Feb 29, 2012

Brandon Beachy - 2012 Fantasy Sleeper Candidate


There are a couple of fantasy baseball deep sleepers each year that can make the difference for your team. The following 2012 profile of Brandon Beachy will analyze the chances that he might be one of the next big sleepers. He was chosen by my sabermetrically-inclined method which is detailed in this introductory post. 

ADP as of this posting: 114.81 (Round 10 in 12-team league)
Projected 2012 Role: ATL #3 SP
2011 Production: 7-3 W/L, 3.68 ERA, 1.21 WHIP, 169 K in 141.6 IP
My 2012 Prediction: 14-10 W/L, 3.00 ERA, 1.15 WHIP, 200 K

Many people were excited about Mike Minor as a sleeper last year but then this Brandon Beachy character came along and stole his spot in the Braves' rotation. Little did we know just how good Beachy was but we should have known good things were coming based on his 11.1 K/9 with 2.1 BB/9 in his previous minor league season. Which means that we shouldn't have been shocked when he blew through the majors with a 10.7 K/9 and 2.9 BB/9.

But, in 2012, the question is: how is a guy with those numbers not being drafted earlier? Tim Lincecum had a lower K/9 and a higher BB/9 than Beachy after all!

The biggest red flag for him is about his ability to pitch a full season without health concerns. Coming into the minors, he was used primarily as a reliever and then slowly started to transition into a starting pitcher. From 2008 to 2010, he only started 21 total games and had 208.6 IP. So there's concern about the toll that suddenly starting 25 games after that with 141.6 IP will have upon him. However, this transition has been done before by folks like C.J. Wilson and it has gone smoothly so this might be an unwarranted concern.

The other concern would be that his BB/9 wasn't exactly at an elite level in 2011. While it was above average, the best of the best have a BB/9 closer to 2.00. That is a slightly fair criticism but it is a small sacrifice to make for one of the best strikeout rates in baseball. However, it should be noted that his BB/9 over his 208 minor league innings was 2.11 so he certainly has potential to reduce that number in 2012.

He becomes even more interesting when you add in the fact that he's pitching in the NL and plays for a team that should accumulate a good number of wins. Beachy may be a surprise addition to your roster this season but he has the potential to be an ace for your team.

Sleeper Verdict: Sleeperific. He established himself as one of the best strikeout artists in the league and has an above average walk rate that is set to improve more. What's not to love in the 10th round?

Feb 27, 2012

How WERTH Roto Values Are Calculated


A couple of friendly readers have e-mailed me in the past couple weeks asking for more information about how the WERTH Roto Values are calculated in the cheatsheets. The process itself isn't overly complicated but the execution of it becomes a bit trickier because of a few wrinkles along the way.

First and foremost, it should be explained that WERTH values are a way of standardizing all stat categories to be on the same scale for roto leagues. The goal is to easily compare players with different strengths such as a 20 HR, 20 SB hitter to a 40 HR, 5 SB hitter.

In statistical terms, WERTH values are just z-scores for each roto category based on the projected fantasy starters. For those unfamiliar with z-scores, it is the measurement of how many standard deviations away from average a data point is. A piece of data within a set that has a z-score of 0 would be the absolute average, a z-score of 1 would show that it is one standard deviation above the average while a z-score of -1 would be one standard deviation below average. The size of the standard deviation depends upon how much variation there is within the data set (a data set with a lot of similar values would have a small standard deviation for instance). Here's a chart that shows examples of that concept from a sample fantasy league:

StDev Avg -1 z-score 0 z-score 1 z-score
HR: 7.5 19 11.5 19 26.5
RBI: 16 76 60 76 92
SB: 10 11 1 11 21
So, in this league, a player's 21 SB's has the same relative value as a player's 92 RBI's. To reflect that, you'd see in your Mr. Cheatsheet spreadsheets that the player has a 1.0 WERTH in both RBI's and SB's and you could recognize that he's projected to deliver relatively similar value in both.

For those that are interested in doing their own WERTH-type calculations so you can maybe make some personal modifications to it, here are the basic steps to it:

Step 1: Calculate who the expected league starters are in your league. A person in a 12 team league with 10 starting hitters would need to identify the 120 expected starting hitters. My 120 hitters in this scenario are chosen based off the top 12 drafted at each position (from their ADP).

Step 2: For those projected 120 starters, calculate the average and standard deviation for each projected stat.

