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:
RankPlayersMINUARPM100Wins1Kevin Durant31226.720.02LeBron James29026.718.63Kevin Love27966.417.64Stephen Curry28466.117.35Blake Griffin28635.215.86Chris Paul21717.214.77Joakim Noah28204.614.38Carmelo Anthony29824.114.39James Harden27774.514.110DeAndre Jordan28704.213.911Paul George28984.113.912DeMarcus Cousins22985.913.713John Wall29803.513.114LaMarcus Aldridge24994.713.015Kyle Lowry28623.512.616Dwight Howard23964.612.217Goran Dragic26683.611.918Anthony Davis23584.411.719Al Jefferson25533.811.720Serge 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.
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.
In my last post, I mentioned that I would, from time to time, produce UARPM100 numbers that were prior-informed by xRAPM numbers from Stats for the NBA. Today, after roughly 30 games played for each team, I’ve gone ahead and produced those numbers. Enjoy!
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!
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.
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???
I recently began tinkering with a new boxscore based catch-all stat after reading a post on the APBR metrics board. The post, by a poster named v-zero, indicated that he had come up with a simple formula for including a usage and efficiency tradeoff in linear weights based metrics. This immediately got me thinking about Alternate Win Score, the best simple box-score based all-in-one stat for predicting future outcomes. The formula for Alternate Win Score, via Neil Paine of Basketball Prospectus is: (pts+0.7*orb+0.3*(reb-orb)+stl+0.5*blk+0.5*ast-0.7 * (fga-fg)-fg-0.35*(fta-ft)-0.5*ft-tov-0.5*pf)/mp. I quickly got excited about the possibilities of including the linear usage and efficiency trade off described by v-zero in that metric to create a better metric for rating players, relatively quickly.
So that’s what I did. Introducing, the rather boringly titled, Usage Adjusted Rating. I pulled the data, updated to include numbers from last night’s game, from the NBA.com/Stats page using the pace adjusted per 48 minutes numbers. I then set a minimum minutes cutoff of 50 (roughly 5 per game in the young season) and ran the numbers. Here they are:
(For your reference, league average UAR translates to roughly 5.5–5.6)
So far about 14–15% of the way through the season, Anthony Davis looks like the most productive player in the entire league on a per minute basis. He’s not even old enough to drink. Goodness. Also, Jordan Hill, look at you! Anyway, this is by no means a perfect metric. It still has all the same problems that plague other box-score based metrics- namely, not properly evaluating defense- but it’s another fun way to look at it. I plan to try to keep the numbers updated as close to daily as I can (they will be located in a new page in the menu of the site) and maybe I will make a weekly feature out of writing about the Usage Adjusted Ratings. Maybe I’ll even come up with a better name for it. Help me out in the comments!
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Stats used for this post derived from NBA.com/Stats
When I was on the Bulls vs Blazers podcast a few days ago, I recommended the Mavericks-Timberwolves matchup as my game of the week to watch. Both teams are high powered offenses that offer little in the way of defensive personnel. I expected a high scoring game, with plenty of well-spaced offensive sets and the game certainly did not disappoint, as the Wolves bested the Mavs 116 to 108. What I did not predict, but greatly enjoyed watching, was Kevin Love finding Corey Brewer at seemingly every opportunity.
Love has always been a tremendous passer, especially when it comes to throwing outlet passes off the defensive rebounds he is so adept at grabbing. At the time Love was drafted, he drew glowing comparisons to Wes Unseld for his ability to toss chest passes the full length of the court off of his rebounds. With the return of Corey Brewer to Minnesota, the Wolves appear to have found Love a perfect target for those brilliant outlet passes. Against the Mavericks, in the first half of the game, Brewer received 4 outlet passes from Love resulting in 3 made baskets and a trip to the foul line.
When you watch the outlets, you can see in each one that as Love is going up for the rebound, Brewer is already leaking out to beat the Mavericks defense down the floor.
In the clip above, the first of Love’s four outlets to Brewer, Brewer contests Shawn Marion’s corner three attempt with a somewhat lackluster closeout, but then he immediately begins sprinting down the floor, anticipating the Love rebound and bullet pass, which comes and hits him right in stride, allowing him to get the easy, uncontested finish.
On the second outlet, above, we see a similar situation, though instead of being the man closing out, Brewer watches as the action moves away from him and towards the painted area. He sees a heavily contested shot go up in the paint and knows Love is there. Relying on Love’s tremendous defensive rebounding ability, Brewer makes the educated guess that Love will end up with the ball and hit him in stride again for another easy two points, which is exactly what happens.
In this clip, Love gets an uncontested defensive board, as all of Dallas’s personnel are at the foul line or further away from the basket. Dallas has three men back in transition defense, as Love throws the outlet ahead to Brewer. Despite this, Brewer is able to get the ball in full stride and get a head of steam going towards the Mavs. The Mavs are put on their heels and unable to react in time, leading to Jose Calderon fouling Brewer at the rim.
