Counting the Baskets

SportVu Data: Failing to Rehabilitate Ball Movement

Thanks to SportVu the NBA now tracks 'touches' of the basketball for each player in the league. Touches is sort of interesting on a player level because is forms a new baseline for all sorts of other stats we're interested in measuring such as Assists per touch or turn overs per touch, as well as where certain players tend to get the ball with the location break outs.

 

In the Regressing column of Dead Spin, Reuben Fischer-Baum took an interesting look at what the aggregate number of touches per possession means for an offense.  In short, generally higher touches is not such a good thing, with more touches being correlated to less efficiency on offense.

 

However, as Fischer-Baum notes, faster/shorter possessions are both generally more efficient and lead to fewer touches per possession.  The number of touches per possession was strongly negatively correlated with pace a -.44 correlation, while pace was positively correlated with offensive rating and touches per possession in nearly equal amounts.  This leaves open the question of causation, are teams with less ball movement scoring more efficiently or getting more touches because they are not able to get into transition as often.  

 

To dig into it a bit, I decided to try to control for possession length.  I looked at both the estimated average possession length and the total player 'possession time' as calculated by SportVu.  The SportVu description for Time of Possession is given simply as "how long the player possessed the ball."  

 

The estimated possession length is calculated simply using Pace to divide the number of offensive possessions into 48 minutes to get an Estimated Possession Length.  The Estimated Possession Length is, of course, only approximate as pace is symmetrical, including defensive possessions.  To date I have not found any public NBA data that tracks possession length on the offensive and defensive end separately. However, Ken Pomeroy released a study earlier this year that indicated that in the NCAA pace for any given team is driven primarily by their offensive style of play because teams have significantly more control over the action on the offensive end.

 

Then touches per possession is adjusted to seconds per touch by dividing the estimated possession length by the number of touches.  Teams with a lower seconds per touch are moving the ball faster while  those with higher are moving the ball slower with the ball possibly 'sticking' more.  For example, the Brooklyn Nets have an average possession length of 15.18 seconds and get 4.21 touches per possession, for 3.6 seconds per touch.

 

The stickiest team in the league by a couple of standard deviations according to these figures are the Golden State Warriors with 4.2 seconds per touch, but a slightly above average efficiency.  Meanwhile the Philadelphia 76ers move the ball the fastest, but are one of the least efficient teams in the league.  In fact, the overall correlation is for teams with slower ball movement to be more efficient, though not statistically significant at the 95% confidence level. 

 

I also looked at Pace and the Seconds per Touch and found that the link between Touches and Pace was indeed broken with no correlation between the two.  

 

Lastly, I added up SportVu's Time of Possession (TOP) for each player on every team and divided that by their number of  possessions.  The SportVu Player TOP was well correlated with the seconds per touch from average possession length and showed same lack of relationship with offensive efficency.

 

Below are visualizations of the relationships between ball movement speed and offensive efficiency, as well as pace and SportVu and Seconds per Touch relationship.

 

 

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SportVu Data: Contested Rebounds are Offensive

I have been taking a look at the stats released by the NBA using their new SportVu cameras, including Drives to the basket, Pull Up shots and Catch and Shoot jumpers.  All of them have given some interesting insights into the game.  This time I am looking at their rebounding stats.

 

The new wrinkles added are that SportVu tracks how many rebound opportunities a player had, as defined as a rebound within 3.5 feet, what percentage they end up with and how many of those were contested by the opposing team.  All interesting stuff.  

 

But the question is how useful is it and in what ways.  To kick the tires a bit, I did two things, I aggregated all of the SportVu rebounding data from NBA.com as of Dec, 11th, 2013 by team and then linked it to the NBA.com advanced stat data for both players and teams. 

 

In theory, having a higher percentage of a player's rebounds coming as contested should be valuable indicating that the rebounds grabbed could have ended up in the opponent's hands.  On the other hand, it could be that players with better positioning or fearsome reputations dissuade opponents from contesting.

 

Also, grabbing a higher percentage of rebound opportunities could be valuable, in terms of maximizing total rebounds.

 

So I tested the aggregated SportVu rebound data for each team against the team rebounding percentages as well as looking at correlations within the SportVu data.  Here are the findings:

 

• A slight negative correlation for the percent of team rebounds contested and the percentage of opportunities grabbed.

 

• A small positive correlation with Contested Percentages and total rebound %

 

• No correlation with defensive rebounds and contested rebounds.

 

• A significant (statistically and practically) correlation between number of contested rebounds per game and offensive rebounding percentage and correlation between the percent of team rebounds contested and team offensive rebound percentage. The R² for all 30 teams being .29.

 

 

This basically confirms what we knew already.  Many of the defensive rebounds any individual player grabs could have been taken by one of his teammates.  In fact, Dave Berri eventually changed the Wins Score and Wins Produced formulas when convinced of the evidence to devalue defensive rebounds in player evaluation.