Step 3: For each stat for each player, compare that to the average and standard deviation of that stat. The formula for calculating a z-score when x represents a player's projected output in a stat is: (x - average) / stdev. So, in that sample league above, (21 SB - 11 SB) / (10 StDev) equals 1.00 for instance.

That's it! Well, except for rate stats like ERA, WHIP and AVG. It's a bit more complicated for those. For ERA, you have to factor in projected innings (a reliever projected to pitch 60 IP with a 3.00 ERA will have a smaller effect on your team ERA than someone who pitches 220 IP of 3.00 ERA). So, you'd calculate the average total innings that each fantasy team would have in a season and the average projected ERA across the league. Then for each player, you have to substitute in his projected innings and ERA to see how the average team ERA would change. For instance, maybe 220 IP of Justin Verlander would lower an average team's ERA by 0.20. Now, for each player, you would compare all of those Changed ERA amounts to each other by doing Step 2 and Step 3 above.

So, that's the basic thought process to calculate any WERTH value you'd want. Luckily, the cheatsheets here do all of that calculation for free for almost any imaginable league setting you could think up. But, let's address some possible criticisms of these WERTH values:

Why not adjust for position scarcity within the roto values? Well, that could be done quite simply in a number of ways. But, really, your place in the standings doesn't change because you got 20 HR from a catcher instead of an OF. In the end, I wanted a raw piece of data which would easily allow us to look at positional averages and compare. Positional scarcity helps to measure economical value which is needed for auction leagues but the goal of WERTH is to standardize the stats first and foremost. By putting players on different scales based on position, it would complicate that goal since Matt Kemp's WERTH wouldn't really be on the same scale as Dan Uggla's WERTH. In the cheatsheets, you can see the average for each position anyhow to make an adequate comparison on your own.

Why not do Standings Gain Points? Standings Gain Points look at the final standings of roto leagues to try to determine the average for what you need to go up one spot in the standings in a year (could be 6 HR's move you up one spot in the standings, for instance). If there was one standard league type that everyone was using then this work great. But, the number of people who play in the exact same league type are few and far between. Every league is wildly different and the SGP's from one league do not apply to another. For an already established league, one minor change such as adding a team would completely negate the past SGP's that you may have calculated. Basically, they are highly individualized and not very flexible to apply to each and every league, which is the goal of these dynamic WERTH roto values.

Why compare to "league average starter" instead of "replacement level player"? The original goal of WERTH was to simply provide a point of comparison among players in a standard roto draft where stats are all on different scales. It has since evolved to include auction drafts where a league's economy becomes important and using replacement players as a baseline becomes necessary for dollar value. In the auction cheatsheets here, the dollar values are projected by taking replacement level players into consideration but the goal of WERTH is still to compare one player against all other players in a league (as opposed to comparing that one player just to a minimum baseline). Thus, WERTH gives you a sense of each player's worth (pun intended) in your fantasy baseball league's unique universe.

Feb 24, 2012

John Mayberry Jr. - 2012 Fantasy Sleeper Candidate


There are a couple of fantasy baseball deep sleepers each year that can make the difference for your team. The following 2012 profile of John Mayberry will analyze the chances that he might be one of the next big sleepers. He was selected based on my simple selection process which is detailed in this introductory post. 

ADP as of this posting: 227.37 (Round 19/20 in 12-team league)
Projected 2012 Role: Possible Starting OF
2011 Production: .273 AVG, 15 HR, 37 R, 49 RBI, 8 SB in 296 PA
My 2012 Prediction: .270 AVG, 25 HR, 80 R, 85 RBI, 15 SB if he starts

Mayberry has pummeled the baseball in every chance he's gotten in the big leagues with a .253 Isolated Slugging Percentage (ISO) thus far in his short career. That type of ISO would be better than the likes of Albert Pujols and Miguel Cabrera so let's not foolishly expect that to continue. While he was always a bit of a slugger in the minors, it wasn't quite this apparent with his .199 ISO in the lower levels. If he reverts back to that .199 ISO, he'd still be in good company with players like Andrew McCutchen or Aramis Ramirez who posted a similar number last year. Regardless, it seems likely that he could hit 20-25 HR's in a full year of play with potential to even approach 30 HR's based on his current body of work.