Similar to the previous clip, on this play Love gets the ball to Brewer leaking out in transition with the Mavs having guys back in transition defense (Dirk Nowitzki and Monta Ellis), but it simply doesn’t matter. Brewer is moving too quickly and Love’s pass is too on the money for the defense to have time to properly react and stop Brewer from getting to the rim. The result is a dunk for Brewer at the 1st half buzzer.
Love was also able to find Brewer for an additional couple of baskets in the first half, one of which came off a great pass and smart cut out of the Horns set Rick Adelman is so fond of running.
Love catches the pass on the left elbow and then Rubio and Pekovic both set down screens on the right side of the court for Brewer. Jose Calderon, who gets switched onto Brewer, anticipates the cut and jumps the passing lane, only to see Brewer smartly bend his cut the other way towards the basket, allowing Love to fit in a nifty bounce pass to give Brewer the easy two points.
Finally, Brewer was even able to get a wide open jump shot from Love’s passing and the defensive attention the big man draws. Here the Wolves run a number of cross screens, none of which is set very solidly, but Love is nonetheless able to establish deep position in the paint on Shawn Marion. Love’s positioning near the rim draws Jae Crowder’s attention away from Brewer as Love receives the pass from J.J. Barea, and as a result, Love hits Brewer with a quick pass in the corner for a wide open look, which he knocks down.
If these early returns are any indication, Flip Saunders and the rest of the Timberwolves front office should be applauded for the decision to reunite Corey Brewer with Kevin Love.
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This offseason it came out that the Chicago Bulls were installing a new offensive system, which was to be based off of “read and react” principles. The goal is, according to Coach Tom Thibodeau:
If [the opponent’s defense] get[s] set we want to move them side to side. But we want the ball in to the paint. When you have a player like Derrick [Rose] who can force the defense to collapse, now you’re going to get some high scoring or very efficient shooting out of that. Everyone has the responsibility to hit the open man, keep the ball moving.
Getting the ball into the paint for shots close to the basket remains the most efficient way to score in the NBA, with the exception of getting to the foul line, so it’s no surprise that Thibodeau wants to emphasize getting those shots for his team.
The Bulls so far have done fantastically well at getting shots in the paint. They are second in the league, behind only the Houston Rockets, in percentage of shots taken within 5 feet of the basket, with 111 of their 250 field goal attempts coming in close. The Bulls are converting those shots at an above average rate, too, coming in 8th in the league in FG% on shots in that range, at 62.2%. As a result, the Bulls are third in the league in Points in the Paint per pace adjusted 36 minutes at 38.0, behind only the Sixers and the Pistons. All of this seems great, so why are the Bulls 24th in offensive efficiency at an abysmal 95.6 points per 100 possessions?
The Bulls are tied for 6th highest in the league in Team Turnover Percentage (TmTOV%) at 19.1%. A lot of that comes back to Derrick Rose playing out of control and forcing things, a topic which was well covered by Ricky O’Donnell over at BlogaBull. Rose has 17 turnovers in just 3 games for an average of nearly 6 a game. That’s obviously too many, but the Bulls have been averaging roughly 13 turnovers per game from non-Rose players, too. That’s a tremendously high number. So what’s driving all these turnovers? I went back and watched every turnover the Bulls committed over the first 3 games and what stood out was just how many of the Bulls turnovers were a product of lazy passes or miscommunications between a passing Bull and the teammate to whom his pass was directed. There were so many times when guys attempted to throw an entry pass just to initiate the Bulls base offensive set and instead just threw the ball away. It seemed like a total lack of focus, for three games running, on valuing possession of the ball. I mean just watch this montage I made of bad Bulls entry passes from the first three games:
Chicago is clearly making getting the ball into the paint a point of emphasis and trying to get it in to Carlos Boozer for paint catches. The problem has been that they seem to be struggling with the touch on those entry passes, as you can see from all of these needless turnovers. There have been a lot of other turnovers that derive from a lack of focus, like this turnover from Kirk Hinrich as he bounces a lazy pass to Mike Dunleavy Jr., which Carmelo Anthony easily sniffs out and steals:
Or this one against the Knicks, which was a team wide failure, starting with Derrick Rose’s not initiating the offense until 14 seconds remained on the shot clock, and then, the rest of the team seeming quite confused about what they were each supposed to do, resulting in a Jimmy Butler long 2 point jumper which was blocked by Tyson Chandler, forcing Jimmy to scramble to get the ball and then chuck up an air ball at the buzzer.