 

Then I looked at the SportVu rebounding data against the individual rebounding percentages for players.  I found a largely similar pattern.  Percent contested rebounding percentage found in the SportVu data have modest with a player's defensive rebounds, but they are more closely related to offensive rebounding percentages.

 

Below are the results of a simple regression for offensive rebounds for players with 4 or more rebounding opportunities per 48 minutes, showing a R² of .499.

 

 

 Using the same filter the R² of .17 for Contested rebound percentage and defensive rebounds:

 

 

What the data released by the NBA does not show, is the percentage of contested opportunities a player is able to pull down.  Some players may have fewer contested rebounds because the coaching scheme does not emphasize offensive rebounds.  Alternatively knowing how often a player is successful in contested rebound situations may help a coach decide which players to send on offensive rebounds and the ones to drop back. 

Looking for Coach Effects

The stats and the commentariat all agree, the Phoenix Suns are most unexpectedly good team and the Brooklyn Nets are the most unexpectedly bad team.

Recently over at Hickory-High, I looked at surprises in the Eastern and Western conference in terms of player production as measured by ASPM, box score based metric scaled like a plus/minus score.  Here I am going to apply it to the whole team. 

In the table below I have the average of the projected ASPM score for returning players that had at least 160 minutes, the average actual ASPM to date this year, and the difference in standard deviations. Then a couple of columns that I think put change in ratings in perspective, the percent of the players in the comparison returning, the average age of those players and whether the team has a new coach.  

Row Labels	Projected ASPM	2013-2014 ASPM	ASPM Diff (Std Dev)	Percent Starters & Role Player Vets on Same Team	Average of Age	New Coach?
East	-0.18	-0.44	-0.12	69%	27.2
ATL	0.47	0.2	-0.13	50%	27.5	New
BOS	-0.79	-0.11	0.31	60%	26.7	New
BRK	0.42	-1.54	-0.9	45%	31.1	New
CHA	-1.34	-0.63	0.33	75%	25.3	New
CHI	0.65	0.99	0.16	86%	29.4
CLE	-0.18	-1.55	-0.63	75%	25.5	New
DET	0.57	-0.19	-0.35	71%	25.4	New
IND	0.7	1.32	0.28	78%	27.3
MIA	0.72	1.39	0.31	90%	30.9
MIL	-0.66	-2.44	-0.82	40%	27.2	New
NYK	0.26	-0.91	-0.54	75%	30.4
ORL	-1.4	-0.22	0.54	86%	25.7
PHI	-1.92	-1.84	0.03	44%	23.7	New
TOR	0.01	0.03	0.01	78%	25.2
WAS	0.29	-0.54	-0.38	89%	26.6
West	-0.09	0.49	0.27	62%	27.4
DAL	0.88	0.91	0.02	40%	30.6
DEN	0.03	0.42	0.18	58%	26.5	New
GSW	-0.19	0.03	0.1	60%	26.8
HOU	0.44	1.11	0.31	64%	26.7
LAC	1.52	1.84	0.15	50%	27.8	New
LAL	-1.56	0.21	0.82	50%	27.9
MEM	0.64	-0.4	-0.48	67%	28.3	New
MIN	0.44	0.88	0.2	75%	26.3
NOP	-0.34	0.27	0.28	60%	25.1
OKC	-0.39	0.35	0.34	83%	30.4
PHO	-1.97	1.37	1.53	50%	25.5	New
POR	-0.73	0.03	0.35	50%	25.7	New
SAC	-1.04	-0.29	0.35	70%	26.5
SAS	1.63	2.76	0.52	90%	29.5
UTA	-0.95	-1.92	-0.44	63%	26.5
Grand Total	-0.13	0.05	0.08	65%	27.3

 

In the table there are a couple interesting results.  There has been a great deal of talk about the historic gap between the Eastern and Western conferences.  The players in the West came in with a slightly higher average projected ASPM, but just slightly -.09 for the West and -.18 for the East.   However, to date players in the West as a whole has been out performing  their projections .58 points while the East players are under performing their projection -.26.  

The discrepancy gets even more pronounced when looking at average ASPM weighted by minutes played, indicating either that the Western teams have more star power and less 'flat' rosters or that the coaching desicions are better lined up with ASPM's measurements, or some combination.  

