In 2014, we got up to 84 Razzball Commenter Leagues (1,008 teams).  (Sign up for this year’s leagues here!!!!!!!!!!!!!!)

We appreciate all the participation and came up with a way to show it. I have all the 2014 RCL draft data + team stat totals (including # of moves) + final standings all loaded into a database.

Enter a theory you want me to test into the comments (or via twitter @rudygamble) and I will spit back the RCL results for it. Just remember this is 12-team MLB roto – if you want me to test NL-only auction $, you are SOL.

For example, below are the average team’s RCL standings points based on their 1st round pick. Anyone thinking “You CAN’T win with an SP as your 1st pick?” Stupendously wrong. Did teams selecting 1/2 have an advantage because Trout/Cabrera were such easy picks? Most likely, yes. What is the average points impact of selecting Carlos Gonzalez over Cano? About 13 standings points (note: might be a bit exaggerated because CarGo owners probably checked out early more often than Cano owners).

Player # of Teams Avg 1st Rd Selection Avg League Standing Pts
Jose Bautista 1 9.0 101.0
Clayton Kershaw 39 8.2 80.8
Carlos Gomez 1 12.0 73.0
Troy Tulowitzki 8 9.5 71.9
Mike Trout 84 1.1 70.9
Miguel Cabrera 84 1.9 70.3
Robinson Cano 64 8.6 69.5
Adrian Beltre 29 10.6 68.4
Paul Goldschmidt 84 3.5 68.0
Andrew McCutchen 84 3.8 66.8
Edwin Encarnacion 49 9.6 66.4
Ryan Braun 56 8.8 66.1
Bryce Harper 47 9.0 62.8
Prince Fielder 59 8.9 62.2
Adam Jones 74 8.2 60.8
Joey Votto 7 11.4 59.6
Chris Davis 84 7.4 59.1
Hanley Ramirez 13 9.8 58.3
Carlos Gonzalez 84 6.0 56.7
Jacoby Ellsbury 45 10.0 53.8
Yasiel Puig 4 8.3 53.3
Yu Darvish 2 9.5 53.0
Evan Longoria 5 11.6 50.4
David Wright 1 6.0 37.0

Here’s another one. This is number of pitchers selected per team in the first 5 rounds. Basically, the hitter/pitcher mix in the first 5 rounds had no impact on team’s place in the final standings. You like drafting aces, cool. You like to wait on pitching, that is cool also. Those that paint one strategy as inherently better, uncool.

# of Pitchers Count Avg Standing Pts
0 243 65.5
1 478 63.7
2 237 66.8
3 41 66.7
4 9 63.7
5 0

Okay, your turn.

  1. Ryan says:
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    How did owning Billy Hamilton or Dee Gordon correlate to winning steals?

    • @Ryan: Here’s a top 20 of Avg Team SB Points for each drafted player (minimum 50 of 84 teams)

      Name # of Teams Avg Standing Points Avg SB Pts
      Jose Altuve 84 78.2 9.8
      Dee Gordon 67 69.3 9.6
      Billy Hamilton 84 76.5 9.4
      Albert Pujols 84 75.5 8.1
      Michael Brantley 84 74.1 7.9
      Carlos Gomez 84 66.6 7.8
      Jose Reyes 84 64.7 7.8
      Todd Frazier 84 72.2 7.8
      Howie Kendrick 84 74.8 7.8
      Jimmy Rollins 84 67.3 7.7
      Ben Revere 83 66.1 7.7
      Anthony Rizzo 84 72.7 7.7
      Jose Bautista 84 75.6 7.7
      Mike Moustakas 83 70.9 7.6
      Austin Jackson 84 69.0 7.6
      Jay Bruce 84 68.7 7.6
      Elvis Andrus 84 64.5 7.6
      Giancarlo Stanton 84 74.9 7.6
      Paul Goldschmidt 84 68.0 7.5

      • Prime Numbers says:
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        @Rudy Gamble: Pujols being #4 on that list is hysterical. I wonder how that worked out.

        • Small sample-driven fluke

  2. Bterry says:
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    How did drafting top closers. (Kimbrell, chapman, jansen, holland) compare with waiting on guys.

    • @Bterry: Here is the Avg Standings Pts + Avg SV Pts for all relievers drafted on 50+ of 84 teams:

      Name # of Teams Avg Standing Pts Avg SV pts
      Fernando Rodney 84 74.0 8.9
      Mark Melancon 84 73.2 8.5
      Trevor Rosenthal 84 72.7 8.6
      Jose Veras 84 72.5 7.4
      Koji Uehara 84 72.3 7.6
      Rafael Soriano 84 71.9 8.0
      David Robertson 84 71.1 8.6
      Steve Cishek 84 70.7 8.1
      Sergio Santos 53 70.3 7.5
      Jason Grilli 84 70.1 7.0
      Darren O’Day 57 70.0 6.8
      LaTroy Hawkins 82 68.9 7.3
      Joakim Soria 82 68.5 7.1
      Tyler Clippard 82 68.5 6.7
      Cody Allen 84 68.4 7.5
      John Axford 84 68.4 6.7
      Jesse Crain 58 68.4 7.1
      Chad Qualls 70 68.0 7.0
      Tommy Hunter 84 67.8 6.8
      Kenley Jansen 84 67.6 8.1
      Craig Kimbrel 84 67.4 8.9
      Glen Perkins 84 67.4 8.1
      Joe Nathan 84 67.3 7.6
      Jim Henderson 84 66.5 6.4
      Rex Brothers 84 66.4 6.5
      Huston Street 84 66.3 8.2
      Bobby Parnell 84 66.0 5.8
      Sergio Romo 84 65.8 6.4
      Greg Holland 84 65.8 8.0
      Neftali Feliz 75 65.4 6.5
      Addison Reed 84 65.4 7.3
      Grant Balfour 84 64.9 6.6
      Danny Farquhar 82 64.7 6.6
      Joaquin Benoit 84 64.4 6.8
      Casey Janssen 84 64.2 6.9
      Ernesto Frieri 84 64.2 6.7
      Adam Eaton 84 64.2 6.9
      Nate Jones 84 63.4 6.2
      Aroldis Chapman 84 62.4 7.5
      Jonathan Papelbon 84 61.7 7.0
      Jim Johnson 84 60.5 5.1

      • Rufus T. Firefly says:
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        @Rudy Gamble: That’s some heavy info!!!

  3. Bterry says:
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    How does transaction number correlate with final standings. More active the better?

    • Kid A says:
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      @Bterry:

      How does maxing out starts as quickly as possible correlate to final standing?

      Nyuk Nyuk Nyuk.

      • @Kid A: That i don’t know. Only have final results.

        • Kid A says:
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          @Rudy Gamble:

          It’s ok. Bterry already knows the answer.

          • Bterry says:
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            @Kid A: @Kid A: ohh kid a. I am have to get an an rcl with you again this year. Strategy still worked with another person in league doing same thing (but with garbage closers and stream oer full year) and it still worked. Ill do same strategy again this year, without spending 4 of my first ten picks on closers of course. Comin for ya.

            • Spammer jay says:
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              @Bterry: @Bterry: last year was A closerpocolyps year though.

              • Every year is one for bottom half of closers. Top 4 were great while rosenthal disappointed a bit

          • Bterry says:
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            @Kid A: i will say it makes the end of the year extremely boring, not being able to stream any starts tho. Def a downside. I still think without the cargo trade, and rios trade i woulda won it. Two horrible deals, i thought they would turn it around.

