How progressive is your NBA team’s offense?

By Stephen Shea, Ph.D. (@SteveShea33)

July 17, 2017

NBA offenses are evolving. The increased reliance on 3-point shooting gets the most fanfare, but there is more to it than that. Teams are restructuring lineups and redesigning plays in hopes of improving all facets of shot selection, counterattacking with speed, and moving the ball faster.

When analytics assess offenses, it’s always a question of efficiency. Efficiency is the ultimate goal, but it relies on both good strategy and proper execution. And execution requires talent.

How does one evaluate scheme independent of efficiency? Doing so would be a means to better understand if recent Brooklyn or Philadelphia squads were adapting to the modern game even when their efficiencies were below average. In other words, it would be a way to see if these teams that were thin on talent were “playing the right way.”

At the other end, there are almost certainly talented teams that aren’t keeping pace with recent trends among NBA offenses. It’s the best teams that have the least incentive to change. Said another way, desperation tends to precede innovation.

But talent can override a suboptimal offensive design, and so, efficiency metrics blur systems’ flaws.

We look back at the last three NBA seasons, and with a heavy reliance on spatial-tracking data, offer ways to assess shot selection, ball movement and counterattacking. In the end, we aggregate these markers to see which offenses have been the most progressive.

Shot Selection

Shots at the hoop, from behind the arc and at the free-throw line are the game’s most efficient.  The analytics are clear that teams should be building rosters and offenses with the intent of shifting a greater percentage of their shots to these attempts (where shots include trips to the free-throw line). To measure shot selection, we can look at just that—the percentage of a team’s shots that come from at the hoop, behind the arc or at the free throw line. (Again, a trip the free-throw line for two or three is considered a “shot.”)

Note that we’re looking at FGA and not FGM. This is a measure of shot choice and not efficiency.

Not surprisingly, Daryl Morey’s Rockets have had the three highest seasons in the last three years in regards to this metric.  (All seasons are listed in the table below.) The highest such percentage was the 2017 Rockets at 84%.

Teams are trending towards better shot selection. After the Rockets, the next five highest seasons in this metric came from 2017. Six of the bottom seven came from 2015.

The league average has risen from 63% in 2015 to 65% in 2016 to 67% in 2017.

RankYearTeamGoodShot%
12017Houston Rockets0.84
22015Houston Rockets0.77
32016Houston Rockets0.77
42017Brooklyn Nets0.75
52017Boston Celtics0.74
62017Cleveland Cavaliers0.73
72017Denver Nuggets0.73
82017Philadelphia 76ers0.72
92015Philadelphia 76ers0.72
102016Golden State Warriors0.72
112017Golden State Warriors0.72
122016Atlanta Hawks0.72
132016Philadelphia 76ers0.71
142017Oklahoma City Thunder0.71
152017Milwaukee Bucks0.71
162017Atlanta Hawks0.70
172016Denver Nuggets0.70
182017Utah Jazz0.70
192015Cleveland Cavaliers0.69
202017Portland Trail Blazers0.69
212016Cleveland Cavaliers0.69
222017LA Clippers0.68
232016Portland Trail Blazers0.68
242015Detroit Pistons0.68
252016Charlotte Hornets0.68
262015Atlanta Hawks0.68
272017Memphis Grizzlies0.68
282017Charlotte Hornets0.67
292016Oklahoma City Thunder0.67
302016Boston Celtics0.67
312016Phoenix Suns0.67
322017Miami Heat0.67
332017Los Angeles Lakers0.67
342015Denver Nuggets0.66
352016Detroit Pistons0.66
362015New Orleans Pelicans0.66
372016Utah Jazz0.66
382015Chicago Bulls0.66
392015Golden State Warriors0.66
402016Toronto Raptors0.66
412017New Orleans Pelicans0.66
422015Utah Jazz0.66
432016Sacramento Kings0.66
442015Dallas Mavericks0.65
452016Dallas Mavericks0.65
462016LA Clippers0.65
472015Phoenix Suns0.65
482017Sacramento Kings0.65
492017Washington Wizards0.65
502016New Orleans Pelicans0.65
512015Toronto Raptors0.64
522015Oklahoma City Thunder0.64
532015Los Angeles Clippers0.64
542017Phoenix Suns0.64
552016Washington Wizards0.64
562016Milwaukee Bucks0.63
572017Toronto Raptors0.63
582017New York Knicks0.63
592015Miami Heat0.63
602017Orlando Magic0.63
612015Milwaukee Bucks0.63
622017Dallas Mavericks0.63
632017Minnesota Timberwolves0.63
642015Boston Celtics0.63
652015Portland Trail Blazers0.63
662016Los Angeles Lakers0.63
672015Sacramento Kings0.62
682015San Antonio Spurs0.62
692016Memphis Grizzlies0.62
702015Orlando Magic0.62
712016Indiana Pacers0.62
722016Orlando Magic0.61
732017Chicago Bulls0.61
742016Chicago Bulls0.61
752017San Antonio Spurs0.60
762017Indiana Pacers0.60
772016Miami Heat0.60
782016Minnesota Timberwolves0.59
792015Brooklyn Nets0.59
802015Indiana Pacers0.58
812016Brooklyn Nets0.58
822016New York Knicks0.58
832017Detroit Pistons0.57
842015Los Angeles Lakers0.57
852015Memphis Grizzlies0.57
862015Washington Wizards0.56
872015Charlotte Hornets0.56
882016San Antonio Spurs0.56
892015New York Knicks0.56
902015Minnesota Timberwolves0.56

