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Introducing the Razzball Player Rater + FIP/ERA & BABIP/AVG Comparisons

April 18, 2012 By: Rudy Gamble Category: Player Raters, Rudy Gamble 100 Comments →

With great pride and bland post titling, I’d like to announce a Beta release of our fantasy baseball in-season player rater as well as two charts that highlight the differences between pitcher FIP vs. ERA and batter BABIP vs. AVG.

The player rater work is an adaptation of the Point Shares methodology I’ve used the last couple of years for pre-season and post-season player estimates.  Here is a link to a favorable test I did earlier this year vs. ESPN’s player rater methodology.  After some trial and error plus assistance from a variety of folks (Eric K at my favorite fantasy baseball escort service – EliteFantasyPlayers.com - and Doug at Dougstats.com among others), we now have a fairly automated system for updating in-season player rankings on a daily basis.

The Razzball Player rater is at –> Fantasy Baseball Player Rater and covers the following 5×5 league formats:

  • ESPN Roster format (C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team / 14 Team / 15 Team / 16 Team MLB
  • Yahoo! Roster format (C/1B/2B/SS/3B/3 OF/2 UTIL/2 SP/2 RP/4 P) – 10 Team / 12 Team / 14 Team / 15 Team / 16 Team MLB
  • AL-Only (2 C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team
  • NL-Only (2 C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team

The table is ranked based on a players’ projected Point Shares (a player’s value in standings points vs. the average player with some factoring in of position).  Dollar estimates are provided both for in-season as well as comparisons vs. pre-season estimates.  Take the dollar estimates with a grain of salt for now – they should become more stable as the season goes on.  You can filter by position (P for all pitchers, -P for all hitters) and sort by any of the columns (1 click ascending, two clicks descending).

There are two pages focused on popular hitter/pitcher stats outside of 5×5 (popularity based on our pre-season poll results).  These tables are filterable/sortable as well.

  • Hitting – OBP, SLG, OPS, Hits, Total Bases
  • Pitching – Quality Starts, Holds, Losses

Lastly, there are two tables that highlight differences between pitcher FIP vs. ERA and hitter BABIP vs AVG.

  • The pitcher table is sorted based on the ‘luckiest’ pitchers – i.e. pitchers ranked in descending order based on the difference between their FIP and their ERA.  For those wondering why I chose FIP vs. xFIP, I do not have access to the league-average fly ball to home run ratio nor pitcher HR:FB ratio.  You may also find that my FIP estimates are slightly off from other sources – this is mainly because I cannot currently separate out intentional from non-intentional walks but it can also be due to how the ‘constant’ is applied to bring the league average FIP in the 3.20 range.
  • The hitter table is sorted based on the ‘luckiest’ hitters - i.e. hitters ranked in descending order based on the difference between their current AVG and their expected AVG.  A hitter’s expected AVG is calculated by applying a hitter’s 3-year BABIP to their in-season performance.  3-year BABIP was used as this stat does vary per hitter based on various factors (line drive rate, their speed, GB to FB ratio, etc.) but a hitter’s BABIP tends to be steady in the long run.  Hitters with less than 100 AB in the previous 3 years are given the league average BABIP of .300.

I’ll do my best to keep these tables updated daily (generally by 10 AM EST).  The last column of each chart reflects the last games included so it will be transparent when it has not been updated for a couple days.  While I will do my best to keep on top of the moving pieces, please do not hesitate to provide the following information in the comments section of Grey and/or my posts:

  • Any missing players from the tables (for now, I’m including any hitter/pitcher with 1+ AB or 0.1+ IP.  That minimum threshold will likely increase as the year goes on.
  • Any position eligibility changes based on 10 game in-season eligibility (I know Yahoo! is 5 games but prefer to make one change across both).  For hitters where position eligibility seems imminent (e.g., Jesus Montero at catcher), I include the additional position and add an asterisk at the end of it.
  • Any wonky data or functionality

Other potential FAQ:

  • Will you ever have a ‘rest of season’ player rater?
    • Maybe.  Would be dependent on a respected projection source providing an uploadable file that is 1) updated on a regular basis, 2) accounts for expected playing time, and 3) is free.
  • Will you create a dynamic player rater to reflect any conceivable league format?
    • Not planning on it.
  • Was your wife turned on by this accomplishment?
    • Nope.  She prefers it when I go Don Draper and fix shit around the house in a white t-shirt.

Review of 2011 Fantasy Baseball Player Rankings

April 03, 2012 By: Rudy Gamble Category: Rudy Gamble, Testing Projections + Player Raters 109 Comments →

In a previous post, I laid out a methodology for testing fantasy baseball player rankings/auction values and all the components involved in projecting player values.  I got feedback from some smart folks that didn’t ‘get’ the test.  Since the common variable in that equation was me, I’m going to try explaining it one more time before I jump into the results of my test across 14 player rankings across 12 sources (2 f0r ESPN & Razzball) + the Average Draft Position (ADP) for the 456 (38 leagues of 12 teams) Razzball Commenter Leagues participants.  (feel free to skip the next paragraphs if you just want to see the results).