      • Bterry says:
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        @Kid A: actually, it worked out quite well for this format. Wht didnt work out quite well. IS spending ao big on closers. I was able to finish top ten in entire rcl in k’s, saves, and mid pack in whip and era. Id say tht was a sucess. Yuk yuk. Drafting chapmen kimbrell jansen and trading for holland. Not so much. Still finished third or so in league tho. I also traded for cargo and one other guy that ended up a dud i cant remember.

    • @Bterry: 42% correlation. The formula is 57.6 standings points + .08*Moves

      • Yabu says:
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        @Rudy Gamble: How about # of transactions compared to average for that league — seems like there would be less to gain if competitors are active too

        • @Yabu: 20% correlation based on Total Moves / Avg Moves in League. 0% correlation based on Total Moves – Avg Moves in League.

          • Yabu says:
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            @Rudy Gamble: Interesting. And was that correlation about the same no matter how active the league was, or was there a diminishing return as the league average went up? (Not sure if I’ve asked that the right way — hopefully that makes sense…)

            • @Yabu: Okay, a new day and I found something much better that fits your train of thought. I converted each team’s # of moves into % of League Moves. Huge difference. % of League Moves represents 57.7% of a team’s standings points. The End of Season $ of all draft picks (floor of $0/player) came in almost identical 58% and the combination of the two comes in at 80%.

              I ran the same regression against teams in the 20 most active leagues and the 20 least active leagues. The correlation of % of Moves and End of Season $ was between 79-81% for both groups.

      • Bterry says:
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        @Rudy Gamble: thanks. Great info here.

  4. L-Boogie says:
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    What about examining how long to wait on a 2B or SS. Maybe points based on if you took your first 2B, SS, or both in rounds 1-3, 4-6, 7-9, 10-15, or >15?

    • L-Boogie says:
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      @L-Boogie: Or whatever round increments you think would be most appropriate.

    • @L-Boogie:
      # of MI 1st 5 rounds | # of Teams | Avg St Points
      0 | 330 | 67.5
      1 | 513 | 64.7
      2 | 152 | 61.1
      3 | 13 | 58.2

      • Hawk says:
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        @Rudy Gamble:

        SS / 2b are normally the position used to argue in favor of “position scarcity”.

        The fact that the more 2b / SS you drafted in the first 5 rounds, the worse you did kinda blows a hole in that theory.

        • Ryan says:
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          @Hawk: No it doesn’t.

          • L-Boogie says:
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            @Ryan: It doesn’t completely negate the theory but it is interesting data.

        • McNulty says:
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          @Hawk:

          no it’s more of a commentary on Andrus and others. If your projection is wrong, it doesn’t matter what position the guy plays

  5. Nico says:
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    Rudy, cool stuff! Can you input my decisions into this thing, and see how it has affected my life thusfar? No? Point taken.

    I’d be curious to know something Grey brought up in his post today. What was the average points for team’s who selected their first pitcher outside the top-50, 75 and 100? There are obviously a ton of other things that can influence this (when was the second pitcher taken?), but I was wondering if it could reinforce Grey’s theory. Also assuming people drafted using Grey’s theory…

    Thanks!

    • Nico says:
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      @Nico: were the average points* hello grammar…

    • @Nico:
      Here’s the average points per team based on first round they selected a pitcher.

      Net-net, there is no systemic benefit or penalty for waiting on SPs. Just personal preference (and, obviously, quality of draft).

      Round # of Teams Avg Pts
      1-3 | 286 | 66.3
      4-6 | 557 | 63.8
      7-9 | 133 | 66.9
      10+ | 32 | 65.6

      • Nico says:
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        @Rudy Gamble: Very cool. Thanks!

        • Rufus T. Firefly says:
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          @Nico: amen!

  6. mauledbypandas says:
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    Did you win an RCL last year?
    Want to challenge other RCL winners?

    Join the RCL Champion’s League!!

    email for invite [email protected]

  7. mauledbypandas says:
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    Champions League would love to have you Rudy if you want to prove yourself :)

  8. J-MoneyIV says:
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    Love this kind of stuff.

    Anyway to compare stats of teams that had the following piutching breakdown:
    – standard 6 SP rotation, a few closers and maybe a MR or two.
    – one or two SP, streaming starts, with a majority of their pitchers as RP.
    I hope I explained that right.

    Also, not sure how informative this would be, but is there a way to determine team points by what type of players(pitchers or hitters) were primarily used in bench spots?

    • @J-MoneyIV:
      completely validates my low SP / high RP strategy :)

      # OF DRAFTED SP | # of Teams | Avg St Pts
      0 | 3 | 60.8
      1 | 2 | 78.5
      2 | 6 | 76.0
      3 | 17 | 72.6
      4 | 46 | 77.2
      5 | 130 | 72.2
      6 | 245 | 65.8
      7 | 242 | 65.4
      8 | 177 | 62.4
      9 | 93 | 56.9
      10 | 32 | 47.5
      11 | 12 | 46.8
      12 | 3 | 64.3

      # Of RP Drafted | # of Teams | Avg St Pts (note: no one drafted 11 RP)
      0 | 30 | 49.1
      1 | 45 | 52.7
      2 | 122 | 60.2
      3 | 268 | 63.7
      4 | 216 | 67.9
      5 | 161 | 65.5
      6 | 98 | 71.8
      7 | 43 | 70.9
      8 | 19 | 80.7
      9 | 4 | 86.9
      10 | 1 | 42.0
      12 | 1 | 37.0

      • L-Boogie says:
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        @Rudy Gamble: Is this strategy (high RP, low SP) really only good for daily leagues or can it work well in weekly leagues too? Seems less than ideal for weekly leagues.

        • For daily. Can only use a few spots for RP in weekly.

        • Aubrey Plaza's Pillow says:
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          @L-Boogie: remember this likely applies much less so (if at all) in H2H leagues.

  9. Sky

    Sky says:
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    So is Grey just inherently uncool then?

    • @Sky: He is very liphairently cool

      • Sky

        Sky says:
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        At first I thought you were saying something about a hair-lip; honestly had to think about it for a second.

  10. Peter says:
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    what are your thoughts

    Need 2 keepers

    Brandley rd 24
    Baez rd 25
    Blackmon Rd 27
    Sale Rd 6

  11. Ross says:
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    Just curious if there was a correlation between a team’s K:BB ratios and overall performance. Even if it were just for pitcher’s points I’d be curious to see. Thanks again and keep up the great work.

    • @Ross: i don’t have team BB since it’s not collected.

      • Ross says:
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        Ah no worries thanks anyway.

  12. Yabu says:
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    Not sure if you could calculate this easily enough, but I’d be curious for the stats of the average SP-stream (define however you like).

    • @Yabu: no way to separate stream from non-stream starts.

  13. J_FOH says:
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    Is there a correlation of groups of players drafted in rounds 10 thru 15 and winning their leagues? I have always found those to be the toughest and most game changing rounds in a draft. Now my question is based on observations and no actual data but since you have data

    • Carnac says:
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      I like this question.

    • Cram It says:
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      @J_FOH: I observed that 7 of my 10 most productive players in the FCL last year were from rounds 10 and later. So I’m going to say no. Or that’s just a sad job by me in the relatively easier part of the draft.

    • @J_FOH: Here’s a correlation of total end of season $ value per draft round bucket to standings points. (Players with negative value capped at $0).