Ball Movement

The NBA’s abolishment of the illegal defense rule allowed NBA teams to help off the ball. Help defense limited the efficiency of isolation-driven offenses. The three-point line together with stricter whistles on physical play on and off the ball have provided an offensive counter-strategy. Teams that space with 3-point threats and quickly swing the ball force defenses into rotations that will free up a cutter to the hoop or a catch-and-shoot opportunity on the perimeter.

Shot selection metrics helps in the understanding of offensive spacing, but don’t directly get at ball movement. Two modern metrics constructed on spatial-tracking data do.

Seconds per touch is the average amount of time a player holds the ball before passing, shooting, drawing a foul, or turning the ball over. Quick ball movement leads to a lower average seconds per touch for the team.

In this metric Golden State is king. They’ve had three of the four best scores over the last three seasons.

The worst team in 2017 was Toronto. DeMar DeRozan doesn’t do much for the Raptors’ shot selection or ball movement.

Ball movement is good, but it’s often the specific action of stringing two swift passes together that generates great opportunities.

Secondary assists occur when a team makes two quick passes to a made shot. They are the so-called “hockey assists,” and an indicator of smart and rapid ball movement on offense.

Secondary assists per game are presented with seconds per touch in the table below. Golden State had the three best seasons.  Beyond Golden State, this is an area where San Antonio, Atlanta and Boston scored well.

(Secondary assists are linked to efficiency. It would be better to use secondary assist opportunities—two quick passes to a FGA—but this is not publicly available.)

YearTeamSec/Touch RankSec/Touch2ndAst/Gm Rank2ndAst/Gm
2016Golden State Warriors12.3919.68
2017Golden State Warriors42.4329.65
2015Golden State Warriors22.4137.91
2015San Antonio Spurs92.5247.51
2016Atlanta Hawks82.4957.29
2016San Antonio Spurs252.6467.12
2015Atlanta Hawks112.5477.07
2017Boston Celtics142.5686.84
2015Milwaukee Bucks362.7296.73
2015Los Angeles Clippers322.70106.57
2017San Antonio Spurs302.69116.31
2017Atlanta Hawks222.61126.29
2016Chicago Bulls282.66136.29
2016Cleveland Cavaliers632.85146.20
2015Memphis Grizzlies462.77156.17
2015Chicago Bulls552.79166.09
2016Boston Celtics62.46176.06
2015Houston Rockets272.66186.00
2017Denver Nuggets422.74196.00
2017Charlotte Hornets482.78205.99
2015Indiana Pacers532.79215.99
2017Cleveland Cavaliers722.90225.95
2015Boston Celtics162.58235.79
2017Milwaukee Bucks472.78245.78
2015Cleveland Cavaliers662.87255.72
2016Houston Rockets292.68265.71
2016Denver Nuggets582.83275.71
2015Washington Wizards382.73285.65
2017Memphis Grizzlies672.87295.65
2017Sacramento Kings352.71305.62
2017New York Knicks192.60315.59
2016Washington Wizards212.60325.57
2017Orlando Magic372.73335.54
2016New York Knicks122.54345.50
2016Milwaukee Bucks542.79355.50
2016Phoenix Suns152.57365.49
2017Philadelphia 76ers32.42375.46
2017Indiana Pacers642.86385.44
2016Charlotte Hornets442.76395.44
2016Dallas Mavericks242.63405.40
2016Minnesota Timberwolves702.89415.39
2017Minnesota Timberwolves762.92425.39
2015Charlotte Hornets412.74435.37
2016Indiana Pacers312.70445.34
2015Brooklyn Nets853.00455.34
2016Orlando Magic342.71465.32
2015Utah Jazz52.46475.26
2017Chicago Bulls332.71485.26
2016Sacramento Kings452.77495.25
2017Utah Jazz502.78505.21
2015Portland Trail Blazers712.90515.20
2015New York Knicks72.48525.19
2015Minnesota Timberwolves682.89535.19
2016Portland Trail Blazers802.95545.07
2016Brooklyn Nets602.84555.06
2017Houston Rockets492.78565.02
2016Memphis Grizzlies562.80575.00
2015Toronto Raptors863.00584.99
2015Dallas Mavericks182.59594.98
2017Brooklyn Nets132.55604.95
2016LA Clippers622.85614.94
2017Portland Trail Blazers873.00624.91
2017New Orleans Pelicans262.66634.90
2015Detroit Pistons772.93644.84
2017Phoenix Suns202.60654.84
2017LA Clippers512.78664.80
2017Washington Wizards792.93674.78
2016Oklahoma City Thunder832.96684.77
2015Orlando Magic612.85694.68
2017Los Angeles Lakers432.75704.67
2015Denver Nuggets752.92714.67
2015Sacramento Kings692.89724.65
2015Philadelphia 76ers102.53734.55
2017Detroit Pistons522.79744.51
2016Miami Heat652.87754.50
2016Detroit Pistons812.95764.49
2016Toronto Raptors732.91774.48
2016New Orleans Pelicans402.73784.48
2017Toronto Raptors883.02794.44
2017Miami Heat742.92804.33
2016Utah Jazz232.61814.27
2015Phoenix Suns392.73824.18
2015Miami Heat572.82834.14
2015New Orleans Pelicans903.13844.10
2016Philadelphia 76ers172.59854.09
2015Oklahoma City Thunder592.84864.06
2017Oklahoma City Thunder782.93874.06
2017Dallas Mavericks822.95884.05
2015Los Angeles Lakers893.09893.89
2016Los Angeles Lakers842.98903.34