These kinds of mental errors and miscommunications are somewhat surprising from a Tom Thibodeau coached team, but when you consider: (1) they’ve installed a new offensive system, (2) their core guys all missed time during the preseason to deal with injuries, and (3) their starting unit had never played a minute together as a whole prior to opening night against the Heat, the miscues become much more understandable. I’d expect Tom Thibodeau will hammer out the kinks in short order, especially given that he’s had a lot of time between the Sixer game and tonight’s tilt with the Pacers to get in practice time.
Despite all the bad passes and dumb turnovers, though, the Bulls have still been getting the ball inside well and converting well on the shots they have gotten inside. So what else, besides the turnovers and resulting empty possessions, is causing the Bulls to be so bad on offense? Well, there’s this:
The Bulls are shooting an absolutely abysmal 24.8% on all jumpshot attempts and an even worse 23.2% as a team from behind the three point arc. As a result of this terrible shooting from anywhere outside the immediate basket area (5 feet and in), the Bulls are 25th in the league in effective field goal percentage, which is just astonishing given how high a percentage of their overall shot attempts have been near the basket and the fact that they are converting those high percentage looks at a top 10 rate. This kind of jump shooting futility is certainly very unlikely to sustain. The Bulls might not have a lot of great shooters, but they do have guys who are better shooters than this awfulness. For reference, the Bulls, as a team, shot 31.5% on all jumpshots last year and 34.3% on three point shots, and all of that was without Derrick Rose creating open shots by drawing the defense’s attention. Going forward, we should expect the Bulls to shoot better on jump shots than their current terrible mark, which, if they continue their effectiveness at getting paint shots and converting them at a high level, should buoy their effective field goal percentage and their overall offensive efficiency to much more respectable levels. The turnovers and bad shooting still don’t tell the whole picture though. There’s one more piece of the puzzle that has held the Bulls back.
As I alluded to above, there is no more efficient way to score in the NBA than to get to the foul line. For a team that’s been getting a lot of shots in the paint, the Bulls have an absurdly low free throw attempt rate (free throws attempted per shot attempt), clocking in at 26th in the league. Derrick Rose, in particular, seems to have reverted back to his rookie days of getting loads of contact, but getting no calls. A team getting the ball inside at such a prolific rate and still getting so few FTAs per shot attempt seems like a circumstance that is simply very unlikely to continue. The Bulls were also unlucky in that they played their first three games against teams that were all pretty good at avoiding fouls last year. Miami ranked 22nd in fouls called against, New York was 15th, and Philadelphia was 25th. As a group, these three clubs seem to have done a great job at either not fouling or not being called for fouls, depending on your level of cynicism about NBA refereeing. Either way, the larger point is that the Bulls played a tough stretch in terms of getting calls on the offensive end, so like the other numbers, expect this one to improve going forward.
The Bulls have been below average to bad in these key areas of scoring efficiently as a team. They’ve still rebounded the ball well, which helped prevent them from being the worst offense in the league thus far. They’ve gotten good shots, for the most part. Looking at things from a process based perspective, rather than a results based one, there’s much to be excited about. The Bulls will shoot better. As they get more familiar with their new offense and their responsibilities within it, they will almost certainly clean up the unfocussed, lazy passes and miscommunication issues which have caused the turnovers which have been a big part of their early season struggles. Finally, they will probably (hopefully?) start getting more of the benefit of the doubt from the referees, especially if they continue living in the painted area.
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After running the team win projections using Nathan Walker’s projected xRAPM numbers, I decided to try a different method for projecting things. I blended a number of plus-minus based advanced stats: xRAPM, RAPM, ezPM, Estimated Impact, ASPM, and IPV, then I added an aging curve. I also added Evan Zamir’s work on home court advantage with a mean regression factor added in. I then ran the relative net ratings of all the team’s through all of their schedules with their HCAs added in. I also adjusted for the Suns’ trade of Marcin Gortat to the Wizards. The results are as follows:
TeamWinsMiami Heat59Houston Rockets56San Antonio Spurs56L.A. Clippers54Brooklyn Nets53Chicago Bulls53Indiana Pacers53Oklahoma City Thunder53Memphis Grizzlies52Golden State Warriors46Denver Nuggets45New York Knicks45Atlanta Hawks44Dallas Mavs43Toronto Raptors40Cleveland Cavs39Minnesota T-Wolves38Portland Blazers38Detroit Pistons36Milwaukee Bucks35New Orleans Pelicans35Utah Jazz35Washington Wizards35Charlotte Bobcats30Sacramento Kings30Boston Celtics29L.A. Lakers28Orlando Magic25Philadelphia Sixers23Phoenix Suns22 The results aren’t terribly different from what I had previously projected, which should make sense as the scale is roughly the same, and the minutes projections are basically the same, except for adjustments for trades. The Suns dumping of Gortat apparently has them in poll position in the Riggin for Wiggins sweepstakes.