      I have no idea why 16th to 20th correlates better than 11th to 15th. Assuming it’s a data anomaly. but, otherwise, looks like as one should expect. earlier rounds more important than later rounds.

      1st to 5th $ ($75) – 34%

      6th to 10th $ ($44) – 31%

      11th to 15th $ ($34) – 16%

      16th to 20th $ ($31) – 27%

      • anthony c says:
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        @Rudy Gamble: I suspect the number of highly touted young pitchers that got injured or sucked in 2015 contributed to the severe round 11-15 drop (salazar, wacha, cingrani, etc.). Would be interesting to see if hitters or pitchers contributed more to the dollar value drop – also during those rounds a lot of people are filling holes in their teams instead of necessarily looking for best value – maybe that has something to do with it as well.

  14. mauledbypandas says:
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    Is there some correlation between when a team takes its first closer and league position?

    • @mauledbypandas:
      Here you go….basicallly, not seeing much of a difference here. feel like any spikes driven by season-specific performances. data says volume of RPs important, round for 1st RP, not important.

      Round # of Teams Avg St Pts
      2nd-6th | 198 | 67.1
      7th-9th | 196 | 64.1
      10th-11th | 184 | 67.4
      12th-13th | 178 | 65.6
      14th+ | 234 | 63.2
      No closers | 18 | 45.2

      Round | # of Teams | Avg Pts
      2 | 2 | 63.25
      3 | 9 | 57.72222222
      4 | 27 | 63.66666667
      5 | 74 | 67.77702703
      6 | 86 | 68.65697674
      7 | 73 | 64.93150685
      8 | 60 | 60.625
      9 | 63 | 66.34126984
      10 | 71 | 66.9084507
      11 | 113 | 67.69469027
      12 | 99 | 65.10606061
      13 | 79 | 66.1835443
      14 | 82 | 62.33536585
      15 | 51 | 67.11764706
      16 | 38 | 66.42105263
      17 | 21 | 58.88095238
      18 | 13 | 62.11538462
      19 | 7 | 69.78571429
      20 | 8 | 60.1875
      21 | 4 | 57
      22 | 5 | 49.4
      23 | 3 | 50.33333333
      24 | 2 | 45.75

  15. Hawk says:
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    Is there a correlation between your first two picks returning expected round value (i.e. 1st round pick returns 1st round value, 2nd round pick returns 2nd round value) and final league position?

    • @Hawk:
      Here are the correlations to the end of season $ values to a team’s final standings points (negative players capped at $0):

      1st – 19%
      1st & 2nd – 24%
      1st through 3rd – 31%
      1st through 4th – 33%
      1st through 5th – 34%
      all 25 rounds- 58%

  16. goodfold2 says:
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    is yes… many not in OPS player rater yet since we have no idea about his at bats?

  17. Hawk says:
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    Also, I absolutely love this kind of thinking. Fantasy Alarm, Rotowire…meh. This is the best fantasy site on the web. Great, great job!

  18. GhostTownSteve says:
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    “You like drafting aces, cool. You like to wait on pitching, that is cool also. Those that paint one strategy as inherently better, uncool.”

    Kind of missed the boat here, Rudy. You merely correlated the drafting of pitchers by round to results. However, you have no underlying data about whether the drafting of those pitchers had strategic intention. You have no information about what the overall strategy applied by the various teams within the buckets even for those who may have drafted pitching with a certain strategic intention. You have no information about the relative skill level or level of engagement was for the players who drafted according to the various tranches.

    Boo on those who continue to insist that strategic considerations are not paramount!

    • GhostTownSteve says:
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      @GhostTownSteve:

      In many of these cases, let’s be very careful not confuse correlation and cause. That is to say, this is not how we draw conclusions. For instance, in the closer example above regarding closers. Do teams who draft no closers perform poorly because drafting no closers is strategically weak or because this particular population of drafters who took that path did so because they were poor or inattentive drafters. Impossible to say.

      • GhostTownSteve says:
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        @GhostTownSteve:

        I would suspect, in fact, the clustering of results in the “when did they draft pitchers” analysis, suggests lack of strategic intent. That is, if each of those tranches represented a diverse and comprehensive playing strategy, I would suspect that there would be more divergent results. The cluster kind of makes me think that it’s an arbitrary correlation without cause.

      • @GhostTownSteve:
        1) This is based solely on RCLers so I think we’re dealing w/ more informed drafters than the average ESPNer and Yahooer.

        2) Is there going to be some noise in this data based on potential differences in drafter skill? Is there sample-driven noise? Is there noise based on season-to-season volatility in success rates per position? On injury rates per position? Noise because of varying degrees of involvement (e.g., # of moves)? All yes.

        3) None of #2 changes the fact that this data can help test the validity of draft strategies based on the observed average users. None of #2 change the fact that the vast majority of opinions that X draft strategy is better than Y draft strategy are almost all completely unfounded.

        4) The reality in most cases is that the three biggest factors to success (in no particular order) are: a) Drafter skill, 2) Luck, 3) Drafter involvement/# of moves

        5) This type of analysis should embolden the vast majority of people to try new strategies based on their perceived strengths/weaknesses or how they perceive market strengths/weaknesses.

        • GhostTownSteve says:
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          @Rudy Gamble:

          Couldn’t disagree more. You have absolutely no data about strategic intent. You have only draft results from which you (erroneously) infer strategic intent. Even if we allow the inference, we still have only one component of the assumed strategic intent (when pitchers were drafted) and no information about the comprehensive strategies of which early round starting pitch would be only a piece.

          #3 and #4 above are assertion without proof.

          This doesn’t mean that there isn’t something of note here. It is interesting to know that SP draft order does not intrinsically advantage or disadvantage a large population of sampled teams. But this cannot be confused with advantage or disadvantage of strategic intent.

          And as to the average skill level of RCL player, I will give a nod over public leagues. But I can tell you from playing in leagues here and everywhere else that a very small population of players understand and can skillfully apply a strategic framework.

          Isn’t it true that the strongest correlation to success in RCL is number of moves made in a season? Transaction volume is directly tied to streaming, which is a strategic consideration. Streaming is not a tactic, but a strategy and one which virtually must be deployed to succeed in RCL. So to say that no one strategic consideration has sway is contradicted by the apparent evidence.

          • @GhostTownSteve: Okay, we’re getting somewhere. I have total # of Moves and total IP. (Vin pulled me total AB but don’t have it loaded right now). So we can isolate the factors.

            So what theory do you want tested accounting for total IP and/or total moves?

            My upfront hypothesis is that maximizing IP is the #1 driver of pitching success (measured by Pitching standings points), followed by average quality of drafted SP, followed by # of relievers.

            • GhostTownSteve says:
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              @Rudy Gamble:

              Rudy…you might be done for the day, but I’d want to see

              All teams in the top 10% of total transactions
              All teams in the top 10% of transactions who took 1 SP in the first 3 rounds
              All teams in the top 10% of transactions who took 1 SP in the first 3 rounds and who did not take another pitcher until after round 10

              • @GhostTownSteve:
                Here are the results for the top 200 most active teams (129+ moves) EXCLUDING the ECFBL which has a crazy # of moves in its league.

                # of SPs in 1st 3 rounds | Count | Avg Pts
                0| 154 | 81.0
                1 |42 | 87.9
                2 | 5 77.0

                Given only 42 teams had drafted 1 SP in first three rounds, doesn’t pay to add in a qualifier on their next SP.

