The Hardball Times -- Jesse Sakstrup http://www.hardballtimes.com/main Baseball. Insight. Daily. en studes@hardballtimes.com Copyright 2013 2013-05-21T08:09:15+00:00 Early 2013 fantasy baseball top 300 http://www.hardballtimes.com/main/fantasy/article/early-2013-fantasy-baseball-top-300/ http://www.hardballtimes.com/main/fantasy/article/early-2013-fantasy-baseball-top-300/#When:09:02:15

Ranking Methodology


I have created my own regression-based projections for well over 300 players, looking at a profusion of peripheral data to arrive at projections that I feel are reasonable, break-even points for each player. Back in August, I outlined my personal method for valuing players, called 'roto points above average' (rPAA), a method that is both quantitative and objective (outlined here and here). I use the same methodology in these rankings, with the sole exception that I am using my own projections instead of projections generated by ZiPS, which were used in the August rankings.

Once all players have been projected, positional adjustments are then made to account for dearth and abundance. Once this is completed, the values are set and a rough outline is created. My rankings do not reflect the precise ordering of players by projected roto point total, as my own subjectivity does move players up or down when the difference between them is merely fractions of a point.

Understanding my personal biases


I tended to rank catchers, starting pitchers and relief pitchers slightly lower than where their projected roto point total suggested they should go. This is because these positions usually have less certainty with regards to health, and because the surface numbers of pitchers (ERA, WHIP, wins and saves) are more vulnerable to wild fluctuations due to random variation or team context rather than skill elevation or degradation. And with closers, roles are in constant flux, making values extremely mercurial, rendering closers more replaceable with shrewd waiver-wire manipulation.

Remember that while these rankings are reasoned and were contrived deliberately, I cannot claim to have dissected every player profile with a surgeon's precision. In the coming months, I will delve deeper into plate discipline statistics, pitch-f/x data, and more individualized qualitative analysis. And, as always, roster moves, playing time expectations, closer roles, and injury status will become more salient in the coming months, inevitably affecting subsequent rankings.

I will update this list periodically throughout the offseason, but for now this is what my initial top 300 for 2013 looks like. Comments, critique, suggestions and questions are welcome.




Num
Player Name
Team
Position




1
Miguel Cabrera
Det
3B




2
Ryan Braun
Mil
OF




3
Mike Trout
LAA
OF




4
Matt Kemp
LAD
OF




5
Robinson Cano
NYY
2B




6
Joey Votto
Cin
1B




7
Albert Pujols
LAA
1B




8
Carlos Gonzalez
Col
OF




9
Andrew McCutchen
Pit
OF




10
Justin Verlander
Det
SP




11
Jose Bautista
Tor
OF




12
Prince Fielder
Det
1B




13
Josh Hamilton
Tex
OF




14
Adrian Beltre
Tex
3B




15
Giancarlo Stanton
Mia
OF




16
Clayton Kershaw
LAD
SP




17
Evan Longoria
TB
3B




18
Hanley Ramirez
LAD
SS/3B




19
Ian Kinsler
Tex
2B




20
Stephen Strasburg
Was
SP




21
Buster Posey
SF
C/1B




22
David Wright
NYM
3B




23
Jose Reyes
Mia
SS




24
Dustin Pedroia
Bos
2B




25
Troy Tulowitzki
Col
SS




26
Jason Heyward
Atl
OF




27
Justin Upton
Ari
OF




28
Starlin Castro
ChC
SS




29
David Price
TB
SP




30
Matt Cain
SF
SP




31
Adrian Gonzalez
LAD
1B




32
Felix Hernandez
Sea
SP




33
Edwin Encarnacion
Tor
1B




34
Matt Holliday
StL
OF




35
Curtis Granderson
NYY
OF




36
Adam Jones
Bal
OF




37
Bryce Harper
Was
OF




38
Cliff Lee
Phi
SP




39
Brandon Phillips
Cin
2B




40
Cole Hamels
Phi
SP




41
Jered Weaver
LAA
SP




42
Joe Mauer
Min
C/1B




43
Jacoby Ellsbury
Bos
OF




44
Jay Bruce
Cin
OF




45
B.J. Upton
TB
OF




46
Billy Butler
KC
1B




47
CC Sabathia
NYY
SP




48
Ben Zobrist
TB
2B/SS/OF




49
Alex Rios
CWS
OF




50
R.A. Dickey
NYM
SP




51
Aramis Ramirez
Mil
3B




52
Mark Teixeira
NYY
1B




53
Roy Halladay
Phi
SP




54
Aaron Hill
Ari
2B




55
Jason Kipnis
Cle
2B




56
Ryan Zimmerman
Was
3B




57
Craig Kimbrel
Atl
RP




58
Corey Hart
Mil
1B/OF




59
Shin-Soo Choo
Cle
OF




60
Ian Desmond
Was
SS




61
Brett Lawrie
Tor
3B




62
Yoenis Cespedes
Oak
OF




63
Jimmy Rollins
Phi
SS




64
Zack Greinke
LAA
SP




65
Chase Headley
SD
3B




66
Madison Bumgarner
SF
SP




67
Pablo Sandoval
SF
3B




68
James Shields
TB
SP




69
David Ortiz
Bos
DH




70
Chris Sale
CWS
SP




71
Paul Konerko
CWS
1B




72
Aroldis Chapman
Cin
RP




73
Michael Bourn
Atl
OF




74
Gio Gonzalez
Was
SP




75
Allen Craig
StL
1B/OF




76
Adam Wainwright
StL
SP




77
Melky Cabrera
SF
OF




78
Yadier Molina
StL
C




79
Jose Altuve
Hou
2B




80
Hunter Pence
SF
OF




81
Yu Darvish
Tex
SP




82
Carlos Santana
Cle
C/1B




83
Shane Victorino
LAD
OF




84
Alex Gordon
KC
OF




85
Kris Medlen
Atl
SP




86
Derek Jeter
NYY
SS




87
Michael Morse
Was
OF




88
Mat Latos
Cin
SP




89
Carlos Beltran
StL
OF




90
Asdrubal Cabrera
Cle
SS




91
Paul Goldschmidt
Ari
1B




92
Elvis Andrus
Tex
SS




93
Max Scherzer
Det
SP




94
Jonathan Papelbon
Phi
RP




95
Kenley Jansen
LAD
RP




96
Matt Wieters
Bal
C




97
Desmond Jennings
TB
OF




98
Freddie Freeman
Atl
1B




99
Yovani Gallardo
Mil
SP




100
Eric Hosmer
KC
1B




101
Dan Haren
LAA
SP




102
Wilin Rosario
Col
C/1B




103
Johnny Cueto
Cin
SP




104
Neil Walker
Pit
2B




105
Jason Motte
StL
RP




106
Mark Trumbo
LAA
1B/OF




107
Carl Crawford
Bos
OF




108
Jordan Zimmermann
Was
SP




109
Nelson Cruz
Tex
OF




110
Mike Napoli
Tex
C/1B




111
Ian Kennedy
Ari
SP




112
Josh Willingham
Min
OF




113
Ernesto Frieri
LAA
RP




114
Matt Garza
ChC
SP




115
Austin Jackson
Det
OF




116
Tim Lincecum
SF
SP




117
Matt Moore
TB
SP




118
Salvador Perez
KC
C




119
Sergio Romo
SF
RP




120
Dan Uggla
Atl
2B




121
Victor Martinez
Det
C




122
Alexei Ramirez
CWS
SS




123
Andre Ethier
LAD
OF




124
Hiroki Kuroda
NYY
SP




125
Ichiro Suzuki
NYY
OF




126
Joe Nathan
Tex
RP




127
Doug Fister
Det
SP




128
Ike Davis
NYM
1B




129
Glen Perkins
Min
RP




130
Miguel Montero
Ari
C




131
Jake Peavy
CWS
SP




132
Martin Prado
Atl
3B/OF




133
Jayson Werth
Was
OF




134
Tom Wilhelmsen
Sea
RP




135
Lance Lynn
StL
SP




136
Alejandro De Aza
CWS
OF




137
Josh Johnson
Mia
SP




138
Matt Harvey
NYM
SP




139
Adam LaRoche
Was
1B




140
Marco Estrada
Mil
SP




141
Brett Anderson
Oak
SP




142
Fernando Rodney
TB
RP




143
Alcides Escobar
KC
SS




144
Brian McCann
Atl
C




145
Chase Utley
Phi
2B




146
Rickie Weeks
Mil
2B




147
Carlos Gomez
Mil
OF




148
Nick Swisher
NYY
OF




149
Josh Rutledge
Col
SS




150
Jon Lester
Bos
SP




151
Will Middlebrooks
Bos
3B




152
Tommy Hanson
Atl
SP




153
Drew Storen
Was
RP




154
Anthony Rizzo
ChC
1B




155
Greg Holland
KC
RP




156
Tim Hudson
Atl
SP




157
Jeff Samardzija
ChC
SP




158
Ryan Howard
Phi
1B




159
Shaun Marcum
Mil
SP




160
C.J. Wilson
LAA
SP




161
Coco Crisp
Oak
OF




162
Adam Dunn
CWS
1B




163
Erick Aybar
LAA
SS




164
Grant Balfour
Oak
RP




165
J.J. Putz
Ari
RP




166
Angel Pagan
SF
OF




167
Michael Cuddyer
Col
1B/OF




168
A.J. Burnett
Pit
SP




169
David Freese
StL
3B




170
Danny Espinosa
Was
2B/SS




171
Kyle Lohse
StL
SP




172
Nick Markakis
Bal
OF




173
Wade Miley
Ari
SP




174
Alex Rodriguez
NYY
3B




175
Ben Revere
Min
OF




176
Jon Niese
NYM
SP




177
Jaime Garcia
StL
SP




178
Chris Davis
Bal
1B/OF




179
Ryan Dempster
Tex
SP




180
Rafael Betancourt
Col
RP




181
Edwin Jackson
Was
SP




182
Mike Moustakas
KC
3B




183
Joel Hanrahan
Pit
RP




184
Manny Machado
Bal
3B




185
Josh Beckett
LAD
SP




186
Howie Kendrick
LAA
2B




187
Jarrod Parker
Oak
SP




188
Alex Cobb
TB
SP




189
Matt Harrison
Tex
SP




190
Carlos Ruiz
Phi
C




191
Casey Janssen
Tor
RP




192
Ryan Vogelsong
SF
SP




193
Jason Kubel
Ari
OF




194
Jim Johnson
Bal
RP




195
Josh Reddick
Oak
OF




196
Anibal Sanchez
Det
SP




197
Brett Gardner
NYY
OF




198
Huston Street
SD
RP




199
Addison Reed
CWS
RP




200
J.J. Hardy
Bal
SS




201
Brandon Morrow
Tor
SP




202
Torii Hunter
LAA
OF




203
Chris Capuano
LAD
SP




204
Rafael Soriano
NYY
RP




205
Lorenzo Cain
KC
OF




206
Wilton Lopez
Hou
RP




207
Jason Hammel
Bal
SP




208
Justin Ruggiano
Mia
OF




209
A.J. Griffin
Oak
SP




210
Andrew Bailey
Bos
RP




211
John Axford
Mil
RP




212
Kendrys Morales
LAA
1B




213
Everth Cabrera
SD
SS




214
Ted Lilly
LAD
SP




215
Marco Scutaro
SF
2B/SS




216
Yasmani Grandal
SD
C




217
Chris Perez
Cle
RP




218
Drew Stubbs
Cin
OF




219
Todd Frazier
Cin
1B/3B




220
Alfonso Soriano
ChC
OF




221
Homer Bailey
Cin
SP




222
Brandon Moss
Oak
1B/OF




223
Steve Cishek
Mia
RP




224
Jesus Montero
Sea
C




225
Daniel Murphy
NYM
2B




226
Jeremy Hellickson
TB
SP




227
Carlos Marmol
ChC
RP




228
Norichika Aoki
Mil
OF




229
Jon Jay
StL
OF




230
David Murphy
Tex
OF




231
Justin Morneau
Min
1B




232
Phil Hughes
NYY
SP




233
Dustin Ackley
Sea
2B




234
Dexter Fowler
Col
OF




235
Emilio Bonifacio
Mia
OF




236
Mariano Rivera
NYY
RP




237
Wandy Rodriguez
Pit
SP




238
Trevor Bauer
Ari
SP




239
Jarrod Saltalamacchia
Bos
C




240
Chad Billingsley
LAD
SP




241
Tyler Colvin
Col
1B/OF




242
Pedro Alvarez
Pit
3B




243
Mark Buehrle
Mia
SP




244
Jean Segura
Min
SS




245
Trevor Cahill
Ari
SP




246
Frank Francisco
NYM
RP




247
Drew Smyly
Det
SP




248
Cameron Maybin
SD
OF




249
Kevin Youkilis
CWS
3B




250
Kelly Johnson
Tor
2B




251
Andrew Cashner
SD
SP




252
Shelby Miller
StL
SP




253
Rajai Davis
Tor
OF




254
Hisashi Iwakuma
Sea
SP




255
Chris Young
Oak
OF




256
Colby Rasmus
Tor
OF




257
Mark Reynolds
Bal
1B




258
Zack Cozart
Cin
SS




259
Starling Marte
Pit
OF




260
Logan Morrison
Mia
OF




261
Jonathan Lucroy
Mil
C




262
Tyler Clippard
Was
RP




263
Brandon Belt
SF
1B




264
Michael Fiers
Mil
SP




265
Matt Joyce
TB
OF




266
Joaquin Benoit
Det
RP




267
Dayan Viciedo
CWS
OF




268
Bobby Parnell
NYM
RP




269
Mike Minor
Atl
SP




270
Omar Infante
Det
2B




271
Jose Valverde
Det
RP




272
Tommy Milone
Oak
SP




273
Brian Wilson
SF
RP




274
Garrett Jones
Pit
1B/OF




275
Carlos Quentin
SD
OF




276
Ryan Ludwick
StL
OF




277
Vinnie Pestano
Cle
RP




278
J.P. Arencibia
Tor
C




279
Cody Ross
Bos
OF




280
Jhonny Peralta
Det
SS




281
Kyle Seager
Sea
3B




282
David Robertson
NYY
RP




283
James McDonald
Pit
SP




284
Russell Martin
NYY
C




285
A.J. Pierzynski
CWS
C




286
David Hernandez
Ari
RP




287
Jed Lowrie
Hou
SS




288
Andrelton Simmons
Atl
SS




289
Johan Santana
NYM
SP




290
Wilson Ramos
Was
C




291
Michael Brantley
Cle
OF




292
Junuchi Tazawa
Bos
RP




293
Jason Vargas
Sea
SP




294
Ryan Doumit
Min
C/OF




295
Justin Maxwell
Hou
OF




296
Lucas Harrell
Hou
SP




297
Sean Marshall
Cin
RP




298
Alex Avila
Det
C




299
Brandon McCarthy
Oak
SP




300
Jake McGee
TB
RP


Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-11-05T09:02:15+00:00 Felix Hernandez and arbitrarily historic performances http://www.hardballtimes.com/main/article/felix-hernandez-and-arbitrarily-historic-performances/ http://www.hardballtimes.com/main/article/felix-hernandez-and-arbitrarily-historic-performances/#When:10:25:15 Felix Hernandez was in the eighth inning of a “perfect game.”