As shown below:

 

Team	AVERAGE ASPM	Ave ASPM Weighted by Minutes	Difference From Average
ATL	0.24	0.67	0.43
BOS	-0.25	-0.26	-0.02
BRK	-1.59	-1.42	0.17
CHA	-0.72	-0.85	-0.13
CHI	0.52	0.58	0.06
CLE	-2.04	-1.75	0.29
DET	-0.19	0.08	0.27
IND	1.32	1.95	0.63
MIA	1.39	1.89	0.50
MIL	-2.33	-2.13	0.20
NYK	-1.42	-0.96	0.46
ORL	-0.22	0.21	0.43
PHI	-1.88	-1.44	0.44
TOR	0.11	0.42	0.31
WAS	-0.64	0.02	0.66
East Total	-0.53	-0.17	0.36
DAL	1.00	1.10	0.10
DEN	0.46	0.95	0.49
GSW	0.75	1.48	0.74
HOU	1.26	1.50	0.24
LAC	1.56	2.15	0.60
LAL	0.03	0.23	0.20
MEM	-0.18	0.48	0.66
MIN	0.88	1.68	0.79
NOP	0.23	0.65	0.41
OKC	0.73	1.43	0.71
PHO	1.50	1.34	-0.16
POR	0.40	1.16	0.77
SAC	-0.17	0.02	0.19
SAS	2.38	2.64	0.27
UTA	-1.49	-1.55	-0.06
West Totals	0.62	1.04	0.42
League	0.05	0.41	0.36

 

Really interesting here we see that Hornacek, who has coached the team most overperforming their stats, has played his veteran players with the least correlation to ASPM (though the correlation moves positive when rookies and back benchers are inclyded).  On the other hand Portland shows the highest correlation, reflecting, in part, the differential in talent between the bench and starters even after this year's upgrade.

Not only do thirteen teams have new coaches, on the other seventeen teams nearly 30% of the players included in the comparison changed teams, so 60% of the players in the comparison are in the first year with their coach, and that is not including rookies new to the league.

A great deal of blame and credit gets heaped on head coaches in the NBA depending on how the team is doing.  But it is a real question as to how much of it is justified.  It is the job of the coach to put their players in positions to be effective, take the best shots available, know when to crash the glass, where to be positioned on defense.  Zach Lowe at Grantland had a nice piece on the progress of Steve Clifford, the new Bobcats coach, in that regard.  We can also see that, for example, the Phoenix Suns under Jeff Hornacek have increased the their expected points per shot (Xpps) from 1.o27 per shot last year to 1.057 this year, putting the team in a better position to score efficiently.

However, one the things we know from examining player metrics over the years is that player performance is less consistent when players change teams and on teams that have high turnover in players have less consistent results.  With the current CBA favoring shorter contracts and shorter coaching stint, it seems that player performance will be less predictable and the nuances of the game will be even more important for analytics.

 

 

What Happens to the Celtics Back Court When Rondo Comes Back?

With the start of December I can not help but turn my thoughts to when Rajon Rondo is going to return to the court and what it will mean for this Celtics team.  For a while it has been projected that Rondo may return sometime later this month, which I think is the most likely at the end of the month.

After a frentic November the Celts find themselves a win or two over the otherwise lowly Bucks away from respectabiliy, sitting at 7- 12 for a .368 winning percentage.  Whether or not the team wants to make the playoffs they are a game and a half out in the underwhelming Eastern conference and a half game out of the Atlantic division.

There are, I think, a couple of different questions we need to answer to estimate what Rondo's return might be for the team.  For the purpose of this post I will be assuming a healthy return for Rondo allowing him to play at a comparable level to his preinjury production, which is, I think, likely but certainly not guaranteed.  We also have to look at how good Rondo is, whose minutes he takes, and any affect he may have on his teammates and the line ups used.

Before the season started I had a post that used some of the advanced one number player metrics to evaluate the Celtics roster and settled on comparing the wins above replacement each player would be worth assuming the player played 36 minutes a game for every game.  The idea wasn't that I assumed any of the players would do that, just to scale their expected production on the same level.  Rondo was projected to have the highest wins above replacement per 36 of any player on the Celtic roster.

The before season projection used five different metrics to project player production, Wins Produced, ASPM, EZPM, xRAPM and Win Shares.  Rondo's median projection was 10.5 wins above starting a replacement level Point Guard for 36 minutes a night.  Of course, part of that equation is the production of a replacement level guard. So we need to look at exactly whose minutes a returning Rondo is taking and how productive they have been.

 For the most part one would expect Rondo's return will affect the guard rotation for the Celtics.  The primary player taking the roll of Point Guard over the last fifteen games has been Jordan Crawford, but there will almost certainly be a knock on effect, with Crawford sliding into some of the minutes from other guards.  Below I have a table with the production of each Celtic guard by the advanced metrics, including a column with the projected number to start the season and, on the right the median of the metrics:

 

