Study Hall: Spurs stars outshine Bulls

The Spurs gave up lots of good outside looks and failed to connect on their own looks from deep, but dominated the turnover battle and got good looks inside the arc to secure their 31st home victory of the season.

By BrettJones@comradebernout Mar 11, 2016, 6:30am CST

Spurs 109, Bulls 101 - Mar 10, '16

The Spurs rode a huge night from their new Big Three to move to 31-0 at home on the year. Leonard, Aldridge and Parker combined for 75 points, 21 rebounds, 15 assists, 3 blocks, 5 steals and just 4 turnovers, good for 71.4% of San Antonio's total production. Despite their heroic efforts, San Antonio didn't run away with this one and had to fight to put away a Bulls squad which trailed by just 3 at one point in the 4th quarter.

On most teams, a Parker-Aldridge-Leonard trio would just alternate isolation plays, putting up ungodly individual stats and winning a decent majority of their games. But while it's always nice to be able to ride heroic performances from star players to victory, San Antonio are at their collective best when the ball flows more freely and role players make a bigger impact. Of course, as we saw many times last season, there are always games where the machine just doesn't run as smoothly as you'd like, and all those "good to great" shots dry up.

Perhaps the greatest strength of this year's Spurs squad is that while they are still capable of playing the Beautiful Game for stretches of time, when things get gunked up, they have a glut of individual offensive firepower they can rely on in a pinch. The ceiling for this style of play may not be as high, but good golly if the floor isn't high as well.

Even when playing disjointed basketball, this year's Spurs squad - with Parker comfortably orchestrating things and still capable of blowing up from time to time, Leonard spending half his time in god-mode and Aldridge moving beyond "comfortable" and closer to "transcendent" - is almost incapable of losing to an average NBA team even when they're playing below their potential.

Case in point: the last time San Antonio lost to a team with a losing record was November 20 of last year. Back then, everyone just knew that Donald Trump would flame out any day, nobody had heard of Flint, Michigan and you were rationalizing impending holiday weight gain with ill-fated New Year's resolutions. It's been awhile.

Outside of the phenomenal performances from San Antonio's stars, I suppose the biggest lesson from this game was that boy, three point shooting percentage matters. The Spurs whipped the Bulls inside the arc - 50% to 43.8%, but Chicago made it rain from deep, connecting on 50% of their 22 looks from deep to just 35.3% for San Antonio.

It's really weird when a team's three point FG% exceeds their two point by over 6 percentage points. There were definitely some defensive breakdowns on San Antonio's part that led to a number of open looks, but good golly, even Derrick Rose got in on the action! Take away the unusually Bulls' high three point accuracy and this game looks a lot more like the blowout it always felt like it should have been.

The Spurs collect yet another home win and have a day of rest before playing host to the Thunder on Saturday. The Spurs will probably need some bigger contributions from their vaunted bench and role players to secure a win against an OKC squad coming off of a much-needed victory over the Clippers on Wednesday. Fortunately for San Antonio, fatigue may be a factor, as the Thunder will be playing the back end of a SEGABABA after hosting the T-Wolves the night before.

Four Factors (def.)

	Spurs	Bulls
Shooting (eFG%)	51%	52%
Ball Handling (TO%)	8%	21%
Off Rebounding (OR%)	19%	26%
Shooting FTs (FT Rate)	20%	15%

Team Stats (Definitions at bottom of post)

	Spurs	Bulls
Pace (No. of Possessions)	101.0
Points Per Possession (PPP)	1.08	1.00
Points Per Shot (PPS)	1.17	1.17
2-PT FG%	50.0%	43.8%
3-PT FG%	35.3%	50.0%
FT%	78.9%	92.3%
True Shooting %	53.8%	55.1%

	Spurs	Bulls
Offensive Rating	108.6	99.3
Defensive Rating	99.3	108.6
Net Rating	9.3	-9.3

	Spurs	Bulls
Passes / poss.	3.1	3.2
% of FGA uncontested	53.8%	47.7%
Points in the paint	46	36
Second chance points	12	17
Fast break points	8	9

	Spurs	Bulls
Assists	29	23
Steals	14	5
Turnovers	8	21
Ball Control Index (BCI) (Assists + Steals) / TO	5.38	1.33