I had been unaware that the Mariners’ righty was on the verge of making history. So what did I do immediately upon reading this news? I proceeded to check the box scores of the early games. Hernandez went on to complete his historical feat, and I didn’t watch a single pitch and it didn’t bother me.

As someone who delves into sabermetrics on a regular basis, it seems odd to me that we are still placing the perfect game on such a pedestal without question or further analysis. Granted, a pitcher who has thrown a perfect game has probably pitched well that night, but that doesn’t mean that he didn’t make any mistake pitches or that hitters didn’t get unlucky on balls in play.

Rewinding to Matt Cain’s perfect game earlier this season, Gregor Blanco made an outstanding catch, deep in the right-center gap, to preserve the unblemished outing. But, had Blanco been playing a step closer to the plate or a step closer to the line, or had he taken a slightly less optimal path to the ball, or had Aubrey Huff or Brandon Belt been playing right field that day, the ball would have dropped in for extra bases and Cain’s performance would have faded into history as just another forgotten one-hit, no-walk, 14-strikeout masterpiece.

Cain would have pitched the exact same game, and all of the balls in play would have been identical, but if Blanco didn’t make that catch, Cain’s performance would have been treated differently.

This, to me, is wrong. It is shallow analysis and it is emphasizing the result more heavily than the process. That isn’t how we measure two-week samples, or how we measure expected future value, so why do we measure performance in a given game this way?

The idea that the end outcome of a pitch doesn’t determine the quality of the pitch shouldn’t be an obscure idea to any analytical baseball fan. A pitch right down the middle is usually not ideal, even if the batter swings and misses or pops it up. The same can be said for a pitch down at the ankles that is put into play for a hit. Provided that that particular hitter doesn’t thrive on pitches at his ankles, then it was a good pitch, but a bad outcome.

Bill James’ game scores attempts to measure performance in a somewhat objective way, giving credit to strikeouts and innings pitched, while punishing for hits, earned runs and walks. This is a quick way to approximate the value of a start without actually watching it or looking at the advanced data, but we could probably do a lot better if we wanted to take the time or had access to the proper data.

So, if looking at process is a better way of evaluating a start than looking at results are, then what are the factors that should be considered? Well, the list could be just about as lengthy as you wanted, but we will stop short of forecasting the marginal value of what a player ate for breakfast. This is the list that I came up with:

The uncontrollable environmental factors: This encompasses weather and ballpark. Balls fly further and hitters typically hit better when the weather is warm, so a each out in 80-degree weather is probably more difficult to achieve than an out in 60-degree weather. Wind is also a major factor. If a ball is hit with precisely enough power to send it 400 feet in a context-neutral environment, but wind resistance causes it to travel 375 feet, which results in an out rather than a home run, the pitcher should not be credited for this positive outcome. The same goes for ballparks; how a ballpark affects home runs, hits and pitch movement cannot be ignored either.

Opponent: Who are you playing against? A perfect game at home against the Rockies is probably an easier achievement than a one-hitter with no walks on the road against the Yankees or in Texas. Not only must you factor in opponent, but the venue as well, as some hitters hit better at home than on the road, for whatever reason.

Additionally, handedness and platoon splits must be considered. A left-hander who saws through a lineup stocked full of lefties with a same-handed platoon split isn’t as big an accomplishment as the glowing box score might indicate.

Actual on-field performance: This is what the pitcher actually does. How hard is the pitcher throwing? How well are his pitches located? How well is he mixing his pitches? How much is each pitch moving? Is he locating pitches primarily in batters’ cold zones? Does the pitcher have a consistent release point and a consistent delivery?

This is really all a pitcher can control. Once the ball leaves the pitcher’s hand, the result is out of his control. A perfect swing is going to be able to hit the perfect pitch, because the swing is in response to the pitch. The hitter has a distinct advantage here in the ability to reduce luck or variance. A pitcher must hope his pitch is good enough that the hitter has little chance of hitting it. Pitchers can maximize their chances of a positive outcome with good velocity, movement, location, sequencing, exploitation of hitter weaknesses, and a repeated delivery, but these just reduce the chances of a negative outcome, they can never eliminate them.

Type of contact: While batters can, and often do, make good contact with good pitches, it happens a lot less against quality pitches than it does with poor pitches. Tracking the velocity, angle and direction off of the bat would be a much better quantification of a pitch result than simply applying the hit or out tag to a ball in play.

Why is it that we can see a line drive that gets caught and say that the pitcher got lucky, or we can see a dribbling ground ball stop half way between home plate and third base, allowing the batter to leg out an infield single, and say that the pitcher got unlucky, and yet, at the same time, we (some of us) look at box scores and draw conclusions about the performance of a pitcher?

If all you knew was that Felix Hernandez threw a perfect game, could you make a reasonable estimation about the quality of his performance? You could conclude that he probably pitched well, but to say it was one of the top 23 performances of all time would be overestimating the accuracy of the box score line. This, however, is how history evaluates performance. Hernandez’s perfect game will go down as one of the better perfect games of all time and may be considered a top-10 pitching performance.

Balls not put in play: Does the pitcher have swing and miss stuff? Swinging strikes are a pretty good indicator of how a pitcher’s stuff is working. The more a batter swings and misses, the better the likelihood that the pitcher is throwing difficult pitches to hit. Similarly, the more called strikes a pitcher gets is usually a positive reflection on his pitch location and his pitch sequencing. A pitcher never tries to split the heart of the plate, but if he does and the hitter is frozen, then he probably did a good job of sequencing his pitches and fooling the hitter. This isn’t great execution of a pitch, but it is good execution of being unpredictable.

Strikeouts are the best outcome a pitcher can get, and, while a walk isn’t the worst outcome for a pitcher, throwing the fourth ball outside the strike zone guarantees that a batter reaches base, and that is bad. If the batter puts the ball in play, the expected value of the average outcome is not as bad as a walk, so a walk is the worst result that a pitcher has control over.

Perhaps an algorithm could be created that could encompass all of these data—and anything I am leaving out—into an expected outcome value based on what usually happens when a pitcher does certain things. But for now we are going to have to draw our own arbitrary conclusions about the data we have.

Lets put the Felix Hernandez perfect game under the microscope to the extent we can.

The game-time temperature was 79 degrees and the wind was 4 mph. The temperature certainly wasn’t optimal for pitching, but not terrible, either, and the wind was probably a non-factor. He pitched at home, in Safeco, a park that increases strikeouts and decreases all four types of hits, so this was definitely beneficial.

Taking the field in Hernandez’ opposition were the Tampa Bay Rays. Here is the batting lineup they rolled out and their wRC+ against righties and away from home in 2012.

1. Sam Fuld (93 against righties for career in 367 at bats; and 87 on the road)
2. B.J. Upton (96 and 87)
3. Matt Joyce (154 and 131)
4. Evan Longoria (105 and 94)
5. Ben Zobrist (123 and 140)
6. Carlos Pena (101 and 78)
7. Jose Lobaton (61 for career and 95)
8. Elliot Johnson (96 and 129)
9. Sean Rodriguez (55 and 58)

The starting lineup that Tampa put out there had a collective wRC+ of 98 against righties and a wRC+ of 100 on the road. There was basically no advantage gained by Felix in facing this version of the Rays.

Hernandez’ actual outing was a smooth, 113-pitch performance. He struck out 12 hitters (44.4 percent), he induced 26 swing and misses (23 percent swinging strike rate) and his fastball registered an average of 93.2 on the radar gun. Felix is striking out 24.4 percent of all batters this season, is garnering swinging strikes 10.4 percent of the time and is averaging about one mph less on his heater for the season than he did this outing. On the surface, his stuff appears to have been tremendous on Wednesday.

He mixed his pitches well, too, throwing 28 fastballs, 24 curves, 23 change-ups, 18 sliders, 13 cutters and seven sinkers. Looking at the PITCHf/x data, there may have been a couple of incorrectly classified pitches, but the general idea is the same: Hernandez made himself unpredictable by mixing in a great variety of pitches.

Here is the pitch plot of his pitches from his perfect game.

image

He stayed near the edges pretty well, but there were plenty of pitches in the middle and near the middle that were likely mistakes and he was probably lucky to get away with every single one of them.

Here is the pitch plot of his previous start on Aug, 10, in which he allowed five hits, four earned runs and one walk.

image

His location here isn’t quite as good—there are more pitches in the center area of the plate—but it doesn’t seem like too substantial a difference.

Since we don’t have access to HITf/x data and have no way of determining ball angle off of the bat and velocity off the bat with any exactitude, grading the type of contact that the Rays made on Felix’s pitches will lack precision. We can look to the batted ball classifications—if you trust them—to see that Hernandez gave up eight ground balls, five fly ball and two line drives. Again, velocity off the bat does matter, but most line drives end up as hits, so there is a good chance there was some luck involved in both line drives being caught. Still, eight ground balls and two line drives out of 15 balls in play represents relatively poor contact.

Hernandez’ outing looked perfect in the box score. His outing still looked very good under a more analytical lens. He struck out more than 40 percent of the Rays hitters he faced, he generated loads of swinging strikes, induced what appears to be weakish contact, and stayed around the strike zone all night. The venue was to his benefit, but the opposition was neutral. And the game-time conditions couldn’t have swayed very far in either direction.

If we were able to look at the HITf/x data and at each pitch’s location in relation to each hitter’s hot and cold zones, and if we were able to quantify the value of both, then we could better analyze this, or any start.

Despite the fact that Hernandez was brilliant in his dismantling of the Rays lineup, labeling his outing as perfect would be sensationalizing what was simply an excellent or exceedingly excellent outing.

Was this one of the 23 best single-game pitching performances of all time? Possibly, but we simply do not have the requisite tools to make an objective conclusion about that.

It can be exciting to watch historic performances, but the arbitrary filters for what is and isn’t considered historic are not the best measurements of true performance. They measure the result, but not the process, the pitcher who had the best outcomes, but not the pitcher who pitched the best.

Is a perfect game better than a one-hit, no walk game? Sure, usually, but is the difference between a perfect game and a one-base runner game so many magnitudes larger than the difference between a one-base runner and a two-base runner game that it requires accentuation and cajolery on such a historic level? No, it just isn't.

Unfortunately, until we can quantify the expected value of every individual action on a baseball field, there will be no sterling measurement of single-game performances.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-17T10:25:15+00:00 August starting pitcher rankings http://www.hardballtimes.com/main/fantasy/article/august-starting-pitcher-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-starting-pitcher-rankings/#When:09:21:15 Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s ZiPS' rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers
First Basemen
Second Basemen
Third Base
Shortstop
Outfield{/exp:list_maker}
Quick Note: ZiPS is a projection system that takes past performance into consideration. For many pitchers, the past applies less to the present than it does for hitters, as is the case with pitchers who have experienced significant velocity loss; this is something that pitchers generally don’t recoup. ZiPS may not recognize diminished skill sets, but I will try to cover what I can in the analysis section.