Name	POS	Median Projected Wins Over Replace per 36	MP	13-14 EZPM WOR per 36	13-14 ASPM WOR per 36	13-14 WP WOR per 36	13-14 WS WOR per 36	Median WOR per 36
Avery Bradley	SG	0.6	568	-2.3	1.5	-5.6	-1.1	-1.7
Courtney Lee	SG	3.8	271	7.1	6.3	9.3	6.6	6.9
Jordan Crawford	PG	-1.0	543	9.9	7.6	10.9	7.1	8.7
Keith Bogans	SF	-0.2	28	4.8		-6.8	-7.3	-6.8
Marshon Brooks	SG	2.2	41	-4.5		-8.0	-3.2	-4.5
Phil Pressey	PG	-0.3	197	1.5	-0.6	1.8	-0.4	0.6
Rajon Rondo	PG	10.5

One of the issues for Boston is that all of the player metrics agree that the guy Rondo is going to replace in the line up, Jordan Crawford, has been their best guard to date, and, in fact, probably their best player.  Interestingly those same metrics projected Crawford at below replacement level before the season started, which leads to the question of how sustainable Crawford's current production is going forward.

In those terms, in Crawford's favor are his relative youth, the change in position that seems to better focus his talents and the change in coaches as most playing statistics are less stable when they change teams.  Below are Crawford's career stats by year from Basketball Reference showing a steady progression to date:

 

Season	Age	Tm	Lg	Pos	G	MP	PER	TS%	eFG%	FTr	3PAr	ORB%	DRB%	TRB%	AST%	STL%	BLK%	TOV%	USG%	ORtg	DRtg	OWS	DWS	WS	WS/48
2010-11	22	TOT	NBA	SG	42	1027	11.8	.456	.417	.168	.256	3.4	8.6	5.9	19.8	2.0	0.2	13.4	27.0	92	112	-1.0	0.5	-0.6	-0.026
2011-12	23	WAS	NBA	SG	64	1753	14.5	.488	.446	.233	.313	2.8	8.1	5.4	19.3	1.7	0.2	12.8	28.1	97	110	0.3	0.7	1.1	.029
2012-13	24	TOT	NBA	SG	70	1709	13.8	.512	.472	.219	.339	2.9	10.9	6.9	23.5	1.2	0.3	15.1	25.3	98	106	0.2	1.7	1.9	.052
2013-14	25	BOS	NBA	SG	19	543	17.8	.544	.487	.268	.304	2.1	10.8	6.5	30.5	2.0	0.3	15.6	21.3	109	105	1.0	0.5	1.5	.135
Career			NBA		195	5032	14.1	.494	.451	.217	.308	2.9	9.4	6.1	22.0	1.6	0.3	13.9	26.2	98	108	0.5	3.4	3.9	.037
3 seasons		WAS	NBA		133	3747	14.1	.489	.447	.215	.302	2.9	8.6	5.7	21.4	1.7	0.3	13.4	27.3	97	109	0.0	2.2	2.2	.028
2 seasons		BOS	NBA		46	1125	14.8	.527	.478	.255	.326	2.4	11.4	7.0	24.8	1.5	0.3	15.5	22.2	103	106	1.0	1.1	2.0	.087
1 season		ATL	NBA		16	160	7.6	.421	.409	.078	.351	6.8	14.2	10.5	16.0	1.0	0.0	15.8	28.1	83	109	-0.4	0.1	-0.3	-0.090

Provided by Basketball-Reference.com: View Original Table
Generated 12/1/2013.

 

Notably, Crawford's usage has declined each year while his true shooting percentage and assist percentage have gone up each year.  Though, in the shooting area, in particular, we should expect to see some regression, Crawford has proven that he deserves some time after Rondo comes back.

The question is how to accomplish that and what backcourt combos to use to maximize production. That leads me to Courtney Lee, the Celtics Guard with the second most efficient production so far this year who has played the third most minutes.  Lee has been close to what was expected of him with even better three point shooting, an important point in a backcourt that lacks stretch shooters.

So far this year Brad Stevens has yet to start Courtney Lee, starting Jeff Green out of position and Avery Bradley instead.  Bradley is a difficult player to value because of the disconnect between his defensive reputation and his offensive production.  He has played better since being moved to his more natural Shooting Guard position, with a somewhat better TS% and fewer turn overs. But, Bradley does not have a starting NBA Shooting Guard shot and lacks consistent three point range.  It is also not clear that he has the defensive impact of a Tony Allen and occasionally seems wear down.  

When Rondo comes back floor spacing in the starting backcourt will be even more of an issue. Rondo is a career 24% three point shooter and has only shot over thirty percent once in his career.(Even though I have an irrational belief that Rondo is going to come back with new found three point range based a vague memory of a report that he was working on them in rehab).   Either Crawford or Lee should be on the court for the bulk of the time with Rondo to help with spacing.

Given that a significant chunk of Crawford's newfound value is based on his increased assist percentage and more selfless play, he should take on the sixth man/back up point guard roll and Lee starts, if not necessarily finishes.  Until Bradley finds a consitent jump shot he needs to be moved to the bench as a defensive energy guy.