Counterattack

It’s easier to score when the defense isn’t ready. Teams that can get out in transition will be rewarded with better opportunities.

Leicester City shocked the English Premier League with a counterattacking style in 2016. While not quite as shocking, Golden State has been the NBA’s equivalent in terms of scheme.

When Golden State gets possession, they counter fast. In 2014-15, 36% of their offense came between 2 and 9 seconds on the shot clock. That led the league, where the average was 26%. In total, the Warriors outscored their opponents by 1062 points (or 13 points per game) in that stretch of the shot clock. In the rest of the time, they were outscored by 229 points.

When teams attack fast, it also means that they usually get a shot up before all their players get down the floor on offense. This has the added benefit of providing good position for preventing opponents’ transition.  The offensive and defensive strategies complement each other, and the teams that execute it well will get out and score quickly while forcing long and difficult halfcourt possessions on their opponents.

A good measure of the extent to which a team attempts to counterattack is how fast they move on offense relative to defense. The table below displays the average speed of a player for the given team divided by the average speed of a player on defense only for each team.

RankYearTeamRelOSpeed
12016Golden State Warriors1.13
22015Golden State Warriors1.13
32017Philadelphia 76ers1.12
42017Golden State Warriors1.12
52016New Orleans Pelicans1.12
62017Denver Nuggets1.11
72015San Antonio Spurs1.11
82017Portland Trail Blazers1.11
92016Charlotte Hornets1.11
102017Charlotte Hornets1.11
112016Oklahoma City Thunder1.11
122016Denver Nuggets1.10
132016Portland Trail Blazers1.10
142017Los Angeles Lakers1.10
152015Charlotte Hornets1.10
162017Brooklyn Nets1.10
172016Washington Wizards1.10
182017New Orleans Pelicans1.10
192017San Antonio Spurs1.10
202015New Orleans Pelicans1.10
212017Detroit Pistons1.10
222016Boston Celtics1.10
232015Portland Trail Blazers1.10
242016Orlando Magic1.10
252015Boston Celtics1.10
262017Atlanta Hawks1.09
272017Phoenix Suns1.09
282017Oklahoma City Thunder1.09
292015Oklahoma City Thunder1.09
302015Orlando Magic1.09
312016San Antonio Spurs1.09
322015New York Knicks1.09
332015Detroit Pistons1.09
342016Atlanta Hawks1.09
352016Chicago Bulls1.09
362017Miami Heat1.09
372016Detroit Pistons1.09
382016Dallas Mavericks1.09
392015Chicago Bulls1.09
402015Philadelphia 76ers1.09
412015Utah Jazz1.09
422017Houston Rockets1.09
432015Washington Wizards1.09
442016New York Knicks1.09
452015Atlanta Hawks1.09
462017Orlando Magic1.09
472016Sacramento Kings1.09
482016Utah Jazz1.08
492015Denver Nuggets1.08
502016Miami Heat1.08
512015Milwaukee Bucks1.08
522017Utah Jazz1.08
532015Miami Heat1.08
542017New York Knicks1.08
552015Dallas Mavericks1.08
562016Indiana Pacers1.08
572015Sacramento Kings1.08
582015Minnesota Timberwolves1.08
592017Boston Celtics1.08
602016Philadelphia 76ers1.08
612017Chicago Bulls1.08
622015Memphis Grizzlies1.08
632016Los Angeles Lakers1.08
642017Toronto Raptors1.08
652015Los Angeles Clippers1.08
662015Phoenix Suns1.08
672016Brooklyn Nets1.08
682017Indiana Pacers1.08
692015Houston Rockets1.08
702016Houston Rockets1.08
712017Washington Wizards1.08
722015Brooklyn Nets1.08
732016Toronto Raptors1.08
742017Dallas Mavericks1.08
752016Phoenix Suns1.07
762017Milwaukee Bucks1.07
772017Sacramento Kings1.07
782016Milwaukee Bucks1.07
792016LA Clippers1.07
802015Cleveland Cavaliers1.07
812016Memphis Grizzlies1.07
822015Los Angeles Lakers1.07
832016Minnesota Timberwolves1.07
842017LA Clippers1.07
852015Indiana Pacers1.07
862017Memphis Grizzlies1.06
872015Toronto Raptors1.06
882016Cleveland Cavaliers1.06
892017Cleveland Cavaliers1.06
902017Minnesota Timberwolves1.06

Modern Offensive Strategy Score

The four statistics detailed in the previous three sections are not independent. Rather, the ideal modern offense will get out in transition with quick passing, and in doing so, create open looks from favorable locations.