Num
Name
IP
SO
W
SV
ERA
WHIP
rPAA (ROS)
EYES (ROS)
Full Season*




1
Justin Verlander
72
74
6
 
3
1.1
1.93
1.61
13.03




2
Clayton Kershaw
69
72
5
 
2.61
1.09
1.82
1.54
12.66




3
Felix Hernandez
73
70
5
 
2.96
1.15
1.45
1.20
11.49




4
Cole Hamels
67
64
5
 
3.36
1.13
1.11
0.92
10.43




5
Cliff Lee
66
62
4
 
3.14
1.09
1.02
0.87
10.12




6
Jered Weaver
64
57
5
 
3.23
1.13
1.00
0.83
10.06




7
Roy Halladay
60
52
5
 
3
1.12
0.98
0.82
10.02




8
Tim Lincecum*
66
70
5
 
3.27
1.26
0.90
0.75
9.76




9
David Price
67
64
5
 
3.36
1.21
0.88
0.72
9.68




10
Matt Cain
69
59
4
 
3.13
1.13
0.87
0.73
9.65




11
Adam Wainwright
59
54
5
 
3.2
1.2
0.72
0.60
9.18




12
Gio Gonzalez
62
64
5
 
3.34
1.29
0.65
0.53
8.94




13
James Shields
71
63
5
 
3.68
1.23
0.64
0.49
8.92




14
CC Sabathia
69
62
5
 
3.52
1.25
0.64
0.50
8.91




15
Zack Greinke
62
62
4
 
3.34
1.18
0.63
0.55
8.90




16
Yovani Gallardo
65
67
5
 
3.74
1.29
0.52
0.41
8.54




17
Mat Latos
62
60
4
 
3.63
1.21
0.37
0.31
8.07




18
R.A. Dickey*
61
48
4
 
3.25
1.18
0.36
0.30
8.04




19
Madison Bumgarner
65
57
4
 
3.6
1.23
0.27
0.21
7.75




20
Chris Sale
54
55
4
 
3.5
1.26
0.18
0.17
7.45




21
Ian Kennedy
57
49
4
 
3.63
1.19
0.17
0.14
7.43




22
Johnny Cueto
61
46
4
 
3.25
1.25
0.13
0.10
7.30




23
Stephen Strasburg
37
42
3
 
2.68
1.08
0.13
0.20
7.29




24
Josh Johnson
49
45
3
 
2.94
1.16
0.11
0.14
7.23




25
Dan Haren
64
52
4
 
3.94
1.2
0.08
0.04
7.14




26
Tommy Hanson
55
53
4
 
3.76
1.27
0.00
0.01
6.90




27
Jon Lester
61
57
4
 
3.84
1.3
-0.04
-0.04
6.78




28
C.J. Wilson
64
57
4
 
3.66
1.33
-0.04
-0.06
6.76




29
Max Scherzer
59
61
4
 
4.12
1.31
-0.09
-0.08
6.60




30
Chad Billingsley
59
52
4
 
3.66
1.32
-0.11
-0.10
6.54




31
Yu Darvish
62
60
4
 
3.92
1.34
-0.12
-0.11
6.51




32
Matt Garza*
60
56
3
 
3.9
1.27
-0.27
-0.22
6.03




33
Tim Hudson
53
34
4
 
3.57
1.25
-0.29
-0.26
5.97




34
Shaun Marcum
46
41
3
 
3.52
1.2
-0.29
-0.20
5.96




35
Jordan Zimmermann
47
37
3
 
3.45
1.19
-0.33
-0.24
5.83




36
Doug Fister*
55
36
4
 
3.93
1.24
-0.37
-0.33
5.72




37
Jaime Garcia
50
40
3
 
3.42
1.28
-0.44
-0.34
5.49




38
Mark Buehrle
60
34
4
 
3.75
1.28
-0.44
-0.41
5.48




39
Jake Peavy
50
44
3
 
3.96
1.24
-0.47
-0.37
5.40




40
Mike Fiers
33
31
3
 
3.27
1.18
-0.47
-0.32
5.40




41
Trevor Cahill
64
47
4
 
4.08
1.34
-0.49
-0.46
5.32




42
Chris Capuano
51
43
3
 
4.06
1.25
-0.55
-0.44
5.14




43
Ricky Romero*
66
52
4
 
4.23
1.37
-0.56
-0.52
5.12




44
Josh Beckett
49
40
3
 
3.86
1.27
-0.58
-0.47
5.04




45
Vance Worley
54
44
3
 
3.83
1.31
-0.59
-0.48
5.02




46
Hiroki Kuroda
56
42
4
 
4.18
1.34
-0.59
-0.52
5.01




47
Ubaldo Jimenez*
61
54
4
 
4.28
1.41
-0.60
-0.54
4.99




48
Matt Moore
51
52
3
 
3.88
1.39
-0.60
-0.48
4.98




49
Anibal Sanchez
58
49
3
 
4.03
1.33
-0.63
-0.53
4.89




50
Brandon Morrow
48
49
3
 
4.13
1.35
-0.65
-0.51
4.83




51
Wandy Rodriguez
62
46
3
 
3.92
1.34
-0.70
-0.60
4.68




52
Justin Masterson
65
50
4
 
4.15
1.42
-0.70
-0.64
4.68




53
Jeff Samardzija
39
36
3
 
3.69
1.33
-0.70
-0.54
4.66




54
Edwin Jackson
62
49
3
 
4.21
1.34
-0.77
-0.66
4.46




55
Bud Norris
55
53
2
 
4.09
1.33
-0.82
-0.65
4.29




56
Matt Harrison
56
36
4
 
4.18
1.38
-0.82
-0.73
4.28




57
Jeremy Hellickson
52
37
3
 
3.98
1.33
-0.84
-0.70
4.23




58
Wei-Yin Chen
56
40
3
 
4.34
1.29
-0.85
-0.72
4.19




59
Jon Niese
54
47
3
 
4.33
1.37
-0.87
-0.73
4.13




60
Ryan Vogelsong
53
42
3
 
3.91
1.4
-0.87
-0.73
4.13




61
Ivan Nova
62
44
4
 
4.5
1.39
-0.88
-0.79
4.10




62
Gavin Floyd
58
47
3
 
4.5
1.33
-0.88
-0.75
4.09




63
Bartolo Colon
46
31
3
 
4.11
1.28
-0.88
-0.73
4.09




64
A.J. Burnett
57
48
3
 
4.26
1.39
-0.90
-0.76
4.04




65
Randy Wolf
60
40
3
 
4.2
1.33
-0.92
-0.80
3.98




66
Johan Santana
37
31
2
 
3.65
1.24
-0.92
-0.70
3.98




67
Mike Minor
57
52
3
 
4.58
1.39
-0.95
-0.80
3.88




68
Tommy Milone
55
40
3
 
4.42
1.33
-0.97
-0.83
3.80




69
Alex Cobb
39
29
3
 
3.92
1.36
-0.98
-0.79
3.79




70
Homer Bailey
55
42
3
 
4.42
1.35
-0.98
-0.83
3.79




71
Clay Buchholz
43
30
3
 
3.98
1.35
-0.98
-0.80
3.78




72
Francisco Liriano
50
52
3
 
4.5
1.46
-0.98
-0.81
3.77




73
Jair Jurrjens*
42
26
3
 
3.86
1.33
-0.99
-0.81
3.76




74
Joe Blanton
44
35
2
 
4.09
1.25
-0.99
-0.78
3.75




75
Dillon Gee
51
41
3
 
4.41
1.37
-1.00
-0.84
3.71




76
Lance Lynn
47
41
3
 
4.21
1.43
-1.01
-0.83
3.68




77
Kyle Lohse
49
30
3
 
4.22
1.31
-1.02
-0.86
3.65




78
Ricky Nolasco
59
46
3
 
4.58
1.36
-1.03
-0.88
3.63




79
Clayton Richard
51
31
3
 
4.06
1.35
-1.04
-0.88
3.59




80
Bronson Arroyo
58
35
3
 
4.66
1.28
-1.09
-0.95
3.44




81
Paul Maholm
54
37
3
 
4.33
1.37
-1.09
-0.93
3.44




82
Ryan Dempster*
54
44
3
 
4.5
1.41
-1.10
-0.93
3.40




83
Erik Bedard
35
32
2
 
3.86
1.34
-1.11
-0.86
3.37




84
Aaron Harang
48
35
3
 
4.31
1.4
-1.13
-0.94
3.31




85
Jason Hammel
52
40
3
 
4.5
1.4
-1.14
-0.96
3.28




86
Brandon McCarthy
40
27
2
 
4.05
1.25
-1.14
-0.91
3.27




87
Derek Holland
54
45
3
 
4.67
1.41
-1.15
-0.97
3.25




88
Luke Hochevar
55
38
3
 
4.58
1.36
-1.16
-0.99
3.22




89
Jeremy Guthrie
57
31
3
 
4.42
1.32
-1.16
-1.01
3.20




90
Jake Westbrook
49
30
3
 
4.22
1.39
-1.19
-1.01
3.10




91
Phil Hughes
47
38
3
 
4.98
1.36
-1.21
-1.01
3.06




92
Ervin Santana
59
44
3
 
4.73
1.39
-1.21
-1.05
3.03




93
James McDonald
50
45
2
 
4.14
1.44
-1.22
-1.00
3.00




94
Jarrod Parker
44
35
3
 
4.3
1.48
-1.24
-1.02
2.97




95
Rick Porcello
58
33
3
 
4.34
1.38
-1.25
-1.08
2.94




96
Hisashi Iwakuma
38
30
2
 
4.26
1.32
-1.25
-0.99
2.93




97
Josh Tomlin
48
27
3
 
4.69
1.31
-1.25
-1.06
2.92




98
Mike Leake
54
38
2
 
4.5
1.31
-1.26
-1.05
2.88




99
Bruce Chen
50
34
3
 
4.68
1.38
-1.27
-1.08
2.86




100
Joe Saunders
56
34
3
 
4.5
1.39
-1.30
-1.12
2.78

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each player would score above an empty spot in a lineup over a full season.

Pitchers I like less than their ZiPS ranking


Tim Lincecum—What to make of Tim Lincecum? He has undoubtedly been one of the most disappointing fantasy pitchers in all of baseball. His ERA sits at 5.43, his WHIP at 1.49. But, despite his woeful surface numbers, Lincecum has maintained a 9.67 K/9 rate and his FIP, xFIP and SIERA are all sub-4.00. Even his 4.31 walk rate has a silver lining, as his first strike percentage of 58.2 percent is the highest it has been since 2007, which suggests that he should be demonstrating better control.

Lincecum has been bitten by a strand rate that is 10 percent lower than his career average, a BABIP 27 points higher than his career mark, and a four percent spike in his HR/FB rate. ZiPS is looking at Lincecum’s dominant 2008-2011 where he posted an average FIP of 2.82, and his 3.82 FIP this season and is expecting some regression toward’s his personal mean.

DIPS don’t absolve Lincecum of all blame for his results, he has not been sharp with his command, but this type of thing generally doesn’t continue to happen over extended periods of time. We should expect Lincecum to improve, but lets not go overboard and write off the first four months of his season solely as bad luck.

I would certainly not treat Lincecum as a top-10 pitcher going forward, but his underlying numbers make a good case for him as a buy-low.

Matt Garza— Garza was placed on the disabled list on Tuesday with a stress reaction in his right elbow—an elbow-related trip to the disabled list is typically not a good thing. The move was made retroactive to July 28, but that doesn’t mean he is close to returning. There is currentlyno word on whether or not Garza will return in 2012. Garza is the Cubs’ biggest trade chip, so they won’t want to rush him back before they are certain he is healthy. Since there is no timetable, it is almost futile to put a rank on him, just be judicious in your valuation.

Ricky Romero— Unlike Tim Lincecum, Romero’s peripherals don’t exonerate him of any blame. His ERA is 5.47 and his FIP (5.05) and xFIP (4.62) agree that he has been awful. His walk rate has shot up to 4.87 and his F-Strike percentage (53.1 percent) is far below his career average. His strikeout rate is down to 6.39 and his swinging strike rate (7.8 percent) confirms that he is missing less bats. He is probably stranding fewer runners than he should and is giving up home runs a little more frequently than he should, but he has been really bad, without any real positives.

With Lincecum, we are hoping that he sharpens control enough to allow his underlying numbers to show through. With Romero, there appears to be no quick fix. We aren’t puzzled as to why Romero is struggling. We may wonder what has caused the degradation of his skills, but the numbers are clearly telling us that there are ample differences with the 2012 version of Ricky Romero.

The American League East is unforgiving, so when you pitch poorly you don’t often get away with it. Romero is showing no signs of turning things around, and even if he does, the division he pitches in will limit his upside. With less than two months remaining in the season, expecting Ricky Romero to return to some semblance of his former self is probably unwise. ZiPS is regressing what he is doing this year towards what he has been in the past, but Romero has not been that pitcher at all this year. He should get a little better, but you don’t want to be rolling him out there unless you are trying to attain a better draft slot for 2013.

Ubaldo Jimenez— ZiPS is, again, taking Ubaldo’s 2008-2011 into consideration. But, Ubaldo is simply not the same pitcher. He has lost 3.3 mph off of his fastball since 2010 (down 1.4 mph from 2011) and hasn’t developed enough command to mitigate these loses. Without that mid-90’s heater, Ubaldo is not generating swinging strikes—just 6.5 percent in 2012 (career 8.6 percent), well below league average—and batters aren’t going out of the zone as much, either—22.5 percent in 2012 (career 25.9 percent). And, to add to his woes, his control has completely abandoned him, as he is walking an outrageous 5.36 batters per nine innings (13.2 percent).

His ERA is 5.29 and all of his DIPS agree that an ERA over 5.00 is appropriate. He isn’t even getting unlucky with his BABIP or his strand rate. The story is similar to that of Romero, only Ubaldo offers less hope of respite.

Jair Jurrjens — In 2010, Jurrjens averaged 91.3 mph on his fastball. This year, his average heater is registering just 88.6 mph. As a result, Jurrjens isn’t missing bats. He is striking out just 3.54 batters per nine, a level that is fully embraced by his 4.6 percent swinging strike rate (career 8.0 percent). With a 6.89 ERA, it is difficult to imagine he won’t improve some, but he just doesn’t have the same stuff as he used to and a return to fantasy usefulness in any format seems unlikely.

Pitchers I like More than their ZiPS ranking


RA Dickey— Dickey’s swinging strike rate of 12.1 percent ranks fourth among all starting pitchers this year. Since he began resorting to the knuckleball as a main component of his arsenal, he had not posted a swinging strike rate of higher than 8.4 percent in any season prior to this one. It is difficult to pinpoint exactly what is causing all of the extra whiffs. Batters are chasing pitches out of the zone more and he is throwing the knuckler 1.1 mph faster this year than he did in 2011, but it isn’t as if he has added a breaking pitch or drastically changed the usage of any single pitch. He is just getting batters to swing and miss more often than ever before.

ZiPS projects a conservative 7.08 strikeout rate going forward, but given his newfound ability to miss bats, I think Dickey has the upside to maintain a strikeout rate of nearly a batter an inning. Thus, I see Dickey as more of a top-15—or fringe top-10—pitcher than top-20.

Doug Fister— Fister upped the usage of his slider and curveball in 2011 and saw his strikeout rate climb from 4.89 to 6.07, validated by a 2.3 percent increase in his whiff rate. This year, Fister has increased his curveball usage further and is also utilizing his changeup more. The result: an increase in swinging strike rate from 6.7 percent to 8.1 percent and a jump to 7.68 strikeouts per nine.

