In fantasy baseball, plate appearances and walks aren’t counted as primary statistics.  The importance of a players batting average has everything to do with his at-bats.  After that, we’re dealing with some speedsters that deal with hammy and quad injuries every second week.

At shortstop, more than any other position, there’s uncertainty around a lot of players about their health or role on the club.

Based on stats alone, we’re dealing with 12 mandatory shortstops as well as Aybar slipping into a UTIL position on someone’s squad.  With that in mind, here’s the distribution charts:

As you can see with the histogram, there’s some depth at the back end of this. The 13 players we use don’t begin to enter the back-end of the normal distribution curve.

Ryan Theriot, J.J. Hardy, Miguel Tejada Jhonny Peralta, Marco Scutaro and Evereth Cabrera are projected to finish within about 10-15pts away from Aybar.

Quite simply, instead of wasting a draft-pick on the lower tier of shortstops, you might as well wait until the later rounds and grab someone that’s just on the cusp.  Maximizing value per pick is important and unless you’re in love with a name — I’d say get your SS early or get him late.

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and here are the Z-Scores, comparing them ONLY to their peers at second base:

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The Differences:

Based on ESPN’s predictions, Jimmy Rollins should be a lot higher.  I’m not sure that I agree with this, but the man contributes across the board.  Rollins, Jeter and Tulo are essentially the same person and unless you’ve got a gut feeling I’d be grabbing the cheapest of the bunch.

Elvis Andrus shouldn’t be that high based on his numbers alone.  ESPN went out on a bit of a limb predicting a return to form for Stephen Drew, but if you’re going to do that than put him where he belongs, ahead of Andrus.

Other than that, they do a fairly solid job putting their rankings together.  There’s a tonne of mediocrity after the first few tiers and there should be some terrific value if you need a shortstop late.

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Utley will be 31 years old for the entire 2010 season, but the steroid-era has fooled us into believing that age doesn’t matter until you’re at least in your mid-30s.

You’ll notice in the BOXPLOT to the left, a good chunk of players are bunched above the median which means pretty much after Utley and Kinsler, there’s a load of talented guys that’ll produce around the same level.

With the larger spread in the 2nd-quartile, we’re dealing with a rather large drop-off as you get to the Cabreras, Polancos and Stewarts of the world.

Tiering this up with a histogram is pretty simple. As you can see, the histogram once again shows us that it’s Utley, then Kinsler, and then the rest.  There are seven projected players between 260 and 300, so you should be able to snag a decent second basemen even if you miss out on the top-tier.

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There’s the averages for the second-basemen and here are Z-Scores:

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The Differences:

Robbie Cano and Brandon Phillips are essentially switched around in this model.  Once again, I’m completely puzzled how a .320 projected average tops a .275 average with 21 extra stolen bases.  The runs, home runs and rbi are almost identical yet Brandon Phillips ranks three spots behind Cano.

If you draft Cano as a MI, you’ve really got to stretch to find stolen bases elsewhere.  Looking at the top few tiers of second basemen, you’re almost guaranteed 20SB.

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The only problems you’ll run into with first basemen is getting enough of them before they start falling off the board into UTIL spots across your league.

The Box-Plot to the left contains a total of 27 first basemen, all of whom exceed the minimum required for a beneficial UTIL-spot.

You’re almost guaranteed to get 30HR out of your first basemen and should be planning a contingency plan if you’re drafting a light-hitting first-basemen like James Loney.

As ESPN refuses to allow us access to their H/AB and rather just throws us a bone with AVG, we have to use CHONE’s AB guestimates.

Presented below are the standardized total points (Tts) and as you can see, the difference between Pujols’ and Howard’s batting average is large, but almost eliminated by Howard’s impressive HR and RBI totals.

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As you can see in the Histogram to the right, there’s one or two players in each tier after 300pts.  At 300pts, it becomes a complete and total crap-shoot.  If you care to compete, you absolutely need to get a first baseman no later than this tier.

Kevin Youkilis, Justin Morneau, Joey Votto, Derrek Lee, Billy Butler, Kendry Morales, Carlos Pena and Adam Dunn are all very capable starters at first base.