We standardized the four statistics and then summed them. The result, which we call Modern Offensive Strategy Score (MOSS), is displayed below.

RankYearTeamGoodShot%Sec/Touch2ndAst/GmRelOSpeedMOSS
12016Golden State Warriors0.722.399.681.1310.27
22017Golden State Warriors0.722.439.651.129.19
32015Golden State Warriors0.662.417.911.137.32
42017Philadelphia 76ers0.722.425.461.125.60
52016Atlanta Hawks0.722.497.291.094.65
62015San Antonio Spurs0.622.527.511.114.18
72017Boston Celtics0.742.566.841.083.76
82017Denver Nuggets0.732.746.001.113.65
92017Brooklyn Nets0.752.554.951.103.60
102016Boston Celtics0.672.466.061.103.40
112015Atlanta Hawks0.682.547.071.093.25
122017Houston Rockets0.842.785.021.092.99
132017Atlanta Hawks0.702.616.291.092.99
142015Houston Rockets0.772.666.001.082.56
152016Houston Rockets0.772.685.711.082.13
162017Charlotte Hornets0.672.785.991.112.10
172015Philadelphia 76ers0.722.534.551.091.89
182016Charlotte Hornets0.682.765.441.111.88
192016Denver Nuggets0.702.835.711.101.71
202015Utah Jazz0.662.465.261.091.70
212016Washington Wizards0.642.605.571.101.64
222015Boston Celtics0.632.585.791.101.40
232017San Antonio Spurs0.602.696.311.101.06
242017New Orleans Pelicans0.662.664.901.100.98
252016New Orleans Pelicans0.652.734.481.120.93
262016Dallas Mavericks0.652.635.401.090.84
272016San Antonio Spurs0.562.647.121.090.81
282016Phoenix Suns0.672.575.491.070.70
292016Chicago Bulls0.612.666.291.090.70
302015Milwaukee Bucks0.632.726.731.080.69
312015Chicago Bulls0.662.796.091.090.63
322017Los Angeles Lakers0.672.754.671.100.60
332017Phoenix Suns0.642.604.841.090.48
342015Los Angeles Clippers0.642.706.571.080.46
352017New York Knicks0.632.605.591.080.34
362016Philadelphia 76ers0.712.594.091.080.30
372017Milwaukee Bucks0.712.785.781.070.26
382015Dallas Mavericks0.652.594.981.080.16
392017Utah Jazz0.702.785.211.080.15
402017Portland Trail Blazers0.693.004.911.110.12
412016Portland Trail Blazers0.682.955.071.100.09
422015New York Knicks0.562.485.191.09-0.08
432016Orlando Magic0.612.715.321.10-0.13
442016New York Knicks0.582.545.501.09-0.17
452016Utah Jazz0.662.614.271.08-0.26
462017Orlando Magic0.632.735.541.09-0.31
472016Sacramento Kings0.662.775.251.09-0.32
482016Oklahoma City Thunder0.672.964.771.11-0.38
492017Cleveland Cavaliers0.732.905.951.06-0.67
502015Charlotte Hornets0.562.745.371.10-0.68
512017Sacramento Kings0.652.715.621.07-0.78
522016Cleveland Cavaliers0.692.856.201.06-0.82
532015Detroit Pistons0.682.934.841.09-0.83
542017Oklahoma City Thunder0.712.934.061.09-0.85
552016Indiana Pacers0.622.705.341.08-0.87
562015Cleveland Cavaliers0.692.875.721.07-0.88
572015Portland Trail Blazers0.632.905.201.10-0.94
582017Chicago Bulls0.612.715.261.08-1.18
592015Washington Wizards0.562.735.651.09-1.33
602015Memphis Grizzlies0.572.776.171.08-1.46
612017Miami Heat0.672.924.331.09-1.53
622017LA Clippers0.682.784.801.07-1.57
632015Orlando Magic0.622.854.681.09-1.59
642016Milwaukee Bucks0.632.795.501.07-1.66
652017Memphis Grizzlies0.682.875.651.06-1.66
662015Oklahoma City Thunder0.642.844.061.09-1.67
672015Denver Nuggets0.662.924.671.08-1.72
682016Detroit Pistons0.662.954.491.09-1.74
692015Phoenix Suns0.652.734.181.08-1.75
702017Detroit Pistons0.572.794.511.10-1.83
712016LA Clippers0.652.854.941.07-2.25
722017Indiana Pacers0.602.865.441.08-2.29
732015Miami Heat0.632.824.141.08-2.33
742015Indiana Pacers0.582.795.991.07-2.44
752017Washington Wizards0.652.934.781.08-2.46
762016Toronto Raptors0.662.914.481.08-2.50
772016Memphis Grizzlies0.622.805.001.07-2.52
782015Sacramento Kings0.622.894.651.08-2.52
792015New Orleans Pelicans0.663.134.101.10-2.58
802016Miami Heat0.602.874.501.08-2.71
812016Brooklyn Nets0.582.845.061.08-2.95
822015Minnesota Timberwolves0.562.895.191.08-3.29
832016Minnesota Timberwolves0.592.895.391.07-3.32
842017Minnesota Timberwolves0.632.925.391.06-3.36
852015Brooklyn Nets0.593.005.341.08-3.45
862017Toronto Raptors0.633.024.441.08-3.51
872015Toronto Raptors0.643.004.991.06-3.66
882017Dallas Mavericks0.632.954.051.08-3.72
892016Los Angeles Lakers0.632.983.341.08-4.42
902015Los Angeles Lakers0.573.093.891.07-6.16

