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Playoffs 2016
Posted: Sat Apr 16, 2016 12:35 pm
by mystic
I didn't see a thread yet ...
Anyway, I calculated the odds for each playoff series, maybe someone is interested in the results. No surprises, I would say, besides the matchup between the Heat and the Hornets.
Code: Select all
Cle in 4: 0.1342
Cle in 5: 0.2426
Cle in 6: 0.1873
Cle in 7: 0.1856
Det in 4: 0.0217
Det in 5: 0.0457
Det in 6: 0.0959
Det in 7: 0.0869
Cle wins: 0.7497
Det wins: 0.2503
Tor in 4: 0.0959
Tor in 5: 0.1982
Tor in 6: 0.1697
Tor in 7: 0.1922
Ind in 4: 0.0349
Ind in 5: 0.0685
Ind in 6: 0.1303
Ind in 7: 0.1102
Tor wins: 0.6561
Ind wins: 0.3439
Mia in 4: 0.0264
Mia in 5: 0.0775
Mia in 6: 0.0859
Mia in 7: 0.1380
Cha in 4: 0.1178
Cha in 5: 0.1756
Cha in 6: 0.2306
Cha in 7: 0.1481
Mia wins: 0.3279
Cha wins: 0.6721
Atl in 4: 0.1471
Atl in 5: 0.2552
Atl in 6: 0.1907
Atl in 7: 0.1813
Bos in 4: 0.0188
Bos in 5: 0.0402
Bos in 6: 0.0866
Bos in 7: 0.0800
Atl wins: 0.7743
Bos wins: 0.2257
Gsw in 4: 0.3469
Gsw in 5: 0.3488
Gsw in 6: 0.1660
Gsw in 7: 0.0914
Hou in 4: 0.0026
Hou in 5: 0.0065
Hou in 6: 0.0181
Hou in 7: 0.0199
Gsw wins: 0.9530
Hou wins: 0.0470
Sas in 4: 0.4989
Sas in 5: 0.3360
Sas in 6: 0.1115
Sas in 7: 0.0412
Mem in 4: 0.0006
Mem in 5: 0.0015
Mem in 6: 0.0047
Mem in 7: 0.0056
Sas wins: 0.9876
Mem wins: 0.0124
Okc in 4: 0.2555
Okc in 5: 0.3258
Okc in 6: 0.1896
Okc in 7: 0.1324
Dal in 4: 0.0061
Dal in 5: 0.0147
Dal in 6: 0.0373
Dal in 7: 0.0386
Okc wins: 0.9033
Dal wins: 0.0967
Lac in 4: 0.1763
Lac in 5: 0.2803
Lac in 6: 0.1948
Lac in 7: 0.1698
Por in 4: 0.0136
Por in 5: 0.0303
Por in 6: 0.0690
Por in 7: 0.0660
Lac wins: 0.8212
Por wins: 0.1788
Re: Playoffs 2016
Posted: Sat Apr 16, 2016 6:50 pm
by Mike G
While Mia is the 3rd seed and Cha is 6th, they're part of a 4-way tie for 3rd, at 48-34.
Cha has the better SRS, 2.36-1.50
Is that what you used to calculate these?
Re: Playoffs 2016
Posted: Sat Apr 16, 2016 7:38 pm
by mystic
Mike G wrote:
Cha has the better SRS, 2.36-1.50
Is that what you used to calculate these?
1. minute projection for each team
2. calculating team rating based on player metric and minute distribution
3. calculation of matchup ratings (regression analysis of the pbp data of the games against each other, coefficients then weighted according to the previously calculated minute distribution)
4. linear combination of team and matchup ratings
Hornets have the better team rating as well as the better matchup rating. But so have the Raptors ...
Re: Playoffs 2016
Posted: Sun Apr 17, 2016 3:23 pm
by Mike G
League eFG% was .502 this year. Saturday's 8 teams averaged .513 in the season, and their opponents shot .492
So they might have been expected to shoot right around .502 ?
Combined eFG% was .434 -- leading to an avg of just 94 ppg
Lots of rebounds, though -- 48 per team.
About 7 eFG missed that normally are made by these teams, on avg.
Two teams shot better than their season eFG%: Ind .500 (vs .493) and OKC .525 (vs .523). The others were from .071 to .182 (Dal) worse than normal.