While this type of strikeout rate surge will probably not be completely sustained, a change in pitch mix, especially when increasing the usage of off-speed pitches, makes an increased strikeout rate more explainable. ZiPS projects a strikeout rate of 5.94 going forward, but given the pitch mix/swinging strike rate combination, it seems reasonable that Fister could outperform that projection, and, in turn, produce a lower ERA and WHIP, as well.

Ryan Dempster— With a BABIP 46 points lower than his career average and a strand rate 8 percent higher, it is reasonable to expect a lot of regression from Dempster down the stretch. And swapping the NL Central for the AL West will only fuel that regression further. The question is how much regression, though? ZiPS thinks a 4.50 ERA is an appropriate adjustment given Dempster’s track record and league switch. Maybe ZiPS will end up being correct, but given the way he has pitched so far this year, and given his control gains both in walk rate and in his first strike percentage—the best of his career—I would give Dempster more credit than that.

Arbitrary Adjustment: Giving him a 4.00 ERA, 1.35 WHIP would move him up to 56th among starters. A 3.75 ERA and 1.30 WHIP moves him to 46th, and add a win to the latter projection and he slides up to 33rd. Dempster is a sell-high right now, there is little debate about that, but I would still move him up to the 40-50 range among starting pitchers.

Matt Harvey—ZiPS only projects Harvey for six starts. If we translate his numbers to nine starts, he would move up to 89th.

Wade Miley—One would assume that a pitcher with a 3.64 FIP over his first 172.2 major league innings would get more love from ZiPS, but this is not the case. Miley has never really had the type of success in the minors that he is having this season. He is probably getting some BABIP assistance and some good fortune with his strand rate, too, but he has demonstrated tremendous control and is crafting a profile that is beginning to look very reminiscent to that of Doug Fister - good ground ball rate, very low walk rate, modest strikeout numbers. He isn't quite in Doug Fister's range yet, but he has probably established himself as a top-60 option.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-10T09:21:15+00:00 August outfield rankings http://www.hardballtimes.com/main/fantasy/article/august-outfield-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-outfield-rankings/#When:09:05:15 Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s ZiPS' rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers
First Basemen
Second Basemen
Third Base
Shortstop{/exp:list_maker}



Num
Name
AB
R
HR
RBI
SB
AVG
rPAA (ROS)
EYES (ROS)
Full Season*




1
Ryan Braun
203
34
12
37
8
0.3
1.87
2.81
15.19




2
Carlos Gonzalez
202
35
10
36
8
0.307
1.76
2.76
14.86




3
Andrew McCutchen
199
36
8
28
9
0.302
1.33
2.15
13.53




4
Mike Trout
189
38
6
22
14
0.291
1.28
2.03
13.39




5
Matt Kemp
162
30
9
29
8
0.296
1.01
1.50
12.56




6
Curtis Granderson
194
36
12
31
5
0.253
0.88
1.27
12.16




7
Justin Upton
189
33
8
27
7
0.28
0.72
1.15
11.67




8
Josh Hamilton
178
27
10
35
3
0.287
0.69
1.04
11.60




9
Matt Holliday
182
30
8
31
3
0.302
0.64
1.14
11.43




10
Michael Bourn
214
33
2
18
17
0.276
0.64
0.97
11.41




11
Adam Jones
204
30
9
28
4
0.284
0.52
0.86
11.07




12
Jose Bautista*
157
29
12
30
2
0.261
0.42
0.55
10.76




13
Alex Rios
197
28
7
26
7
0.279
0.37
0.56
10.62




14
Giancarlo Stanton
165
26
11
29
2
0.273
0.28
0.35
10.34




15
Melky Cabrera
196
29
5
23
5
0.306
0.25
0.62
10.25




16
Hunter Pence
204
28
7
31
4
0.275
0.25
0.43
10.24




17
Jay Bruce
183
28
10
31
3
0.257
0.25
0.29
10.23




18
Corey Hart
188
28
9
26
3
0.271
0.12
0.18
9.83




19
Nelson Cruz
175
24
9
30
4
0.263
0.11
0.04
9.82




20
Ichiro Suzuki
218
30
3
16
11
0.284
0.11
0.28
9.82




21
Mark Trumbo
189
24
10
31
2
0.265
0.09
0.02
9.74




22
B.J. Upton
187
26
6
23
12
0.241
0.08
-0.14
9.73




23
Alex Gordon
197
30
6
24
4
0.284
0.08
0.28
9.71




24
Drew Stubbs
184
30
6
19
12
0.239
0.07
-0.08
9.70




25
Michael Cuddyer*
182
27
7
28
3
0.28
0.06
0.17
9.66




26
Jacoby Ellsbury
157
23
4
17
11
0.287
-0.04
-0.14
9.35




27
Desmond Jennings
184
30
4
17
12
0.25
-0.07
-0.19
9.26




28
Ben Zobrist
175
28
6
25
6
0.257
-0.09
-0.16
9.18




29
Shane Victorino
191
26
5
20
9
0.267
-0.09
-0.20
9.18




30
Austin Jackson
193
30
4
19
7
0.275
-0.15
-0.10
9.01




31
Yoenis Cespedes
178
24
7
26
3
0.275
-0.19
-0.28
8.89




32
Jason Heyward
172
26
7
22
5
0.262
-0.22
-0.38
8.79




33
Jason Kubel
162
22
8
30
 
0.278
-0.26
-0.38
8.68




34
Carlos Beltran
150
22
7
25
3
0.28
-0.26
-0.42
8.66




35
Josh Willingham
153
24
9
28
1
0.255
-0.29
-0.51
8.59




36
Colby Rasmus
180
28
8
26
2
0.25
-0.30
-0.46
8.56




37
Alejandro De Aza
178
28
3
17
8
0.275
-0.32
-0.38
8.50




38
Shin-Soo Choo*
168
25
5
21
5
0.274
-0.37
-0.52
8.35




39
Torii Hunter
165
23
6
25
3
0.273
-0.39
-0.57
8.29




40
Ben Revere
177
23
 
12
13
0.288
-0.42
-0.60
8.20




41
Andre Ethier
170
24
6
27
1
0.276
-0.42
-0.54
8.20




42
Angel Pagan
170
22
3
18
9
0.276
-0.42
-0.68
8.18




43
Martin Prado
190
26
4
21
3
0.289
-0.43
-0.44
8.17




44
Michael Brantley
193
26
2
19
7
0.28
-0.45
-0.54
8.11




45
Dexter Fowler
177
29
3
18
6
0.271
-0.46
-0.54
8.06




46
Carl Crawford
135
20
4
17
8
0.281
-0.50
-0.83
7.96




47
Nick Swisher
169
24
8
28
 
0.254
-0.50
-0.78
7.94




48
Nick Markakis
188
25
5
22
2
0.282
-0.50
-0.61
7.94




49
Juan Pierre
166
22
 
13
12
0.289
-0.51
-0.74
7.93




50
Alfonso Soriano
169
20
8
27
2
0.254
-0.52
-0.94
7.87




51
Dayan Viciedo
190
23
7
25
1
0.268
-0.53
-0.78
7.86




52
Delmon Young
186
22
6
26
1
0.274
-0.56
-0.79
7.78




53
Rajai Davis
129
19
2
13
13
0.264
-0.58
-1.07
7.71




54
Ryan Ludwick
153
20
8
29
 
0.255
-0.60
-1.00
7.65




55
Garrett Jones
163
22
7
24
2
0.258
-0.61
-0.98
7.62




56
Jeff Francoeur
199
23
6
23
3
0.261
-0.61
-0.95
7.60




57
Allen Craig
135
21
6
24
1
0.281
-0.61
-0.89
7.60




58
Norichika Aoki
191
27
2
18
4
0.288
-0.62
-0.67
7.59




59
Carlos Lee
175
19
5
28
1
0.28
-0.63
-0.92
7.54




60
Chris Young
161
23
7
21
5
0.236
-0.63
-1.13
7.54




61
Matt Joyce
146
22
6
22
3
0.26
-0.66
-1.03
7.46




62
Cameron Maybin
169
26
3
16
9
0.243
-0.68
-1.10
7.39




63
Alex Presley
171
24
4
17
6
0.263
-0.69
-1.03
7.38




64
J.D. Martinez
184
22
5
27
1
0.266
-0.71
-1.01
7.32




65
Raul Ibanez
159
20
7
27
1
0.252
-0.72
-1.18
7.28




66
Bryan LaHair
166
20
8
21
1
0.265
-0.72
-1.17
7.26




67
Brennan Boesch
171
22
6
22
2
0.263
-0.74
-1.12
7.23




68
Bryce Harper*
164
21
5
18
7
0.244
-0.77
-1.34
7.12




69
Josh Reddick*
182
23
7
20
3
0.247
-0.77
-1.26
7.11




70
Tyler Colvin
152
21
6
23
2
0.257
-0.78
-1.23
7.09




71
Cody Ross
148
22
6
24
1
0.257
-0.79
-1.19
7.07




72
Michael Morse*
142
19
6
23
 
0.282
-0.81
-1.19
7.00




73
Denard Span
178
25
1
15
6
0.281
-0.82
-1.05
6.97




74
Gerardo Parra
157
20
3
17
5
0.28
-0.86
-1.26
6.85




75
David Murphy
139
17
4
19
4
0.281
-0.89
-1.39
6.75




76
Coco Crisp
134
18
2
13
10
0.261
-0.91
-1.53
6.69




77
Anthony Gose
145
18
4
11
12
0.228
-0.92
-1.74
6.67




78
Peter Bourjos
151
22
3
14
7
0.258
-0.92
-1.43
6.66




79
Jon Jay
148
21
3
14
4
0.284
-0.99
-1.40
6.46




80
Jose Tabata
159
24
2
11
7
0.264
-1.00
-1.46
6.41

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each player would score above an empty spot in a lineup over a full season.

Analysis


Sometimes ZiPS has trouble projecting playing time for players who are currently on the disabled list. Since there are a lot of outfielders to get to, I will provide detailed analysis where I see fit, but if the issue is playing time, the analysis will be brief.

Playing time alterations:

Jose Bautista – Bautista’s wrist is still bothering him and there is no timetable for his return. ZiPS projects Bautista to play in 45 of the Blue Jays’ final 54 games. I’d exercise caution and arbitrarily project closer to 33 games. Using the same per-game production that ZiPS had projected, the lower playing time projection makes Bautista the 45th ranked outfielder going forward.

Michael Cuddyer – Cuddyer was placed on the disabled list on Saturday - retroactive to August 1 – with a strained oblique muscle, so he will be out until the middle of the month at the earliest. This means that Cuddyer will certainly fall shy of the 49 game projection that ZiPS has created for him. Reworking Cuddyer’s playing time projection to 40 games would slide him down to the number 50 slot among outfielders.

Bryce Harper – Harper has played in nearly every game since his call-up. I see no reason why we should expect that to change. Hence, an adjustment from 44 to 50 games played. This moves Harper up to number 40 at outfield.

Michael Morse – Since returning from the disabled list on June 2, Morse hasn't shown any signs that warrants distrust in his health. I am confident enough in his ability to stay on the field to project 50 games played, an adjustment that boosts his ranking to 38th at outfield.

Shin-Soo Choo – Almost everything about Choo’s 2012 production is in symbiosis with his career averages. He is on pace to finish with roughly 20 home runs, 20 steals and an average around .290, which is basically what he did in both 2009 and 2010. In 2011, however, Choo struggled through injuries, playing just 85 games. If you believe that Choo’s performance at the plate last year was partly due to his injuries, then it shouldn’t be hard to fathom that he could be back to his pre-2011 form. I am a believer, but ZiPS doesn’t project on assumptions, just numbers.

ZiPS projects Choo to appear in just 44 games down the stretch, to go along with a .333 BABIP (career .353). I don’t see why Choo’s production needs to drop at all – his .355 BABIP and 21.9 percent strikeout rate are, again, in congruence with his career marks – so I would put far more weight on his 2009, 2010, and 2012 production than his injury riddled 2011, which appears to be an outlier of a season.

Arbitrary Adjustment: In 191 at bats and a fantasy line of 28/6/25/6/.290 makes Choo the 16th ranked outfielder.

Josh Reddick – Reddick has come out of relative obscurity this year to post very good fantasy and real life numbers. Perhaps ZiPS has been caught off guard. Reddick’s huge HR/FB spike from 7.4 percent to 16.4 percent is probably unsustainable, but with a fly ball rate just shy of 50 percent, he will give himself plenty of opportunities to deposit baseballs over outfield fences. ZiPS projects seven more home runs. That may be a bit stingy, but not egregious, so I won't spend anymore time undressing Reddick's power peripherals. The real issue I have with the projection is the low run and RBI totals.

Reddick is currently scoring 0.135 runs and driving in 0.124 runners per plate appearances. ZiPS projects 0.115 runs/PA and 0.100 RBI/PA going foreward. Over the course of a full season those rates would translate to about 12 runs and 14 RBI less than what Reddick is currently doing. Oakland’s lineup is currently producing a wRC+ of 90, so Reddick isn’t in a great situation to rack up counting stats, but he should still do better than what ZiPS is projecting.