For fantasy purposes, there's reason to be excited about a guy with good power that is available in the later rounds. It gets even more exciting when we see that he has the ability to steal some bases. In the minors, he stole 63 bases while only getting caught 13 times (20 SB and 3 CS recently in 2010). So, he's an efficient base stealer which means they should continue to let him run periodically and 10-20 SB in a season isn't out of the question.

There's been nothing not to like about Mayberry thus far in this post so that batting average can't be sustainable, right? Sorry, my friend, but it seems that the .273 AVG last year was legit (based on xBABIP) though it was higher than his AVG  from any of his minor league seasons. This has the most potential to drop a little but it shouldn't drop very far if it does. For roto teams, he won't help you in this area but he won't hurt you either.

But, finally, here's the bad news: Mayberry may be in an outfield platoon in 2012. I've seen many good sleepers ruined this way and he certainly could fall into that trap. There's been a ton of mixed signals thus far from the Phillies about whether Mayberry is the starter or not. This offseason the Phillies brought in Laynce Nix as a possible platoon partner for Mayberry in LF (Hunter Pence and Shane Victorino already taking up the other OF spots) and there's still the issue of Domonic Brown not wanting to go back to Triple-A. So he certainly has some competition that will make it hard for him to be the clear-cut starter all year long unless he comes out swinging a hot bat. Combine those issues with the fact that Mayberry is already 28 years old and you can see the dilemma. It's for these reasons that Mayberry isn't being touted as an earlier round pick. which adds to his possible value for you in the case that he does get the job.

Sleeper Verdict: Sleeptastic. Mayberry has the potential to deliver huge rewards for those who take a risk on him. A 20/15 season is possible or, very optimistically, even a 30/20 season could happen. It will be a matter of if he can claim the starting LF role and avoid being caught in a platoon.

Feb 22, 2012

2012 Fantasy Cheatsheets | Late February Update


Another round of fantasy baseball cheatsheet updates is here today! The three available 2012 cheatsheets are powerful Excel files made for your roto draft, roto auction or point-based league drafts. They each pull in data from projection systems, expert rankings and ADP sites and let you choose what you want to display so you can make the cheetsheet unique to you. For roto leagues, the spreadsheet also has the neat feature of showing you the expected roto points gained (WERTH value) in each stat based on your individual league settings and the projections you select.

The following blog posts about the initial release of the cheatsheet have much more of the basic information about what to expect out of these: Point Leagues, Roto Draft, Roto Auction. When opening up the spreadsheet, you may be asked to enable macros... Please enable macros as that is the only way this spreadsheet can work! 

The latest updates to the cheatsheets contain eight new roto categories for the roto sheets and include new ADP data (from Yahoo), expert rankings and projections (ZiPS). For hitters, the new roto categories are Total Bases, Total Bases + Walks, Doubles, and Triples. For pitchers, the new roto categories are Innings Pitched, Strikeouts - Walks, Hits Allowed, and Walks Allowed. Aside from those big updates, the most important change here is getting the ZiPS projections and Yahoo ADP added into the sheets. At this point, we're just waiting on the Marcel and Steamer projections before I can add in the Combined projections for the final release.

In addition to those features, any ADP or projection data that was previously in the cheatsheets has been updated with whatever updates were available.

Download the 2012 Roto Draft Cheatsheet or
Download the 2012 Roto Auction Cheatsheet or
Download the 2012 H2H Points League Cheatsheet

Data included for this release:
  • CAIRO projections
  • RotoChamp projections
  • Fangraphs Fan projections
  • ZiPS projections
  • MockDraftCentral ADP data
  • Yahoo! ADP data
  • ESPN staff rankings
  • ESPN (T. Cockcroft) rankings
  • RotoChamp rankings
  • Roto Summit rankings
  • Yahoo! rankings
  • Clubhouse GM rankings
  • Combined rankings (average of available rankings)
New features for 2012 version compared to 2011:
  • Team Summary tab added (as explained above)
  • Live Standings now only based on team's starters as opposed to all drafted players
  • Draft Central tab is redesigned to be easier to read and follow
  • Customize button added to have more control over what is shown and not shown
  • Ability to sort by position now added so you can more easily target a specific position when needed
New features in this version compared to last:
  • Four new roto categories for batters and four for pitchers
  • Added ZiPS projections to the available projections to choose from
  • Added Yahoo! ADP data and rankings
  • Updated Fangraphs Fan projections and MockDraftCentral ADP data
  • Updated any expert rankings that have changed
  • Added 3 new expert rankings and updated combined rankings
  • Added a few extra players to the default draft pool
So, that's the latest update to the sheets. There will be additional updates in the near future as additional projections become available. Any major updates to the cheatsheets may be tougher to do at this point but let me know if there any features that might be worth adding. I still haven't addressed problems for Mac users or those with Excel 2003 or earlier. I'm going to look into the possibility of doing AL and NL only versions of the cheatsheet as well, if the demand is large enough.