	Spurs	Bulls
Expected Offensive Rebounds	12.0	10.5
Offensive Rebounds	9	11
Difference	-3.0	0.5

Spurs Shot Chart

Bulls Shot Chart

Players (Definitions at bottom of post, columns sortable)

Spurs

Player	Min	AdjGS	GS/Min	Line	Usage%	Floor%	OffRtg	DefRtg	NetRtg
Kawhi Leonard	39	31.0	0.80	29 Pts (10-15 FG, 4-5 3PT, 5-5 FT) 7 Reb (1 Off), 2 Ast, 2 Blk, 2 Stl, 1 TO, 1 PF	21%	70%	118.0	92.7	25.3
LaMarcus Aldridge	33	23.7	0.72	26 Pts (10-19 FG, 6-6 FT) 10 Reb (1 Off), 1 Ast, 1 Blk, 2 Stl, 1 TO, 2 PF	31%	59%	111.5	97.2	14.3
Tony Parker	32	22.5	0.71	20 Pts (10-16 FG, 0-1 3PT, 0-1 FT) 4 Reb (0 Off), 12 Ast, 1 Stl, 2 TO, 1 PF	26%	64%	114.1	95.2	18.9
Tim Duncan	28	9.0	0.32	7 Pts (3-7 FG, 1-3 FT) 3 Reb (1 Off), 4 Ast, 2 Stl, 1 PF	13%	52%	106.2	101.4	4.8
David West	19	6.8	0.35	8 Pts (3-8 FG, 2-2 FT) 6 Reb (4 Off), 1 Ast, 1 Blk, 1 TO, 3 PF	21%	43%	120.8	88.9	31.9
Kyle Anderson	14	5.2	0.38	3 Pts (1-4 FG, 1-1 3PT ) 4 Reb (0 Off), 2 Ast, 2 Stl, 1 PF	12%	38%	100.8	118.2	-17.4
Danny Green	24	3.8	0.16	4 Pts (2-6 FG, 0-2 3PT ) 2 Reb (1 Off), 1 Ast, 2 Stl, 2 PF	11%	37%	111.6	109.3	2.3
Manu Ginobili	20	3.2	0.16	4 Pts (2-6 FG, 0-2 3PT ) 1 Reb (0 Off), 2 Ast, 2 Stl, 1 TO, 1 PF	16%	35%	94.1	80.7	13.3
Kevin Martin	8	1.7	0.23	1 Pts ( 1-2 FT) , 1 Stl,	5%	63%	114.2	106.6	7.6
Patty Mills	17	1.4	0.08	5 Pts (2-9 FG, 1-5 3PT ) , 4 Ast, 1 TO	25%	31%	89.8	104.2	-14.4
Boris Diaw	3	1.4	0.45	2 Pts (1-2 FG, 0-1 3PT ) 3 Reb (1 Off), 1 TO	43%	29%	66.7	85.0	-18.4
Andre Miller	3	0.0	0.00	0 Pts ,	0%	0%	100.0	189.0	-89.0
Boban Marjanovic	1	-0.8	-1.31	0 Pts (0-1 FG, ) ,	100%	0%	0.0	150.0	-150.0

Show Bulls Players

Player	Min	AdjGS	GS/Min	Line	Usage%	Floor%	OffRtg	DefRtg	NetRtg
E'Twaun Moore	36	24.3	0.68	20 Pts (8-12 FG, 4-5 3PT ) 6 Reb (0 Off), 4 Ast, 1 Blk, 1 Stl, 3 TO, 2 PF	17%	57%	102.5	115.9	-13.4
Pau Gasol	35	21.7	0.62	21 Pts (8-16 FG, 1-1 3PT, 4-4 FT) 12 Reb (4 Off), 4 Ast, 2 Blk, 7 TO	29%	44%	94.9	103.8	-8.9
Derrick Rose	36	19.4	0.54	21 Pts (9-21 FG, 1-2 3PT, 2-2 FT) 3 Reb (2 Off), 6 Ast, 2 TO	28%	49%	100.2	104.4	-4.2
Nikola Mirotic	28	10.7	0.38	8 Pts (3-7 FG, 2-5 3PT ) 10 Reb (1 Off), 2 Ast, 1 Blk, 2 Stl, 3 TO, 3 PF	15%	34%	107.7	98.4	9.3
Justin Holiday	21	10.7	0.51	12 Pts (5-8 FG, 2-3 3PT ) 2 Reb (1 Off), 1 Ast, 1 Blk, 1 Stl, 3 TO, 3 PF	24%	45%	91.7	110.7	-19.0
Taj Gibson	22	7.2	0.33	8 Pts (3-7 FG, 2-2 FT) 5 Reb (3 Off), 1 Ast, 1 Stl, 2 TO, 4 PF	19%	43%	91.1	126.5	-35.4
Mike Dunleavy	29	4.9	0.17	7 Pts (2-8 FG, 1-5 3PT, 2-3 FT) 6 Reb (0 Off), 3 Ast, 1 TO, 3 PF	14%	39%	104.6	112.4	-7.8
Doug McDermott	22	4.5	0.21	4 Pts (1-4 FG, 0-1 3PT, 2-2 FT) 2 Reb (0 Off), 2 Ast, 1 PF	10%	55%	92.3	96.2	-3.9
Bobby Portis	11	-2.4	-0.21	0 Pts (0-3 FG, ) 4 Reb (0 Off), 2 PF	13%	0%	109.7	115.1	-5.4