Arbitrary Adjustment: I would project a fantasy line of 26/8/25/3/.247. This would move Reddick up to 39th at outfield.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-07T09:05:15+00:00 August shortstop rankings http://www.hardballtimes.com/main/fantasy/article/august-shortstop-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-shortstop-rankings/#When:08:40:15 Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s ZiPS' rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers
First Basemen
Second Basemen
Third Base{/exp:list_maker}



Num
Name
AB
R
HR
RBI
SB
AVG
rPAA (ROS)
EYES (ROS)
Full Season*




1
Starlin Castro
226
29
4
25
8
0.292
0.71
1.19
10.20




2
Troy Tulowitzki
160
26
8
28
4
0.294
0.69
1.03
10.12




3
Hanley Ramirez
176
27
6
25
9
0.267
0.59
0.76
9.83




4
Jose Reyes
182
27
3
15
12
0.297
0.52
0.80
9.62




5
Elvis Andrus
213
31
1
19
12
0.282
0.48
0.84
9.52




6
Jimmy Rollins
190
27
5
21
9
0.258
0.23
0.23
8.77




7
Asdrubal Cabrera
200
27
6
24
4
0.275
0.20
0.34
8.68




8
Alexei Ramirez
205
26
5
25
4
0.273
0.07
0.15
8.31




9
Ian Desmond
188
23
5
22
7
0.266
0.02
-0.08
8.17




10
Alcides Escobar
199
25
2
17
9
0.281
-0.02
0.00
8.03




11
Dee Gordon
191
23
 
10
17
0.262
-0.08
-0.33
7.86




12
Danny Espinosa
200
27
6
22
7
0.235
-0.11
-0.35
7.77




13
Derek Jeter
202
28
3
19
4
0.282
-0.16
-0.04
7.64




14
Emilio Bonifacio
156
22
1
9
12
0.263
-0.47
-0.83
6.71




15
Erick Aybar
178
22
2
16
7
0.27
-0.49
-0.75
6.66




16
J.J. Hardy
186
24
7
21
 
0.253
-0.55
-0.85
6.49




17
Jhonny Peralta
184
21
5
25
 
0.266
-0.56
-0.82
6.45




18
Rafael Furcal
155
24
3
15
5
0.265
-0.57
-0.82
6.43




19
Zack Cozart
187
27
5
16
3
0.251
-0.57
-0.83
6.42




20
Trevor Plouffe
164
23
7
19
1
0.25
-0.58
-0.94
6.40




21
Mike Aviles
158
18
4
19
4
0.266
-0.64
-1.06
6.22




22
Yunel Escobar
186
24
4
18
1
0.269
-0.68
-0.92
6.09




23
Brian Dozier
188
22
2
17
5
0.25
-0.86
-1.33
5.57




24
Andrelton Simmons
162
19
1
14
5
0.278
-0.87
-1.28
5.53




25
Cliff Pennington
172
21
2
15
7
0.238
-0.90
-1.50
5.44




26
Marco Scutaro
177
21
2
15
3
0.266
-0.96
-1.39
5.26




27
Yuniesky Betancourt
163
16
4
21
1
0.258
-0.99
-1.58
5.18




28
Ryan Theriot
169
20
 
14
5
0.272
-0.99
-1.42
5.18




29
Pedro Ciriaco
122
15
1
14
5
0.262
-1.17
-1.84
4.67




30
Sean Rodriguez
138
18
4
16
3
0.225
-1.18
-1.95
4.62




31
Everth Cabrera
117
16
1
9
9
0.231
-1.18
-2.01
4.62




32
Willie Bloomquist
122
16
1
8
5
0.279
-1.23
-1.86
4.49




33
Ruben Tejada
161
18
1
13
2
0.273
-1.27
-1.83
4.37




34
Alexi Amarista
152
17
1
15
4
0.25
-1.27
-1.97
4.36




35
Eric Sogard
138
19
2
13
3
0.246
-1.27
-1.94
4.36




36
Stephen Drew
124
16
3
15
1
0.258
-1.28
-1.97
4.33




37
Jed Lowrie
111
14
4
14
1
0.252
-1.33
-2.12
4.19




38
Elliot Johnson
111
12
2
11
6
0.243
-1.34
-2.25
4.16




39
Cody Ransom
121
14
5
18
1
0.215
-1.37
-2.31
4.07




40
Brendan Ryan
139
18
1
12
4
0.23
-1.50
-2.34
3.69

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each player would score above an empty spot in a lineup over a full season.

Analysis


Troy Tulowitzki – Tulo took batting practice Tuesday for the first time since undergoing surgery on his groin. There is still no timetable for his return, however, and with the Rockies’ position, 29 games below .500, it would be prudent to make sure he is fully healthy before bringing him back. There is no indication that the Rockies are planning on, or considering, shutting Tulowitzki down for the remainder of the season, so without any major setbacks, he should see action at some point in 2012.

ZiPS projects Tulowitzki to play in 43 of the Rockies’ remaining 57 games. Since there is still no timetable for his return, this seems overly optimistic.

Arbitrary adjustment: Taking my best guess, I would project around 34 games for Tulo. This would move his value to 0.05 (rPAA) and 0.03 (EYES), ninth among shortstops.

Starlin Castro – At the end of April, Castro had 10 stolen bases and looked like he might be on his way to a 30-steal season. He followed that up with five steals in May, but since then he has stolen just one base in six attempts over the past two months. ZiPS has Castro down for eight more steals the rest of the way. Steals can come at random, but unless his current stolen base trajectory takes an drastic turn, paying for eight steals the rest of the way might not be the wisest move.

Arbitrary adjustment: Four steals? Does that sound fair? That is what I am going with. This would move his expected roto score down to 0.36 (rPAA) and 0.76 (EYES), also sliding him down to fourth at shortstop.

Ian Desmond – Desmond is currently on the disabled list and, as with Tulowitzki, there is no timetable for return. Players with this type of oblique injury collectively miss an average of 26 games, according to the Los Angeles Dodgers’ senior director of medical services, Stan Conte, who has studied this sort of injury extensively.

Desmond was placed on the DL on July 22, so look for him to return sometime close to the end of August, which would leave the Nationals with about 40 games left in the season. Assuming he plays all of those games, his new expected roto value becomes -0.53 (rAA) and 0.95 (EYES), moving him back to 15th at the position.

Dee Gordon – Gordon suffered a torn ulnar collateral ligament in his right thumb on July 4. According to Gordon’s original timetable, he is expected to miss at least two more weeks. That timetable was announced about three weeks ago, so it might be outdated by now.

Assuming he is still on course for a five-week recovery, a reasonable expectation might be 35-40 games from this point forward, much less than the 47 games that ZiPS projects. And that is also assuming that the Dodgers don’t get tired of his .280 on base percentage, and .263 wOBA, and cut his playing time now that they have added Hanley Ramirez.

Arbitrary adjustment: With 35 games played, and that might be a bit volatile, his expected roto value moves down to -0.75 (rPAA) and -1.35 (EYES), which makes him the new 22nd ranked shortstop.

Josh Rutledge - ZiPS does not have a projection for Josh Rutledge. However, since he was called-up on July 13, Rutledge has hit .329/.348/.659, with six home runs and three stolen bases, so he is merits mentioning.

Can Rutledge continue at this pace? No. But, there are reasons to believe that he could be useful for owners in need of a middle infield option down the stretch. His .349 BABIP is probably a bit high, but he has maintained a high BABIP throughout his minor league career and is seen as having the hitting tools that might foreshadow a high BABIP. He is also controlling his strikeouts (15.5 percent), as he did in the minors, so while a .329 average is probably out of his reach going forward, we shouldn't expect a complete free-fall either, especially when he plays half of his games in Coors Field.

Coors Field will help his home run totals, too, but he is more of a gap hitter than a home run hitter. Rutledge is also a solid runner and has stolen 30 bases in the minors since the beginning of 2011 (902 plate appearances), so look for a modest contribution on the base paths as well.

The Rockies won't be taking Rutledge out of the lineup while he has a .445 wOBA, but Tulowitzki will be returning at some point this season. With the way Rutledge is hitting, that could mean he moves to second base, pushing DJ LeMahieu to the bench, or, if Rutledge comes back to earth, he and LeMahieu could split time at second. Tulo's return will make this situation much more hazy if Rutledge does begin to slump, but right now he is playing, and playing very well. I'd be skeptical about going all-in, though.

Arbitrary projection: A fantasy line of 21/4/17/4/.272 in 182 at-bats. This would give him expected roto values of -0.53 (rPAA) and -0.86 (EYES), ranking him as the 16th best shortstop for the remainder of 2012.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-06T08:40:15+00:00 August third base rankings http://www.hardballtimes.com/main/fantasy/article/august-third-base-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-third-base-rankings/#When:07:38:15 Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s ZiPS' rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers
First Basemen
Second Basemen {/exp:list_maker}



Num
Name
AB
R
HR
RBI
SB
AVG
rPAA (ROS)
EYES (ROS)
Full Season*




1
Miguel Cabrera
206
34
12
39
1
0.316
1.32
2.17
12.95




2
Jose Bautista
171
32
14
33
3
0.263
0.73
0.93
11.31




3
David Wright
189
29
7
31
7
0.291
0.52
0.79
10.73




4
Brett Lawrie
216
31
8
26
8
0.278
0.45
0.63
10.54




5
Hanley Ramirez
183
28
7
26
10
0.268
0.29
0.27
10.08




6
Adrian Beltre
195
27
9
31
1
0.297
0.18
0.35
9.78




7
Ryan Zimmerman
192
30
8
29
1
0.281
-0.06
0.02
9.09




8
Edwin Encarnacion
171
26
10
27
3
0.269
-0.07
-0.22
9.09




9
Aramis Ramirez
180
26
7
34
1
0.283
-0.11
-0.11
8.95




10
Evan Longoria
148
25
8
29
3
0.277
-0.14
-0.28
8.87




11
Alex Rodriguez
161
24
8
28
4
0.267
-0.23
-0.48
8.63




12
Pablo Sandoval
181
25
7
27
1
0.298
-0.25
-0.28
8.57




13
Mike Moustakas
220
27
8
29
1
0.268
-0.35
-0.52
8.30




14
Martin Prado
205
28
4
23
3
0.288
-0.49
-0.54
7.91




15
Michael Young
219
26
4
27
2
0.283
-0.56
-0.69
7.71




16
Chase Headley
187
24
5
23
5
0.273
-0.56
-0.88
7.70




17
Pedro Alvarez
180
23
10
27
1
0.244
-0.64
-1.18
7.48




18
Mark Reynolds
173
26
10
27
3
0.214
-0.65
-1.29
7.45




19
Chris Davis
187
22
9
26
1
0.251
-0.75
-1.31
7.18




20
Kyle Seager
194
25
4
24
4
0.263
-0.79
-1.18
7.06




21
Todd Frazier
173
22
7
24
3
0.249
-0.84
-1.44
6.91




22
Emilio Bonifacio
167
23
1
10
13
0.263
-0.88
-1.47
6.81




23
Chris Johnson
181
21
5
25
1
0.271
-0.98
-1.48
6.52




24
Kevin Youkilis
149
23
7
22
1
0.248
-1.03
-1.64
6.40




25
David Freese
143
18
5
23
1
0.287
-1.03
-1.57
6.39




26
Juan Francisco
154
18
7
24
1
0.26
-1.05
-1.74
6.35




27
Mike Aviles
166
19
4
20
5
0.265
-1.06
-1.71
6.32




28
Trevor Plouffe
174
24
7
20
1
0.247
-1.12
-1.76
6.15




29
Josh Harrison
161
23
2
16
5
0.267
-1.22
-1.80
5.87




30
Chipper Jones
129
17
5
21
1
0.279
-1.22
-1.90
5.85




31
Steve Lombardozzi
202
22
2
16
5
0.267
-1.28
-1.90
5.71




32
Casey McGehee
176
19
6
25
 
0.25
-1.28
-2.05
5.69




33
Daniel Murphy
155
17
2
21
2
0.29
-1.31
-1.92
5.60




34
Will Middlebrooks
170
15
6
21
2
0.259
-1.31
-2.19
5.60




35
Jose Lopez
165
18
5
22
1
0.261
-1.32
-2.07
5.59




36
Jordan Pacheco
177
17
2
22
2
0.271
-1.46
-2.20
5.19




37
Alberto Callaspo
171
20
3
19
1
0.269
-1.47
-2.16
5.18




38
Ryan Roberts
151
19
4
17
5
0.232
-1.47
-2.42
5.17




39
DJ LeMahieu
156
15
1
16
3
0.295
-1.54
-2.28
4.96




40
Wilson Betemit
135
16
5
17
1
0.259
-1.58
-2.50
4.86

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each player would score above an empty spot in a lineup over a full season.

Analysis


Alex Rodriguez— Rodriguez will have X-rays on his fractured hand sometime in the near future. Until then, we won’t have a timetable for when he might return. The general sense is that he may be out up to two months. With roughly two months left in the season, this could mean that Rodriguez misses anywhere from 75 to 100 percent of the remaining games this year.

Since ZiPS doesn’t know his timetable, it still has him projected for 161 at-bats. This is probably far too high, especially with the Yankees’ position six and a half games ahead of Baltimore and Tampa Bay in the AL East. They could play it safe and wait until the playoffs to bring Rodriguez back. This injury should probably push Rodriguez out of the top-40 third basemen entirely, as he won’t be very useful in fantasy until 2013.

Mike Olt and Michael Young—The Texas Rangers summoned third base prospect Olt on Thursday, leaving Michael Young’s playing time in serious jeopardy. As Dave Cameron mentioned in his analysis of the call-up, Young hasn’t hit a home run since May 7 and has hit just .247/.275/.306 since then.

Young has been an above league-average player every year since 2003, but his -1.5 WAR in 2012—second worst in baseball (Jeff Francoeur)—became too much for the Rangers to take. Olt's arrival likely means the end of Young as an everyday player. Despite Young’s overall ineptitude this year, he has remained solid against lefties, hitting for a 104 wRC+ against them, so he could still see at-bats in a platoon situation against lefties.

In the first game of the Olt era last night, he hit eighth and played first base, while Young remained in the lineup and hit sixth against lefty C.J. Wilson. We still don’t know if Young will be a strict platoon player for the rest of the year, but the Rangers didn’t call up Olt to sit on the bench, and with Young’s wRC+ of 50 against right handers, they would be best served to keep Young on the bench against them in favor of Mitch Moreland and his career 114 wRC+ against righties. However this situation ends up shaking out, it is not going to be good for Young's fantasy value.

Arbitrary adjustments: For Michael Young, it would probably be safe to assume he loses close to half of the 219 at-bats that ZiPS has him projected for the rest of the way, which moves him out of the top-40 third basemen completely.

As for Olt, getting a gauge of what he will do in a two-month sample is tricky. He hit .288/.398/.579, with 28 home runs this year at Double-A, but he also struck out in 24 percent of his plate appearances. ZiPS does not have a projection for Olt yet, but he should provide good power with a low batting average, and will benefit greatly from his home park and the dangerous Rangers lineup. Putting a specific ranking on him is almost futile, due to the excessively large range of reasonable outcomes, but he is absolutely worth an add if you have a need a third base or a corner infield slot.