Also, be sure to check out my Twitter feed to know when any minor updates are made to the cheatsheets.

2011 Projections Flashback - Predicting Hitter Stats


Your fantasy baseball draft preparation will only be as good as the projections that you are working from because good input equals good output. There are a multitude of great projection systems out there right now but which one should you choose? Let's look at last year's results to help answer that question.

Disclaimer: For my casual baseball fans, this is going to be a lengthy data-heavy article so feel free to sit this one out. For my analytical friends, keep in mind that I'm not a statistician and there are likely better methods to use than I chose here. For the people in between, enjoy!

Step 1
I gathered data from 7 different 2011 projections and eliminated the players that weren't shared by all of them. I also averaged the projections from the 4 free options (Marcel, Steamer, ZiPS, and Cairo) to create an 8th projection as well. The systems were:
  • Marcel: The most basic forecasting system around - it takes three years of player data, weights the most recent years heaviest and regresses the players towards a mean (age factor included)
  • Oliver: Similar concept to Marcel with a few wrinkles about how minor league stats are calculated (park/league factors included)
  • PECOTA: A bit more complicated in that it finds comparable players to each projected player and bases the projections on the history of those comparable players
  • Roto Value: Projects a stat, does age regression and then historical skill regression
  • Steamer: I haven't found a full explanation of their current model but historically it takes three years of player data and regresses certain stats more heavily than others without an aging factor
  • ZiPS: Does a little of the weighted regression like Marcel but for four years and does a bit of the comparable player regression based on aging trends
  • Cairo: It's like Marcel but with more bells and whistles (stat-specific regression and position-specific regression for instance)
  • MSZC: Averaging the four free projections for a player into one projection
For fantasy purposes, I eliminated players who ended up with less than 300 AB in 2011 as a majority of them would be part-time players that weren’t necessarily relevant to projecting for fantasy baseball. There were 214 players left over at this point.

Step 2
From those 8 projections and actual 2011 stats, I only included the 5 stats that are most commonly used for fantasy baseball hitters: AVG, HR, R, RBI and SB. As a separate sixth stat, I converted each of those stats for a player to a z-score and then added the five z-scores to create a “total 5x5 value” which gave us 6 points of comparison between the projections and the results.

Step 3
I threw all of the projections into a magical machine, which spat out the correlation to 2011 results (r-value) and the root mean squared error (RMSE). The correlation value helps us see how well the systems ranked the players in each statistic. RMSE will show us badly the projections missed the mark on their projections (with larger errors receiving extra punishment). Basically, in this instance, larger correlation values are better while smaller error values (RMSE) are ideal.

Results – Actual 2011 Stats vs. 2011 Projections (300+ AB)
Correlation
AVG
Rk
HR
Rk
Runs
Rk
RBI
Rk
SB
Rk
5x5
Rk
Marcel
0.42
6
0.71
7
0.50
7
0.57
7
0.78
6
0.51
8
Oliver
0.47
4
0.73
5
0.53
5
0.64
3
0.81
2
0.57
3
PECOTA
0.49
2
0.73
4
0.49
8
0.61
6
0.80
5
0.57
4
RotoValue
0.41
8
0.69
8
0.51
6
0.57
8
0.70
7
0.53
7
Steamer
0.45
5
0.74
3
0.59
2
0.67
1
0.81
1
0.60
1
ZiPS
0.49
1
0.75
1
0.57
3
0.63
4
0.63
8
0.56
5
Cairo
0.42
7
0.72
6
0.55
4
0.61
5
0.81
4
0.55
6
MSZC
0.47
3
0.74
2
0.61
1
0.66
2
0.81
3
0.58
2
RMSE
AVG
Rk
HR
Rk
Runs
Rk
RBI
Rk
SB
Rk
5x5
Rk
Marcel
.026
6
6.94
6
19.1
4
20.5
6
7.1
5
3.38
7
Oliver
.026
4
7.04
7
19.8
6
20.7
7
6.8
3
3.26
4
PECOTA
.025
1
6.82
4
19.9
7
19.8
4
7.1
4
3.26
3
RotoValue
.030
8
7.61
8
22.8
8
24.1
8
8.4
7
3.39
8
Steamer
.026
5
6.72
3
19.0
3
19.4
2
6.7
1
3.16
1
ZiPS
.025
2
6.50
1
18.7
2
19.5
3
11.4
8
3.26
5
Cairo
.027
7
6.88
5
19.2
5
20.2
5
6.7
2
3.30
6
MSZC
.026
3
6.50
2
17.3
1
18.5
1
7.1
6
3.16
2
In terms of correlation, Steamer did quite well across the board here while RotoValue, Cairo and Marcel lagged behind. ZiPS did great with the exception of stolen bases which were so bad that they also hurt the correlation to the 5x5 total roto value stat.