Re: Playoffs 2016
Posted: Sun Apr 17, 2016 6:21 pm
by Crow
Last year's playoffs average efg% was .487 after a regular season of .496. First round has more bad teams than later so it will probably be worse. Perhaps first playoff game is the worst of all, normally? Or maybe it is a fluke.
Re: Playoffs 2016
Posted: Sun Apr 17, 2016 6:24 pm
by Crow
"3. calculation of matchup ratings (regression analysis of the pbp data of the games against each other, coefficients then weighted according to the previously calculated minute distribution)"
I assume you are talking about team matchups? If so, then you are assuming similar patterns on player matchups. A coach could alter results by altering player matchups. Would you have the interest and ability to go to that level and find optimal team rotation / matchup preferences? Maybe there would be some bargaining model to try to sort out likely negotiated matchups? That would be really advanced. Coaching habits of who plays when and how long could narrow the vast choices down to a modest, more manageable set of patterns.
Re: Playoffs 2016
Posted: Mon Apr 18, 2016 6:23 pm
by sideshowbob
mystic!
What are your qualitative/quantitative thoughts on Curry this year? Lot of talk of him playing at GOAT level, would you say you'd agree given what you've seen?
Re: Playoffs 2016
Posted: Tue Apr 19, 2016 12:48 am
by Mike G
In their playoff opener, both Cha and Dal had their worst game of the season, in margin of defeat.
Hou had their 2nd worst, Mem their 4th worst, and Por their 8th worst games so far.
Re: Playoffs 2016
Posted: Tue Apr 19, 2016 3:18 am
by sideshowbob
Miami put up the highest ORTG of this season of any team in game 1 at 147.4.
Re: Playoffs 2016
Posted: Thu Apr 21, 2016 8:22 pm
by sideshowbob
Miami with a crazy 136.9 ORTG (~135 w/team TOs) on Charlotte through two games. Currently the highest ORTG in any series ever since 1984 (record is 127.3 by Phoenix in 1st round of 95 playoffs).
Re: Playoffs 2016
Posted: Thu Apr 21, 2016 8:53 pm
by Mike G
Here are best ORtg-DRtg (Net ORtg) for series at various levels of the postseason.
Code: Select all
NetO 1st Rd W-L opp
32.9 *2016 SAS 2-0* Mem
31.4 1986 LAL 3-0 SAS
27.2 2009 Den 4-1 NOH
25.7 1996 Chi 3-0 Mia
25.4 *2016 Mia 2-0* Cha
25.2 1987 LAL 3-0 Den
25.2 2001 Cha 3-0 Mia
NetO 2nd Rd W-L opp
29.9 2010 Orl 4-0 Cha
22.3 2009 Cle 4-0 Det
16.0 2011 Dal 4-0 LAL
15.5 2013 Mia 4-1 Chi
15.1 1989 Phx 4-1 GSW
NetO 3rd Rd W-L opp
24.8 2001 LAL 4-0 SAS
19.6 1996 Chi 4-0 Orl
15.1 1998 Uta 4-0 LAL
14.9 1986 Bos 4-0 Mil
14.3 2015 Cle 4-0 Atl
NetO Finals W-L opp
16.0 2014 SAS 4-1 Mia
11.2 1991 Chi 4-1 LAL
10.6 2004 Det 4-1 LAL
10.4 2009 LAL 4-1 Orl
10.2 2002 LAL 4-0 NJN
The Nuggets have the biggest
best-of-7 domination (vs Hornets in '09) in any first round series since 1984; the Magic thrashed the other Hornets even worse in the 2nd round of 2010.
In 1st round series, this year's Clippers are next up (8th); the Thunder are 11th; Dubs 18th
Something about 2 games, both at home?
Re: Playoffs 2016
Posted: Sat Apr 23, 2016 9:29 am
by mystic
Crow wrote:
If so, then you are assuming similar patterns on player matchups.
Indeed, that's what the past is telling us. Usually, coaches use rather similar lineups and matchups which worked in the past, even if it didn't quite work against a specific opponent. Coaches are usually reluctant to change things massively; they stick to the plan. There are always the exceptions to the rule, and there are small changes which mostly will only be seen, if we pay attention to detail.
My current approach has the obvious disadvantage of being based on a extremely small sample size. But that isn't too much of an issue, because the overall influence isn't that big (roughly 15% of the overall value is based on the matchup rating). But using that improved the predictive power slightly.