The Differences:

In the ESPN Rankings, Ryan Howard is the fifth overall first basemen despite crazy projections.  It’s become fairly clear that ESPN over-values batting average and what a .270 Batting Average means in the overall scheme of things.

Assuming we have 9 positions with 600 AB each, the difference between Howard’s .270 average and Mark Teixeira’s .300 Average is about 20 Hits over the course of 600 AB.  With Ryan Howard as your first baseman on a team that hits .300 — You end up with a .2965 or almost a .297 Average.  With Teixiera on a .300 Hitting team, you end up with .300 obviously.  Whether or not you think 3pts in BA is equal to 9HR and 20 RBI is up to you…

Pablo Sandoval also takes a huge hit for the opposite reason.  In order to take advantage of Sandoval’s .323 Projected Average, you’ll have to live with the San Francisco Giants inept offense.  Sandoval’s ability to play third definitely helps his overall value, but in the first base rankings, he’s just too high.  I find it hard to believe that he hit line-drives less than 20% of the time, considering that he wasn’t smash-killing homers.

Teams made it abundantly clear that they have absolutely no problem pitching Sandoval outside the zone by throwing 60 percent of pitches off the plate.  Teams will continue to pitch Sandoval way out there until Sandoval actually stops swinging at ‘em:  Sandoval’s 41.5% swing rate on pitches outside of the zone is 16% higher than average and trails only Bengie Molina in the overall standings.   It’ll come down to whether or not you think Sandoval has the type of plate coverage to back it up.  I’m not sure if I throw Sandoval into the Guerrero/Suzuki realm of plate coverage at this point…

He did show the ability to take a walk last year, which is a good sign when you’re 2008 BB% was not even 3%.  In a nutshell, Sandoval absolutely kills the fastball-changeup combination.  Last year, he improved dramatically against the old uncle charlie, but that was more than likely statistical error in 2008– the kid can hit, but just how far he’ll chase pitches off the plate remains to be seen.  At this point, there’s just no reason to throw him anything even remotely close to the strike-zone.

Here are the averages and standard deviations from Pujols to Helton:

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With the Z-Scores, or standard deviations away from the mean, for just first basemen.   This is a great reference point for how much a first basemen will hurt vs. help you in any given category –  We’re assuming everyone get’s the same number of AB in this chart, which is different from the overall rankings which uses projections for AB.

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I ended up using the ESPN rankings along with the AB that CHONE projects to come up with a value for each batting average category.  Ie.  ESPN-HITS = CHONE-AB x ESPN-Batting-Average.  Same thing went for ERA and WHIP on the pitcher’s side of things.

Each of the 12 teams had a catcher, first basemen, second basemen, shortstop, third basemen, three outfielders, a UTIL spot, and a bench spot on offense.  For the UTIL and BENCH spots, the *Best* players were used.  On the pitching side of things, the roster was 2xSP, 2xRP and 3xP plus 3 more bench spots.  It worked out like 7 SP and 3 RP.  I think this is a fair guestimation of the average roster in an active league.

Player Scracity Box Plot Graph

If you don’t know how to read a boxplot, let me take it from the top.  Each position has it’s own graph, as you can see at the bottom.  In the center, there’s the reference plot containing all of the potential players used in our fantasy universe.

The boxes in the center of the graphs are split up into 3 distinct sections.  The line in the middle represents the average or mean.  Of all the usable first basemen and this includes an extra 15 players that would be optimally used in a UTIL or BENCH role, the average score you’ll get is 281 Points.

Both above and below the median are two boxes of various sizes.  They represent 25% of the population above and below the median, respectively.  So the entire box combined, obviously represents 50% of the usable first base population.

The lines ranging up from the boxes have markers at the top and bottom.  These markers represent the most and fewest points a position will give you (according to espn.)  In certain graphs, there are circles above the graphs that represent values that are outliers.  In terms of fantasy, we love outliers because they don’t fit with the normal distribution because they’re that damn good.

Ryan Braun is that much better than the outfielders and Tim Lincecum is that much better than the SP.  Albert Pujols, well, he’s a God.

About 25 percent of the population fall between the top of the box and the market at the top (and the same for the bottom.)