When factoring in the frequency and size of the errors, Steamer and the combination of free projections seem to be kicking the most butt thus far. Towards the end here, we’ll come up with a definitive ranking. But, first, there’s more work to do…

Step 4
Comparing projections to actual results brings back some good information. However, it should be noted that forecasters tend to start by projecting base stats and then adjusting for playing time at the end. We've already compared to that final result but I also want to know how well each system does before playing time is factored in. So, I took all of the projections and actual stats for each player and adjusted them onto the same 500 AB scale (though it could be any amount and the results would be the same). Would the projections change? Are some projections good at predicting player output but not as good with getting playing time correct?

Results – Adjusted 2011 Stats vs. Adj. 2011 Projections (300+ AB)
Correlation
AVG
Rk
HR
Rk
Runs
Rk
RBI
Rk
SB
Rk
5x5
Rk
Marcel
0.42
6
0.77
6
0.59
3
0.65
7
0.82
6
0.58
5
Oliver
0.47
3
0.78
3
0.61
2
0.70
2
0.83
5
0.63
1
PECOTA
0.49
2
0.78
4
0.46
8
0.68
4
0.83
4
0.62
3
RotoValue
0.41
8
0.74
8
0.56
7
0.61
8
0.79
7
0.58
6
Steamer
0.45
5
0.78
1
0.58
5
0.71
1
0.84
2
0.62
2
ZiPS
0.49
1
0.78
5
0.59
4
0.67
5
0.66
8
0.57
7
Cairo
0.42
7
0.77
7
0.57
6
0.66
6
0.84
1
0.57
8
MSZC
0.47
4
0.78
2
0.63
1
0.69
3
0.84
3
0.61
4
RMSE
AVG
Rk
HR
Rk
Runs
Rk
RBI
Rk
SB
Rk
5x5
Rk
Marcel
.026
6
5.65
4
11.3
3
14.6
5
6.1
4
2.71
5
Oliver
.026
4
5.73
7
11.0
1
14.0
4
6.2
5
2.71
4
PECOTA
.025
1
5.65
5
12.4
8
14.0
3
6.1
3
2.71
3
RotoValue
.030
8
6.05
8
12.2
7
16.0
8
6.7
7
2.79
7
Steamer
.026
5
5.51
2
11.8
5
13.5
1
6.0
2
2.65
1
ZiPS
.025
2
5.63
3
11.8
4
14.8
6
11.1
8
2.74
6
Cairo
.027
7
5.70
6
12.2
6
14.8
7
5.8
1
2.82
8
MSZC
.026
3
5.51
1
11.2
2
13.9
2
6.4
6
2.65
2
The results are somewhat similar to what we saw from the results with playing time included except it seems that Oliver seems to improve quite a bit in this scenario. But, let's break this down and see who the actual winners are...

