The Defensive 3-Point Revolution

By Chris Baker (@ChrisBakerAM) and Steve Shea (@SteveShea33)

September 23, 2016

If the city of Boston were redesigned from scratch tomorrow, no sane person would weave the confounding knot of one-way roads that tangle the city today. If considered in a bubble, coaching strategies shortly after the introduction of the 3-point line made as much sense as the North End’s roadways after widespread adoption of the motorcar. Of course, professional coaches don’t develop their strategies overnight, nor do players develop their skills so quickly.

NBA offensive and defensive strategies are evolving.  They aren’t just changing with the whims of a current coach or executive.  They are changing out of necessity.  The current NBA talent pool is different from the past.  For example, current players are much more accustomed to shooting threes than previous generations that grew up without the shot.  There have also been significant defensive rule changes.  Hand-checking fouls now make it easier for perimeter players to drive.  The abolishment of illegal defense allows teams to collapse into the NBA’s version of a zone (which must respect the current defensive 3-in-the-key).

Current strategies find their roots in past practices.  There is value in this model since “survival of the fittest” is at play.  The best past strategies have endured the longest (if perhaps with modifications).  There are consequences as well.  Often elements of past strategies linger beyond their expiration.  Ideally, coaches would be able to harvest the wisdom of past experiences without carrying the burden of a bias towards past success when designing strategies that consider the constraints of today’s game.  But that isn’t realistic.

Analytics isn’t (usually) biased towards past tendency, and sometimes analyzing the game can feel like taking a snapshot at the Target checkout just after a new register has been opened. We wonder why Phil Jackson is standing 10 deep at aisle 7 when Daryl Morey is alone in aisle 2. (Sam Hinkie is first at an unmanned register in aisle 6 playing the odds that the store will open it soon. If Joel Embiid exits the break room in the next 30 seconds, he’ll look like a genius.)

It’s offensive

Red Auerbach once proclaimed, “Basketball is like war in that offensive weapons are developed first, and it always takes a while for the defense to catch up.” Recent years agree with Red; NBA offenses are evolving faster than defenses are responding.

The components of the modern offensive evolution can be subtle, such as replacing a few pick-and-rolls with dribble handoffs or encouraging less pursuit of offensive rebounds to better defend transition.

There is no subtlety to the growing importance of the 3-point shot. It’s become the hallmark of today’s game. In 2015-16, NBA teams averaged 24.1 3-point attempts per game. That’s up 33% from 18.0 attempts per game just 5 years earlier, and up nearly 90% of the 12.7 attempts per game in 1997-98 (which was the first season after a 3-year trial of a shorter 3-point line).

The 3-point growth shows no sign of slowing down. Teams shot 1.7 more 3-point attempts per game last season than the prior season (although a faster pace league-wide contributed to that growth).

It’s not that weaker teams are heaving more threes in desperation. The Eastern and Western Conference Champions, the Cavs and Warriors, were in the top three in both 3-point attempts and in % of the team’s FGA from behind the 3-point line. 10 of the top 11 teams in 3-point attempts made the playoffs.

The increased usage of 3-pointers is justified. As the NBA talent pool became increasingly efficient from deep, the shot’s value surpassed almost any FGA besides a wide-open dunk. In 1982-83, NBA teams shot 23.8% on 3-pointers. That’s equivalent to 0.71 points per FGA. By comparison, teams shot 49.2% on twos or generated about 0.98 points per shot. A two-pointer was a much better shot. Today, teams are still generating about 0.98 points per two-pointer, but improved 3-point shooting has teams averaging 1.06 points per 3-point attempt. For the Golden State Warriors, 3-point attempts generate 1.25 points per shot. (That’s why they take 31.6 of them a game.)

When we focus on the “good” 3-point attempts, efficiencies improve significantly. Teams averaged 1.13 points per corner 3 attempt and 1.11 points per catch-and-shoot 3-point attempt last season.

Compared to a “good” 3-pointer, a “bad” two-pointer looks inexcusable. Teams scored just under 0.8 points per mid-range jumper in 2015-16. An average team could pick up 2 points per game just by replacing 6 mid-range jumpers with 6 corner 3s.

The threat of a 3-point shot can be as valuable as the shot itself in that it provides spacing for the rest of the offense—room to drive, cut or post-up. For more on this idea, see our previous post.

We all agree, but have yet to see

Of course, not all NBA teams adopted 3-point usage at the same rate or embraced the improved spacing provided by 3-point threats to the same degree. Aisle 2 in the checkout line has been open for a while, but we’re still far from equilibrium (as the Bucks so kindly demonstrated last season). The NBA teams that were late to the 3-point party still must properly configure their rosters and transition their offensive schemes to come close to the Cavs or Warriors in effective 3-point usage.

But on-the-court equilibrium isn’t realistic. There will always be talent disparities. There will always be teams at various stages of competitiveness—some bringing back a core for their 10th consecutive playoff run while others are reloading with young talent. The true measure of where we stand in the offensive evolution lies in where basketball executives and coaches side on the issue.

It’s now safe to say that all NBA teams are onboard the 3-point train, even if strategies differ as to how best to implement the shot. If there were still any doubt after the 2015-16 season, the last great hurdle was crossed when Knicks’ President Phil Jackson consented, “The 3-point line has become our affection, because it means more when we make a 3-point shot.” (Thanks Phil.)