There are three main components to developing pre-season fantasy baseball rankings:

  1. Playing Time Estimates (PA/AB and IP)
  2. Statistical Projections (HRs per AB, K/9, etc.)
  3. Methodology For Converting #1 and #2 Into Player Rankings/Value

Most player rankings are published as ‘Top 200′ or ‘Top 300′ with no stat projections.  I call these ‘curated’ lists.  Each curator creates their list differently but I’d gather that they probably all use some source for Playing Time and Stat Projections and then use their fantasy experience to determine each player’s value (vs. an actual formula).  For testing purposes, you have to test the composite effectiveness of these three variables since they aren’t explicitly listed in the rankings.

It would seem straightforward to just test each Player Ranking (or isolated component) against the end of year results.  Matt Swartz of FanGraphs did a great test of Projection systems isolating wOBA and ERA.  But here are the challenges that I see for such a test to have fantasy baseball relevance:

  1. The test needs to consider all the fantasy baseball categories.  If you just focus on ERA, what about Wins, WHIP and K’s?  How would that help with Closers?  So you would need to figure out a way to test and properly weight each statistical category.
  2. There is a BIG difference if your pre-season ranking screw up the value of a Top 3 round player vs a late round player.  So you would need to figure out a way to weight players based on their likely draft status.
  3. If a player misses significant playing time (for a hitter, let’s say 30+ games), the timing and sequencing of these missed days as well as the availability of waiver/FA pickups plays a major role in terms of the player’s impact on a team’s success.  For instance, a player like Chipper Jones who might miss games throughout the season is harder to replace than Nelson Cruz who goes on 15 day DL trips.  If a player gets sent down before opening day (like Lonnie Chisenhall), it’s even easier to adjust for the loss in expected playing time.  So you would need to credit replacement level performance that varies depending on the timing/sequencing of missed playing time.
  4. The timing and sequence of a player who underperforms can dramatically change how a player impacts one’s fantasy team.  Let’s say Player A and Player B had equal pre-season rankings and both underperform, hitting .220 with 15 HRs for the season.  If Player A goes .150 with 0 HR in April and Player B goes .240 with 3 HR, Player A will likely be dropped quicker and that team might find a replacement player who performs above replacement value.  So you would need to account for how quickly/slowly a player was replaced as well as estimate the impact of the replacement player.

Now, I can see an argument that particularly #3 and #4 come down to chance vs. a prognosticator’s skill.  But that’s not entirely true.  Some players are more injury-prone than others and they may be marked down accordingly (e.g., I knocked Kinsler’s playing time down about 50-75 PAs from my sources which took his value down about 10 spots in the rankings.  Shouldn’t I be credited/hurt because of that choice?).  And while there is some chance involved, I’d argue it’s still better to reflect the real impact these chance instances had on teams vs. find ways to remove them (like testing a rate stat like HR per AB instead of total HR).

So here is the test I put together that I think addresses these ‘near impossible to model’ variables:

  1. Take the draft results by team from the 38 Razzball Commenter Leagues in 2011 (hosted on ESPN using ESPN’s default league formats for 12 team 5×5 MLB leagues).  This amounts to 456 teams’ worth of draft data.
  2. Create team total values based on ‘expert’ rankings/$ totals/other arbitrary metric (like Point Shares or THT Z-Score)
  3. See how these team totals correlate with each team’s final Total Standings Points

Since we’re testing based on a team’s pre-season aggregated player value and their end of year Total Standings Points, we are factoring in all the categories (#1).  The pre-season rankings provide a natural way to weight each player’s value/impact by matching them up against ranked auction dollar amounts (#2).  Since the end of year Total Standings Points reflects how fantasy baseball managers behaved when facing situations involved lower than expected playing time or performance, it accounts for these two last variables (#3 and #4).

Hope that makes sense to everyone!  Now on to the good stuff!

2011 Pre-Season Rankings Tested (click here for the aggregated results, links provided for rankings that are still posted):

Here are some general notes before I start throwing data out:

  • Most rankings do not provide $ estimates.  Since there is a larger gap in value between earlier draft picks vs. later (e.g, average gap between 10th and 11th best players greater than difference in 150th vs 151st best players), I converted all rankings into dollar estimates by providing the $ estimate for the corresponding rank from my Point Shares.  I used the published dollar estimates if they were available (vs forcing my $ estimates).  For HardballTimes and my Point Shares, I used the player scores since they estimate the value between picks (vs. just an arbitrary 1 between picks when done as rankings).
  • I believe all these rankings were released between Feb 1 and March 30th with the majority in the March time frame.  To be fair, I gave everyone the same values for the following players whose value dropped significantly during Spring Training – even if they had not ranked them.  The values are based on my Point Share value as of late March (I put a Z-score equivalent to $6 for THT):
    • Adam Wainwright – $0 – Tommy John surgery
    • Chase Utley – $6 – Knee issues became more problematic
    • Kendry(s) Morales – $6 – Comeback plans hit a snag
  • Most rankings do not specify the league format (# of teams, positions, etc).  Since the Razzball Commenter League format is about as standard as it gets (12 team, 5×5, ESPN roster format), I thought it was still fair to include in this test.  When possible, I used a source’s ’12 team MLB’ rankings/estimates.
  • Some rankings (ESPN/Matthew Berry, KFFL, USAToday) ranked only 200 players.  I tested the impact of adding the next 100 players based on a composite of the other rankings and the correlation percentages decreased.  I didn’t feel this was fair so I kept it as just the top 200.
  • Any player not in a source’s rankings who was drafted was valued at $0.
  • I capped (or is it floored?) any estimates at the equivalent of $0 as only some sources such as Point Shares and Last Player Picked report negative dollar values for players.  This is probably for the best, anyway, as players with negative projected value before the draft are dropped like luxury good brand names in rap songs – early and often.
  • In the previous post, I tested whether there was a clear bias in the RCL teams’ draft behavior.  The ADP of the RCL teams’ correlated closer to the ESPN Top 300 (96.7%) than either Grey’s rankings (92.6%) or my Point Shares (81.5%).  While I don’t have a second non-RCL sample to confirm, I would theorize the largest bias in the RCL teams’ behavior is driven by ESPN being the draft host and providing default rankings.

Here are the test results – see the following file for the raw results including the draft picks per team and the total standings points per team:

Chart #1:  Correlation % Between Team Final Standings’ Points & Team Drafted Player Projected Value By Rankings Source 

Rank Source $ Converted Correlation
1 Razzball – Point Shares (Late March) N 8.0%
– Razzball – Point Shares (Late March) Y 7.7%
2 Razzball – Grey’s Rankings Y 7.7%
– Razzball Point Shares (March 8th) N 6.8%
– Razzball Point Shares (March 8th) Y 6.6%
3 RotoChamp top 300 Y 0.0%
4 HardballTimes - Jeffrey Gross (Z-score) N -0.9%
5 RotoExperts Top 300 Y -3.2%
6 KFFL Top 200 N -3.5%
7 FantasyPros Aggregated Top 300 Y -3.88%
8 USAToday Top 200 Y -3.93%
9 RCL ADP Top 100 Y -5.0%
10 Last Player Picked N -5.8%
11 RCL ADP All Y -6.1%
12 SI.com top 300 Y -7.7%
13 CBSSports Top 300 N -8.0%
14 FoxSports Top 300 N -8.1%
15 ESPN Berry Top 200 Y -9.3%
16 ESPN Top 300 N -12.2%

 

For those that care about this sort of thing, here is a link that tests the Point Shares (Late March) against the other sources.  Other that Grey’s rankings, Point Shares performed better at a 99.9+% statistical confidence level.  This means that – based on these results – it would have lost to one of the other sources in less than 1 in 1000 instances.

General takeaways (all specific to the 12-team MLB 5×5 format although I imagine much of it would apply to other formats):

  • The vast majority of pre-season rankings have a slight negative correlation with projected team performance.  Even the few that were positive aren’t very positive (8% being the high) when you consider the previous test showed that 64% of a team’s success is correlated to the actual performance of their draft picks.  It’s likely there are high-stakes fantasy baseball players or subscription sites who could exceeded this 8% but, for now, it’s the ceiling.  So I surmise that…
    • Somewhere around 55% of fantasy baseball team performance is driven by drafted players performing above/below consensus expectations (including injuries) – or, in other words, luck.  (Note: This isn’t the sum of all luck as I would think there is also luck involved around in-season pickups performing above/below consensus expectations)
    • The delta between the best performing rankings and worst performing rankings (~20%) is perhaps a rough estimate of the true difference between the best and worst drafted teams prior to the season (assuming the worst drafter didn’t veer too far from ADP).
    • Further proof can be seen in the nominal difference (-5.0% vs. -6.1%) between the ADP of the top 100 finishing RCL teams vs. all 456 teams.  The Top 100 teams clearly drafted  better judged on final season performance but it couldn’t have been predicted to any significant degree before the season started by any of these sources (and, I’d surmise, anyone)
  • Pre-season standings calculations are – by and large – a waste of time and energy unless it can be shown that the source’s stat + playing time expectations greatly exceeds the 8% ceiling found in this study (i.e., Having the most Point Shares $ value after the draft would increase one’s chances of winning by a negligible amount vs. 1/12th of a percent in a 12-team league) 
  • RCL ADP’s correlation for the Top 100 and All Teams is further from 0% than I expected.  I have a theory later in the post on why.
  • The small delta (0.2-0.3%)  between the $-converted and non-$ converted Point Shares calculations (March 8th and Late March) is a positive sign that no ranking source was significantly helped/hurt by the $ conversion process.
  • Aside from major injury news (Wainwright, Utley, K. Morales), the learnings from early March until opening day are fairly minor.  Point Shares improved from 6.8% to 8.0% in correlation.  This difference is statistically confident at an 81.3% level – so it’s meaningful but not overwhelming so.  So for you multi-league drafters who have a late March draft, don’t feel too much pressure to update your rankings (I would scan for injuries/playing time shifts though).
  • Combining/averaging rankings (like FantasyPros.com does) pays some dividends but is no panacea.  I use multiple sources for both my stat projections and playing time estimates based on the belief that my rankings are more likely to suffer from an outlier than benefit (case in point, if you used just Oliver like THT, you’d have Juan Francisco as a top 10 player in 2011).  But if you are just combining a bunch of ‘safe’ rankings (the next section will define this further), you are not removing any risk.  You are just creating one big vanilla-flavored rankings porridge.  I’d rather identify a base rankings source (I’d recommend Point Shares) and then average it with a 2nd source as a sanity check
    • I averaged my Late March Point Shares with Grey’s rankings and got 8.1% (vs. 8.0% for Point Shares alone).  So no major gain but it didn’t hurt either.
    • I averaged my Late March Point Shares with FPro’s rankings and got 2.76%.  So adding in the safer rankings just dragged Point Shares down towards mediocrity.