Crow wrote:
Would you have the interest and ability to go to that level and find optimal team rotation / matchup preferences? Maybe there would be some bargaining model to try to sort out likely negotiated matchups? That would be really advanced. Coaching habits of who plays when and how long could narrow the vast choices down to a modest, more manageable set of patterns.
In general that is an interest of mine, but the issue is obviously sample size. I'm working on something which should overcome that issue, but while the pre-lim results were rather good (it is boxscore and player measurements based), I once posted player ratings derived from that approach, which had higher correlation to RAPM than BMP), it is still work in progress, because I quite simply don't have the ressources. The idea is to find a pattern (different player types) and then go from there. There is a distinct pattern found, but there seems to be some variance between seasons where I have an idea how to stabilize the results, but haven't found the time yet to bring that work to an end. But if things go well, I probably found someone willing to provide the necessary ressources (including man power) to work on that further. Well, we have to see how much time I can spend on that ...
sideshowbob wrote:
What are your qualitative/quantitative thoughts on Curry this year? Lot of talk of him playing at GOAT level, would you say you'd agree given what you've seen?
I'm following the conversation on RealGM somewhat, and I'm more on the Draymond-Green-side of things here ... I like Curry a lot, he is extremely fun to watch, but I would not say that he is GOAT-level yet. I'm really confident that I would take Jordan, O'Neal, Olajuwon, Garnett and James ahead of him in terms of peak-level. He is the GOAT shooter, without a doubt; he is even more versatile in creating his own perimeter shot than Steve Nash was, which has a lot to do with his extreme quick release. Besides that he can create the offense for others, but I think that in time teams can adapt their defense accordingly to lower Curry overall offensive impact more than they could do that for Jordan or O'Neal. I think that some of that was seen over the course of the season already. We need to see how Currys can further improve and find a counter to the other teams' "counter" ... But he also needs to be healthy. Hopefully, him sitting out now for two games was just a precaution and not a sign of something serious. He had ankle issues in the past and there were some questionmarks, but it did seem to have vanished. Maybe he should ask Nowitzki for advice to get some "rubber-ankles", he rolled his ankles multiple times and still had/has a long career.
Re: Playoffs 2016
Posted: Mon Apr 25, 2016 12:47 pm
by Mike G
Some completed series summaries:
Code: Select all
. Spurs min PER WS/48 BPM e480 WS vorp eWin
Kawhi Leonard 126 35.4 .439 14.9 2.80 1.15 .53 .73