Spurs Index: 98.7 (def.)

Factor	Value	Score
Passing (AST%)	65.9%	31.8
Shooting (eFG%)	50.5%	18.8
Defensive Rebounding (DReb%)	73.8%	19.3
Defense (DefRtg)	99.3	20.2
Opponent % of FGA Uncontested	47.7%	8.6
Total		98.7

Bulls Spurs Index: 95.1 Show Breakdown

Confused? Show Advanced Stats Glossary

eFG%: Effective Field Goal percentage. (via) Effective Field Goal Percentage; the formula is (FG + 0.5 * 3P) / FGA. This statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal. For example, suppose Player A goes 4 for 10 with 2 threes, while Player B goes 5 for 10 with 0 threes. Each player would have 10 points from field goals, and thus would have the same effective field goal percentage (50%).

AdjGS: a take-off of the Game Score metric (definition here) accepted by a lot of basketball stat nerds. It takes points, assists, rebounds (offensive & defensive), steals, blocks, turnovers and fouls into account to determine an individual's "score" for a given game. The "adjustment" in Adjusted Game Score is simply matching the total game scores to the total points scored in the game, thereby redistributing the game's points scored to those who had the biggest impact on the game itself, instead of just how many balls a player put through a basket.

Usage%: This "estimates the % of team possessions a player consumes while on the floor" (via). The usage of those possessions is determined via a formula using field goal and free throw attempts, offensive rebounds, assists and turnovers. The higher the number, the more prevalent a player is (good or bad) in a team's offensive outcome.

Floor%: Via Basketball-Reference.com: Floor % answers the question, "when Player X uses a possession, what is the probability that his team scores at least 1 point?". The higher the Floor%, the more frequently the team probably scores when the given player is involved.

Offensive Rating (offRtg): Points per 100 possessions.

Defensive Rating (defRtg): Points allowed per 100 possessions.

Spurs Index: The Spurs Index © is a just-for-fun formula that attempts to quantify just how "Spursy" a particular game is, based off averages for the 2013-2014 regular season. A perfectly average game would have a Spurs Index of 100. The formula consists of four factors which the Spurs are known for and lead or nearly lead the league in: Shooting (effective Field Goal %), Passing (Assist percentage), Defensive Rebounding Rate, and Defensive Rating. These metrics are weighted as follows:

Factor	Weight	Average
Passing (AST%)	30%	62.1%
Shooting (eFG%)	20%	53.7%
Defensive Rebounding (DReb%)	20%	76.4%
Defense (DefRtg)	20%	100.1
Opponent % of FGA Uncontested	10%	40.8%

The values for each metric are determined based on how a particular game's performance compares to the Spurs 2013-2014 regular season average for that metric. For instance, the average effective Field Goal percentage for 2013-2014 was 53.7%. So if the Spurs shot 60% in a given game, the score for eFG% would be calculated by: (0.6 / 0.537) * 20, which would yield a "score" for that factor of 22.3.

Special thanks to:

Bill Connelly over at our sister blog Rock M Nation, for the idea for Study Hall (and many of the stats and definitions)
Nick Bottomley, whose nba stats API project made it possible to automate the statistical breakdowns for every game