Hopefully this helps you visualize player distribution.

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Now, we’ll put it together.

SB = 1.75
HR = 2.23
RBI = 0.75
R = 0.84
AB = -0.16
H = 0.962

W = 5.21
K = 0.43
SV = 2.23
IP = 1.85
BB or H = -0.5
ER = -1.5

Which presents us with these statistics for 2009 (100 pitchers / 100 hitters):

Looks okay from here, but would you really want Josh Johnson over Joe Mauer?  Of course not, unless your league doesn’t have any position requirements.

That’s what we’ll get into next.

I currently have three sets of projections to work with:  ESPN, CHONE, and MARCEL.  Each of these has it’s strengths and weaknesses.  For the most part, ESPN gets on the hype-train while CHONE and MARCEL heavily favour previous performance.   If you mix ‘em all up, you end up with a nice balance of hype and history.

There’s one glaring problem, though (other than the fact that ESPN doesn’t chart H/AB, but rather just AVG).  It’s impossible to predict the worth of any given player without knowing exactly how many teams are playing in your fantasy league and the roster and line-up requirements for those teams.

Unless there’s a huge drop-off, the difference between 10 and 12 team leagues isn’t going to be huge.  However, the difference between 10 and 16 team leagues makes the majority of the adjusted statistics worthless.  Secondly, if you’re in a 10 team league that requires 2 of each position players with 6 OF; this’ll be of very little use, as well.

So here’s how it’s going to go down from this point forward:

1.  Make Our Universe ( C, 1B, 2B, 3B, SS, OF x 3, UTIL, SP x 2, RP x 2, P x 3) with 12 different teams.

2.  Calculate the means, standard deviations, and distribution for players in OUR universe.  Currently fangraphs and various other sites offer plenty of information regarding replacement level players.  That’s all well and dandy, but those calculations don’t account for our position limitations.

3.  Calculate a modifier for each position, relative to the overall points system.

4.  Put everything in simple, human readable, graphs so that our retarded half-cousins can even understand it.

Our Problems, ahead of time:

There’s going to be absolutely no accounting for the fact that people do weird shit.  Maybe someone only drafts 2 SP and 10 RP — That’s something we can’t account for.  Also, while it makes sense to have your UTIL and BENCH spots filled with the highest output players; even though one-category players sit on benches.  I can’t account for someone stashing Rajai Davis or Nyjer Morgan in their UTIL or BENCH spots because their team lacks speed across the board.

In all cases, we’re going to assume optimal strategy for your standard Yahoo League.  The Bench will contain almost all starting pitchers except for maybe a handful of hitters.

…and that’s that, for now.

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A Quick Note: these rankings do not take into account scarcity, which we’ll get into when I rank the players.  Simply applying the formula by itself to rank players will give you the best players, but you’ll end up with 10 SP and no RP.

Wins:

This is probably the easiest to predict because it remains pretty static from year to year.

Weighted Average: 19.2 Wins

TOTAL POINTS FOR ONE WIN:  5.21

Strike-Outs:

Weighted Average: 233

TOTAL POINTS FOR ONE STRIKE-OUT:  0.43

Saves:

Weighted Average: 44.2

Small Adjustment for K-Rod’s ridiculous 2008 season.

TOTAL POINTS FOR ONE SV:  2.23

IP:

This is where we end up with a bit of a head-ache, at least in terms of explaining it.  IP is used in both WHIP and ERA, and will be split up like this, although it doesn’t really matter.

One Inning Pitched when calculating WHIP = 0.95

One Inning Pitched when calculating ERA = 0.90

One Inning Pitched is worth 1.85

Walks and Hits:

Now we have to start penalizing the things we don’t like.  Walks and Hits are equal when it comes to WHIP in Roto & H2H; so they’re equal here too.

One Walk or Hit is Worth: NEGATIVE 0.5

Earned Runs:

Earned runs follow the same pattern as WHIP and show just how dominant Zach Greinke’s was in the 2009 season.  Not only did he have a 2.16 ERA, but he did it over 230 IP.  It’s almost exactly on par with Roger Clemens’ 1.87 ERA over 211 IP in 2005.