Personal takeaways:

  • I’m obviously quite happy to see how (relatively) well my Point Shares did – although it’s humbling how small a percentage it explains in team performance.  Here’s the humbling math – if one team in a 12-team league used +8% correlated rankings (Point Shares) and the others used -8% correlated rankings, that would increase the +8% team’s chances of winning from 8.33% (1/12th) to 9%.  Perhaps that explains why Grey and I didn’t have a ton of fantasy baseball success last year despite doing so well in this test.  (That and Mornoooooooooooo!)
  • I can’t believe Grey did so well.  Given that he published them in February and his percentages beats my March 8th estimates, I’d say he’s the real (if not statistically significant) winner.  It’s quite annoying because it makes this study seem rigged – all I can say is that I did not make any changes to all the sources’ rankings (other than the 3 players noted ab0ve),  RCL Draft results, or final standings.

I ran two correlations to better understand the similarity (or lack thereof) between the various rankings.  The first chart (Chart #2) shows how each source’s player rankings correlates against Point Shares, Grey’s Rankings, and FantasyPro’s aggregated rankings (average of 18 separate rankings).  The second chart (Chart #3) shows how each source’s projected team values correlate vs those three sources.

Chart #2:  Correlations of Each Source’s Player Rankings vs. Point Shares, Grey’s Rankings, and Industry Average (Sorted by Uniqueness to FPRo Aggregated Rankings)

Rankings Correlations
Source  Point Shares (Late March) Grey’s Rankings FantasyPro Aggregated Rankings
HardballTimes – Jeffrey Gross (Z-score) 57% 62% 67%
Razzball – Point Shares (Late March) 100% 73% 82%
CBSSports Top 300 82% 79% 85%
KFFL Top 200 74% 71% 85%
Rotochamp – Top 300 81% 77% 86%
Last Player Picked 90% 79% 87%
Razzball – Grey Rankings 73% 100% 89%
ESPN Berry Top 200 78% 79% 89%
FoxSports Top 300 70% 82% 89%
SI.com Top 300 80% 83% 91%
ESPN Top 300 80% 85% 92%
USAToday Top 200 78% 80% 93%
RCL ADP Top 100 85% 90% 93%
RCL ADP All 84% 91% 93%
RotoExperts Top 300 83% 90% 94%
FantasyPros Aggregated Top 300 82% 89% 100%
Average 78% 81% 88%

Chart #3 Correlation of Team Projected Values By Each Rankings Sources (Sorted by Uniqueness to FPro Aggregated Rankings)

Team Projected Value Correlations
Source  Point Shares (Late March Grey’s Rankings FantasyPros Aggregated Rankings
Razzball – Grey Rankings 2% — 22%
HardballTimes - Jeffrey Gross (Z-score) 57% -7% 37%
Razzball – Point Shares (Late March) — 2% 59%
RotoChamp top 300 67% -15% 61%
ESPN Berry Top 200 37% -4% 63%
Last Player Picked 73% -21% 64%
RCL ADP Top 100 45% 39% 66%
RCL ADP All 43% 40% 68%
CBSSports Top 300 52% 1% 70%
KFFL Top 200 49% 8% 72%
ESPN Top 300 41% -9% 73%
FoxSports Top 300 36% 17% 74%
USAToday Top 200 49% 6% 78%
SI.com top 300 44% 16% 78%
RotoExperts Top 300 47% 31% 80%
FantasyPros Aggregated Top 300 59% 22% —
Average 47% 8% 64%

Some observations:
  • While Chart #2 is a more straightforward test (e.g., compares rankings directly vs. running the results through 456 RCL teams), I think Chart #3 is the better test.
    • Example #1:  Grey’s rankings look close to the other systems in Chart #2 but are wildly unique when valuing RCL Teams.  If Grey’s rankings were as close as Chart #2 suggests, his team values wouldn’t have been so divergent from the rest of the sources (8% correlation is really low)
    • Example #2:  Point Shares should correlate highest with three other automated/quant-based systems (HardballTimes, RotoChamp, Last Player Picked).  This is not the case in Chart #2 but clearly the case in Chart #3.
  • The four most unique rankings in Chart #3 finished in the top 4 in correlating to team success while the 5th (Matthew Berry) finished 2nd to last.  I don’t think this isn’t a coincidence – the most unique rankings should be furthest above/below the consensus rankings.
  • It is odd how ESPN’s Top 300 could finish last and yet be so safe in terms of similarity to consensus rankings.   FantasyPro’s Aggregated Rankings finished middle of the pack so there appears to be some safety in consensus.  Seems most likely that ESPN just had some bad luck.
  • The 4 quantitative-based solutions (HardballTimes, Point Shares, Rotochamp, and Last Player Picked) finish in the top 6 for uniqueness.