With all of the talent in Golden State, the intelligence in their offensive design is often overlooked. They have been playing a progressive style of basketball for several seasons, and they’ve blown out the field in MOSS.

The Lakers under head coach Byron Scott appeared oblivious to how the game was evolving, but new coach Luke Walton, hired from Golden State’s staff, has caught the team up in a hurry.

Tom Thibodeau hasn’t had the same impact in Minnesota.

MOSS is constructed with a focus on scheme over execution, and so, it should not correlate with offensive efficiency. In fact, as discussed above, it’s often the least talented teams that are the most innovative.

To understand if this modern playing style is effective (to teams other than Golden State), we have to compare teams to themselves.

NBA offensive rating is trending up in recent years. Across the league, it has risen from 105.6 to 106.4 to 108.8 in the last three seasons. MOSS has been trending with it. Average MOSS has gone from -0.50 to 0.10 to 0.40.

Among the 30 NBA teams, 25 saw an improvement in ORtg from 2016 and 2017. There were 17 teams that saw an improvement in MOSS, and all of those saw an improvement in ORtg. This means that among the 13 teams that saw their MOSS decline, 5 saw their ORtg follow.

Among the 17 teams that saw an improvement in MOSS, the average change in ORtg was +3.1 points per 100 possessions. Among the 13 that regressed in MOSS, the average change in ORtg was +1.3.

Final Thoughts

As the game evolves, it can be helpful to have means to assess the extent to which organizations are keeping pace.

MOSS indicates that Golden State, Philadelphia, Boston, Denver, Brooklyn, and Houston employ progressive offenses, even if some of those teams don’t yet have the talent to capitalize.

Is the once innovative Spurs offense now outdated?

By Steve Shea (@SteveShea33)

July 7, 2017

He’s a genius, the NBA’s best coach in recent history hands down. Since Gregg Popovich’s first full season as San Antonio’s head coach, 1997-98, the Spurs have had 20 consecutive playoff appearances, a regular-season record of 1133-459 (a .712 win %), and won 5 titles.

One of the joys of the analytics movement has been uncovering quantitative explanations for the past success of teams, players and coaches.

Modern statistical analyses have demonstrated the value of the corner 3. It’s both an efficient shot when taken and an excellent means to space the floor and stretch the defense.

But long before nerd blogs flooded the internet with arguments for its usage, Popovich was dismantling NBA defenses with Jaren Jackson and Sean Elliot lurking in the corners. As the following chart demonstrates, San Antonio enjoyed over a decade of intelligent corner 3 usage before the league caught on.

Between 2002 and 2012, The Spurs were in the top 3 in corner 3 usage 10 times. They were in the top 2 nine times.

In their awareness of the value in the corner 3 and in other ways, the Spurs were an innovative offense under Popovich. But the tail ends of the above charts suggest that the current Spurs, which must operate in a modern NBA informed by analytics, are no longer ahead of the curve.

Spurs’ Shot Selection

It’s hard to mention analytics without someone associating the movement with the suggestion that teams should shoot less mid-range jumpers and more 3s. Of course, this was a valid suggestion, and teams did improve by simply rerouting a percentage of 18-footers to behind the arc. But, not all 3s are equal. Above-the-break 3s aren’t as efficient as corner 3s, and a truly savvy team will find ways to shift inefficient mid-range attempts to those corners.

A team’s ratio of mid-range jumpers to corner 3s functions as a quick assessment of their shot selection. In 1997, teams averaged over 13 mid-range attempts for every corner 3. This past season, teams averaged less than 3 mid-range attempts for every corner shot.

The following chart shows that by 2005, San Antonio was operating at 2017 league average rates. They were more than a decade ahead of their peers.

