A Quick Prediction Post


Posted By on Sep 22, 2014

I wanted to get this up now that it’s done. I may blow this out into a larger series with fuller explanations of how I arrived at these win totals, but the short version is that it was roughly the same method as last year’s xRAPM predictions, only using ESPN’s Real Plus-Minus (which is largely the same as xRAPM). I also ran the numbers through the actual schedule, so SOS is somewhat adjusted for. Unfortunately, I wasn’t able to include back-to-backs and their influence into the projections, but this should be a decent projection anyway.

Team              Wins
CLE60
GSW60
OKC59
SAS59
LAC57
DAL57
MEM55
CHI51
HOU49
POR49
MIA48
WAS48
TOR48
PHX45
IND43
ATL41
NOP36
BKN35
MIN35
CHA34
ORL32
DEN32
BOS30
DET29
NYK28
UTA25
MIL24
SAC23
LAL20
PHI18

Enjoy responsibly.

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SVG

Pictured: A brilliant basketball man.

Stan Van Gundy was, to my mind, the best coach available on the market this offseason, and for the last few offseasons frankly. That stance, of course, comes with the caveat that there are many assistant coaches and coaches at different levels of basketball with whom I am unfamiliar and therefore, I exclude from any consideration in the “best coach available” discussion. There may be some diamonds in the rough who need a shot to show how good they can be. But I don’t know enough about the coaching development chain to say much about those guys. Still, SVG has proven himself as a great coach in the NBA.

Van Gundy ought never to have lost his job in the first place, but it’s a superstar league and the Magic wanted to appease Dwight Howard, before realizing how futile a game that really was. SVG has a winning percentage of 64.1% in the regular season over 579 games and he is 9 games over .500 in the post-season (48-39). Winning percentage alone, however, is not the only mark of a great coach. If you need any proof of that, check out Avery Johnson’s career after his Dallas turn, Vinny Del Negro’s run with the Clippers last season, or Scotty Brooks’ campaigns the last few seasons. Talent can overcome imperfect or even downright incompetent coaching, at times. Van Gundy isn’t that sort of coach. He’s exceedingly competent, curious, and supremely adaptable. So the following should hardly be surprising:

Stan Van took two separate and totally different style squads through deep playoff runs, ending in the Conference Finals and NBA Finals, respectively. In Miami, he paired Dwyane Wade’s pell-mell drives to the hoop with a still dominant Shaq’s low-post scoring to reach a top 5 offense and a 7 game conference finals loss to Detroit.  It was a distinctly traditional offensive setup, and Van Gundy made it work incredibly well.

Then, from 2008-2011, he helped Dwight Howard reach his peak, which he has still not surpassed or even really approached, with a 4-out spread pick and roll attack which both incorporated the biggest lessons of the modern basketball analytics movement (the importance of spacing, along with the value of the three point shots and dunks) and best catered to the talents of his team. It’s also worth noting that no coach since SVG has been able to get Dwight to buy in to the pick and roll game as thoroughly as he did in Orlando. In 2009, the Magic overachieved in the playoffs, knocking off LeBron’s Cavaliers juggernaut– +8.68 SRS, well ahead of the second best team, the eventual champion Los Angeles Lakers– in the Eastern Conference Finals, only to get beat by that Lakers team in 5 games. It would be easy to be disappointed by the Magic’s performance in that Finals, but really, they shouldn’t have even been in that series at all. Orlando had already overachieved by reaching the Finals. Of course, that’s not the attitude a coach or his team should have in that series, but for fans and media analyzing Van Gundy’s resume, it’s important to note that important context.

So, in short: Detroit has nabbed a great coach. Van Gundy, after being burned by both his former Florida employers and unjustly let go, also managed to negotiate control over Basketball Operations for the Pistons. I don’t know how SVG will do on the management side of things, as there’s no history to go on. Given his understanding of how teams fit together and how to maximize the talents he’s provided, though, I suspect he’ll also do a heck of a job there, as well. He would be hard pressed to do worse than Joe Dumars the last few years.

I see most things in the NBA through the lens of how they will effect my beloved Chicago Bulls. Stan Van Gundy entering the Central Division scares the hell out of me.  I’d imagine most fans of other Central Division teams feel likewise, which is as good an indication as any that Detroit has done very well for themselves here. Now it’s time to fire up the trade machine, because there’s almost no way SVG is keeping this trainwreck together next season.

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Last post, I mentioned that Kevin Durant was the UARPM100 MVP, and I gave a top 10 list of players in Wins Above Replacement as well. After looking through the numbers, something that occurred to me was that the number of total wins under those numbers didn’t sum up to team level wins. That was primarily an effect of including raw per minute plus-minus numbers as part of the UARPM formulation. Basically, good teams had too many wins, and poor teams had too few wins. So I decided to correct that.  I adjusted the UARPM100 numbers using a per minute adjustment for each player on the team so that total team plus-minus was equal to team SRS (basically point differential adjusted for strength of schedule) via Basketball-Reference. The final numbers are posted on the UARPM100 page.

