figuring out replacement level?  So maybe the “free” replacement level SS actually averages 65 R, 11 HR, and 63 RBI whereas you calculated it at 63 R, 12 HR, and 64 RBI because you forgot to put Marco Scutaro in there.  Is that REALLY going to make a significant impact on your “scores” for how valuable Stephen Drew is, overall, relative to Alexei Ramirez?

    Frankly, the level of imprecision inherent in the predictions (which underpin the whole system) is far greater than any imprecision introduced by a situation such as the above. 

    - Yes, ANY such scoring system is not going to be super useful for picking one-category guys… but I believe that “one-category guys” is a stupid philosophy in roto anyway, unless you are making a late-season push and have no change in the categories you sacrifice as a consequence.  There is no “free lunch” there and if you can’t play a Nyjer Morgan for a full season in roto without acknowledging the points he is costing you in HR and RBI.  And if you DO need a one-category guy, it’s not that hard to check. 

    Anyway, honestly it doesn’t REALLY matter which system you use as long as you are using SOME type of objective scoring.  When I rank players, I have tried both approaches and the relative scores correlate nearly 100%.  These systems are only useful to establish general value anyway, they are never going to be “perfect” ordinal rankings.  So if one system tells you Adrian Beltre is worth 5.2 points next season whereas Ryan Zimerman is 5.4… but the other system tells you Beltre is worth 4.8 but Zimm is 4.6… does it REALLY matter?  You are probably going to pick the guy you have a “gut feel” for anyway, these scoring systems are just a guide to help frame the general parameters of value.

  16. batpig said...

    To reinforce the point, I just did a quick check… I have 88 outfielders projected and ranked for next year in my league using my replacement level approach.  I just quickly did a Z-score ranking and the correlation between my replacement scoring and the Z-score method was R = .951

  17. Jeffrey Gross said...

    Batpig,

    I think the idea is quite interesting. I guess it’s just a complicated question of determining scoring and value, because its not a particular matter of, for example, just pulling the top 12 by position in composite z-score. you also need to give room for flex. For example, a lot of projection systems are down heavy on Aaron Hill, but thats because they weight his non-2009 performance poor. His BABIP-luck adjusted 2010 was actually solid and his pre-2009 numbers were stunted by concussion. He’s clearly going to factor in next season, but do you include his numbers? and how? 

    I just prefer to make 0 bias judgments and weight the pool against the players within, taking guys with Z-Score>1 in any given category.

    I will heavily agree with one thing however: i think Roto players overvalued one-category guys. People lament the effect that Carlos Pena will have on your AVG, but they readily draft Rajai Davis. This is why I always draft players based on composites. They may not excel in anything in particular, but it’s about getting as much total surplus as possible, not about “filling holes.” This is why I will draft 10 Shin Soo Choos over a composite of guys Like Adam Dunn, Juan Pierre, etc. to try and “frankenstein” a team. Balanced, less sexy players,  also mitigate risk. If your HR are locked up in 2 players and one goes down, then what?

  18. Jeffrey Gross said...

    Wynn,

    That is a smart strategy. It’s a gut approach to what BatPig is essentially suggesting. I think people overvalue production because they fail to look at the margins. Like last year. Oh your pitcher has a sub-4 ERA? That’s good, right? Well when the league average ERA is <4.2, then a 3.9 ERA pitcher is not exactly as sexy as he may have been 2/3 years ago. Especially in “high downside” pitchers like Brandon Morrow and Justin Masterson, I think the upside is a bit overblown in the absolutes.

    I’m no stats genius by any means. I just come up with ideas and theories that I have others help me test. I just know the basics smile

  19. bluechipper said...

    I have been following this same basic idea for several seasons, with one pretty large exception—positional factoring.  I would assert that if you are trying to measure player value directly against the pool of available players (and ESPECIALLY if you are taking the time/effort to limit your player pool to a draftable population), it only makes sense to separate your pools into position-eligible subsets when you calculate your means and SDs.  Give each player a positional Z-score and then move each positional subset back into the total pool and compare across all positions.  This tells you, for example, if Albert Pujols is truly more valuable than Joe Mauer, based on the value that you could get at the same position with a different draftable player.

    The weakness, I think, is that you never know what previously-considered “undraftable” player will break through and skew your original positional baselines.  Keeping a larger population will limit the effects of such an occurrence, of course, but I think you get more meaningful data by scoring positionally first.

  20. Jeffrey Gross said...

    bluechipper,

    if you score by position, then, its also extra leg work—you have to do CI/MI subsets, etc. I just prefer to do this, then create a sheet of the top 20 indexed SS, 2B, etc. and then i can compare their relative z-scores to each other and the rest of the population. It is that much more valuable in determining which players are filler for the position and WHO you should target scarce resources towards. It’s that much more of an affirmation as to why pay $40 for hanley is NOT necessarily insane.

  21. ChrisS said...

    How does the Z-score method compare to say if you took the THT projections and divided the projected figures over the max of each category and summed up the total?  Any flaws in this approach in evaluating players?