Juan Francisco and Josh Harrison — I am not sure why ZiPS is so aggressive in the playing time projections for Francisco and Harrison. Both players have been used primarily as pinch hitters. Since the beginning of July, Francisco compiled more than one plate appearance in just five games, while Harrison has had six such games. Both have skill sets that would be worth keeping an eye on should either of them stumble their way into regular at-bats, but as of now, they should probably be left on the waiver wire in mixed leagues.

Will Middlebrooks— ZiPS projects Middlebrooks for a 15/6/21/2/.259 fantasy line in 180 plate appearances the rest of the way. I have no problem with the projected batting average regression, and, while six home runs translates out to only 20 over a 600-plate appearance sample, I don’t have a huge problem with being cautious on a player with a 66-game major league track record and horrible plate discipline. I do, however, have an issue with the low run and RBI totals that ZiPS projects for Middlebrooks.

So far this year, Middlebrooks has 32 runs scored (0.125 runs per plate appearance) and 48 RBI (0.188 RBI per plate appearance). ZiPS, however, projects just 15 runs (0.833 runs/PA) and 21 RBI (0.117 RBI/PA). Runs and RBI are difficult to predict and there is surely regression coming in these two areas for Middlebrooks, but that seems too steep.

ZiPS projects a .294 OBP and .165 ISO for Middlebrooks for the rest of the season. Using FanGraphs' new leaderboard filter, I extracted all hitters who have posted an on-base percentage between .290 and .300 in either 2011 or this year. The average runs scored rate for these players was 0.109 runs per plate appearance. I also extracted the hitters who had an isolated power between .155 and .175 over that same time period. The average RBI rate for this batch of players was 0.118 RBI per plate appearance.

The players on these two lists came from various teams, but most teams aren’t the Boston Red Sox. Despite losing key players in Carl Crawford and Jacoby Ellsbury for long periods of time this year, Boston still ranks eighth in baseball in wOBA. The lineup looks fully healthy now and could easily hit like it did in 2011, when it produced a league-best 116 wRC+.

Arbitrary adjustment: If we adjust the average run and RBI rate of players similar to Middlebrooks to reflect a 110 team-wRC+, something Boston should easily be capable of coming close to going forward, then he would average 0.120 runs and 0.130 RBI per plate appearance. I would also bump his playing time projection to 200 plate appearances. Under these circumstances, Middlebrooks’ rest of season projection looks like this: 24 runs, seven home runs, 26 RBI, two steals and a .259 average. Since he typically hits sixth or seventh, we could probably add a couple of RBIs and subtract a couple of runs from that line, but the overall result would be similar, a boost into the 17th spot among third basemen.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-03T07:38:15+00:00 August second base rankings http://www.hardballtimes.com/main/fantasy/article/august-second-base-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-second-base-rankings/#When:11:57:15
Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s ZiPS' rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers
First Basemen {/exp:list_maker}



Num
Name
AB
R
HR
RBI
SB
AVG
rPAA (ROS)
EYES (ROS)
Full Season*




1
Robinson Cano
225
35
10
36
2
0.302
1.27
2.13
11.83




2
Ian Kinsler
214
36
8
27
10
0.271
1.06
1.57
11.24




3
Brandon Phillips
218
32
7
31
6
0.284
0.77
1.24
10.46




4
Jose Altuve
221
29
3
21
11
0.294
0.46
0.78
9.61




5
Neil Walker
213
29
6
32
4
0.277
0.32
0.55
9.22




6
Dustin Pedroia
199
29
6
24
6
0.286
0.30
0.52
9.16




7
Jason Kipnis
214
31
6
27
7
0.262
0.28
0.40
9.10




8
Ben Zobrist
188
31
7
27
6
0.255
0.22
0.29
8.95




9
Aaron Hill
197
26
6
26
4
0.279
-0.01
0.02
8.32




10
Danny Espinosa
218
30
7
24
7
0.234
-0.16
-0.42
7.91




11
Dan Uggla
201
32
9
29
1
0.234
-0.18
-0.33
7.84




12
Howie Kendrick
197
25
5
23
5
0.274
-0.23
-0.35
7.71




13
Kelly Johnson
189
26
7
22
5
0.238
-0.45
-0.86
7.09




14
Kyle Seager
197
26
4
24
4
0.264
-0.46
-0.65
7.08




15
Emilio Bonifacio
169
24
1
10
13
0.266
-0.53
-0.90
6.89




16
Rickie Weeks
177
26
7
20
4
0.243
-0.54
-0.93
6.86




17
Dustin Ackley
209
29
4
18
5
0.249
-0.68
-1.00
6.47




18
Mike Aviles
170
20
4
21
5
0.265
-0.70
-1.15
6.42




19
Darwin Barney
209
26
2
18
4
0.273
-0.77
-1.01
6.22




20
Chase Utley
131
20
5
19
4
0.26
-0.79
-1.30
6.18




21
Omar Infante
178
21
3
18
3
0.281
-0.88
-1.25
5.94




22
Gordon Beckham
190
25
5
21
2
0.247
-0.89
-1.36
5.90




23
Jemile Weeks
201
26
1
15
8
0.249
-0.93
-1.40
5.79




24
Steve Lombardozzi
205
22
2
16
5
0.268
-0.98
-1.45
5.64




25
Daniel Murphy
157
17
2
21
2
0.293
-1.00
-1.44
5.59




26
Marco Scutaro
189
22
3
16
3
0.265
-1.09
-1.61
5.34




27
DJ LeMahieu
159
15
1
17
3
0.296
-1.21
-1.77
5.02




28
Tyler Greene
143
19
3
14
7
0.231
-1.21
-2.04
5.02




29
Ryan Theriot
180
21
 
14
5
0.272
-1.24
-1.78
4.93




30
Ryan Roberts
154
19
4
17
5
0.227
-1.24
-2.09
4.93




31
Jordany Valdespin
140
15
3
13
6
0.257
-1.26
-2.10
4.87




32
Orlando Hudson
151
22
3
17
3
0.238
-1.29
-1.98
4.81




33
Pedro Ciriaco
131
16
1
15
6
0.26
-1.31
-2.08
4.75




34
Sean Rodriguez
147
19
4
17
4
0.224
-1.34
-2.21
4.67




35
Maicer Izturis
126
16
2
13
5
0.262
-1.36
-2.15
4.61




36
Alexi Amarista
163
18
2
16
4
0.252
-1.38
-2.15
4.56




37
Daniel Descalso
157
21
2
16
2
0.255
-1.40
-2.06
4.50




38
Mark Ellis
141
18
2
13
4
0.255
-1.44
-2.22
4.40




39
Ruben Tejada
172
20
1
14
2
0.273
-1.44
-2.05
4.39




40
Alexi Casilla
126
17
1
11
6
0.254
-1.46
-2.30
4.32

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each player would score above an empty spot in a lineup over a full season.

Analysis


Jose Altuve— I like Jose Altuve a lot, and ZiPS does, too. He produces his value in the three categories—runs, steals and batting average—that many fantasy owners seem to value less, but this doesn’t mean that those categories count less.

While Altuve won’t drive in many runs hitting atop an abysmal Astros lineup, he does have some power—five home runs with a .124 ISO—so he shouldn’t be a complete black hole in the home run category. Think Starlin Castro at second base, with a little less power, and a little more speed.

Altuve makes a lot of contact and has drastically improved his plate discipline upon what he did in 2011. He cut his swing rate by 12 percent, and is chasing at far less pitches outside the zone. This has helped him nearly triple his walk rate, albeit only up to 5.9 percent. Altuve, though, has a blossoming skill set and doesn’t look in immanent danger of dropping off in the final two months of the season. I’d probably still take Pedroia over him, but I really like the aggressive ranking here.

Chase Utley— ZiPS’ projection for Utley is very cautious, calling for just 131 more at bats (35 games). The trepidation is not without merit, however, as Utley has played in only 56.8 percent of the Phillies’ games over the past three seasons.

He missed 43 games in 2010 with a torn ligament in his right thumb. In 2011, he missed the team’s first 45 games with right knee problems, but did play 103 of the remaining 117 games after returning. This year, he lost 76 games to left knee issues, but has played decently since his return (.333 wOBA). Those are three pretty serious injuries, but that’s the complete list of Utley’s DL stints, not just over the past three seasons, but since the 2007 season. What I am trying to say is that Utley doesn’t yo-yo on and off the disabled list, and none of his injuries have been reoccurring issues. Once he heals, he is usually good to go, at least for a while.

Since returning from his latest DL trip, Utley has hit .236/.330/.449, but that line is accompanied by five home runs and three stolen bases, a good sign his knees are feeling well.

If I were to arbitrarily adjust Utley's ZiPS’ projection for at-bats, I would bump it up to around 171, which would give him a rest of season projection of 26/7/25/5/.260. This would raise his expected roto values to -0.07 (rPAA) and -0.20 (EYES), and would bump him up to 10th among second basemen.

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-02T11:57:15+00:00 August first base rankings http://www.hardballtimes.com/main/fantasy/article/august-first-base-rankings/ http://www.hardballtimes.com/main/fantasy/article/august-first-base-rankings/#When:10:04:15
Most fantasy rankings are forged on "gut calls" and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s rest-of-season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you won't have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.


Previous installments:
{exp:list_maker}Catchers {/exp:list_maker}



Num
Name
AB
R
HR
RBI
SB
BA
rPAA (ROS)
EYES (ROS)
Full Season*




1
Albert Pujols
191
32
13
39
3
0.305
1.23
1.83
12.79




2
Paul Konerko
201
32
12
39
 
0.311
0.96
1.56
12.06




3
Adrian Gonzalez
203
32
9
36
 
0.323
0.71
1.33
11.37




4
Paul Goldschmidt
205
32
11
37
2
0.28
0.58
0.86
11.02




5
Michael Morse
200
29
10
35
1
0.296
0.40
0.64
10.50




6
Prince Fielder
193
32
10
34
 
0.297
0.39
0.72
10.49




7
Ryan Howard
192
30
11
36
 
0.278
0.26
0.37
10.13




8
Chris Davis
210
28
10
36
 
0.274
0.04
0.02
9.51




9
Justin Morneau
217
30
9
35
 
0.274
-0.02
0.01
9.36




10
Billy Butler
206
28
7
32
 
0.301
-0.07
0.08
9.24




11
Mark Trumbo
192
26
10
33
2
0.266
-0.08
-0.27
9.20




12
Anthony Rizzo
197
27
10
33
2
0.262
-0.09
-0.28
9.17




13
Bryan LaHair
173
26
10
32
 
0.278
-0.16
-0.30
8.99




14
Freddie Freeman
208
28
7
31
1
0.28
-0.28
-0.35
8.65




15
Mark Teixeira
178
26
10
32
 
0.26
-0.35
-0.65
8.45




16
Eric Hosmer
211
28
6
27
3
0.279
-0.36
-0.50
8.41




17
Adam Dunn
193
29
12
35
 
0.222
-0.39
-0.86
8.35




18
Adam Lind
201
25
9
32
 
0.26
-0.51
-0.87
8.02




19
Brandon Belt
192
29
5
26
3
0.274
-0.53
-0.71
7.97




20
Michael Cuddyer
165
24
6
26
2
0.289
-0.56
-0.83
7.86




21
Michael Young
202
26
4
25
1
0.302
-0.61
-0.70
7.75




22
Allen Craig
154
22
7
27
 
0.291
-0.68
-1.02
7.55




23
Kendrys Morales
167
22
7
27
 
0.283
-0.75
-1.14
7.37




24
Adam LaRoche
183
24
8
29
 
0.259
-0.75
-1.23
7.34




25
Carlos Pena
197
28
10
31
1
0.214
-0.80
-1.48
7.23




26
Carlos Lee
187
24
5
25
1
0.278
-0.89
-1.30
6.96




27
Joey Votto
101
19
5
20
1
0.329
-0.99
-1.46
6.70




28
Lance Berkman
132
21
6
21
1
0.283
-1.04
-1.61
6.55




29
Brandon Moss
164
22
7
25
1
0.243
-1.14
-1.88
6.29




30
James Loney
177
21
3
20
1
0.275
-1.43
-2.11
5.48




31
Casey Kotchman
186
22
4
22
 
0.264
-1.43
-2.13
5.48




32
Todd Helton
135
19
4
18
 
0.283
-1.52
-2.26
5.25




33
Ike Davis
129
17
5
20
 
0.256
-1.64
-2.60
4.90

*Full season = the raw (non-adjusted) full season pace roto score using the roto points-above-replacement method. This is, essentially, the amount of expected roto points each catcher would score above an empty spot in a lineup over a full season.

Analysis


Adrian Gonzalez – I can buy into the counting numbers Oliver projects for Gonzalez, but I cannot say I would endorse paying for a .323 average. Gonzalez hit .338 last season with a .380 BABIP (57 points higher than his career average). He also hit for more power last year than he is thus far in 2012, which gave his average a boost, too.

This year, his BABIP is fine at .334, and Gonzalez is striking out just as often as he did in 2011, but he is hitting just .298. Even with a small uptick in power, Gonzalez is going to need some serious BABIP love to hit .323 the rest of the way. Could he do it? Yes. Should fantasy owners pay for that as a baseline batting average? No.

Arbitrary Adjustment: Even with the slight uptick in power Oliver forecasts, I’d expect marginal regression in strikeout rate and BABIP. Giving him a 16.5 percent strikeout rate and a .330 BABIP, the new projected average of .305 seems appropriate. This would bring his roto value to 0.47 (rPAA) and 0.89 (EYES).

Paul Goldschmidt – Really? Paul Goldschmidt? I was surprised when I saw it, too. I know he has good power, and the low-teens stolen base total that he will end up providing is sneaky value, but does that justify a ranking one spot ahead of Prince Fielder?