                • GhostTownSteve says:
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                  @Rudy Gamble:

                  It does pay to add a qualifier because we are trying to zero in on strategic considerations. So we’ve established that one SP in the first three (read) getting an ace was superior to not getting an ace or getting 2 aces. Now what we want to know, strategically, is whether it was better to back up that ace with another pitcher or whether it was better to construct the draft by taking that ace to anchor the staff or to use the initial investment as a reason to delay pitching.

                  • GhostTownSteve says:
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                    @GhostTownSteve:

                    It also points out the bias in these numbers in that the average RCL player is more savvy, but also under the sway of the Grey don’t pick pitchers mantra.

                  • @GhostTownSteve:

                    Here you go. If you find learning here, great. In my eyes, this is worthless b/c of the small sample.

                    # of SPs 4-10th round | # of Teams | Avg Pts
                    0 4 96.8
                    1 19 87.6
                    2 14 85.5
                    3 3 83.5
                    4 2 97.3

                    • GhostTownSteve says:
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                      @Rudy Gamble:

                      I do find it useful. Thanks for the hard work you put in on all this.

                    • Np. All this stuff will hopefully stimulate some other research ideas!

              • @GhostTownSteve: I did a regression based on each team’s total # of Moves, Hitter investment, SP investment, and RP investment. # of Moves explains 42% of standings points. The investment split explains a negligible amount via forced fitting.

                A regression using Moves + End of Season $ for drafted players ($0 floor/player) was 72% (EOS$ was at 58%).

                The standings points equation is -6.4+.08*Moves + .31*EOS$

                So a team with 200 moves and the average EOS$ of $207 would be expected to have about 72 standings points.

                • GhostTownSteve says:
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                  @Rudy Gamble:

                  This is important vis a vis the above comment because what we want to know is how the approach to pitching investment impacted the offensive production you got from the draft.

        • GhostTownSteve says:
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          @Rudy Gamble:

          Also, it’s irrelevant to include “luck” as a factor. It is only a factor when we look at a limited sample size. The whole point of examining strategic methods is to minimize the impact of luck over time.

          • Rufus T. Firefly says:
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            @GhostTownSteve: Great stuff, guys.

  19. halfdonkeyhalfunicorn says:
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    Correlation between total Ks and final standings?

    Correlation between total runs and final standings?

    • @halfdonkeyhalfunicorn:
      Here are the correlations per category to total standings points. I see signs of winning strategies discussed last year like maximizing AB (Runs/RBI) and IP (W/K).
      r 79%
      hr 66%
      rbi 74%
      sb 50%
      avg 33%
      w 67%
      sv 55%
      era 60%
      whip 55%
      k 74%
      IP 68%
      (don’t have AB handy)

      • halfdonkeyhalfunicorn says:
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        @Rudy Gamble: Yeah, I guess it wasn’t really a question. Just highlighting that those are the two you really want. And the way to acquire them have little to do with the draft.

      • Catcher Fever says:
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        @Rudy Gamble: in my 15 year, 12 team, 7×7 (obp%, slug%, innings, losses) rotisserie league the correlation between 1st place and runs is almost 100%.

        Don’t even need to do math…the 2 champs who didn’t come in 1st in runs came in 2nd and 3rd in their winning years.

        Although correlation doesn’t not imply causation. Good hitting teams have batters high in their respective batting orders, hit for average, obp, hit more HR, RBI, etc.

        • Catcher Fever says:
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          @Catcher Fever: auto correct… correlation does not imply causation

        • is it a daily or weekly league? quality hitters at premium batting order spots crucial for both but maximizing ABs through roster churning is key in daily league to score high in Runs/RBI.

  20. Eric says:
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    And how about a list of all of the players drafted, number of teams drafted on, and average standings of teams. Also, if you can impute a salary to each draft slot, the average spent on hitting/SP/RP for teams that finished 1st through 12th. Thanks!

    • @Eric:
      Here’s the hit/pitch split data
      Hit % of Draft | # of Teams | Avg Std Pts
      <55% | 31 | 66.3
      55-60% | 65 | 65.1
      60-61% | 37 | 68.0
      62-65% | 150 | 68.8
      66-67% | 124 | 64.0
      68-69% | 164 | 63.6
      70-71% | 141 | 65.4
      72-73% | 141 | 60.0
      74-77% | 124 | 65.9
      78+% | 67 | 69.2

      will have to circle back for sp/rp splits.

    • @Eric: here’s the average standings points and average pick for all players drafted in 50 or more leagues (out of 84):