    I used the relevant categories of my league (R,H,HR,RBI,SB,BB,AVG) and added a 1.1 scalar to runs and 1.4 scalar to home runs. 
    The resulting top 25 from the sum of those 7 categories:
    1 Albert Pujols
    2 Miguel Cabrera
    3 Adrian Gonzalez
    4 Hanley Ramirez
    5 Mike Stanton
    6 Prince Fielder
    7 Adam Dunn
    8 Ryan Braun
    9 Ryan Howard
    10 David Wright
    11 Matt Holliday
    12 Joey Votto
    13 Nelson Cruz
    14 Mark Teixeira
    15 Matt Kemp
    16 Mark Reynolds
    17 Dan Uggla
    18 Chris Carter
    19 Jayson Werth
    20 Carlos Gonzalez
    21 Evan Longoria
    22 Pablo Sandoval
    23 Juan Francisco
    24 Andrew McCutchen
    25 Shin-Soo Choo

  22. Jeffrey Gross said...

    More details on THT Forecasts. QS is not a stat, apparently. These are the stats you can customize for your league

    Batting Stat Categories
    AVG  
    R  
    RBI  
    HR  
    SB  
    2B  
    3B  
    BB  
    K  
    OBP  
    SLG  
    OPS  
    TB  
    SB-CS  
    TB+BB  
    RunsP  
    2B+3B  

    Pitching Stat Categories
    W  
    S  
    ERA  
    WHIP  
    K  
    K/9  
    BB/9  
    HR/9  
    IP  
    TB  
    K/BB  
    NetWins

  23. Jeffrey Gross said...

    (quality starts is an incredibly hard thing to predict. a 4.5 ERA pitcher could have 30 QS and a 3.5 pitcher could have half that)

  24. ChrisS said...

    The method I mentioned compares the projections of one player with the maximum projection for all categories.  So for example, the greatest projected figure for the category home runs is 45 (Mike Stanton).  So everyone’s projected home run total is divided by 45.  I did the same for the remaining 6 categories though I added a 1.4 multiple to HRs and 1.1 for Runs to give each of them a greater weighting. Finally, I summed up the calculated values to give each player a total “score.”

  25. Andrew said...

    Just noticed this tweet, Jeffrey:

    “Keep also in mind, a single home run is about 6-7% more valuable in fantasy than a single stolen base.” (Feb 12)

    In a word, no. Last year there were roughly 1.5 times as many HR as SB. Make no mistake:  SB is significantly more valuable than a HR.

  26. Dub Boys said...

    @ Andrew,
    I have to disagree that a SB is more valuable than a HR in fantasy. A HR contributes to 4 of the 5 categories for every one hit whereas a SB leads to a greater probability of a run scored (after the obvious benefit to a team for actually getting on base). The elite SB guys almost never are heavy contributors beyond the two categories that directly correlate to a SB. Usually 20 homer and 20 SB guys are available on the wire, but for my money give me the homer guy every time. I would rather build up my lead in OPS and the counting stats in my league and hop on the nyjer morgan bandwagon in August than have Michael Bourn and his negative value in 3 of 5 categories dragging me downt he entire year. We looked at it last year and Bourn cost the manager in my league last year 5-6 points simply by being in the lineup every day. Just because there might be a 3:1 ratio of HR’s to SB’s does not make SB’s more valuable at all…

  27. Ken said...

    you seem to hand waive away the positional adjustments, or at least don’t speak to how important they are.

    if you are going to bother to create a system like this, then you should create Z scores by position. otherwise it is of limited utility.

    we both know that Mauer at #24 is too low. but should be be #15? 10? 5?

  28. Jeffrey Gross said...

    Ken,

    I agree to some extent, but this is all you need to create a list for actual total value. From here, you can just index your top 15 catchers (though I doubt 15 catchers make the cut…) and see how much value they add in comparison to the other positions. For example, you see that getting Joe Mauer in the second round is actually a great value, rather than just a “positional reach” akin to taking Jeter in round 4 or 5

  29. Jeffrey Gross said...

    Travis,

    There are some adjustments that need to be made, but mostly those come in the positional department. That adds the value sufficient to lower dollar values. I’m not sure where you got $10/win. I got $6.23 (which is still extraordinarily high)

  30. Travis said...

    I created some position-adjusted values but I saved the raw z-scores to make a raw $ value as well. The thing is that there are so few raw z-score units. I used a scalar of .77 for xH,(arbitrary, but I wanted to take into account that AVG is much less predictable, especially in a H2H league. Also I could not find any data on the year-to-year correlation between MARCEL projections and results), .9 for R, and .95 for R. My total # of z-scores was only about 12 instead of 300+. So the $ multiplier would have been over 10, and the $ values look pretty inaccurate. So I guess I’ve given up on the raw $ values, but it would be useful in a draft.

  31. Jeffrey Gross said...

    The top 20 fantasy pitchers per EYES:
    Lincecum, Tim
    Hernandez, Felix
    Halladay, Roy
    Wainwright, Adam
    Haren, Dan
    Lee, Cliff
    Lewis, Colby
    Sabathia, CC
    Jimenez, Ubaldo
    Hanson, Tommy
    Kershaw, Clayton
    Verlander, Justin
    Carpenter, Chris
    Latos, Mat
    Greinke, Zack
    Oswalt, Roy
    Johnson, Josh
    Lester, Jon
    Weaver, Jered
    Scherzer, Max

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>

Current day month ye@r *