Two unsubstantiated theories:

  • I have no idea how to prove this but here’s a theory for why so many rankings are below 0%….Most of the ‘curated’ rankings reflect conventional fantasy baseball thinking (e.g., don’t draft pitchers in the first 15 picks).  This conventional thinking either feeds or just mirrors what ends up as the default rankings within draft software.  Weaker players lean on the default rankings more than stronger players.  While success in a 12-team mixed league requires a lot of luck, there is enough skill during the draft (and in post-draft roster moves) that weaker players in aggregate will perform worse than stronger players.  If weaker players depend more on default rankings/ADPs than stronger players, a negative correlation would arise between team success and any ranking system similar to default rankings.  (If correct, this also means that if the RCL somehow used Grey’s rankings or my Point Shares as the default rankings, they’d automatically fare worse in a test like this.)
  • The common advice of “Zag when others zig” seems true for both experts who publish rankings and fantasy baseball players who seek those rankings out.  If I was curating a rankings list, I’d follow Grey’s lead and take a lot of chances.  (We’ll see if Grey has similar success in 2012).  ’Safe’ rankings have value only if people are drafting without default rankings (or with really bad default rankings).  If I were building a system from scratch to quantify player values, I would do my best to avoid using traditional rankings as a benchmark (I’d also advise against this because there’s a lot more learning curve than you think….I’d just use Point Shares for my draft and invest that time doing something that might get you laid like learning to play guitar.)

Other Notes:

  • I plan on running a similar test at the end of 2012.  We’ve got about 10 more RCLs than last year (38 to 48) – more sample is always better.
  • Since I got such a late start on this analysis, I’m going to hold off analyzing stat projections (Marcel, ZiPs, Steamer, etc.) until the offseason and have 2 years of data to reference.  I”ll only be including projections that are directly available for users.  Projections available for subscription will require direct permission from the publisher.
  • The worksheet provided in Google Docs is considered public domain – feel free to create and publish complementary or contradictory analyses  with it.  Please just link to this post and note the data is available courtesy of Razzball.

Final Note:

ESPN’s 2012 Fantasy Baseball Rankings Rankle, Should Be Ankled

March 12, 2012 By: Grey Category: 2012 Fantasy Baseball Draft 149 Comments →

ESPN fantasy baseball rankings are the same old shizz, different effin’ year.  We’ll get to them in a second.  I gotta build up my anger.  Right now, I’m feeling downright jovial because I just watched the coup de grâce of unintentional comedy — ESPN’s Rankings Roundtables.  If you have a few minutes, watch a part of one.  You don’t need to watch the whole thing, unless you’re into Gitmo’ing yourself.  A few things I noticed from watching a minute of one:  1) Cockcroft looks like he wants to stab Berry in the eye with Stephania Bell’s injury reports.  2) The general air surrounding the proceedings is everyone in the room knows what a bad idea the roundtables are because then people will get to see the methodology behind their rankings is Berry whining, “But guys!”  3) Brendan Roberts sits with his hands folded, trying not to get called on.  But what the casual viewer is missing is Brendan’s also mumbling to Cockcroft to nudge him if he falls asleep.  4) They are in a bare room, but sitting in what appears to be Louis Vuitton chairs.  5) Brendan Roberts gelled his hair for this.  6) I wish they sat Karabell and Cockcroft together so it would look like an Office Space reunion with Michael Bolton and Milton.

I don’t bring these videos up simply because there’s a lack of unintentional comedy since Kim Jong-il passed and can no longer look at things.  I think it gives you an eye into the belly of the beast.  There’s no methodology.  There’s no rhyme or reason.  If you held ESPN’s rankings in any regard before this, now you see their rankings come from a room of guys trying not to upset the head bozo.  So when you see ESPN ranks Michael Bourn 35th overall, you say to yourself, “Yeah, well, the head bozo must’ve wanted him early.”  Then when you see ESPN gives Desmond Jennings the projections of 82/16/51/.256/37, you think that’s great projections, but why is he ranked way down at 104th overall?  There’s no reason!  That’s the take away.  (Side note:  I Googled to see if take away was one word or two.  Didn’t really find a definitive answer, but I found this sentence as an example for take away, “The death of her mother removed the last obstacle to their marriage.”  Huh?  That doesn’t even use take away and the mother dying is the last obstacle for their marriage is the best example they can come up with?  I wonder if Free Dictionary dot com has definition roundtables.  Any hoo…)

There’s some players that stand out with a huge difference between me and ESPN.  One guy I’m going to happily own in multiple leagues is Howie Kendrick.  His ranking on most ‘pert sheets is as puzzling to me as Jay Bruce (which you’ll hear more of if you listen to the podcast that is coming later today.  You can hardly wait.  No, you.  The Razzball Podcast:  Where I sound as sharp as Brendan Roberts’s hair!).  People loved Kendrick for a bunch of years and he disappointed, then he has his best year, is 28 years old, gets Pujols into his lineup and NOW (caps for emphasis, not aesthetics) everyone is down on him.  Hey, Talking Heads, stop making sense.