      # Name | count(*) | avg(e.PTS) | avg(e.PTS)
      Jose Altuve | 84 | 79.7 | 78.2
      Billy Hamilton | 84 | 81.2 | 76.5
      Jose Bautista | 84 | 26.3 | 75.6
      Albert Pujols | 84 | 28.5 | 75.5
      Johnny Cueto | 84 | 196.2 | 75.0
      Clayton Kershaw | 84 | 13.3 | 74.9
      Giancarlo Stanton | 84 | 25.8 | 74.9
      Howie Kendrick | 84 | 181.9 | 74.8
      Michael Brantley | 84 | 216.0 | 74.1
      Fernando Rodney | 84 | 189.7 | 74.0
      Jose Abreu | 84 | 80.9 | 73.7
      Mark Melancon | 84 | 244.8 | 73.2
      A.J. Pierzynski | 55 | 272.6 | 72.8
      Trevor Rosenthal | 84 | 98.5 | 72.7
      Anthony Rizzo | 84 | 70.3 | 72.7
      Jose Veras | 84 | 217.0 | 72.5
      Koji Uehara | 84 | 112.7 | 72.3
      Todd Frazier | 84 | 219.5 | 72.2
      Hunter Pence | 84 | 62.3 | 72.0
      Rafael Soriano | 84 | 161.7 | 71.9
      Kole Calhoun | 84 | 205.7 | 71.9
      Anthony Rendon | 84 | 219.5 | 71.6
      Nelson Cruz | 84 | 142.0 | 71.5
      Yordano Ventura | 77 | 220.0 | 71.4
      Corey Kluber | 83 | 227.7 | 71.3
      Madison Bumgarner | 84 | 47.7 | 71.2
      David Robertson | 84 | 122.5 | 71.1
      Max Scherzer | 84 | 39.9 | 71.1
      Mike Moustakas | 83 | 219.0 | 70.9
      Mike Trout | 84 | 1.1 | 70.9
      Everth Cabrera | 84 | 96.8 | 70.9
      Alex Gordon | 84 | 65.3 | 70.9
      Josh Donaldson | 84 | 56.6 | 70.8
      Cole Hamels | 84 | 137.9 | 70.7
      Steve Cishek | 84 | 170.6 | 70.7
      Scott Kazmir | 58 | 263.1 | 70.6
      Jon Lester | 84 | 163.1 | 70.4
      Miguel Cabrera | 84 | 1.9 | 70.3
      Justin Morneau | 67 | 255.6 | 70.3
      Sergio Santos | 53 | 259.0 | 70.3
      Jason Grilli | 84 | 145.5 | 70.1
      Rajai Davis | 75 | 247.9 | 70.1
      Darren O’Day | 57 | 267.0 | 70.0
      Jordan Zimmermann | 84 | 98.1 | 69.7
      Carlos Beltran | 84 | 81.6 | 69.7
      Pablo Sandoval | 84 | 126.3 | 69.7
      Jhonny Peralta | 67 | 256.2 | 69.6
      Carlos Martinez | 67 | 265.2 | 69.4
      Dee Gordon | 67 | 239.0 | 69.3
      Brett Gardner | 84 | 185.3 | 69.1
      Julio Teheran | 84 | 93.0 | 69.1
      Victor Martinez | 84 | 162.2 | 69.0
      Austin Jackson | 84 | 164.2 | 69.0
      LaTroy Hawkins | 82 | 244.0 | 68.9
      Brian Dozier | 83 | 202.6 | 68.9
      Chris Carter | 82 | 224.8 | 68.9
      Robinson Cano | 84 | 10.1 | 68.9
      Peter Bourjos | 54 | 244.3 | 68.8
      Matt Adams | 84 | 106.1 | 68.7
      Avisail Garcia | 84 | 193.7 | 68.7
      Jay Bruce | 84 | 28.8 | 68.7
      Nolan Arenado | 84 | 145.4 | 68.6
      Michael Wacha | 84 | 124.2 | 68.6
      Adrian Gonzalez | 84 | 54.6 | 68.6
      Alex Wood | 82 | 227.5 | 68.6
      Sonny Gray | 84 | 133.1 | 68.6
      Joakim Soria | 82 | 226.0 | 68.5
      Neil Walker | 55 | 257.7 | 68.5
      Tyler Clippard | 82 | 255.8 | 68.5
      Cody Allen | 84 | 248.2 | 68.4
      John Axford | 84 | 194.1 | 68.4
      Jesse Crain | 58 | 271.5 | 68.4
      Jayson Werth | 84 | 86.7 | 68.4
      Adrian Beltre | 84 | 14.2 | 68.3
      Masahiro Tanaka | 84 | 99.8 | 68.2
      Justin Upton | 84 | 33.7 | 68.1
      Chad Qualls | 70 | 259.6 | 68.0
      Paul Goldschmidt | 84 | 3.5 | 68.0
      Kolten Wong | 65 | 246.1 | 67.9
      Tommy Hunter | 84 | 217.9 | 67.8
      Alcides Escobar | 58 | 229.2 | 67.8
      Rick Porcello | 83 | 212.9 | 67.8
      Felix Hernandez | 84 | 37.0 | 67.8
      Michael Cuddyer | 84 | 104.9 | 67.7
      Kenley Jansen | 84 | 67.7 | 67.6
      Brandon Moss | 84 | 112.2 | 67.6
      Matt Holliday | 84 | 62.6 | 67.6
      Craig Kimbrel | 84 | 52.9 | 67.4
      Glen Perkins | 84 | 129.2 | 67.4
      Marco Estrada | 82 | 230.0 | 67.4
      Ryan Braun | 84 | 10.8 | 67.3
      Jimmy Rollins | 84 | 216.7 | 67.3
      Joe Nathan | 84 | 115.0 | 67.3
      Ian Kennedy | 51 | 267.2 | 67.0
      Oswaldo Arcia | 66 | 245.3 | 66.9
      Aaron Hill | 84 | 94.3 | 66.9
      Chris Sale | 84 | 50.0 | 66.9
      Xander Bogaerts | 84 | 145.4 | 66.9
      Brandon Belt | 84 | 125.7 | 66.9
      Andrew McCutchen | 84 | 3.8 | 66.8
      Tyson Ross | 56 | 261.8 | 66.8
      Martin Prado | 84 | 111.6 | 66.8
      Tim Lincecum | 83 | 233.9 | 66.7
      Gerrit Cole | 84 | 97.4 | 66.7
      Nick Markakis | 50 | 264.5 | 66.6
      Carlos Gomez | 84 | 22.0 | 66.6
      Ryan Zimmerman | 84 | 49.3 | 66.5
      James Shields | 84 | 91.9 | 66.5
      Jim Henderson | 84 | 187.0 | 66.5
      Jose Fernandez | 84 | 48.8 | 66.5
      Ubaldo Jimenez | 72 | 259.5 | 66.4
      Josh Johnson | 65 | 249.5 | 66.4
      Jonathan Lucroy | 84 | 127.8 | 66.4
      Yasiel Puig | 84 | 22.5 | 66.4
      Rex Brothers | 84 | 217.7 | 66.4
      Lance Lynn | 83 | 217.2 | 66.3
      Huston Street | 84 | 182.0 | 66.3
      Ryan Howard | 82 | 239.5 | 66.1
      Ben Revere | 83 | 221.9 | 66.1
      Starling Marte | 84 | 56.1 | 66.1
      Bobby Parnell | 84 | 189.7 | 66.0
      Zack Greinke | 84 | 70.4 | 66.0
      Sergio Romo | 84 | 131.9 | 65.8
      Greg Holland | 84 | 77.7 | 65.8
      Alex Cobb | 84 | 85.3 | 65.8
      Yoenis Cespedes | 84 | 60.3 | 65.6
      Jason Heyward | 84 | 63.5 | 65.6
      Wil Myers | 84 | 50.2 | 65.5
      Neftali Feliz | 75 | 216.5 | 65.4
      Addison Reed | 84 | 144.4 | 65.4
      Andrelton Simmons | 84 | 148.2 | 65.4
      Troy Tulowitzki | 84 | 17.1 | 65.4
      Francisco Liriano | 84 | 143.9 | 65.2
      Jonathan Villar | 84 | 199.6 | 65.2
      Mark Trumbo | 84 | 63.9 | 65.2
      Salvador Perez | 84 | 156.0 | 65.2
      Adam Lind | 84 | 193.6 | 65.1
      Norichika Aoki | 84 | 170.7 | 65.0
      Asdrubal Cabrera | 84 | 169.9 | 65.0
      Kyle Seager | 84 | 82.4 | 65.0
      Stephen Strasburg | 84 | 36.5 | 64.9
      Danny Salazar | 84 | 136.1 | 64.9