One Earned Run  is Worth: NEGATIVE 1.5

2005 Perfect Pitcher Results

2006 Perfect Pitcher Results

2007 Perfect Pitcher Results

2008 Perfect Pitcher Results

2009 Perfect Pitcher Results

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Pitching System Available Here

Personally, I love playing fantasy baseball with a point system. It allows you to tailor your fantasy game to just about any taste. Head-to-Head and Rotisserie leagues bind you to a static value on any category that’s added, which isn’t optimal in most cases. If you want to place a negative value on a strike-out, but don’t want to make it equal to a category like HRs or SB, you’re pretty stuck — Unless of course, you embrace the point system!

Points system’s allow me to rank and compare players quite a bit easier as well. So, before I start spitting out rankings, I’ll share with you the method behind the madness. We’ll start with the hitters and get to the pitchers tomorrow or the next day.

The first problem with a points system is variance in any given year. Jacoby Ellsbury had 70 SB in 2009, but Jose Reyes had 78 SB two years earlier. Albert Pujols ended up hitting 47 HR last year, but Ryan Howard bested that mark by 11 in 2006. So how do we deal with this?

First, we have to admit that there’s no way to perfectly assign each category a static 100% value in a points system. This isn’t something we want to do anyways because very rarely are the accomplishments equal. If someone leads the league and sets records with 90 SB that should be worth more than someone that leads the league with a paltry 48 HR.

Using the Top-10 and Top-5 League Leaders (final quartile of usable fantasy players) from the previous five years, we’ll create an average for each year, with which we’ll create a weighted average. We’ll give the greatest importance to the most recent sets of data and descend from there. We’ll then normalize that to a hundred points for ease of use(feel free to use base ten multipliers if you don’t like decimal points).

Then we pray. We pray hard. We pray that the Maximum value attained over the previous five years doesn’t fall outside 30% of our hundred points. Thankfully, as much as stolen bases, homeruns and batting average varies — we’re good to go.

Stolen Bases:

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Weighted Average: 55.6 SB

Small 2007 Jose Reyes Adjustment.

TOTAL POINTS FOR ONE STOLEN BASE:  1.75

Home Runs

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Weighted Average: 44.9 HR

TOTAL POINTS FOR ONE HOME RUN  2.23

Runs Batted In:

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Weighted Average: 134.3

TOTAL POINTS FOR ONE RBI  0.75

Runs Scored:

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Weighted Average: 120.9

TOTAL POINTS FOR ONE RUNS SCORED  0.83

Batting Average

This one’s by far the toughest to get through, but here goes –

An At-Bat is worth NEGATIVE 0.16

A HIT is worth 0.962

or, a hit is worth roughly 6 times as much as an AB.

That’ll give us a Maximum of 120 (Ichiro in 2007) and a minimum of 100 (Dustin Pedroia in 2008).

For Data From The Previous 4 Years

2009 Data Using Point System

2008 Data Using Point System

2007 Data Using Point System

2006 Data Using Point System

2005 Data Using Point System

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First, we’ll have to suspend disbelief, unless you’re a Twins fan, I suppose. Joe Mauer probably isn’t going to duplicate his .365 Batting Average in 2010, but for the purpose of this example; We’ll just assume he does.

So Let’s take these Five BA Leaders and take a deeper look inside their numbers:

All great hitters and if you’re drafting any of ‘em, you’re going to get a nice little bump in the Batting Average category.  However, just how valuable is Joe Mauer’s .365 compared to Ichiro’s .352 or Jeter’s .344?

Well, the easiest way for me to quantify their value is to just add ‘em to everyone’s favourite three-outcome player: Adam Dunn.  In 2009, Dunn had 146 Hits in 546 AB — good for a .267 Batting Average.  Ideally, we’d compare them to the replacement level BA player on your roster, but using Dunn provides a tangible example.

You’ll notice the batting average decrease is disproportional, as expected.   Suffices to say, hitting .365 over 523 ABs is an astounding achievement but it looks a lot better than it actually contributes to the BA category. Ichiro’s extra 100 at-bats almost completely negates Joe Mauer’s original 13pt advantage.