Next guy that stands out is David Ortiz.  They ranked him 67th overall.  About 120 picks before me.  Then they ranked Adam Lind about 70 spots after Ortiz while giving them nearly the same projections.  Sure, Ortiz is 8 years older so, I guess, he’s got experience.  What he doesn’t have is position eligibility.  I guess it’s better to take a 36-year-old utility man in the 6th round and wait on a 28-year-old 1st baseman.  Yeah, I have no idea either.  My head is starting to hurt, so I’m gonna move on.

Boy, everyone hates Mark Reynolds.  I know, he doesn’t hit for average, but are we all playing in one category leagues?  If so, let a brother know and say brother like Hulk Hogan.  That would help explain Reynolds and Bourn’s rankings.  According to their own stupid Player Rater, Reynolds was more valuable than Wright, Zimmerman and A-Rod last year.  He was nearly as valuable as Longoria, according to them.  Sure, those other 3rd basemen had off years, but you know what Reynolds didn’t have?  An off year.  In fact, in his five year career, if you assume anything .220 or higher is fine for Reynolds, then he’s only had one off year.  I think Ryan Zimmerman has only had one ‘on’ year.  Reynolds ranks 156th overall at ESPN right after… Wait for it… Here it comes… Shoot, I think I left it in the other room… Hold on one second… Okay, here it is… Reynolds is ranked after Carlos Lee!  Wait, huh?!  Next year for their roundtables, they should bring in the real wizard behind the rankings:  The homeless person they bought lunch for at Benihana while they picked his brain.

Below is a rough and tumble charts of where some of the bigger discrepancies were between ESPN and me.  The first chart is where I’m higher on someone, which is 95% (no math done for that number) young guys who I see getting better.  The second chart is where I’m lower on players, which is 95% (still no math!) older guys or guys coming off career years that I don’t have much faith in.  The chart was provided by Fantasy Pros.  I’ll warn you; if you go to that site you might find yourself losing three hours of your life while your loved one threatens divorce.

Player Position Grey’s Rank ESPN’s Rank Difference
Peter Bourjos OF 95 239 144
Jose Tabata OF 149 280 131
Yoenis Cespedes OF 117 242 125
Trevor Cahill SP 158 269 111
Ike Davis 1B 98 208 110
Ryan Howard 1B 87 196 109
Danny Valencia 3B 167 268 101
Colby Rasmus OF 115 206 91
Austin Jackson OF 169 257 88
Danny Espinosa 2B 102 189 87
Emilio Bonifacio SS 126 213 87
Alex Rios OF 114 197 83
Delmon Young OF 170 252 82
Carlos Quentin OF 147 224 77
Jhoulys Chacin SP 157 232 75
Howie Kendrick 2B 46 118 72
Mark Reynolds 3B 84 156 72
Alcides Escobar SS 186 258 72
Derek Holland SP 174 244 70
Ian Desmond SS 166 235 69
Jair Jurrjens SP 187 256 69
Brennan Boesch OF 194 261 67
Justin Morneau 1B 205 270 65
Mike Leake SP 238 300 62
Aaron Hill 2B 151 211 60
Logan Morrison OF 94 153 59
Mike Moustakas 3B 135 190 55
Dexter Fowler OF 195 247 52
Adam Lind 1B 85 135 50
Mat Gamel 3B 214 264 50
Jesus Montero C 139 188 49
Jake Peavy SP 234 282 48
Kendrys Morales 1B 203 249 46
Jarrod Saltalamacchia C 247 293 46
Buster Posey C 82 127 45
Gio Gonzalez SP 88 132 44
Mark Trumbo 1B 182 226 44
Kevin Youkilis 3B 56 99 43
Vance Worley SP 209 250 41
Salvador Perez C 246 287 41
Carlos Marmol CL 143 183 40
Jonathan Lucroy C 250 290 40
Michael Young 3B 58 97 39
Drew Stubbs OF 73 112 39
Brandon Morrow SP 132 170 38
Kenley Jansen CL 176 214 38
Anibal Sanchez SP 101 137 36
Alejandro De Aza OF 258 294 36
Desmond Jennings OF 70 104 34
Jason Heyward OF 75 109 34
Russell Martin C 249 283 34
Krispie Young OF 74 107 33
Brandon Belt 1B 202 234 32
Matt Thornton CL 221 253 32
Madison Bumgarner SP 50 80 30
Jemile Weeks 2B 172 202 30

 