      Grant Balfour | 84 | 162.2 | 64.9
      Hyun-Jin Ryu | 84 | 142.3 | 64.9
      Adam Wainwright | 84 | 37.0 | 64.9
      Ian Desmond | 84 | 30.4 | 64.8
      Ian Kinsler | 84 | 42.2 | 64.7
      Jose Reyes | 84 | 41.8 | 64.7
      Danny Farquhar | 82 | 253.8 | 64.7
      Brad Miller | 84 | 156.1 | 64.7
      Hisashi Iwakuma | 84 | 140.1 | 64.7
      Nick Castellanos | 78 | 239.5 | 64.6
      Billy Butler | 84 | 106.7 | 64.6
      Brian McCann | 84 | 114.9 | 64.5
      Elvis Andrus | 84 | 47.3 | 64.5
      Wilson Ramos | 84 | 175.5 | 64.5
      Joaquin Benoit | 84 | 237.9 | 64.4
      Homer Bailey | 84 | 80.6 | 64.4
      Andrew Cashner | 84 | 153.8 | 64.3
      J.J. Hardy | 84 | 125.0 | 64.3
      Wilin Rosario | 84 | 99.2 | 64.3
      Pedro Alvarez | 84 | 83.0 | 64.3
      Ervin Santana | 79 | 223.1 | 64.2
      Matt Carpenter | 84 | 70.0 | 64.2
      Casey Janssen | 84 | 175.6 | 64.2
      Mark Teixeira | 83 | 212.3 | 64.2
      Eric Young Jr. | 79 | 262.7 | 64.2
      Ernesto Frieri | 84 | 166.6 | 64.2
      Adam Eaton | 84 | 191.0 | 64.2
      Matt Kemp | 84 | 71.7 | 64.1
      Gio Gonzalez | 84 | 88.2 | 64.0
      Nick Swisher | 84 | 221.3 | 64.0
      Mike Napoli | 84 | 122.6 | 64.0
      Christian Yelich | 84 | 180.2 | 64.0
      A.J. Burnett | 84 | 211.9 | 64.0
      Tony Cingrani | 84 | 156.0 | 64.0
      Freddie Freeman | 84 | 27.7 | 63.9
      Chris Archer | 84 | 200.8 | 63.9
      Alex Rios | 84 | 35.2 | 63.8
      Kelly Johnson | 77 | 253.6 | 63.6
      Leonys Martin | 84 | 152.7 | 63.6
      Ben Zobrist | 84 | 70.5 | 63.5
      Ivan Nova | 76 | 245.4 | 63.5
      Michael Pineda | 71 | 255.8 | 63.5
      Brett Lawrie | 84 | 121.5 | 63.5
      Nate Jones | 84 | 192.9 | 63.4
      Justin Verlander | 84 | 56.3 | 63.3
      Angel Pagan | 55 | 273.9 | 63.2
      Coco Crisp | 84 | 143.9 | 63.2
      Evan Gattis | 84 | 211.5 | 63.2
      Edwin Encarnacion | 84 | 12.0 | 63.1
      Khris Davis | 84 | 165.3 | 63.1
      Eric Hosmer | 84 | 43.4 | 63.1
      Cody Asche | 52 | 221.5 | 63.1
      Dexter Fowler | 81 | 237.4 | 63.0
      Desmond Jennings | 84 | 99.9 | 62.9
      Erick Aybar | 75 | 246.7 | 62.9
      Taijuan Walker | 84 | 240.7 | 62.9
      Torii Hunter | 84 | 185.9 | 62.9
      Buster Posey | 84 | 51.3 | 62.9
      Evan Longoria | 84 | 17.8 | 62.8
      Carlos Santana | 84 | 79.8 | 62.7
      Shane Victorino | 84 | 124.1 | 62.7
      Chase Utley | 84 | 129.3 | 62.6
      Josh Hamilton | 84 | 93.8 | 62.5
      Justin Masterson | 84 | 153.0 | 62.5
      Jed Lowrie | 84 | 166.7 | 62.5
      Aroldis Chapman | 84 | 86.1 | 62.4
      Curtis Granderson | 84 | 155.7 | 62.4
      Chris Johnson | 63 | 256.2 | 62.4
      Josh Reddick | 84 | 213.0 | 62.3
      David Ortiz | 84 | 55.5 | 62.2
      Yu Darvish | 84 | 25.9 | 62.1
      Matt Moore | 84 | 125.3 | 62.0
      Matt Wieters | 84 | 172.6 | 62.0
      Anibal Sanchez | 84 | 73.5 | 62.0
      Clay Buchholz | 84 | 219.0 | 61.8
      Miguel Montero | 72 | 266.7 | 61.8
      Drew Smyly | 83 | 227.6 | 61.8
      Bryce Harper | 84 | 11.7 | 61.7
      Jonathan Papelbon | 84 | 155.5 | 61.7
      Chris Tillman | 82 | 227.9 | 61.6
      Carl Crawford | 84 | 186.4 | 61.5
      Jeff Samardzija | 84 | 147.5 | 61.5
      Jedd Gyorko | 84 | 87.4 | 61.5
      Jason Castro | 84 | 200.5 | 61.4
      Justin Smoak | 76 | 269.6 | 61.4
      David Wright | 84 | 23.7 | 61.2
      Alfonso Soriano | 84 | 152.5 | 61.2
      Hanley Ramirez | 84 | 15.9 | 61.1
      Alexei Ramirez | 84 | 166.1 | 61.0
      Zack Wheeler | 84 | 180.6 | 61.0
      Doug Fister | 84 | 126.8 | 60.9
      Dillon Gee | 59 | 272.8 | 60.9
      Adam Jones | 84 | 8.9 | 60.9
      Jean Segura | 84 | 41.4 | 60.8
      David Price | 84 | 52.3 | 60.8
      Alejandro De Aza | 84 | 218.0 | 60.7
      Joe Mauer | 84 | 85.7 | 60.6
      Joey Votto | 84 | 17.5 | 60.5
      Jim Johnson | 84 | 152.0 | 60.5
      Yovani Gallardo | 80 | 250.5 | 60.4
      Corey Hart | 84 | 213.7 | 60.3
      Aramis Ramirez | 84 | 105.0 | 60.3
      Shin-Soo Choo | 84 | 37.1 | 60.3
      Shelby Miller | 84 | 116.5 | 60.3
      B.J. Upton | 84 | 198.6 | 60.2
      Jason Kipnis | 84 | 23.2 | 60.0
      Will Venable | 84 | 134.5 | 59.9
      Jurickson Profar | 79 | 155.2 | 59.9
      George Springer | 77 | 243.7 | 59.8
      Prince Fielder | 84 | 10.6 | 59.8
      Manny Machado | 84 | 126.7 | 59.8
      Domonic Brown | 84 | 110.9 | 59.6
      Chase Headley | 84 | 157.9 | 59.5
      Archie Bradley | 57 | 267.5 | 59.4
      Kendrys Morales | 70 | 232.6 | 59.4
      Colby Rasmus | 83 | 241.3 | 59.3
      Allen Craig | 84 | 63.2 | 59.2
      Chris Davis | 84 | 7.4 | 59.1
      Daniel Murphy | 84 | 157.7 | 59.1
      Matt Cain | 84 | 94.8 | 58.9
      Dan Haren | 80 | 220.1 | 58.9
      Mat Latos | 84 | 102.9 | 58.5
      Starlin Castro | 84 | 93.4 | 58.5
      Hiroki Kuroda | 84 | 194.7 | 58.3
      Will Middlebrooks | 84 | 153.5 | 58.2
      Michael Bourn | 83 | 212.9 | 58.0
      Dustin Pedroia | 84 | 28.3 | 57.3
      C.J. Wilson | 82 | 193.0 | 57.2
      Oscar Taveras | 67 | 265.4 | 56.8
      Carlos Gonzalez | 84 | 6.0 | 56.7
      Jacoby Ellsbury | 84 | 14.3 | 56.3
      Nate Schierholtz | 61 | 271.8 | 56.2
      CC Sabathia | 84 | 188.9 | 56.2
      Dan Straily | 72 | 242.3 | 56.0
      Matt Garza | 82 | 216.5 | 55.9
      Mike Minor | 84 | 112.8 | 55.7
      John Lackey | 62 | 261.0 | 55.2
      Bartolo Colon | 57 | 270.6 | 54.4
      Jered Weaver | 84 | 131.3 | 53.3
      R.A. Dickey | 84 | 168.0 | 52.2
      Jake Peavy | 77 | 241.1 | 51.6
      Yadier Molina | 84 | 81.2 | 51.6
      Brandon Phillips | 84 | 77.5 | 51.4
      Cliff Lee | 84 | 38.6 | 49.9

