Tford

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Tford last won the day on May 16 2013

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About Tford

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    Huddler

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    Falcons

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    Great White North

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  1. JStew 2.0? That's a kiss of death if I've ever seen one.
  2. Hard to argue against Jarrad Davis in IDP.
  3. I don't care what you do. Both of your teams suck.
  4. The IDP Free Agent report comes out on Tuesday. http://www.thehuddle.com/2013/season/06/idp-free-agent-report.php
  5. Mucca is undefeated and leading the league at 4-0. Baby Jane is one of two teams that are 0-4.
  6. You can't answer that question unless you describe your team (active roster and other PS roster guys) and the league you play in. At least I can't anyways. If I am in a trade-heavy league where I know that I can flip a guy if he pans out, I am more likely to go with the safer bet simply because I know that I can cash in on value. That said, these leagues are usually trade-heavy because of scarcity of talent because they are deep so usually I won't have the option of who I want for a PS player very often. On a team note, if the players I am evaluating are in an area of strength for my team, I am going to gamble on the guy with the highest ceiling because he has a better shot of improving my team. If it is in an area of weakness, I am more likely to take the safest player with maybe less upside to get a better chance of getting a contributor. Finally, on relative position note, if I am a contender, I am likely choosing the player who I think has a quicker path to playing time in case an injury strikes and I need points right now. However, if I am rebuilding, I am more amenable to taking a player that I may need to wait longer on.
  7. FWIW Jordan played almost exclusively with his hand in the dirt in the HOF game. Not exactly proof positive but at least a small measure of relief for dynasty owners that drafted him to play DE.
  8. When the NHL did this, I loved it. Especially the part where Phil Kessel got picked last.
  9. Still adding owners! Signup in the linked forum above.
  10. Lots of bad blood in here and I don't really know why. Maybe I'm crazy but I don't really see why Baby Jane is getting crucified in here. 1) Baby Jane has opinions on FF. Agree with them on not, that's relevant for this forum. Quite frankly, there are some tendencies in his drafting and methodology that I actually (I refuse to spell it your way AE, that's the line) agree with. Argue about the message, not the messenger. 2) Sure, there was a curveball in the all-waiver league that some people (many who aren't even in the league) were upset over. Am I upset? More at myself for not catching it than anything. I still intend to find a way to beat AE during our matchup and like everyone else, despite this thread, I will play in that league to win. Understandably, others may not share my view of how that went down but that's my stance. I have bigger problems to get bent out of shape over. 3) I've yet to see AE fall prey to some of the antagonizing that he's endured thus far. Antagonizing that I don't think is warranted to be honest. Troll gets thrown around a lot in this thread and another one. I believe that it is many huddlers that are attempting to troll AE and he isn't taking the bait. Frustration leads to an ignore. Maybe it's better that way but there are a lot of huddlers who are looking like the childish ones in my opinion. 4) Love him or hate him, he's given life to a board that has been a wasteland for months. I understand that my viewpoint might earn me an ignore as well. So be it. The threat of an ignore doesn't change how I think about it nor my willingness to share my opinion. -TJ
  11. I like the outside the box thinking here but there are a couple statistical flaws here. Nothing to do with the data itself, but straight math no-nos. Some points I took away from this. 1) In statistics, you cannot apply any mathematically derived solution of a subset onto a whole set and keep it valid. The qualifier that causes it to be a subset in the first place contains assumptions or data characteristics that are not inherit to every piece of data in the whole set. In this case, the extrapolation of win % without a QB within the "win" subset cannot be applied to the "total games" whole set. Other factors caused the win subset to be a win in the first place. Things like good games from your other players and/or a bad game from your opponent. Those factors are not always present in the other data subset "losses". Therefore any derived number from the wins subset, can't apply to the other games, it can only by compared to a similar derivation to the whole set. In order for your win % extrapolation to hold true, every game would have to involve good games from your other players or a bad game from your opponent. We already know this not to be true. 2) I like where you were going with the Average wins for a team with a top-3 QB. I think that analysis is much more relevant. Since we know that in every league with an even amount of teams and a 13 games fantasy regular season, the mean win average must be 6.5, determining the true mean of the top QB subset and comparing that true mean to the whole set true mean of 6.5 will hold much more relevance if we assume that fantasy regular season follow a normal distribution (most things do). That said, three things prevent your analysis from sticking. - In order to compared mean to mean, you need to know what the standard deviation of both the set and subset is to determine whether the null hypothesis is true (subset mean = true mean or rather, subset mean is not significantly different than true mean). In this case, you are trying to prove just that. If the subset mean is the same as the set mean, then a top QB should not provide any statistical advantage. However, simply stating that your calculated subset mean is 6.6 is only half the answer. You need to use standard deviation to determine how far away from the mean is normal. A normal distribution of 6.5 mean and 0.1 standard deviation is drastically different from the same mean and 1.5 standard deviation. You reference CV (coefficient of variation) in your article later on. In this case, a lower CV might prove the null hypothesis false whereas a higher CV might prove the null hypothesis true. - In order to complete that, you need a much larger sample set on the order of 1000s of leagues. You need to eliminate bias from the data set as much as you can. Did the top 3 QB owner tend to draft WR heavy? Did he always lead out RB? Was it the same guy in multiple leagues? You need the answer to all of these questions and any question like it to be sometimes, not usually or seldom. If you cannot say that, then your sample set is not randomized enough to be considered a whole set. - Top 3 QB seems to be an arbitrary number. Using your SOFA classic league, the pick difference between QB3 and QB4 drafted was 7, 7, 7, 5, 2, 1 and 2 going back to 2006. What makes the QB3 selecting team so markedly different from the QB4 selecting team? In addition, sometimes the QB3 was selected as high as round 1 and others it was selected as low as round 5. A round cutoff would have more appropriate since I would argue that the guy who took Drew Brees at QB3 (5.06) in 2007 (whoever drafted for the The Huddle oddly enough) or the guy who took Aaron Rodgers at QB3 (4.12) in 2009, got much better value than the guy who took Drew Brees at QB3 (1.11) in 2012. You go on to speak about VBD in your article but QB3 in round 4-5 is markedly different than VBD in round 1-2. I would argue that lumping those teams into the same data subset isn't as valid as lumping all round 1-2 QB drafting teams together and seeing what analysis that holds. 3) Your analysis involving CV and miss rate is spot on and is very relevant in my opinion. It highlights the disparity between what RB1/WR1 you can draft in round 1-2 versus waiting a round because you drafted QB versus the disparity (smaller) of the QBs were you to wait. I don't know if Olympic miss rate is relevant simply because a top 3 QB drafted will likely be started for his fantasy team every week he is healthy. That said, major points were not made off of Olympic MR so it doesn't really matter. 4) Finally, and I only bring this up because I am an engineer and I am a nerd, theorems are statements that are proven factual time and time again through multiple proofs and derivations from other theorems and accepted fact. Theory, in a statistical sense, is a hypothesis that has been rigorously tested and being proven true through repeated mathematical test. It is not a law as it may not have yet been tested under specific conditions which may test its validity. What you have is a hypothesis. A proposed explanation for a result, behavior or phenomenon that can be tested to prove/disprove validity. All in all, good read.
  12. Still looking to fill up, visit the subforum!
  13. That's awesome. Pretty cool that your boy will always have a great party on his birthday no matter how old he is.
  14. Enjoy your cold beer and hot dogs while I grind it out at work.