Well, I am fine with the power and speed projections. And while a .280 average might seem high, Goldschmidt has cut down on the whiffs and has produced a high BABIP at every level, so I am going to defer to Oliver here, too.

Where I do disagree with Oliver, though, is in the run and RBI totals. In 471 career at-bats, Goldschmidt has averaged 0.163 RBI (0.161 in 2012) per at-bat. Oliver believes that Goldschmidt will produce 0.18 RBI per at-bat from this point forward. Maybe he will, as RBI are unpredictable, but that’s not the point. The point is that we shouldn’t be paying for things that probably won’t happen.

Arbitrary Adjustment: With an adjusted RBI rate of 0.163 (his career rate), Goldschmidt projects to drive in 33 more runs this year. Do the same with runs scored and we get 30, for new valuations of 0.34 (rPAA) and 0.44 (EYES), which moves him behind Fielder but still keeps him fifth overall among first basemen, a spot that I still wouldn't value him at.

Ryan Howard – Since returning from a lengthy stint on the disabled list, Howard has shown good power—a .250 ISO, his best mark since 2009—but also has struck out a ton (35.4 percent). Strangely, Howard’s swinging strike rate is at the lowest of his career, and his contact percentage is at a career best, so the strikeout issues are probably the result of a small 65-plate appearance sample.

The whiffs should come down, and his .214 average should come up, but ranking Howard sixth among first basemen would be extremely aggressive due to the injury risk, the uncertainty of performance, and the departures of Hunter Pence and Shane Victorino, two of the cogs that made the Phillies lineup ... uh, respectable? They are two of the Phillies' best hitters; let's go with that. Considering all of these factors, it would seem that cautiously undervaluing Howard would be more desirable than bullishly overvaluing him.

I am not going to make any arbitrary adjustments, but I will note that Ryan Howard is 32, a health risk, his skills have already been in decline, and the last vestiges of what was once a quality offense are being removed from around him. What's more, he hasn’t exactly set the world ablaze since his return, hitting .214/.323/.464. I would devalue him significantly.

Chris Davis, Justin Morneau and Bryan LaHair – I will make it quick here. I don’t agree with any of these three players’ high run and RBI projections because their traditional rates simply don’t warrant it. I also think Davis’ batting average will be about 20 points lower than his projected .274. He has struck out in nearly 30 percent of his plate appearances, which is actually lower than what he usually does. Both Morneau’s and Davis’ projected at-bat totals of 217 and 210, respectively, are a bit steep, as well.

Arbitrary Adjustments: Davis’ fantasy line changes to 24/9/28/0/.253 in 190 at-bats, moving him down to around 25th among first basemen. Morneau’s line dips to 23/8/27/0/.274 in 180 at-bats, 23rd at first base. And LaHair's adjusted line of 21/10/24/0/.278 drops him to 24th.

Mark Teixeira – This ranking is more a function of playing time than anything else. Adjusting Teixeria’s line to reflect 215 at-bats, rather than 178, gives him new scores of 0.31 (rPAA) and 0.36 (EYES), and a new ranking of 6th. Of course, his potential wrist injury could significantly impact Teixeira's playing time, so keep an eye on those MRI results.

Adam Lind – Blarg! Runs and RBI projections seem a bit high, once again. I am adjusting his projection to 21 runs and 25 RBI. New rank: 27th.

Ike Davis – Davis has been playing every day for a while now, so a projection of 129 at-bats is probably low. Giving him 180 at-bats and a bit more power moves him up to 16th at the position.

Here is how the ZiPS projection system ranks the top-30 first basemen for the rest of the 2012 season.



Num
Name
AB
R
HR
RBI
SB
AVG
rPAA (ROS)
EYES (ROS)
Full Season




1
Miguel Cabrera
213
36
12
40
1
0.315
1.49
2.50
13.06




2
Albert Pujols
208
35
12
37
4
0.293
1.31
2.04
12.56




3
Joey Votto
193
33
10
35
4
0.306
1.09
1.79
11.97




4
Mark Teixeira
209
33
12
41
1
0.263
0.73
1.08
10.97




5
Prince Fielder
209
31
11
39
1
0.282
0.72
1.14
10.95




6
Adrian Gonzalez
217
31
10
37
 
0.3
0.70
1.26
10.90




7
Michael Cuddyer
204
31
8
32
4
0.279
0.39
0.66
10.04




8
Eric Hosmer
231
30
7
29
6
0.281
0.35
0.59
9.93




9
Mark Trumbo
210
27
11
35
2
0.267
0.30
0.36
9.80




10
Paul Goldschmidt
201
27
10
32
4
0.264
0.23
0.23
9.62




11
Paul Konerko
190
26
10
32
 
0.295
0.21
0.39
9.55




12
Edwin Encarnacion
176
27
10
28
3
0.273
0.10
0.10
9.26




13
Freddie Freeman
202
29
8
33
1
0.277
0.05
0.19
9.12




14
Michael Young
226
27
4
28
2
0.283
-0.43
-0.43
7.80




15
Adam Dunn
180
27
12
33
 
0.211
-0.46
-0.97
7.70




16
Allen Craig
149
23
7
27
1
0.282
-0.48
-0.69
7.66




17
Carlos Lee
198
22
6
31
1
0.278
-0.49
-0.69
7.64




18
Mike Napoli
152
23
10
25
1
0.257
-0.50
-0.88
7.62




19
Anthony Rizzo
170
21
7
29
3
0.259
-0.53
-0.93
7.51




20
Mark Reynolds
179
27
10
28
3
0.212
-0.57
-1.12
7.42




21
Garrett Jones
177
23
8
27
2
0.254
-0.58
-0.99
7.38




22
Chris Davis
193
23
9
27
1
0.254
-0.59
-1.01
7.35




23
Ryan Howard
148
21
9
30
1
0.243
-0.61
-1.10
7.31




24
Carlos Santana
181
28
7
27
2
0.243
-0.61
-0.93
7.31




25
Michael Morse
160
22
7
27
 
0.281
-0.61
-0.88
7.30




26
Bryan LaHair
181
22
9
23
1
0.265
-0.65
-1.07
7.20




27
Adam Lind
178
20
8
28
1
0.258
-0.71
-1.20
7.02




28
Brandon Belt
167
23
5
22
4
0.263
-0.78
-1.20
6.83




29
Carlos Pena
176
26
9
26
1
0.21
-0.97
-1.66
6.32




30
Adam LaRoche
160
20
7
27
 
0.25
-1.00
-1.59
6.24


Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-08-01T10:04:15+00:00 August catcher rankings http://www.hardballtimes.com/main/fantasy/article/oliver-driven-ros-catcher-rankings/ http://www.hardballtimes.com/main/fantasy/article/oliver-driven-ros-catcher-rankings/#When:09:46:15 Most fantasy rankings are forged on ‘gut calls’ and the whimsical notions of whoever is compiling the list. Some experts don’t pay for saves, some don’t pay for steals, some wait on pitching, some value upside, some value reliability, and so on. While it might be nice to see plenty of different opinions, it probably doesn’t truly help unless you understand the biases of each individual ranker. This is why some of the writers here at The Hardball Times have created their own, objective valuation methods, outlined here and here (reading, or at least skimming, these introductory articles will give you a much better understanding of the rankings that follow and should help to answer most potential questions).

In accompaniment with Oliver’s rest of season forecasts, we will use these objective formulas to create objective rankings. You may not agree with a particular rank, but you will know how the ranking was calculated and you wont have to guess what the ranker was thinking. This should make adding your own personal adjustments and biases much easier. And perhaps this type of ranking will introduce some potential buys and sells that you may have otherwise overlooked.

These rankings will assume a 12-team league in adjusting for league average. The ordering of players, however, is unaffected; players will rank in identical order for leagues of all sizes.

So, lets get to it. First up are catchers. 



Num
Name
AB
R
HR
RBI
SB
BA
rPAA (ROS)
EYES (ROS)
Full Season*




1
Mike Napoli
171
27
11
33
1
0.271
0.65
0.83
9.51




2
Buster Posey
196
27
7
29
1
0.298
0.43
0.74
8.91




3
Brian McCann
192
27
9
31
1
0.269
0.35
0.44
8.70




4
Joe Mauer
194
28
4
26
1
0.315
0.27
0.68
8.46




5
Miguel Montero
203
27
7
30
 
0.282
0.19
0.35
8.25




6
Yadier Molina
179
24
5
24
2
0.292
-0.08
-0.07
7.52




7
Carlos Santana
195
29
7
28
1
0.248
-0.13
-0.25
7.37




8
Carlos Ruiz
173
24
4
25
 
0.303
-0.20
-0.14
7.17




9
J.P. Arencibia
194
24
10
31
 
0.23
-0.23
-0.63
7.11




10
Alex Avila
191
26
6
26
1
0.258
-0.30
-0.49
6.91




11
A.J. Pierzynski
184
22
5
25
 
0.283
-0.39
-0.56
6.65




12
Jarrod Saltalamacchia
169
21
8
27
 
0.235
-0.57
-1.11
6.15




13
Jonathan Lucroy
181
22
4
22
1
0.275
-0.60
-0.90
6.07




14
Matt Wieters
171
21
6
24
 
0.257
-0.65
-1.09
5.93




15
Salvador Perez
174
19
4
21
 
0.272
-0.88
-1.36
5.30




16
Ramon Hernandez
145
18
4
20
 
0.28
-0.89
-1.37
5.29




17
Rod Barajas
157
18
7
23
 
0.237
-0.89
-1.60
5.27




18
Geovany Soto
151
19
6
22
 
0.238
-0.97
-1.65
5.07




19
John Buck
154
19
6
21
 
0.238
-1.01
-1.72
4.95




20
Ryan Doumit
133
17
4
19
 
0.267
-1.08
-1.71
4.77




21
Miguel Olivo
162
18
5
20
2
0.226
-1.15
-2.00
4.58




22
Chris Iannetta
135
18
5
18
1
0.228
-1.21
-2.05
4.39




23
Russell Martin
139
17
4
17
2
0.234
-1.26
-2.12
4.26




24
A.J. Ellis
163
21
2
17
 
0.259
-1.28
-1.89
4.21




25
Jesus Flores
174
17
4
20
 
0.244
-1.30
-2.12
4.15




26
Ryan Hanigan
129
15
2
15
 
0.272
-1.46
-2.24
3.71




27
Yorvit Torrealba
124
14
2
14
1
0.264
-1.51
-2.38
3.57




28
Devin Mesoraco
89
12
4
14
 
0.253
-1.53
-2.50
3.54




29
Yasmani Grandal
131
15
3
16
 
0.246
-1.54
-2.45
3.51




30
Josh Thole
132
15
1
13
 
0.269
-1.66
-2.51
3.17




31
Jose Molina
133
14
3
14
 
0.236
-1.74
-2.80
2.95




32
Kurt Suzuki
124
13
2
14
 
0.238
-1.84
-2.93
2.68




33
Chris Snyder
73
9
3
10
 
0.227
-1.99
-3.25
2.26




34
Jason Castro
120
13
1
12
 
0.239
-2.00
-3.13
2.25




35
Derek Norris
84
10
3
10
1
0.207
-2.00
-3.31
2.24




36
Wilin Rosario
58
7
2
9
 
0.243
-2.11
-3.40
1.94




37
Tyler Flowers
63
8
2
9
 
0.218
-2.17
-3.52
1.76




38
John Jaso
64
7
1
7
 
0.251
-2.26
-3.59
1.52




39
Jose Lobaton
86
9
1
8
 
0.226
-2.30
-3.67
1.40




40
Michael McKenry
66
7
1
7
 
0.231
-2.34
-3.74
1.29




41
Steve Clevenger
57
6
 
6
 
0.272
-2.35
-3.69
1.27




42
Brett Hayes
68
7
1
7
 
0.225
-2.37
-3.80
1.21




43
Nick Hundley
55
6
1
6
 
0.233
-2.39
-3.82
1.17




44
Kelly Shoppach
43
5
1
6
 
0.229
-2.41
-3.86
1.12




45
Bobby Wilson
66
6
1
6
 
0.227
-2.44
-3.91
1.03




46
Jhonatan Solano
57
5
1
5
 
0.238
-2.45
-3.93
0.99




47
Martin Maldonado
43
4
1
5
 
0.232
-2.48
-3.98
0.93




48
Brayan Pena
57
5
 
5
 
0.249
-2.51
-3.97
0.83




49
Chris Stewart
66
6
 
6
 
0.232
-2.51
-3.98
0.82




50
Hector Sanchez
39
4
 
4
 
0.257
-2.54
-4.03
0.75




51
Erik Kratz
22
2
1
3
 
0.252
-2.55
-4.11
0.72




52
Ronny Paulino
29
3
 
3
 
0.263
-2.60
-4.13
0.59




53
David Ross
26
3
 
3
 
0.258
-2.60
-4.14
0.57




54
Tony Cruz
45
4
 
4
 
0.236
-2.61
-4.14
0.57




55
Mike Nickeas
55
5
 
5
 
0.215
-2.62
-4.18
0.53




56
Rob Johnson
33
3
 
3
 
0.215
-2.70
-4.31
0.32




57
Wil Nieves
22
2
 
2
 
0.243
-2.70
-4.30
0.31




58
Gerald Laird
22
2
 
2
 
0.243
-2.70
-4.30
0.31




59
Lou Marson
32
3
 
2
 
0.225
-2.71
-4.32
0.28




60
Carlos Corporan
22
2
 
2
 
0.227
-2.72
-4.34
0.26




61
John Baker
32
3
 
2
 
0.221
-2.72
-4.33
0.25




62
Jeff Mathis
33
3
 
3
 
0.201
-2.73
-4.36
0.24




63
Matt Treanor
31
3
 
2
 
0.216
-2.73
-4.35
0.24




64
Ryan Lavarnway
10
1
 
1
 
0.273
-2.74
-4.37
0.21




65
Henry Blanco
15
1
 
2
 
0.232
-2.74
-4.38
0.21




66
Francisco Cervelli
10
1
 
1
 
0.241
-2.76
-4.40
0.16




67
Welington Castillo
11
1
 
1
 
0.243
-2.76
-4.40
0.16




68
Drew Butera
45
3
 
3
 
0.208
-2.76
-4.42
0.14




69
John Hester
11
1
 
1
 
0.235
-2.76
-4.41
0.14




70
Tim Federowicz
11
1
 
1
 
0.231
-2.76
-4.42
0.13




71
Anthony Recker
10
1
 
1
 
0.223
-2.77
-4.42
0.13




72
Luis Exposito
13
1
 
1
 
0.231
-2.77
-4.43
0.12




73
Taylor Teagarden
13
1
 
1
 
0.205
-2.79
-4.46
0.06

*Full season = the raw (non-adjusted) full season pace roto score using the roto points above replacement method. This is, essentially, the amount of expected roto points each catcher would score above an empty spot in a lineup over a full season.

