Fantasy Baseball Advice

We’ve added two new data grids to our Fantasy Baseball Player Rater to help you identify streaking hitters/pitchers (streaking in the non-Ferrellian sense):

• Hot Hitters:  This has hitting stats for the past 20 days of all hitters with 1+ AB.  Besides your typical 5×5 stats, it includes BABIP, OBP, OPS, SLG, and K%.  Players are ranked in descending order of their ‘Heat Index’ which is calculated using a mix of Hits, Runs, HRs, RBI, SBs, and ABs.  Each player is indexed against the 156th most valuable hitter for the period (156 hitters are active on a given day in a 12-team league).  While it isn’t possible for us to know if he’s available or not in your league, we provide pre-season and in-season $ estimates (based on 12 team 5×5 ESPN rosters) – the higher those values, the more likely a player is already owned.
• Hot Pitchers:  This has pitching stats for the past 20 days of all pitchers with 0.3 IP.  Besides your typical 5×5 stats, this includes FIP, Blown Saves, Holds, K/BB, K/9, and Quality Starts.  Players are ranked in descending order of their ‘Heat Index’ which is calculated using a mix of Wins, Saves, Earned Runs, Hits, Walks, Innings, and Holds.  Each pitcher is indexed against the 108th most valuable pitcher for the period (108 pitchers are active on a given day in a 12-team league).  Pre-season and in-season $ estimates (based on 12 team 5×5 ESPN rosters) are provided for each player to help identify players more likely to be free agents in your league.

The inaugural #1 hot hitters/pitchers are Carlos Gonzalez (edging out Josh Hamilton) and Johnny Cueto (edging out CC Sabathia).

As with our other Player Raters, the grids are sortable and you can filter based on position (including SP vs. RP).  The data should update every weekday by around 10AM EST – the last column will indicate the last day’s worth of games that are included.

If you see a player missing or set at the wrong position, please mention it in the comments section of the latest post.  Please note that players just called up may take a week or so to be added.

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.

Over the past 2 years, Grey and I have had several conversations around joining a high-stakes baseball league.   We are intrigued by the NFBC  but our math keeps suggesting that their rake is too high (e.g., I calculated a 20% rake on the $150 slow draft).

We decided to take the plunge this year with a league consisting mainly of poker degenerates (Derek Carty of BaseballProspectus is the only other ‘pert).  The league is sponsored by daily fantasy sports site DraftDay (“Where every day is draft day!” exclaims Rudy Gamble) with the fantasy baseball league’s blog hosted at http://www.ddfantasybaseball.com.

This was the first time that Grey and I have been in a 10-team AL auction.  I’ve been doing an expert 12-team AL auction the past 2 years and Grey popped his AL-only cherry in a 12-team CBSSports AL league we are co-managing (FWIW, that CBS team is my least favorite team going into the season).

There were two quirks to the draft:

1. The $260 could be used to fill bench spots as well.  Once every team with $ has filled all their starting + 5 reserve spots, the remaining teams fill out the rest of their rosters via a snake draft.
2. The draft was done via an AIM chat room + Skype

The first quirk plays to one of our strengths as we tend to have a little money left over at the end of the draft.  So this could allow us to stock up on some reserves with assured playing time or high-end prospects.  The second quirk made it impossible for Grey to bid so he listened in via Skype while I did Razzball’s bidding (cue evil laugh).

Below is our team.  Here are the full draft results (along with Point Share $ per player at a straight 154/96 Hitter/Pitcher split):

One constant throughout our drafts this year has been a concern for value at 1B/3B.  We’ve gotten a 1B every time in the first round and snagged Longoria in the 2nd when available and punted for someone like Moustakas in late rounds.  Thus, it should come as no surprise that we budgeted $35 for A-Gonz (assuming M-Cab and Pujols would go for more) and $33 for Bautista or Longoria.  We were psyched to nab A-Gonz and Longoria for $2 less than budgeted (and bumped Bautista up to $34 in the process for a competitor).