Player Position Grey’s Rank ESPN’s Rank Difference
Carlos Beltran OF 145 115 -30
Jose Altuve 2B 267 237 -30
Mariano Rivera CL 104 73 -31
Tyler Clippard MR 282 251 -31
Dee Gordon SS 165 133 -32
Andrew Bailey CL 190 158 -32
Jim Johnson CL 274 241 -33
Sean Rodriguez 2B 278 245 -33
Jered Weaver SP 65 31 -34
Mike Napoli C 81 47 -34
David Freese 3B 228 194 -34
Mitch Moreland OF 261 227 -34
Brandon League CL 226 191 -35
Chris Perez CL 273 238 -35
Jason Bartlett SS 289 254 -35
Ian Kennedy SP 108 72 -36
Doug Fister SP 180 144 -36
Ryan Dempster SP 239 203 -36
Bud Norris SP 237 198 -39
Tim Hudson SP 179 139 -40
Jason Motte CL 177 136 -41
Joel Hanrahan CL 162 119 -43
Gaby Sanchez 1B 206 162 -44
Neil Walker 2B 211 167 -44
Ben Zobrist 2B 96 51 -45
Kyle Farnsworth CL 218 172 -46
Matt Moore SP 121 74 -47
Wandy Rodriguez SP 213 166 -47
Melky Cabrera OF 197 149 -48
James Shields SP 113 64 -49
Alexei Ramirez SS 136 87 -49
Daniel Murphy 2B 285 236 -49
Tim Stauffer SP 231 181 -50
Brandon McCarthy SP 230 179 -51
J.J. Hardy SS 164 111 -53
Jordan Walden CL 217 163 -54
Adam Wainwright SP 155 98 -57
Ichiro Suzuki OF 148 89 -59
Martin Prado 3B 229 168 -61
Carlos Pena 1B 253 192 -61
Michael Pineda SP 154 91 -63
Ryan Madson CL 163 100 -63
Chris Carpenter SP 142 78 -64
Ted Lilly SP 232 164 -68
Matt Capps CL 275 205 -70
Javy Guerra MR 280 210 -70
Scott Baker SP 233 157 -76
Jhonny Peralta SS 204 126 -78
Angel Pagan OF 262 178 -84
Freddie Freeman 1B 207 120 -87
Daniel Bard SP 272 185 -87
Yunel Escobar SS 288 200 -88
Michael Bourn OF 125 35 -90
John Danks SP 301 209 -92
Chris Sale SP 271 177 -94
Jeremy Hellickson SP 189 93 -96
Jason Kubel OF 295 199 -96
Josh Willingham OF 286 184 -102
Rafael Betancourt CL 220 114 -106
David Ortiz U 184 66 -118
Stephen Drew SS 293 160 -133
Carlos Lee OF 298 155 -143
Hiroki Kuroda SP 300 147 -153

What Yahoo Is Putting Together These Fantasy Baseball Rankings?

March 09, 2012 By: Grey Category: 2012 Fantasy Baseball Draft 165 Comments →

On one hand, we have Evan Longoria at 7 and Yahoo has him at 13 in their O-Ranks, so we can get Longoria.  On the other hand, they have Dee Gordon at 96 in their O-Ranks and we have him at 165, so we’re not getting Gordon without reaching.  On the lesser known, third hand that is actually a foot wearing a mitten, what on earth is an O-Rank?  Sounds like something a teenager would say when they forgot to take their garbage out for six months.  Alas, I found the definition, “The “O-Rank” is an overall player rank based on current and prior seasons. Since it provides such a full spectrum analysis of a players abilities, the “O-Rank” is the method used for determining auto-picks in our draft for full Yahoo! Fantasy Sports games.”  A full spectrum analysis?  Who wrote this, Stephen Hawking?  I Googled “full spectrum analysis” and Google said “Do you mean gobbledygook?”  Actually, no lie, but the only other time “full spectrum analysis” was used since Al Gore invented the internet was in regards to gamma rays.  I don’t know the first thing about gamma rays, but the term “full spectrum analysis” actually makes sense to me more in regards to gamma rays than fantasy baseball rankings.  Well, whatever the case may be, we’re gonna say Yahoo’s O-Ranks are their rankings.  Key word is rank, I suppose.

“Ooh, I’m a Yahoo ‘pert and I love Michael Bourn and anyone covering Total Eclipse of the Heart.”  That’s Funston and Evans while they compile the rankings and watch The Voice.  They are just as high on Michael Bourn as every other ‘pert this and that side of the Mississippi minus Razzball.  I already went over my thoughts on Bourn’s supremacy and how everyone’s forgot his true identity:  SAGNOF!   Same goes for Curtis Granderson, who I labeled a schmohawk.  At least Yahoo didn’t put him in the top 20, that’s just common Curtis-y.  Pun point!

No one likes Jay Bruce, but me.  Honestly, I have no idea how this happened and it’s about the most puzzling thing going on this preseason.  When Bruce was first called up, the fantasy baseball community embraced him immediately, labeled him Bruuuuuuuuuuce and sold the Ohio Turnpike to the Middle East in his honor as reported by Matt Taibbi.  Now, we’re coming off a season where he finally put everything together and he’s going to be 25 years old.  This is the time to move on from him?  I nearly put Bruce in my top 20 overall (ended up just outside of it at 27) and Yahoo has him at 51.  I’ll happily go with Bruce in every league, and that’s not just the Jersey in me talking.  Though I do write with one hand and fist pump with the other.