The top 10 is basically the same, with Carmelo Anthony jumping into the 8th spot, and DeAndre Jordan sliding to 10th. The total number of wins are reduced across the board, and they are no longer set to above replacement, because I decided it’d be more interesting to just have total wins contributed. You can easily turn wins into Wins per 48 minutes by dividing by minutes played and multiplying by 48. Durant and LeBron were nearly exactly the same in per possession impact by UARPM100, with Durant’s heavier minutes load giving him the edge in wins. Chris Paul also remains the best per-possession player in the league, even after the team adjustment.

Here’s the updated top 20:

RankPlayersMINUARPM100Wins
1Kevin Durant31226.720.0
2LeBron James29026.718.6
3Kevin Love27966.417.6
4Stephen Curry28466.117.3
5Blake Griffin28635.215.8
6Chris Paul21717.214.7
7Joakim Noah28204.614.3
8Carmelo Anthony29824.114.3
9James Harden27774.514.1
10DeAndre Jordan28704.213.9
11Paul George28984.113.9
12DeMarcus Cousins22985.913.7
13John Wall29803.513.1
14LaMarcus Aldridge24994.713.0
15Kyle Lowry28623.512.6
16Dwight Howard23964.612.2
17Goran Dragic26683.611.9
18Anthony Davis23584.411.7
19Al Jefferson25533.811.7
20Serge Ibaka26663.411.6

All in all, this seems like a pretty credible list. For what it’s worth, Rookie of the Year award winner, Michael Carter-Williams produced 6.1 wins under UARPM100, well ahead of runner-up Victor Oladipo who clocked in at 4.7 wins. The voters appear to be doing a pretty good job.

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I recently updated my UARPM100 numbers to reflect the end of regular season statistics. You can see the final numbers here. After all the games were played, Kevin Durant was the UARPM100 MVP in my version of Wins Above Replacement (WAR). Durantula provided roughly 21.8 wins over what we’d expect from a replacement level player taking over his minutes, while LeBron James came in as a close second providing 21.5 WAR. LeBron was a bit more productive per possession, by UARPM100, than Durant (+7.6 to +7.1), but Durant played more minutes, which ultimately made the difference.

Interestingly, Chris Paul was tops in per possession productivity clocking in at +8.1 points per 100 possessions better than average. CP3 missed a number of games with injury, which knocked him out of MVP consideration. Paul was still able to contribute 16.8 WAR despite only playing 61 games, which is pretty amazing.

The top 10 in WAR via UARPM100 were:

1. Kevin Durant, 21.8 WAR

2. LeBron, 21.5 WAR

3. Kevin Love, 19.4 WAR

4. Stephen Curry, 18.9 WAR

5. Blake Griffin, 18.2 WAR

6. Chris Paul, 16.8 WAR

7. Joakim Noah, 16.1 WAR

8. DeAndre Jordan, 16.0 WAR

9. James Harden, 15.7 WAR

10. Carmelo Anthony, 15.1 WAR

(Way to waste a really great season from Carmelo, Knicks.)

Also notable: Goran Dragic, who recently received the NBA’s Most Improved Player award, finished 20th overall in WAR. In 2012-13, Dragic put up a +1.3 UARPM100, while this season he put up a +3.8, along with the aforementioned 20th place finish in WAR. A pretty big leap, and one of the more difficult things a player can do- go from being the pretty good player he’s been his whole career- to jumping into the top echelon.

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Last time out I explained, in detail, how I calculate Usage Adjusted Rating (a usage adjusted version of Alternate Win Score). I liked the results, but I thought that they could be better. In order to try to better value defense, I decided to try to include a weight to factor in minutes per game played. I made this decision based on the idea that coaches, generally, won’t play someone a lot of minutes if he’s got shaky counting stats- which basic UAR covers- unless he’s providing other value. So I added a factor that gives a slight boost to players who play 20 minutes or more per game and gives a slight negative to players who play under 20 minutes a game.

In addition, I took the UAR with the minutes per game adjustment (70%) and blended it with non-adjusted +/- per pace adjusted 48 minutes (20)% and added a zero-weight to regress it to the mean (10%), as this was the blend that best correlated with xRAPM. Then I made the metric 100 possessions, instead of per 48 minutes pace adjusted. I call this new metric UARPM100, which is a bit of a mouthful, but it conveys the information contained within the metric, so I’m sticking with it.

I ran a correlation of UARPM100 over past years against xRAPM from Jeremias Englemann at stats-for-the-nba.appspot.com. The r-squared for UARPM100 against xRAPM was roughly .67. The r-squared becomes much, much stronger if prior year xRAPM is blended with UARPM100. The r-squared for blended prior year xRAPM and UARPM100 is roughly .82 with in-year xRAPM, which is obviously very strong. Given my belief that xRAPM is probably the best one-number metric in the public domain, I feel pretty good about UARPM100’s results. Here are the results for UARPM100 through the December 16, 2013 games (minimum 120 minutes played):

Going forward, I will be updating UARPM100 as close to daily as possible. Periodically, I will also post UARPM100 that’s prior informed by 2012-13 xRAPM.  Hope you enjoy!