Popovich’s progressive approach is also reflected in their league ranks in the stat (where less mid-range per corner 3 means a higher rank).

In 2012, San Antonio placed 3rd in the league, the 9th time they did so in an 11-year span. But in 2013, they slid to 5th. After that, they were 10th and 9th. Two seasons ago, they were an abysmal 27th, and last season, they weren’t much better.

On defense, San Antonio continues to be elite, and Popovich continues to innovate. But, the once progressive San Antonio offense now appears to be a step behind their competition. It’s not so much that San Antonio has regressed in their shot selection. It’s that they stayed stagnant as the rest of the league passed them by. In 2003, The Spurs were second in the league with only 3.4 mid-range attempts for every Corner 3 attempt. In 2017, they ranked 24th with a ratio of 3.8.

The Spurs’ offense hasn’t fallen off a cliff. Last season, they were ranked 9th in ORtg. But, for an immensely talented roster with an elite coach, should that be satisfying? They were only a hair more efficient than the very young Timberwolves and behind the Nuggets, Celtics and Wizards.

LaMarcus Aldridge

When the Spurs signed LaMarcus Aldridge before the 2015-16 season, many saw it as brilliant, a means for the Spurs to transition from the Duncan-led era to a new dynasty without suffering years of a poor product in order to rebuild through the draft. Together, Kawhi and Aldridge were arguably as good as any other pair in the NBA.

Aldridge is certainly very talented, but he also plays a game that was more the flavor in the previous decade than the current one. In 2015, his last season in Portland, Aldridge led the NBA with 6 FGA per game between 15 and 19 feet.  He averaged 11.1 FGA in total from mid-range, also the most in the NBA.

And it’s not like Aldridge was unusually efficient at them. Aldridge made 41.5% of his mid-range attempts his last year in Portland. His mid-range jumper was as efficient as a 27.6% 3-point attempt.

But in 2015, the NBA was evolving rapidly, and with it, many of its players.  The coming years would see traditional bigs like Marc Gasol and Brook Lopez step back and start launching from behind the arc.

Also, what many teams saw as power forwards in the early 2000s were getting relabeled as small-ball centers. In 2015-16, Aldridge’s first year in San Antonio, Golden State’s death lineup with 6’7’’ Draymond Green at center (and Curry, Thompson, Barnes and Iguodala on the perimeter) torched teams, outscoring opponents by 166 points in 172 minutes.

The game was trending smaller and to the perimeter, and Aldridge appeared to have the ideal combination of size, athleticism and skills to excel in that environment.

It hasn’t happened.

In his last season in Portland, Aldridge went 37 for 105 (35.2%) from 3. Those numbers suggested the 3 could be a regular part of his game, and that he could be the rare big that could space the floor and pull opposing forwards and centers away from the hoop, clearing the path for his teammates to drive.

But in his first season with San Antonio, Aldridge made zero threes. ZERO THREES!  He only had 16 attempts.

To understand the impact of shot selection on Aldridge’s production consider how his selection compares to that of Houston’s Ryan Anderson.

If Aldridge matched Anderson’s 1.12 points per shot on his 626 attempts, he would have scored an additional 162 points for San Antonio.

Along with the shot selection issue, San Antonio hasn’t found ways to use Aldridge as the big in smaller lineups.

There are reasons Golden State’s death lineup only sees limited minutes. For one, they don’t want to give opponents too much practice defending it. In addition, it can be incredibly taxing for a small center like Draymond Green to bang with bigs like Marc Gasol, DeAndre Jordan or Andre Drummond.

Similar to the situation in Golden State, San Antonio wouldn’t want to overuse smaller lineups and wear down Aldridge, but they should turn to it on occasion.

In 2016-17, Golden State’s upgraded death lineup with Durant in place of Barnes saw 224 minutes (and was +123).

I went in search of San Antonio lineups with Aldridge and 4 wings/guards (which excludes bigs like Dedmon, P. Gasol, Lee, and Bertans). It turns out that the most commonly used lineup to meet that criteria played only 23 minutes. In total, there were 4 such lineups that played more than 6 minutes together.

The small lineups worked to the tune of +39 in 50 minutes.

Golden State is able to play small so effectively because they have the most appropriate personnel. The purpose of playing small is to be able to switch screens on the perimeter on defense and to bring more perimeter skills (shooting, ball handling, passing, and driving) to the offense. But it’s hard to find players with those small-ball characteristics that teams can’t bully with size near the hoop.

But San Antonio also has the appropriate personnel. Aldridge and Leonard are dream players for small-ball lineups. And according to NBAwowy.com, when San Antonio went small with Aldridge and Leonard, the team had an ORtg of 116 and a DRtg of 98. Both rating would have been league bests for teams on the season. Yet, San Antonio only went to such lineups for a total of 112 minutes or about 82 seconds per game.

