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Dantoni's historical H/A splits as a baseline for '16/'17 Rox W/L

Discussion in 'Houston Rockets: Game Action & Roster Moves' started by dream2clips, Dec 6, 2016.

  1. dream2clips

    dream2clips Member

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    [​IMG]screen capture windows

    W@H = Wins @ Home
    W@A = Wins @ Away
    H-A = Home wins - Away wins
    For my W/L projections:
    2016 = Baseline trying to fit the W/L around the 5.4 average
    eH = extreme Home split; fitting W/L to Dantoni's +8 historical ceiling
    eA = extreme Away split; fitting W/L to Dantoni's =/- 0 historical floor
    bold = My pick for most likely outcome in a given subset

    Off day for the Rox so good time to assess the big picture. I think the consensus now is that this is a 55 win team if things break right and a 41 win team if they don't - exception being Harden missing time.

    It did surprise me a bit that despite some dominant 60 win PHX teams that Dantoni's best Home showing was 33-8. Now, this side of the Spurs, Warriors, and Cavs, 33-8 checks the box for every other team in the league - but, again, I was a bit surprised to not find more dominance. Conversely, you can interpret that as I was surprised his teams were so successful on the road - 31-10 in his best year!

    I originally set out to deduce whether Dantoni's pace of play neutralizes H/A splits. Does the extreme nature of the pace - vs. normal - overwhelm other typical H/A factors? I decided pretty quickly that I didn't want to screw around with 10 other data points so this is all I gots for ya...oh well. I'll revert back to the thread at the all-star break and at the end of the year to tie up loose ends and see how the season fit w/in his overall H/A historical splits. Will be pretty cool if one of the bolded selections winds up being the nut.
     
  2. FTW Rockets FTW

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    LOL 1 hour in and no one has replied.

    I pity you OP but we are in the midst of the Dmo movement.

    If it ain't a DMo thread, you will not get responses. No popcorn for this one.
     
  3. snowconeman22

    snowconeman22 Member

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    This is an interesting breakdown . I would like to see what the typical ft home and away splits are for d'antonis teams and how those compare to league average splits .

    I think there is something to be said for a 7 seconds or less offense negating a typical home ft advantage, but this team isn't built like that and our best player should tribe off getting to the line more at home ... So I'm not sure the historical trend will hold .

    Nonetheless great topic and solid breakdown
     
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  4. RocketsFan92

    RocketsFan92 Member

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  5. jbasket

    jbasket Member

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    Don't let this thread distract you from the fact that DMo did not show up to his physical.
     
  6. Nivos

    Nivos Member

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    First, I do appreciate the effort of making a thread that is basketball related.
    But I don't think you can do such a comparison between 4 completely different teams that the only common thing between them was the coach.
    How can you learn anything for it?
     
  7. CantBeRight

    CantBeRight Member

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    Interesting.
     
  8. dream2clips

    dream2clips Member

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    Thanks for the sympathy breh lol but I already acknowledged I did a half-ass job b/c when I started deep-diving I realized how many inputs the analysis would take anddddddddd I passed :). What I posted is far less interesting than the drivers, but that takes far more time.


    Fair point. And I thought about this as well, but the data spans his entire NBA coaching career. I'm most interested in the constraints or the floor/ceiling. His +8 eH ceiling was hit several times and across all 3 of his stints - PHX, NYK, LA. In my profession, there can be extraordinary value in not believing in accidents or coincidences. It could just be an accident

    And to your last point - precisely! The only theme is that it is the same coach. He has a very specific, and extreme, style of play. It was my goal to assess whether his style overrides things like players (stars specifically), personnel (rotations), and home court advantage. Looking at the data at a high-level, most of which I didn't share in the post, there certainly seems to be something to it. I'm not big on fluffing up conclusions with half-ass work so I'll save it until such time as I can.
     
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