Using the roto points above average (rPAA) formula, the average catcher is projected to score 2.82 raw roto points prior to adjusting for league average, with Mike Napoli projected at 3.46. After adjustments, though, you can see there is little difference between any of the players. Napoli and Jarrod Saltalamacchia, the 12th ranked catcher, are separated by just 1.22 expected rest of season roto points. The E.Y.E.S. method sees a bit more of a difference between first and 12th, but at a gap of just 1.73 roto points, the difference isn’t substantially greater.

The main take-away – at least what the numbers are saying anyways—is that there isn’t much to be gained or lost at catcher from this point forward. There will probably be a catcher or two who far outperforms the rest for the remainder of the season, but none have been outstanding to the point where Oliver believes in them enough to rank them as such.

Analysis


Mike Napoli – Oliver has Mike Napoli as the top ranked catcher going forward, but much of that expected value is tied to a .271 projected batting average. While Napoli is certainly capable of hitting for an average that high for the remainder of the year, he is currently hitting just .227 and is striking out in 30.0 percent of his plate appearances (career 25.2 percent). His plate discipline looks fine, but both his contact percentage and swinging strike rate are higher than his career averages, although not quite enough to justify a 30.0 percent strikeout rate.

I would guess Napoli cuts his strikeouts down to around 27 percent and raises his slightly low .285 BABIP to around .295 the rest of the way. These slight improvements would vastly improve Napoli’s batting average, but only to around .260, which happens to be his career average.

Another objection I have with Napoli’s projection is his RBI rate. Oliver thinks he will drive in about 0.19 runs per at bat. Over the course of his first two seasons in Texas he has averaged just 0.17 RBI per at bat and he has played much worse this season (0.13 RBI/AB). Given his struggles, and the fact that he is hitting eighth in the Rangers lineup, I wouldn’t pay for much more than 0.15 RBI per at bat, which would equal about 26 RBI from this point forward.

These may seem like minor or futile adjustments, but they move Napoli's down to a score of 0.26 roto points above replacement (0.23 EYES), which, over a full season, would translate to a difference of about 1.07 roto points. This also moves Napoli down to catcher number three according to the rPAA formula and fifth according to EYES.

Miguel Montero – Currently hitting .282, Oliver believes that Montero can maintain that same average for the remainder of the season. But, Montero is striking out at a rate that would tie his career high (23.7 percent) and is benefiting from a .356 BABIP, 43 points higher than his career average. He is a career .273 hitter, and with an inflated strikeout rate, BABIP regression should probably send his average into the .260s.
This adjustment doesn’t change Montero’s ranking, but it does move his roto scores below league average (-0.02 and -0.03).

Ramon Hernandez and Wilin Rosario – Oliver seems to think that Hernandez is going to get 145 at bats from this point forward. Hernandez, however, has only played in seven games since July 13 when he came off the disabled list. During that time Rosario has only played in eight games, so it isn't like there is a clear starter among the two. Given that Hernandez is 36 years old, and that Rosario is the Rockies future at the position, and that the Rockies are nowhere near competitive this season, it would seem like Rosario would get at least a slight majority of the playing time behind the plate going forward. And at the very least, it would be hard to project much more than a 50/50 split between the two for the remainder of the year.

Adjustments: Hernandez to 100 at bats (moves down to 28th and 29th among catchers). Rosario to 125 at bats (moves up to 26th and 26th among catchers).

Click here to learn about THT's download subscriptions.]]> Jesse Sakstrup 2012-07-31T09:46:15+00:00 Fantasy valuation systems: adding more context http://www.hardballtimes.com/main/fantasy/article/fantasy-valuation-systems-adding-more-context/ http://www.hardballtimes.com/main/fantasy/article/fantasy-valuation-systems-adding-more-context/#When:09:06:15 fantasy baseball valuation systems. I promised positional rankings for the rest of the season, but today we're going to get a little more familiar with the numbers before delving into those rankings tomorrow.

When playing in a rotisserie fantasy baseball league, you want to accumulate the most roto points. Simple concept, right? Well, now we have a couple of systems that try to quantify each player’s roto point values. This should be helpful in valuing players, especially when preparing for a draft or when making a trade.

But, even as straightforward of a concept roto point values is, it can still seem a little abstract before we familiarize ourselves with some of the nuances, like what is league average for leagues of various sizes? What does the positional scarcity spectrum look like? Are two top-40 players really worth a top-5 player? If not, what is? And so on. So, without any more foreshadowing, let's build a little framework for reference.

(For the sake of simplicity, I am only going to use one formula—my roto points above average formula (rPAA)—instead of all three. This shouldn’t change much, as all three formulas showed strong correlations with one another in their valuation of players, both hitters and pitchers.)

League Average


League average, in this case, is simply the average roto point score for all players that would be inserted into any starting lineup in a league, with hitters and pitchers having separate calculations. A typical 10-team league has 13 starting hitters and nine starting pitchers, so in this case, league average is the average score from the top-130 hitters and top-90 pitchers. For 12-team leagues, it is 156 hitters and 108 pitchers.

Bench utilization, pitcher streaming, daily lineup adjustments and individual league rules can muddy the water a little bit with regards to a true league average, but for now we are going to continue the theme of simplicity and use this non-flexible definition of starters.

The league-average amount of raw (non-adjusted) roto points for hitting over the past three full seasons (2009-2011):
{exp:list_maker}10-team leagues: 9.61
12-team leagues: 9.03
14-team leagues: 8.53
16-team leagues: 8.08 {/exp:list_maker}This graph is displaying the top 156 hitters in terms of their roto point production in relation to league average in a 12-team league in 2011. 
image

Only 71 hitters (45.5 percent) scored above zero. This is because there is so much roto value created by the top few players. Most seasoned players already know this. The total roto point value of those 71 players scoring in the positives was 161.12 rPAA. The top-20 accounted for 101.03 (62.7 percent) of those marginal points. So yeah, it pays to have stars on your team.

On the pitching side, league average is a little lower, again these numbers are averaged from 2009-2011:
{exp:list_maker}10-team league: 7.48
12-team league: 6.98
14-team league: 6.56
16-team league: 6.18 {/exp:list_maker}This graph shows the rPAA of the top 108 pitchers in a 12-team league for 2011. 
image

As with hitters, pitching scores at the top are disproportionately more valuable than scores throughout the rest of the curve are, but, with pitchers, the dropoff is much steeper. Only 37 players (34.3 percent) recorded a roto point value of above average. And of the 104.38 points produced by those top-37 players, the top-10 earners raked in 65.49 (62.7 percent) of them.

2011 rPAA Percentiles for Hitters (12-team leagues; Non-Positional Adjusted)



Percentile
Hitter
rPAA




1
Matt Kemp
9.95




10
Melky Cabrera
3.54




25
Jay Bruce
1.59




50
Alex Avila
-0.28




75
Logan Morrison
-2.09




90
Casey Kotchman
-2.91




100
Ryan Ludwick
-3.31

2011 rPAA Percentiles for Pitchers (12-team leagues)



Percentile
Pitcher
rPAA




1
Justin Verlander
10.73




10
C.J. Wilson
3.36




25
Jon Lester
0.94




50
Fernando Salas
-0.83




75
Aaron Harang
-1.67




90
Jason Motte
-2.36




100
Edwin Jackson
-2.66

Positional Adjustments


It is easier to find production at some positions than others. This is no secret. Thus, we need to adjust for positional value. To do this we simply find the number of players at each position likely to be owned for a particular league size. Let's call this number X. Now, we take the average scores of the top-X players at each position (X will vary by position). After we find the average for each position, we measure each positional average against the others, giving extra credit to the positions with lower positional averages and giving less credit to positions where production is more abundant.

Let's use a 12-team league as an example. There are 12 starting shortstops and 12 middle infielders. Without knowing the player pool for a particular year, it would be safe to assume that roughly 18 shortstops will be owned in a 12-team league.

Obviously, the utility slot will create some difficulties when looking at positions of abundance, but since the distribution of positions used at utility throughout a league will vary from year-to-year and league-to-league, we will ignore this variable for this summary. When actually calculating an overall list at any specific point in time using a static player pool, this can be accounted for with much better accuracy.

Moving on, now that we have a number, 18, we take the average value of the top-18 shortstops in relation to the average at other positions. Let's say it is -1.0. Once we have averages for all other positions, we can use the most abundant position as the benchmark for what all other positional averages should be raised to after positional adjustments. Lets say that first base is the most abundant position, with an average score of +2.0. In this case we will give a +3.0 bump to each shortstop. Now we can value them on the same scale.

Here are the positional averages for 12-team leagues for each position. These are raw scores and have not been set to league average.
image

First base is clearly the most productive position, with outfield a distant second. Contrary to traditional beliefs, shortstop is not substantially scarcer than third base or second base, as all three positions have scored about equally in recent years. Bringing up the rear is catcher, which has a raw score of an average of 3.8 points lower than first base over the past three years.

Again, how middle and corner infielders are distributed, and what positions are commonly play in the utility slot will alter the data some, but this should give us a decent idea of what positions to pay the extra buck for.

How much is the No. 1 player worth?


Fantasy players often wonder whether they should sell the best, or one of the best, fantasy baseball players to try to shore up weak spots in another areas or if they should just hold tight? It is always a good idea to sell a player if you are being overwhelmed with value, but what is the tipping point between accepting and rejecting a proposal?

The top offensive player in 2011 was Kemp, who provided nearly 18.6 raw roto points and just under 10.0 marginal roto points over a league-average player. To figure out what type of two-for-one trade would have worked out in retrospect—say if you made the deal at the beginning of 2011—we can add up Kemp’s 18.6 to a replacement level player worth around 5.4 roto points. (Factoring in a replacement-level player is just a quick way to estimate the value of the player you would have to drop from your roster to make room for two incoming players, as chances are this player will be close to replacement level.)

To trade away Kemp last season, you would have needed at least 24.0 roto points of value in return just to theoretically break even. A couple of combinations that would have just broke even are Joey Votto and Hunter Pence, Prince Fielder and Mark Teixeira, or Jose Bautista and Jay Bruce. And that’s without figuring in that you are probably eventually going to find a player who will produce more than replacement level value.

All of those hypothetical deals would have broken even, but Kemp nearly went 40/40 and competed for a Triple Crown. His 2011 season was easily the best (fantasy) offensive season of the last three years. So this is by no means the actual break-even point when selling the game’s best player.

Entering a season, the top-ranked player is typically valued at an expected 14-15 raw roto points, depending on the degree of aggression the projection exhibits. In this case, two top-40 hitters should be a “fair deal,” but again, that assumes that everything will happen in a vacuum. Chances are that the attentive owner will end up with a player better than replacement level to supplement that top-tier talent, and any player capable of being selected No. 1 overall is almost surely going to be more reliable than two random top-40 hitters. If it was my team and I owned the game’s best hitter in a 12-team league, I would need a bit more than just hypothetical "break-even" value, so something around two top-25 hitters might be the minimum package I'd be looking for.

Whether or not you are trading for the best player in the game, one can use this valuation system to get a general sense of where he or she stands in the deal. The more players that are involved in a deal, the more complex that deal becomes. With a simple calculation of expected roto value, an eight-player trade transforms from a complete uncertainty to a positive or negative number that should eliminate most of the guesswork. Categorical needs will have to be considered, as well, but absent of obscure, league-specific context, this method of trade evaluation should save a lot of time.

Year-to-Year Roto Point Correlations


Pitching correlations:
image

From 2009 to 2010, roto point totals by pitchers correlated at 0.6154 (r^2 = .3787), and from 2010 to 2011 the correlation was 0.6331 (r^2 = .4107). I also isolated the top-20 pitchers to see if the best pitchers were more reliable year to year. The correlations were 0.2165 (2009-2010) and -0.0989 (2010-2011).

So, while pitchers like Zack Greinke in 2009 and Justin Verlander in 2011 have produced like a top-5 overall player, there is far too much variance with regards to the spectrum of reasonable, expected outcomes to justify taking a pitcher that early. This is why axioms like "wait on pitching" exist. We obviously can dissect the underlying numbers to get a little closer in our projections than these correlations might suggest, but even consistently good pitchers have a high volatility range.

Hitting correlations:
image

From 2009 to 2010, the roto point totals for hitters showed a correlation of 0.7739 (r^2 = 0.5990). From 2010 to 2011, the correlation was 0.7706 (r^2 = 0.5939). This correlation is much higher than pitchers. It's not ideal, but at least there is more signal than noise with hitters. The top-20 hitters also showed much better year-to-year correlations of 0.4314 (2009-2010) and 0.4543 (2010-2011). These correlations are by no means precise, but hitters are way more reliable than pitchers, which is a predominant factor in why pitchers are infrequently selected in the first few round of fantasy drafts.

Concluding Thoughts


Hopefully, all of this has enhanced your understanding of my methodology. Jeff Gross' E.Y.E.S. method and Mike Silver's FantasyPlayerRater formula, as we discussed Friday, showed strong correlations both to each other and to my method, so once we start getting into some Oliver-driven objective rankings, which are slated to begin Tuesday, following along with their valuations shouldn't be much trouble, either.

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