Final Thoughts

It’s out with the post-ups and mid-range jumpers and in with drives, bigs that shoot 3s, small-ball lineups, and off-the-ball cuts. The NBA game is evolving rapidly, and to be successful, teams need to keep pace. It appears as though the great Gregg Popovich is struggling to stay ahead of an analytics-infused NBA, and the once inventive Spurs offense is trending towards obsolete.

Do analytics deserve a seat at the table?

By Steve Shea (@SteveShea33)

June 30, 2017

Often, professional sports organizations employ the “go-for” model for incorporating analytics. Picture a group of executives seated around a large table deciding what to do for lunch. They may toss around a couple options—Italian or Chinese—before bickering on whether they should order individual entrees or share crowd-sized portions.  Eventually, pen goes to paper and an order is scratched out.

Then, the young intern is called in to make the run, to execute the portion of the operation where the he has some authority. He will have to decide whether to pick up the food or have it delivered. If it’s pick-up, he’ll have to find and decide on a parking space. At the restaurant, he may make the call to toss in a few more soy sauce packets. And should the restaurant not be able to fill a portion of the order, he may have to make the split-second decision on a replacement item, provided it’s an appetizer or dessert and not something central to the meal.

In the sports world, the owners, the president of [pick your sport] operations, the general manager, assistant general managers, and coaches are the decision makers.

When they gather to hash out the basics of an offseason plan, the group might be unanimous that the team needs an upgrade at point guard, but argue over the means to make the acquisition. After some discussion, it may be decided that free agency has the most appealing and feasible options, and a short list of candidates could be assembled.

Then the analytics are brought in. The front office or coaching staff would like evaluations on the candidates, specifically focusing on their playmaking ability.  The analytics staff will have some freedoms in the analysis. For example, they might decide to split pick-and-roll situations depending on if the screening teammate rolled or popped, or they might look at the point guard’s turnover rates split by whether or not the opposition switched.

The analytics will help the decision makers zero-in on a first, second and third choice among the group. They’ll help the general manager and his team understand which available option is the best match for what the group wants.

But maybe, analytics should have been part of that group. Perhaps, analytics should have been at the table before the short list of free-agent options was assembled and before free-agency was decided as the most appealing path.  If in the room, analytics might have suggested that the group’s desire for a ball-dominant point guard is outdated, that the team should instead try to build a lineup that shares the playmaking.  And that if anyone should be the primary creator, it’s a forward already on the roster. Analytics may have argued that the team should be giving as much consideration to any potential addition’s off-the-ball value as they do his on-the-ball skills. Or, analytics may have suggested that if the team moves away from the traditional point guard mold, they can find a player that is better defensively and with the length to switch onto bigger wings on the perimeter.

What should be the role of analytics?

Should analytics simply run the post-up efficiency of the bigs the team is considering for the upcoming draft, or should analytics have the forum to suggest that the team shouldn’t be so interested in posting up on offense and should instead judge centers on their ability roll to the rim, pass, knock down a perimeter shot, and switch screens on defense?

Should analytics stick to tracking the team’s paint touches and ball reversals and their influence on the offense’s efficiency, or should they be able to question the coach’s lineups, suggest swapping the rebounding power forward for another small forward, and push for a 1-in and 4-out formation?

The analytics

Ever notice how a journalist or TV commentator will reference “the analytics,” as in “the analytics say James Harden should win MVP.”  The analytics don’t say Harden should be MVP because he has a higher true-shooting percentage. The analytics don’t say Rudy Gobert is the NBA’s best defender because he has the best defensive real plus-minus. The analytics don’t say anything, because a quantitative analysis isn’t the equivalent of punching an addition problem into a calculator.

Teams fill analytics positions based on the applicant’s degrees, the programming languages she or he knows, and to some extent, the individual’s fluency in the sport. But job ads don’t often ask for a demonstration of quantitative creativity. They might pry for problem-solving skills, but the kind that have answers in SQL code.

Sports mirror many industries in their increased reliance on data to make decisions. In this new world where numbers have power, everyone wants to be comfortable with statistics. But I’ve seen comfortable with statistics, and it’s not what anyone should be aiming to achieve.

Mathematics is taught as black and white. 2+2 is always 4. A teacher asks her students to journal about their dream day and expects a variety of responses, but every math exam comes with a fixed answer key. We teach that math is either right or wrong.

So, when a crowd gathers for a PowerPoint presentation with a few pie charts and vague references to statistical significance, we all nod in approval as if the sky opened before us and the speaker’s conclusions descended from the heavens.

Stats are facts, but every situation can be analyzed in numerous ways, and each analysis has a multitude of interpretations. We should get as cozy with one particular approach as we would a bed of skunks and porcupines.

In the go-for model, analytics are a simple stat run that leaves little for the decision-makers to interpret. It’s one approach with strict parameters. When analytics are in the room, they are not just the numeric answers to a specific query but a perspective when shaping the questions. It’s not just providing the team’s offensive rebounding rates, but it’s posing the question of if they should be considered in conjunction with the transition points surrendered. And any assortment of statistical information on that front leads to a complex discussion of alternate interpretations. There are analytics to support aggressive play on the glass, and there are stats that argue for the team’s need to get more bodies back to stop transition.