 

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Usage Adjusted Rating, as I discussed previously, has Alternate Win Score (AWS) as its base. Alternate Win Score is a simple per minute measure of performance, which has proven to be the best linear weights metric for prediction across high continuity and low continuity contexts. High continuity contexts are situations where a team is the largely the same as it had been when the players compiled the statistics being used to make predictions. Low continuity contexts are the opposite. AWS, as Neil Paine has demonstrated, is the best linear weights metric for prediction when dealing with both of those situations. So how is Alternate Win Score defined?

AWS equals Points+0.7*(Offensive Rebounds)+0.3*(Defensive Rebounds)+Steals+0.5*(Blocks)+0.5*(Assists)-0.7*(FG missed)-(FG made)-0.35*(Free Throws Missed)-0.5*(Free Throws Made)-Turnovers -0.5*(Fouls Committed) all divided by Minutes Played.

I wanted to make some tweaks to this basic formula. Namely, I wanted to include a usage-efficiency tradeoff. As I mentioned in the previous post, APBRmetrics forum poster v-zero provided a way to do that. I incorporated his math into the formula for AWS and after some tweaking, I arrived at UAR.

About that tweaking. Some people have expressed interest in knowing exactly how I arrived at the numbers I came up with. So here goes. I broke AWS into two separate figures. The scoring (and offensive turnover) portion and the Non-Scoring aspect. The Non-Scoring portion of UAR simply is equal to .7*OREB+.3*DREB+Steals+.5*Blocks+.5*Assists-.5*Fouls Committed per pace adjusted 48 minutes.

Then I moved on to the Scoring portion of UAR, which includes turnovers because turnovers use a possession just the same as a shot attempt or free throw attempts, except turnovers obviously always result in 0 points. I calculated the league average for points per possession (PPP), using the simple formula for possessions (FGA+.44*FTA+TOV), and similarly calculated the league average for possessions per 48 minutes (USGper48), again using the simple possession definition. I then used the coefficients v-zero provided to create what I call average ScoreRating, which is simply 5*(PPP)+.076*(USGper48).  For this season, thus far, the league average for that number has been roughly 6.2. Next I calculated the Score Rating for every player in the league and subtracted out the league average rate, so that if you’re an average scorer you break-even in Score Rating, if you’re above average you contribute a positive value through your combined scoring volume and efficiency whereas if you’re below average, you detract value from your team through your inability to score. I also had to multiply Score Rating by a coefficient in order to properly value scoring in UAR relative to the NonScoring parts of UAR. The Scoring Rating needed to be worth roughly 2.7 times the Non-Scoring Rating, based on some math resulting from the Four Factor weights discovered by Evan Zamir here. In order to get the scale right, the coefficient turned out to be roughly 2.4. This owed to the league average for Score Rating being 6.2 and the league average for Non-Score Rating being about 5.5. Then I set total league average UAR to 0.

These numbers change year over year but they are pretty consistently in this range.  I then added the Scoring and Non-Scoring parts together to get UAR.  The equation for this year basically looks like this:

UAR = (2.4*(5*(PPP)+.076(USGper48))+ (7*OREB+.3*DREB+Steals+.5*Blocks+.5*Assists-.5*Fouls Committed per pace adjusted 48 minutes)-((lg avg Score Rating)+(lg avg non-score Rating))

The numbers, as I said, vary year over year depending on what the average numbers league wide are.

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Derrick Rose hurt his right knee (the other one, not the one he injured over 18 months ago) in a non-contact injury during the third quarter of tonight’s tilt with the Portland Trailblazers. Rose was just running along normally, making an off-ball cut into the paint on offense and his knee buckled beneath him.

I don’t know how bad the injury is at this point, but I do know Rose couldn’t put any pressure on his leg as he left the floor to go back to the locker room.


I also know that I can’t go through this again. I’m an NBA fan, but I’m first and foremost a Bulls fan, and this is just awful. There were so many years of just terrible, ugly basketball following the Jordan glory days abruptly coming to a halt, then there was improvement to mediocrity with dreams of something more. Derrick Rose was that promise of something more and for the too brief moments he’s been healthy since he entered the league, Rose has delivered on that promise. But he just missed over 18 months with a knee injury and now he appears to have hurt his *other* knee pretty badly. If Rose is done for the year again, the Bulls are, obviously, cooked. If his tests come back with bad news, the Bulls absolutely need to blow things up and fast. The franchise’s only hope for relevance will be to draft a new savior.

This is maybe (hopefully) overreaction, but the creeping doubts that had been lingering about Rose’s return had, for me, been on the verge of full blown panic about whether he will ever be the same player. Now, with this newest injury, the concern is about whether his body will stop betraying him long enough to let him continue to play professional basketball at all- never mind whether he will be able to do it at a high level again.

This is just the absolute worst. There is no positive spin on this. The Bulls blew a 20 point lead and Derrick Rose, for the second time in under 2 years, could not walk off the hardwood without assistance. Ugh. Why???

 

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