    • @Eric: Too much work to note a team’s finish (calling from a table that has their total points) but this was interesting.

      The average team had a Hit/SP/RP split of 68.7%/22.6%/8.7%.

      The correlation of $ to final standings points was: 0% / -9% / 19%

      So it appears that teams investing heavier in relievers fare better than average.

  21. Razzball Fan says:
    (link)

    Are OBP and WHIP the best categories to target for overall teams performance?

    • @Razzball Fan:
      Here’s a correlation of team projected stats (Based on preseason Steamer) vs their final standings Hitting points

      r hr rbi sb avg
      11.8% 15.8% 13.8% 12.1% 11.0%

      And for standings pitching points by category. I added k/ip and wins per GS as the negative values on Wins and K’s indicate to me the counterproductive strategy of drafting a lot of starting pitchers.

      w sv era whip k k/ip w/gs
      (14.4%) 25.6% 31.2% 27.0% (12.0%) 21.6% (1.2%)

      So my focus on hitters is balance with a slight bias towards power. For pitchers, it’s ignore Wins completely and focus on ERA/WHIP/K per inning/Saves

  22. goodfold2 says:
    (link)

    from above, the yes…many mentioned was y.thomas ARI, and was wondering if the reason he’s not listed in OPS player projections (specifically yahoo 6X6 OPS) due to his not being assured at bats?

    • Tomas not there because steamer hadn’t projected him yet. Difficult with foreign players

      • goodfold2 says:
        (link)

        @Rudy Gamble: thanks. Whatever site fantasy pros goes with for their auction projections is having exact same thing, but from other things i’m seeing they don’t use steamer.

  23. marti says:
    (link)

    I would love a 2nd opinion on who to keep in my 12 team mixed league dynasty league roto league. We keep 6 players and lose that round. Each year that round gets higher by 2 rounds. We play 1 catcher and 5 outfield spots and 2 utility spots. Its a 5 by 5 roto league standard scoring.

    Bumgarmer round 6
    Desmond round 11
    A Garcia round 15
    Chris Carter round 16
    Dee Gordon round 17
    A McCutchen round 18
    A J Pollock round 21
    K Jansen round 26
    Z Britton round 31
    C Dickerson round 32

    A few of these guys are obvious keepers but I need to keep 6. Thoughts appreciated

    • @marti: Dickerson, Jansen, McCutchen, Gordon, Desmond, Britton

  24. Happy Vegans says:
    (link)

    What’s the correlation between checking out the stacked waitress who bends over to serve me and my date our drinks at dinner, having my date notice my ill-timed glance, and then getting “lucky” with said date later that evening?

    • GhostTownSteve says:
      (link)

      @Happy Vegans:

      High negative correlation.

      • Happy Vegans says:
        (link)

        @GhostTownSteve: What if I said I was looking at her interesting necklace? Weave in some meso-american art BS? Or what if I tell her I have a very lazy eye? Or how ’bout I have a lactose addiction that instantly went pavlovian? Or I have some weird version of synesthesia and I tend to mash up the sight of round objects with the sound of “Will that be all for you two?”

      • Happy Vegans says:
        (link)

        @Wake Up: Ha Ha! Still the best place to receive a morning smile & laugh. It’s better than ‘Open Mic’ night I tell ya!

  25. Tehol Beddict says:
    (link)

    How come I had the worst year of my fantasy baseball career last season? #Lost

      • Tehol Beddict says:
        (link)

        @GhostTownSteve: id say you’re correct but it’s never stopped me before……….i probably should tone it down tho. My therapist thinks its a good idea

        • GhostTownSteve says:
          (link)

          @Tehol Beddict:

          I’m all for hubris when it’s a means to an end but not the end in and of itself. I think that iconclastiscism is the heart of Razzball when it’s paired with a grounding in solid analysis.

        • Sky

          Sky says:
          (link)

          Please don’t tell me it’s Tobias Funke…

          • GhostTownSteve says:
            (link)

            @Sky:

            Analrapist…

  26. Matt says:
    (link)

    RCL limits pitchers by GS, not IP, yeah? Makes sense that stacking RP = more IP, more K, more success. Be quite interested to see if that held up in a league with an IP cap rather than a GS cap. Anyone got another 1000 teams that can be looked at? ;)

    Interested to see round the first catcher is drafted vs total points.

    • @Matt: Yes, it’s a GS cap vs IP cap. I think closers and middle relievers are valuable in IP capped leagues but it’s not the same.

      Here’s the average points based on round in which first catcher taken:

      Round # of Teams Avg Pts
      3-5 | 92 | 62.2
      6-7 | 145 | 59.3
      8-9 | 146 | 63.1
      10-11 | 99 | 67.2
      12-13 | 94 | 64.0
      14-15 | 106 | 65.4
      16-17 | 85 | 63.7
      18-19 | 71 | 65.6
      20-23 | 76 | 68.0
      24+ | 94 | 76.0

      Here’s another view of it. Maybe drafting a catcher early pays off but it was generally a negative last year…

      Catcher # Avg Pick Avg Pts
      A.J. Pierzynski | 55 | 272.6 | 72.8
      Devin Mesoraco | 34 | 289.3 | 69.9
      Yan Gomes | 39 | 270.6 | 69.6
      Jonathan Lucroy | 84 | 127.8 | 66.4
      Salvador Perez | 84 | 156.0 | 65.2
      Brian McCann | 84 | 114.9 | 64.5
      Wilson Ramos | 84 | 175.5 | 64.5
      Wilin Rosario | 84 | 99.2 | 64.3
      Evan Gattis | 84 | 211.5 | 63.2
      Buster Posey | 84 | 51.3 | 62.9
      Carlos Santana | 84 | 79.8 | 62.7
      Matt Wieters | 84 | 172.6 | 62.0
      Miguel Montero | 72 | 266.7 | 61.8
      Jason Castro | 84 | 200.5 | 61.4
      Joe Mauer | 84 | 85.7 | 60.6
      Yadier Molina | 84 | 81.2 | 51.6

    • @Matt: GS = volume
      IP = quality innings

  27. Bill says:
    (link)

    Streamer vs zips vs grey…
    Projections vs end of year, broken down into hitting and pitching. Seems in the past it was grey and/zips were better for hitting and streamer for pitching. Thoughts…

    • Steamer is better than zips now for hitting too – http://blog.rotovalue.com/comparing-2014-projections-woba/.

      Should be clear if u read this blog that I think Steamer projections better than Grey (or any other non-algorithmic projections). I like that Grey provides projections behind rankings as it shows his underlying assumptions that go into his rankings.

  28. Grays Sports Almanac says:
    (link)

    As a test Of how much being active correlates to winning, how did the people that make the most moves in a season fare?

    • @Grays Sports Almanac: covered above. a team’s % of total moves has a huge correlation w/ final standings points (~58%). Almost equal to the correlation between the end of season $ value of a team’s draft and final standings points.

  29. So, punt catcher and don’t draft elite closers, both confirmed?

    • Sky

      Sky says:
      (link)

      And make sure to call your mother at least once a week.

      • @Sky: Funny, cuz your mother was calling me last night, Alex!

    • @Wake Up: I’ve seen nothing, per se, against drafting elite closers. But you definitely need to draft volume as well.

      • @Rudy Gamble: I saw above that there doesn’t seem to be an advantage to drafting a closer in the early rounds. But I hear what you’re saying. Thanks!

  30. Yescheese says:
    (link)

    As a twist on the other question on stat category correlation to winning (R, RBI, W, K) … what is the correlation of total team transactions to finishing in the top 3 for those categories?

    Said another way, if you have the most transactions, did you typically finish near the top in R, RBI, W and/or K?

    • @Yescheese: Ks for sure. If you come out of the draft loaded in RPs you are at a big disadvantage in total Ks obviously. So Ks will be directly related to total # of moves.