If teams are looking for a lackey to calculate a team’s efficiency on drives, then they are searching for the analytics, because there is only one correct answer.  But should they be asking for more?

Illumination or just support?

At a 2012 MIT Sloan Sports Analytics Conference panel, longtime hockey executive Brian Burke said, “Statistics are like a lamp post to a drunk, useful for support but not for illumination.”

There’s a day-to-day component of analytics—for example, tracking and aggregating shots in practice, or assembling and communicating pre and post-game reports—that support the organization in its activities.

But Leicester City didn’t win the Barclay’s Premier League with pretty shot charts. They won because they dramatically altered their philosophy on how to build a team and implemented an innovative strategy on the field (built largely on an understanding of transition).

Teams need daily maintenance, but the supporting basketball activities described above don’t move the needle like finding Draymond Green in the 2nd round.

Can analytics be so illuminating? Recognizing the value of positional versatility and Draymond Green’s potential in that NBA goes beyond calculating college players’ efficiency by play type. It requires an understanding of the game’s trends and in that context, an intelligent interpretation of data. It requires not just the ability to crunch numbers, but the creativity to pose original questions.

But isn’t this precisely where analytics should be strong? At their core, analytics are a different perspective. As much as many quantitative analysts are fans of the game, numbers eventually beat the fan bias out of the observer. Analytics are fresh eyes, activity that very much lives outside of the traditional box in which sports organizations operated. If a team is looking for new ideas, true innovation that can distinguish them among their competitors, could there be a more fertile land than analytics?

Some NBA organizations are already onboard with this approach. Houston is the glaringly obvious example. But to what extent was analytics involved in Phil Jackson’s decisions in New York? And when we move to less progressive leagues, like the NHL, we find decision-makers that would more willingly welcome a plague than conversations on per possession efficiency.

Certain organizations still view analytics as glorified stat trackers, like the kind you’d see behind the bench charting shots at a high school basketball game. I’d argue that the true value in analytics is only found when teams recognize them as a fundamental mode of thinking in their own right, a unique approach to solving sports problems.

Analytics deserve a seat at the table, not to provide all the answers, but to question conventional thinking.

Draft Day!

June 22, 2017

By Steve Shea (@SteveShea33)

College Prospect Ratings (CPR) are a formula meant to approximate a college player’s pro potential. It’s based on college box score production, and so, it is limited in its ability to assess certain elements of a player’s game (and certainly, his character). Still, it’s been successful in its predictions. CPR suggested Klay Thompson and Kawhi Leonard should have been top 5 picks in 2011. And, it found 2nd round steals, Draymond Green and Jae Crowder, in 2012. (For more details, see this previous post.)

Here’s what CPR says about this year’s class:

RankPlayerCollegeCPR V3
1Malik MonkKentucky18
2Jayson TatumDuke15
3Markelle FultzWashington14
4Lonzo BallUCLA14
5Caleb SwaniganPurdue14
6Dennis SmithN.C. State11
7Josh JacksonKansas10
8Thomas BryantIndiana10
9Jonathan IsaacFlorida St.9
10Cameron OliverNevada9
11Zach CollinsGonzaga8
12Luke KennardDuke8
13Donovan MitchellLouisville8
14Tyler LydonSyracuse8
15T.J. LeafUCLA8
16Alec PetersValparaiso8
17De'Aaron FoxKentucky7
18Lauri MarkkanenArizona7
19John CollinsWake Forest7
20Ivan RabbCAL7
21Jawun EvansOklahoma St.7
22Bam AdebayoKentucky6
23Sindarius ThornwellSouth Carolina6
24D.J. WilsonMichigan6
25Monte MorrisIowa St.6
26Josh HartVillanova6
27OG AnunobyIndiana5
28Dillon BrooksOregon5
29Justin PattonCreighton5
30Jordan BellOregon5
31Luke KornetVanderbilt5
32V.J. BeachemNotre Dame5
33Malcom HillIllinois5
34Nigel Williams-GossGonzaga5
35Frank JacksonDuke4
36Kobi SimmonsArizona4
37L.J. PeakGeorgetown4
38Devin RobinsonFlorida4
39Justin JacksonUNC4
40Frank MasonKansas4
41Jarrett AllenTexas3
42P.J. DozierSouth Carolina3
43Isaiah BriscoeKentucky3
44Johnathan MotleyBaylor3
45Kyle KuzmaUtah3
46Melo TrimbleMaryland3
47Dwayne BaconFSU3
48Derrick WhiteColorado3
49Semi OjeleyeSMU3
50Wesley IwunduKansas St.3
51Tony BradleyUNC2
52Antonius ClevelandSoutheast MO St.2
53Andrew WhiteSyracuse2
54Ike AnigboguUCLA1
55Harry GilesDuke1
56Isaiah HicksUNC1
57Nigel HayesWisonsin1
58Jaron BlossomgameClemson1