LaMar. Aldridge 113 22.0 .253 -1.0 1.68 .60 .03 .40
Tony Parker 89 12.7 .084 -5.2 1.18 .16 -.07 .22
Danny Green 83 13.0 .150 5.4 .80 .26 .15 .14
Boris Diaw 82 13.9 .170 .8 .92 .29 .06 .16
Tim Duncan 81 19.6 .251 7.9 1.55 .42 .20 .26
Patrick Mills 78 20.9 .258 6.3 1.45 .42 .16 .24
Manu Ginobili 76 19.8 .347 8.9 1.47 .55 .21 .23
David West 72 22.4 .299 10.1 1.35 .45 .22 .20
Kyle Anderson 68 7.6 .105 .1 .41 .15 .04 .06
Kevin Martin 29 19.6 .166 2.2 1.67 .10 .03 .10
Jonathon Simmons 26 17.6 .228 6.1 1.08 .12 .05 .06
Boban Marjanovic 23 29.8 .447 2.6 2.43 .21 .03 .12
Andre Miller 15 11.1 .228 -.2 .40 .07 .01 .01
totals 961 19.8 .247 4.8 1.46 4.95 1.64 2.92
. Grizzlies min PER WS/48 BPM e480 WS vorp eWin
Matt Barnes 139 7.7 -.107 -3.7 .44 -.31 -.06 .13
Zach Randolph 120 12.2 -.092 -5.9 .90 -.23 -.12 .23
Jordan Farmar 113 6.0 -.120 -7.2 .04 -.28 -.15 .01
Lance Stephenson 95 17.4 .066 -1.7 .81 .13 .01 .16
Tony Allen 94 7.1 -.107 -4.6 .24 -.21 -.06 .05
Vince Carter 91 20.0 .140 2.4 1.16 .27 .10 .22
Xavier Munford 89 7.0 -.111 -3.0 .25 -.21 -.02 .05
Chris Andersen 79 14.9 .016 -1.2 .78 .03 .02 .13
JaMychal Green 72 16.8 .054 1.5 .89 .08 .06 .13
Jarell Martin 46 7.4 -.071 -3.5 .00 -.07 -.02 .00
P.J. Hairston 22 .6 -.173 -8.4 -.44 -.08 -.04 -.02
totals 960 11.2 -.044 -3.1 .54 -.88 -.27 1.08
. Cavs min PER WS/48 BPM e480 WS vorp eWin
LeBron James 165 23.3 .164 5.6 2.28 .56 .31 .78
Kyrie Irving 151 27.7 .239 5.1 2.34 .75 .27 .74
Kevin Love 143 21.4 .170 -.4 2.03 .51 .06 .60
J.R. Smith 142 16.4 .172 5.1 .74 .51 .25 .22
Tris. Thompson 113 11.6 .128 4.6 .04 .30 .19 .01
Matt Dellavedova 72 25.8 .349 3.2 1.73 .52 .09 .26
Rich. Jefferson 66 5.8 .055 -1.3 -.44 .08 .01 -.06
Iman Shumpert 61 8.1 .048 -.3 .15 .06 .03 .02
Channing Frye 29 2.8 .027 -2.0 -.60 .02 .00 -.04
Timofey Mozgov 14 -11.4 -.333 -14.2 -1.80 -.10 -.04 -.05
totals 956 18.2 .161 2.9 1.24 3.21 1.17 2.48
. Pistons min PER WS/48 BPM e480 WS vorp eWin
K Caldwell-Pope 161 16.7 .070 5.0 .69 .23 .28 .23
Tobias Harris 156 17.0 .070 3.4 1.06 .23 .21 .34
Reggie Jackson 147 18.7 .049 2.4 1.14 .15 .16 .35
Marcus Morris 144 17.3 .097 2.5 .77 .29 .16 .23
Andre Drummond 131 18.1 -.011 -10.0 1.26 -.03 -.26 .34
Stanley Johnson 81 12.7 .026 -4.9 .18 .04 -.06 .03
Aron Baynes 44 6.2 -.086 -4.6 -.47 -.08 -.03 -.04
Steve Blake 43 .6 -.166 -9.3 -.87 -.15 -.08 -.08
Anthony Tolliver 26 10.4 .007 1.1 -.29 .00 .02 -.02
Reggie Bullock 22 32.2 .330 11.7 1.48 .15 .08 .07
Spencer Dinwiddie 2 66.0 .970 7.7 4.85 .04 .00 .02
Jodie Meeks 2 47.0 .310 7.9 1.76 .01 .00 .01
totals 959 16.1 .045 .1 .74 .90 .49 1.48
Re: Playoffs 2016
Posted: Tue Apr 26, 2016 11:01 am
by Mike G
Code: Select all
. Thunder min PER WS/48 BPM e480 WS vorp eWin
Kevin Durant 191 16.4 .109 -3.8 1.54 .43 -.09 .61
Ru. Westbrook 180 32.4 .362 14.2 2.93 1.36 .73 1.10
Serge Ibaka 162 20.7 .282 8.1 1.37 .95 .41 .46
Steven Adams 138 18.1 .221 5.3 1.11 .64 .25 .32
Dion Waiters 132 15.1 .176 3.7 .89 .48 .19 .25