The easiest way to view batting average without putting too much thought into it is to subtract a single point for every 25 (or so) ABs below 600 and to add a point for every 25 or so above that number.  This is just a rough work-around and the effect will decrease as you get closer to your team’s average BA.

Here are some of the players that are overvalued based on limited ABs:

1. Chris Coghlan, .321 AVG  — 504 ABs
2. Jason Bartlett, .320 AVG — 500 AB
3. Erick Aybar, .312 AVG — 504 AB
4. Magglio Ordonez, .310 AVG — 465 AB

…and the undervalued players:

1. Ichiro Suzuki, .352 — 649 AB
2. Robbie Cano, .320 — 637 AB
3. Braun, Jeter, and Miguel Cabrera as well.

Obviously, this doesn’t take into account WHY a player missed his AB, just that he missed them.  In some cases, players miss AB due to their position in the line-up or even the line-up itself.  A catcher will generally take more off-days than a fielder and a lead-off hitter will get more ABs than someone in the 9-hole.  In other cases, players just refuse to take a walk.  These variables stay pretty constant from year to year, though.  Ichiro’s Batting Average is consistently over a larger number of ABs because of the previous reasons.  If a player posted a solid batting average in 2009, but missed the 600 AB mark due to injury; it’s best to just look at his historical ABs, assuming that he hasn’t changed teams.

Your best bet is to focus on good hitters that bat in the top-3 of a good offensive line-up while converting his plate appearances into hits rather than walks.

Also keep in mind that the reverse is true:  Jimmy Rollins’ .250 Batting Average over 672 at-bats hurts a lot more than Russell Martin’s .250 Batting Average over 505 at-bats.

The worst case scenario is to both overvalue Joe Mauer’s .365 batting average while undervaluing the impact of Jimmy Rollins’ .250 Batting Average.

Say you drafted Joe Mauer’s .365 BA and Jimmy Rollins’ .250 Batting Average.  At first glance (a straight average) would give you a .308 Batting Average which makes for a solid draft-strategy.  However, if you factor in the number ABs the averages were achieved over, you’ll end up with a .300 BA — Not nearly as impressive.

A Caveat:

It’s almost impossible to predict a players Batting Average within 10 points, so if you’re feeling a certain player for a break-out year — Go ahead and take ‘em.

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Unfortunately, this career path ends with everyone ignoring me because the game’s gone-done changed. You used to be able to throw together some sleeper candidates based solely on a gut feeling, but now the public wants statistical proof. Everything is expected to be justified by BABIP’s and LD rates, even if they’re not pertinent.

So, with my fantasy baseball rankings just about complete, I’ve decided to welcome myself back in style.

Just like Mase…

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Buy or Sell: Probably Sitting on It

Projected Value: Medium-

As someone who attends most of the Blue Jays’ games, I didn’t mind this selection.  All things considered, this was probably a great pick for the Blue Jays.

Jenkins appears to have put it together through dedication and a bulldog mentality.  Jenkins is built like a horse, and was one of the better college pitchers entering the draft.

Jenkins isn’t a front-line starter, but he’s built to eat up innings with the best of ‘em.  Even if Jenkins performs to his ceiling — an innings eater that can dance around the corners with solid sinkin’ heat and throw a potentially plus slider — his value in Toronto isn’t all that great.

The Jays have had solid luck with their pitchers lately, but injuries have been a problem.  If Jenkins picks up the cutter, as just about every Jays’ pitcher has, his ceiling could get a nice little bump.

I don’t think you can get fair market value for this card if you’re selling it out of the gate.  I also won’t be rushing out to buy a tonne of low-numbered refractors.

Think about it, how well will a solid number 2 in Toronto appreciate anyways?

Jenkins is a potential trade-chip though as he advanced through the Minors.  Players with Jenkins build and stuff often find themselves as a very useful commodity.  If anyone in the Jays farm system would benefit from a sit down with Roy Halladay before he’s shipped off, it’s Jenkins.

Obviously, if Jenkins autographs dip to amongst the lowest in the class, I’ll be grabbing more than a couple.  He’s still a first rounder so I don’t exactly expect that to happen for at least a  month or two after the release date.

The Jays farm system isn’t pretty, so you may be able to make a quick buck when Jenkins makes the list.

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