Step 5
We have a ton of funky numbers on all sorts of different scales and we still don't have an answer on which system does the best for fantasy baseball hitters. If I were to add up the rankings for each projection, we would have an answer but it wouldn't recognize those times when 1st, 2nd and 3rd were a virtual tie and when last place was far, far behind the others. To account for that, I converted the rankings to standardized z-scores to show how far above or below average each projection was for each stat. So, in comparison to the actual 2011 statistics (playing time included), here are the overall results for correlation, RMSE and the combination of the two:

Correlate AVG HR Runs RBI SB 5x5 Corr. Total
MSZC 0.5 0.8 1.5 1.0 0.6 1.0 5.3
Steamer -0.1 0.7 1.2 1.3 0.7 1.3 5.1
PECOTA 1.1 0.3 -1.1 -0.2 0.4 0.4 1.0
Oliver 0.5 0.3 -0.3 0.5 0.7 0.5 2.2
ZiPS 1.2 1.1 0.5 0.4 -2.1 -0.1 1.1
Cairo -1.0 -0.5 0.1 -0.2 0.6 -0.5 -1.5
Marcel -0.9 -1.0 -1.0 -1.4 0.2 -1.6 -5.7
RotoValue -1.3 -1.8 -0.8 -1.4 -1.0 -1.0 -7.5
RMSE AVG HR Runs RBI SB 5x5 RMSE Total
MSZC 0.5 1.1 1.4 1.1 0.3 1.2 4.4
Steamer 0.2 0.4 0.3 0.6 0.6 1.3 2.1
PECOTA 0.8 0.2 -0.3 0.3 0.4 0.2 1.3
Oliver 0.3 -0.5 -0.2 -0.2 0.5 0.2 .0
ZiPS 0.8 1.1 0.5 0.5 -2.3 0.2 0.5
Cairo -0.2 .0 0.2 0.1 0.6 -0.4 0.7
Marcel -0.1 -0.2 0.3 -0.1 0.3 -1.3 0.3
RotoValue -2.3 -2.1 -2.1 -2.3 -0.5 -1.4 -9.2
All
MSZC 9.7
Steamer 7.2
PECOTA 2.3
Oliver 2.2
ZiPS 1.6
Cairo -0.8
Marcel -5.4
RotoValue -16.7
The combination of the free projections is the winner here mainly because of how much better those projections are at minimizing the size of the errors as seen by that great RMSE z-score. That shouldn't be all too surprising since any extreme projection is brought closer to normal when projections are combined with each other. It takes some of the crazier data and brings it all closer to a safe middle ground.

Now, when we look at the results that remove playing time from the equation, the rankings end up shifting around quite a bit with Oliver and Marcel taking huge leaps while ZiPS takes a huge drop:

300 Adj. Corr. Total RMSE Total All
MSZC
4.0
3.5
7.5
Oliver
4.3
2.3
6.6
Steamer
3.5
2.6
6.2
PECOTA
0.9
0.7
1.7
Marcel
-2.0
1.1
-0.9
ZiPS
-1.5
-1.9
-3.4
Cairo
-2.5
-1.1
-3.6
RotoValue
-6.8
-7.4
-14.2
When all is said and done, Steamer handily wins when it comes to actual results yet Oliver narrowly wins when playing time isn't factored in. However, neither can beat the power of a simple combined projection system in this experiment.

Step 6
Maybe I picked the wrong amount of AB’s to use as my filter though. Perhaps, if I included players with less playing time then the results would shift around. Well, let's see! I ran the same experiment to include all shared players above 100 AB in 2011 (321 of them). Here is what the final z-score rankings were in that case:

100 ActualCorr. TotalRMSE TotalAll
MSZC
5.5
4.0
9.6
Steamer
5.0
2.0
7.0
PECOTA
1.7
2.4
4.1
Oliver
2.9
0.8
3.7
Marcel
-0.6
3.5
3.0
Cairo
-1.5
-1.3
-2.8
ZiPS
-1.7
-3.1
-4.7
Roto Value
-11.3
-8.5
-19.8
100 Adj. Corr. Total RMSE Total All
MSZC
3.8
2.5
6.2
Oliver
3.6
2.1
5.8
PECOTA
2.0
2.9
4.9
Steamer
2.7
2.1
4.7
Cairo
0.4
0.3
0.7
Marcel
-0.9
1.0
0.1
ZiPS
-1.4
-2.4
-3.7
RotoValue
-10.2
-8.5
-18.7
The gaps aren't quite as wide but the standings are similar with Steamer doing well when it comes to actual results but Oliver doing better when playing time isn't a factor. However, it should be noted that Marcel does markedly better here when taking into account these players who got less playing time.

When it comes to 2011 forecasts for hitters for fantasy baseball purposes, Steamer gets the gold medal with Oliver and PECOTA getting silver and bronzes. Despite that, I still bow down to the power of combining projections to help reduce the size of any projection errors.