An. Roberson 117 10.9 .130 4.6 .53 .32 .19 .13
Enes Kanter 102 35.5 .447 8.6 2.66 .95 .27 .57
Randy Foye 61 1.8 -.057 -4.2 .06 -.07 -.03 .01
Nick Collison 49 8.4 .153 9.0 .14 .16 .13 .01
Anthony Morrow 25 15.2 .197 -.3 .76 .10 .01 .04
Kyle Singler 19 12.6 .198 -.4 .47 .08 .01 .02
Cameron Payne 10 13.6 .190 -3.4 .79 .04 .00 .02
Josh Huestis 10 11.3 .163 -5.3 .58 .03 -.01 .01
Nazr Mohammed 4 -7.7 -.212 -7.4 -1.24 -.02 -.01 -.01
totals 1200 19.2 .218 4.9 1.41 5.45 2.06 3.53
. Mavericks min PER WS/48 BPM e480 WS vorp eWin
Wes. Matthews 173 10.2 -.042 -2.4 .31 -.15 -.02 .11
Raymond Felton 172 14.1 -.040 -1.5 .75 -.14 .02 .27
Dirk Nowitzki 170 19.4 .036 -1.7 1.11 .13 .01 .39
Devin Harris 121 10.8 .004 -1.4 .30 .01 .02 .07
Zaza Pachulia 112 17.5 .108 2.4 .95 .25 .12 .22
J.J. Barea 100 2.4 -.244 -12.5 -.14 -.51 -.26 -.03
Ju. Anderson 95 19.7 .060 3.2 1.12 .12 .12 .22
Salah Mejri 76 14.9 .082 2.3 .58 .13 .08 .09
Dwight Powell 64 17.4 .063 -.4 .75 .08 .03 .10
Deron Williams 49 1.7 -.257 -12.0 -.17 -.26 -.12 -.02
David Lee 33 14.5 .101 -1.5 .55 .07 .00 .04
Ch. Villanueva 20 1.0 -.289 -14.8 .01 -.12 -.06 .00
JaVale McGee 14 -0.2 -.303 -14.6 -.41 -.09 -.04 -.01
totals 1199 13.1 -.019 -2.3 .59 -.48 -.10 1.47
The Mavs played like a below-replacement team (BPM), with negative Wins Shared.
Durant worst player on the winning team (vorp).
Re: Playoffs 2016
Posted: Thu Apr 28, 2016 10:36 am
by Mike G
Code: Select all
. Warriors min PER WS/48 BPM e480 WS vorp eWin
Draymond Green 177 17.2 .219 7.1 1.58 .81 .40 .58
Klay Thompson 170 22.2 .266 6.4 2.03 .94 .36 .72
Harrison Barnes 147 6.7 .060 -2.3 .23 .18 -.01 .07
Andre Iguodala 139 18.8 .263 6.5 1.31 .76 .30 .38
Sh. Livingston 135 21.2 .291 5.5 1.77 .82 .25 .50
Andrew Bogut 90 19.3 .313 11.9 1.63 .59 .31 .31
Marr. Speights 72 19.5 .186 .0 1.86 .28 .04 .28
Leandro Barbosa 67 16.5 .196 3.5 .97 .27 .09 .14
Ian Clark 57 21.3 .321 8.4 1.58 .38 .15 .19
Festus Ezeli 40 11.1 .047 -5.1 1.04 .04 -.03 .09
Stephen Curry 38 24.0 .144 8.0 2.81 .11 .10 .22
Brandon Rush 37 15.3 .190 2.8 .88 .15 .04 .07
James McAdoo 17 9.3 .028 6.5 .70 .01 .04 .02
Anderson Varejao 14 6.6 .091 -2.0 .26 .03 .00 .01
. totals 1200 17.4 .215 4.8 1.42 5.37 2.03 3.56
. Rockets min PER WS/48 BPM e480 WS vorp eWin
James Harden 193 22.6 .113 5.9 1.72 .45 .38 .69
Trevor Ariza 181 2.0 -.123 -5.5 -.29 -.46 -.16 -.11
Dwight Howard 180 16.1 .045 -1.0 1.13 .17 .05 .42
Patr. Beverley 129 5.3 -.037 -5.4 .13 -.10 -.11 .04
Jason Terry 124 5.8 -.016 -5.6 -.09 -.04 -.11 -.02
Do. Motiejunas 98 12.1 -.044 -3.9 .81 -.09 -.05 .17
Michael Beasley 80 16.6 .017 -6.6 1.16 .03 -.09 .19
Corey Brewer 77 1.6 -.128 -9.8 -.23 -.21 -.15 -.04
Clint Capela 43 13.6 .006 -1.7 .84 .01 .00 .08
Josh Smith 38 8.1 -.048 -5.6 .24 -.04 -.03 .02
K.J. McDaniels 34 7.6 -.031 -6.6 .35 -.02 -.04 .02
Montrezl Harrell 12 -.8 -.164 -17 -.46 -.04 -.05 -.01
Andrew Goudelock 11 .7 -.363 -24 -.15 -.08 -.06 .00
. totals 1200 10.7 -.017 -3.4 .58 -.43 -.42 1.44