    • @Yescheese:
      Below is the correlation of category standings points to a team’s % of league moves. There might be the potential for a strategy to de-emphasize certain stats while drafting and emphasize others if you know you’ll be one of the more active teams (i.e., draft some high AVG hitters and low ERA/WHIP pitchers).

      PTS | 58%
      R_PTS | 55%
      HR_PTS | 41%
      RBI_PTS | 51%
      SB_PTS | 34%
      AVG_PTS | 0%
      W_PTS | 43%
      SV_PTS | 49%
      ERA_PTS | 23%
      WHIP_PTS | 20%
      K_PTS | 53%

  31. FrankGrimes says:
    (link)

    in 14 team H2h points league where hitter Ks are -1
    best way to figure out hitters to draft mid rounds?
    my keepers are already high K guys

    • I would take the hitter projections and convert to points. I’d weigh down the SBs by .5. Then compare those ranks with who is available then with ADP.

      • FrankGrimes says:
        (link)

        @Rudy Gamble:
        thanks

  32. Yescheese says:
    (link)

    As a twist on my previous question… What is the correlation when you have the most moves in your league and win it all … And 3 or more members have 10 moves or less.

    Basically how many leagues had artificially low competition from abandoned teams?

    There’s probably a couple of twists on this metric

    • @Yescheese: i kind of answered this above. % of League Moves seems to drive an equal % of success independent if it’s an active or inactive league.

  33. Happy Vegans says:
    (link)

    In a typical RCL 5X5 league, how closely have the category totals correlated to each other year over year? For example, Runs in 2014 to Runs in 2013, HR in 2014 to HR in 2013? Thank you!

    • @Happy Vegans: Not sure where the 2013 totals are right now.. I assume this is super-high. Not seeing any insights here.

  34. Yescheese says:
    (link)

    What is the correlation with winning vs total number of moves… By ranges? Did anybody win with 10 or less moves? 10-20? Etc

    • Answered this above. 42% correlation to moves and standings points. Second biggest factor to success after end of season $ value of drafted players

      • Yescheese says:
        (link)

        @Rudy Gamble: what is the average delta for drafted $ for winners vs the average RCL? Is it $15 more for winners? Just curious if it’s the equivalent of hitting it big with one player, several players, or just marginal

        • @Yescheese:
          The average draft’s End of Season $ was $207. The average of the top 84 teams (in terms of draft end of season $) was $284. The average of the bottom 84 teams was $134. So the range seems to be about +/-$70 from 1st to 12th and the difference per place is about $11-$12.

  35. JR says:
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    Rudy, how often do you update the Razzball/Steamer 2015 projections?

    • Every day or two to reflect new signings and playing time changes

  36. J-FOH says:
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    I have a DFS bot question. Is there any way to get it updated the day before?

    • @J-FOH: This is an ask for all DFS sites I’m talking to this offseason. Planning for 3 additions (which ones are still TBD).

  37. Will says:
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    What’s the dollar value of a draft pick?

    Like, the average 1st round pick is typically held all year (regardless of how well they do) and contributes a certain amount of $ value over the season. As you get into later rounds, players present less value AND get dropped sooner. Let’s say that every player in the 10th round is worth $5 total on the season, and 50% of them get dropped halfway through the season — and I’m going to say 10-team, because math is hard — that’ a $3.75 value per pick, right? It’d bereally interesting, especially in keeper leagues where picks get traded willy-nilly — to have something approaching an empirical value for a draft pick. (I’ve been in a league for a while where people do goodplayer-badplayer trades with a 13th and a 17th switching and I keep screaming THAT DOES NOT REPRESENT VALUE, ASK FOR MORE). For this exercise, undrafted players are worth $0. And players dropped even once represent $0 for the rest of the season.

    is that the kind of shizz you can pull?

    • All u have to do is look at my preseason player rater. Just ignore the player and pretend the rank = the draft pick.

      • Will says:
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        @Rudy Gamble:

        But that is kind of like the “potential” value of a pick… what I am talking about was the 2014 “true” value of the pick. Does that make sense? Like — take the 91-100 ranked players from 2014 pre-season rankings, find their end-of-year value, multiply by the average % of the season each was actually on someone’s team before getting dropped (trades are fine) — does the methodology on this even make sense? Maybe I’m kind of out of my mind. I’m definitely a little bit out of my mind.

  38. Spammer jay says:
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    Rudy, is this data from last season only or the entire rcl history?

  39. Tom says:
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    13 team 6×6 (OPS and QS) Roto – 2 C, 1B, 2B, 3B, SS, IF, 4 OF, 3 U, 4 SP, 3 RP, 2 P, 6 Bench, 3 DL

    I can keep up to 4 of these players for just this year while forfeiting the provided round’s pick:

    Jose Abreu – 6
    Jose Altuve – 7
    Corey Kluber – 16
    George Springer – 19
    Corey Dickerson – 26

    • Dickerson, springer, kluber, Abreu

  40. Bill G says:
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    Hey man – draft strategy question here…

    In a 12 team h2h league with 7×7 categories (OPS/Ks added for hitters and Losses/Quality Starts added for pitchers), do you think this strategy could work?

    Stocking up on hitters and closers. Try to win every hitting category possible then get a bunch of good closers and hope to win 4 of 7 pitching categories (Losses, Saves, ERA, WHIP)

    Keep in mind the inning minimum every week is 10 Innings Pitched and our pitching roster spots looks like this…
    SP-
    SP-
    SP-
    SP-
    SP-
    RP-
    RP-

    Stud closers tend to go LATE in this league, only Chapman got taken in the top 100 last year. What do you think?

    • I don’t like it. Ur not going to get that much better of an offense to afford punting W and QS. I can see grabbing one SP and a top closer in first 10 rounds and then picking up a lot of quality arms in 11-20th round.

  41. KCatthebat says:
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    Here is a test question for you Rudy – not sure if the data will support reporting along these lines – I am trying to figure out if there is a correlation between draft and final standings such that one could project finish immediately following the draft. Specifically I am wondering what percentage of a teams final point totals results from draft vs. in-season management, and more specifically what the impact of regulars / reserves (essentially a regular / reserve split) is. Currently I use a weighted average of the projections for regulars (85%) and reserves (15%) and calculate projected finish. But this is a pretty crude methodology so wondering if your data could lead to a better one.

    • I have done this before. Projected standings after the draft have at best a 10% correlation. Useless. End of season $ value explains 58% in 2014 and % of league moves added an additional 22%.

      • KCatthebat says:
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        @Rudy Gamble: thanks

  42. KCatthebat says:
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    Here is another one – but if you have moved on to other things no worries. I am wondering if you can calculate Z-scores for finish position by category. I have just done this for my home league and found that over the past 9 years Z-scores for Batting Average have averaged:

    1st: 1.773
    2nd: 1.192
    3rd: 0.793
    4th: 0.652
    5th: 0.355
    6th: 0.078
    7th: -0.140
    8th: -0.226
    9th: -0.640
    10th: -0.833
    11th: -1.267
    12th: -1.739

    Given the offensive shifts and differing projection methodologies it really does me no good to set a specific Batting Average target, but if I can set a target of 0.793 relative to the projections I am using I should be in good place to finish in third or better. Which is where I try to target my finish for each category.

  43. goodfold2 says:
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    i’m guessing with kenley ranked only 8th at $10.7 on holds/player rater you’ve already adjusted his playing time to miss a month?

  44. goodfold2 says:
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    8th among RP.

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