Rather than going through all of their wonky player rankings, I’ve made a chart.  “Yo, Differences Between My Rankings and O-Ranks, you’ve been charted!”  The positive numbers mean you have a good chance of getting those players if you follow my rankings.  The negative numbers mean Yahoo’s ranked these players higher than me.  Help for this chart was provided by frequent commenters, MattTruss223 and Cheese.  Promotional consideration was provided by Wheat Thins, “Now go eat yourself thin!”

Some of the Biggest Differences Between Grey’s Rankings and O-Ranks
Player Grey’s Y! Difference
Jay Bruce 27 51 24
Dan Uggla 33 48 15
Curtis Granderson 38 22 -16
Howie Kendrick 46 72 26
Madison Bumgarner 50 80 30
Chase Utley 54 71 17
Aramis Ramirez 55 74 19
Lance Berkman 57 86 29
Brian McCann 60 83 23
Matt Wieters 61 90 29
Jered Weaver 65 35 -30
C.J. Wilson 66 88 22
Desmond Jennings 70 43 -27
Krispie Young 74 122 48
Brett Gardner 79 111 32
Jayson Werth 80 108 28
Mike Napoli 81 53 -28
Mark Reynolds 84 143 59
Adam Lind 85 155 70
Billy Butler 86 119 33
Gio Gonzalez 88 118 30
Matt Cain 89 54 -35
Stephen Strasburg 91 57 -34
Jordan Zimmermann 93 130 37
Peter Bourjos 95 144 49
Ben Zobrist 96 52 -44
Anibal Sanchez 101 156 55
Danny Espinosa 102 253 151
Mariano Rivera 104 76 -28
Alex Rios 114 168 54
Michael Bourn 125 67 -58
Emilio Bonifacio 126 174 48
Vernon Wells 141 229 88
Carlos Quentin 147 210 63
Aaron Hill 151 264 113
Michael Pineda 154 114 -40
Adam Wainwright 155 99 -56
Dee Gordon 165 96 -69

2012 MLB Point Shares v1.0

February 27, 2012 By: Rudy Gamble Category: 2012 Fantasy Baseball Draft, 2012 Fantasy Baseball Rankings, Rudy Gamble 77 Comments →

Point Shares are up for the following mixed-league formats: 5×5 for 10/12/14/15/16 team in both ESPN and Yahoo! roster formats.

For those of you unfamiliar with Point Shares, they represent the estimated difference in an average team’s points if they were to substitute a given player for the average player at his position. For more information, see here.

Here are some additions vs. last year:

  • Included three $ estimates to broadly reflect drafting styles from even weighting to hitters and pitchers to a slight skew towards hitters to a heavy skew towards hitters.  For instance, the ESPN roster versions have $156/$104, $165/$95, and $180/$80 dollar splits.  The rankings are based on even weighting of hitters and pitchers but you can cut/paste the rankings into an Excel spreadsheet or Google doc to sort by one of the other two figures (to cut/paste, right click within the Point Shares spreadsheet, choose ‘Select All’, copy, then paste.
  • Color-coded the category point shares so it’s easier to tell who are great/bad in each category.
  • Added the ADP from ESPN/Yahoo then showed the difference vs. the Point Share ranking.  Color-coded the difference so you can see which players are 5 or more picks different in ADP vs. Point Share ranking.
  • Added Steamer as a 2nd projection source (here’s one of the reasons why) and Rotochamp as a second playing time source.

Many thanks to:

  • Dan Szymborski (the man behind ZiPs projections) and Jared Cross (the man behind Steamer projections) for all the work that goes into providing free, high quality projections.
  • The folks at Fantistics and Rotochamp for contributing playing time estimates.
  • Everyone who responded to our recent polls and shared their fantasy baseball league info.

Things I’m still mulling over:

  • How to provide guidance for popular categories like OBP, OPS, and Quality Starts

Potential answers to questions asked in the comments:

  • “Yes, these apply for both Roto and H2H but make sure to load up your bench with SPs for matchups.”
  • “Yes, I’ll try to update the Point Shares again before the beginning of the season.”
  • “Yes, balance these rankings against your gut vs. treat them like gospel.  I created these Point Shares and spent countless hours cobbling them together and even I balance this against my gut.  Jeff Francouer was at the top of the available OFs in an early draft and I still couldn’t pull the trigger on Frenchy.”
  • “Yes, you could win a league using any of the three hitter/pitcher splits.  For mixed leagues, I prefer using the unweighted version (hitters + pitchers valued equally) or a slight hitter skew.  I’ll have a post up soon showing why that’s my preference.”
  • “Yes, I know the Point Shares spreadsheet looks wonkier in Firefox (random gridlines missing).  Not sure why.  Looks better in IE and Chrome.”
  • “Well, it matters your criteria.  If you go by sheer size and impressiveness, you have to choose my afro.  But if you go strictly by softness or ease of combing, Grey’s moustache is your choice.
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