Our third offensive pillar was an OF in the $25-$30 range.  We got Granderson at $29 but don’t love the pick given that OFs went for less than I expected – e.g., Hamilton for $25, Cruz for $23, Choo for $22.  The prices were discounted enough that we nabbed a 2nd strong OF in Adam Jones.

I overpaid on Bourjos but we needed speed – Grey was pissed that I didn’t bid more aggressively on Gardner who went for a fair price at $22.  I think I’d be a bigger fan of Gardner if he had either HR or AVG upside but I don’t think he has either.  I addressed speed as well by only going $1 over the budgeted $20 on the 2B/SS pair of Jemile Weeks and Cliff Pennington.  The best thing I can say for the rest of the hitters is that they’ll have solid playing time and we may have some upside in June with Mike Trout, Ryan Kalish, and Salvador Perez.  I’m not thrilled about getting stuck with Abreu after no one topped my $1 bid but hopefully he’ll get 300+ AB with the Angels or another team.  Also, while I’m glad I didn’t get caught overbidding on Valencia or Betemit, it’s TBD whether Lillibridge gets enough playing time to be an adequate CI.  I had Parmelee on my ‘late round’ checklist and wished I got him for $1 during the later rounds.

As for pitching, I really wanted an ace that wouldn’t cost more than $24.  I went last year in an AL 12 team with Hellickson and Phil Hughes as my aces.  Our 2012 CBSSports AL team has Justin Masterson as the ace.  Thus, I was happy (Grey less so) that we were able to nab Lester for only $22.  Colby Lewis is a solid #2, Sale/Peavy make for higher risk/upside #3/#4, and we got an Athletic (Milone) and three Marginers on the cheap in Millwood, Beavan, and Hultzen (rookie who could get called up in May/June).   Real happy to get such SP depth in a league like this as it can cost a lot of FAAB to acquire it once the season starts.  We only needed one closer and Walden is fine at $12.  K-Rob seems like a bargain at $2 given his K-rate and that he’s next in line for saves in New York in the spare chance that Mariano Rivera ever ages.  F-Cord was our last pick who’ll generally be on the bench (it’s a weekly league) but might deliver some cheap saves if Santos gets hurt.

Overall, I think we have a contending team.  I don’t think we have any weak spots, we have decent depth, and have a good mix of reliable and high upside players.  Our luck, the shitty CBSSports AL team will catch all the breaks (1st prize – you can mention you won on your own blog!) and this team will implode.

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:

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)

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

As we mentioned earlier this month, we created an ‘expert’ league that follows the same rules as the Razzball Commenter League and will be included in the master standings.  Will the Expert League reign supreme (Iron Chef^TM) in competitive index or will several RCL leagues put the experts in their place?  We shall see…

Here are the participants in the first annual Razzball ‘Expert’ League (links if they posted a draft review):

Razzball – Grey Albright
Razzball – Rudy Gamble
Yahoo! – Brandon Funston
Yahoo! – Scott Pianowski
Yahoo! – Andy Behrens
FanGraphs – Eno Sarris
Hardball Times – Jonathan Halket
MLBTradeRumors.com/RotoAuthority – Tim Dierkes
Mastersball – Ryan Carey
Rotowire – Dalton Del Don
SI.com – Eric Mack
Steamer Projections – Dash Davidson

Here’s a link to the entire Draft Recap:

Grey:

As mentioned in our 2nd podcast, ESPN screwed me for messing with them for the last five years.  Right before my 1st pick, my computer crashed.  Here’s me during the draft.  “Hey, I have the 7th pick.  Awesome.  I’m gonna have a 1st baseman.  I might even get Votto.  I wonder who that lady is watering her lawn across the street.  Maybe I’ll stand up carefully to make sure I don’t knock over my coffee and get a better look–NOOOOOO!  Crap, mother-effin’, son-of-a-motherless-goat!  Reboot!  Reboot!  Reboot!  Okay, it’s rebooting… I have a minute and thirty seconds… Reboot!  DAH!  I drafted Robinson Cano!”  And then that dictated just about all my other hitter picks.  Since I had Cano, I couldn’t grab Kinsler, didn’t want a shortstop and the only 1st baseman or 3rd baseman within the vicinity was Tex and I wasn’t drafting him with my 18th pick.  So I took Giancarlo.  Then I really felt like I needed to make sure I had some sorta corner man so I reached for Zimmerman.  Not feeling totally comfortable with Zimmerman as my 3rd baseman, I reached for Hosmer for my other corner.  Then I felt like if Zimmerman got hurt again, I should have another 3rd baseman, so I grabbed Aramis, then I realized later on that Aramis wasn’t a clean bill of health either so I grabbed Chisenhall.  Then I dropped Chisenhall when he was demoted and grabbed Smoak for the two game Japaning Day, then, when those games ended, I grabbed Eric Thames.

I may have 5 aces when it’s all said and done.  I wouldn’t even need Gio on this staff, but he fell so far down that I wasn’t going to let him go.  I’m not worried about saves, even if my closers are little iffy.  SAGNOF!  Since I had Cano and Hosmer, I took some guys that may be average drains, but should give steals and power.  Though, I do think average will be one of my biggest concerns.  In true Grey fashion (I love to pick up and drop players and talk about myself in third person), I’ve already made a few moves on pitching too.  Dropped Fuentes (right after Balfour became the closer) and grabbed Bourgeois.  Bourgeois is the kinda of guy that if he sneaks into a large playing role or if I switch him in only when he plays, he could get me 30 cheap steals.  SAGNOF!  I dropped Stauffer and grabbed Lidge when Storen was hurting (Clippard was drafted) and dropped Crain for Henry Rodriguez.  It’s a bit of a shizzshow, but, in some ways, I like to be able to juggle my last roster spots so I don’t care I wasted a pick on Chisenhall, Fuentes, etc.  Rudy gets a lot more tied to his drafts than I do.  In the end, I think I still have a solid team.  By the time you read this, I may have made three more pick-ups and drops.

Rudy:

At this point in the draft season, I’m starting to get predictable in my early round draft behavior.  I was really happy picking 10th with the confidence that either Joey Votto or (more likely) Adrian Gonzalez would fall to me and I wouldn’t have to worry about overpaying for a 1B later in the draft.  As luck should have it, Grey’s computer crashed and he auto-picked Robinson Cano vs. his preferred Joey Votto pick who came gift-wrapped to me at #10.   Longoria was an easy choice at #15 as I figured there would still be top SPs on the board by the time I picked next at #34.  The experts were more aggressive than I figured at drafting SPs however as Halladay, Kershaw, Verlander, Lee, F-Her, and Lincecum were all off the board.  Luckily, I liked Greinke slightly more than F-Her and Lincecum.  But I also liked Jay Bruce for this pick and, in retrospect, probably should’ve gamed Greinke was the more likely of the two to make it me at pick #39.  Hunter Pence was the consolation prize.

I spread out my SP selections and seemed to have a lot of success nabbing K-friendly pitchers (Lester, Beachy, Morrow).  I specifically drafted Lewis and Nolasco in later rounds because of their solid WHIPs (which help balance out Morrow).  For the first time in years, I drafted the first closer off the board (Kimbrel – 6th round/63rd pick) as the value was too great given his obscene K-rate.  Marmol and Putz came at decent values at Rounds 11/12 and I was glad to be done with closers before a closer run occurred (10 closers went in the next 33 picks after Putz).

As for offense, I just drafted for value and was able to avoid inadvertently punting AVG or SBs.  I reached for Jose Altuve whom I think has 30 SB upside with solid AVG and, like several of my teams this year, got solid R/RBI value out of my other MI spots (Alexei Ramirez, Neil Walker).

All in all, this was about as good of a draft as I could’ve hoped for.  I don’t think my team has any major weaknesses and hopefully my team has good injury karma.  It’ll be interesting to see how well I do in this type of format (12 team, daily changes) – especially without Grey as co-manager