An opposite (but not equal) reaction

If offenses have changed so radically, so then must the defense. As the 3-point shot became more efficient and as teams began implementing offenses to specifically generate 3-point attempts, teams needed to devote more defensive resources to paroling the 3-point line, running off shooters and heavily contesting shots. The defensive changes needed to be as extreme as the offensive shift.

As Red Auerbach realized, the defensive adjustments always lag behind the offensive maneuvers. The NBA is in the early stages of a defensive 3-point revolution, a counter to the offensive development. Here, the Target checkout phenomenon is real; some teams are more progressive than others, not just in on-the-court performance, but also in front office philosophies.

Penetration and Kick outs

If an NBA player wants to pull-up for 3 in traffic, there is little that the defense can do about it. Fortunately for the defense, that’s not the type of 3-point attempt that they need to be concerned about. Rather, it’s the catch-and-shoot opportunities that are troubling.

NBA defenses won’t intentionally leave a capable shooter open enough to catch and shoot a 3 in rhythm. Instead, NBA offenses have to penetrate, draw help defenders and force defensive rotations to create enough space for perimeter shooters.

Understanding how teams defend the 3 means understanding how teams defend penetration and kick outs.

The best defense of a catch-and-shoot 3 is to not allow the shot. In theory, there are two ways this can happen. First, a team can guard the perimeter players so closely that a kick out isn’t attempted, or when it is, the perimeter player is coaxed to drive or pass. The second method would be to position the defense to create turnovers either by occupying passing lanes or by aggressively trapping the ball handler before the pass attempt.

That’s the theory, but are teams practicing either strategy and if so, are they successful? There is a remarkable amount of real estate on the 3-point line, and many teams now play lineups with at least 3 perimeter threats. Can teams consistently and significantly create turnovers or reduce catch and shoot 3-point attempts?

Yes, and the defensive systems that are being employed are remarkably different than those from the past.

The minimal help model

All other variables the same, the closer the shot, the easier it is to make. Prior to the 3-point line and even in the first few years of its existence, the best shots (by far) were those near the hoop. As a result, NBA defenses protected that region at all costs. Teams collapsed with help defense to the best of their ability (under the old illegal defense rules). In those days, forcing a kick out on penetration was a win for the defense.

Today, the best defenses are doing the opposite. They are sending minimal help on the drive. In particular, teams are not leaving the corners open, and they are terrified to leave an elite shooter (e.g. Steph Curry, Kyle Korver or J.J. Redick) alone anywhere behind the arc.

To assess defensive strategy on penetration we’ll use measurements of offensive and defensive stretch, which were introduced in Basketball Analytics: Spatial Tracking.

Consider the image below taken from an movement animation. It is the 3rd quarter of the Clippers-Warriors game on March 31, 2015. Notice the blue polygon wrapped around the Clippers’ defenders and the red polygon wrapped around the offensive players. The area of the blue polygon is CHAD (the Convex Hull Area of the Defense), and the area of the red polygon corresponds to what we called the Convex Hull Area of the Offense (or CHAO).

Screen Shot 2015-05-29 at 2.05.51 PM

When CHAD is close to CHAO, we have a situation like the one above where the defense is aggressively guarding the 3-point line.

The second image below was taken from a Knicks-Clippers game on March 25th.  Here, the Knicks are well spaced. Their area (or CHAO) is large. However, the Clippers have a very small CHAD.  When CHAD is small relative to CHAO, the defense has collapsed and left open players on the perimeter.

Screen Shot 2015-05-29 at 2.20.20 PM

We looked at every halfcourt possession in the NBA in 2014-15. (In other words, we eliminated transition.) In those halfcourt possessions, we marked the first instance the offense penetrated (moved from possession outside 15 feet to possession inside 10 feet.) This could be a pass to the post or a cutter, or it could be a drive from the perimeter. Using spatial tracking coordinates provided by SportVU, we looked at the position of all players on the court the first instant the offense had possession within 10 feet of the hoop. Then, we calculated the offense’s spacing (CHAO) and the defense’s stretch (CHAD). The teams with the smallest difference in CHAO and CHAD are the ones that help off the perimeter the least. Here are the results

RankTeamCodeCHAO-CHAD (sq. ft)

Chicago, Portland, Cleveland, San Antonio, and Golden State helped the least. These teams were the leaders in the minimal help model, a defensive strategy on penetration that is in direct contrast to traditional defense.

(We don’t mean to suggest that the above 4 teams employ identical defenses.  The teams employ defenses strategy that align in their stretch during penetration and in their strategy to reduce 3s on kick outs.)

Does it work? There are costs to not helping as much on defense. It means less obstacles for the penetrating player, and possibly higher opponents’ efficiency around the rim. It can mean less resources around the hoop for rebounds. So, if a team is going to intentionally not help, there must be a benefit. The goal of not helping is to prevent catch-and-shoot threes. Were these teams able to do that?

We looked at how many catch and shoot 3-point attempts (C&S 3PA) each team gave up in 2014-15. We adjusted for opponent tendencies by looking at each game individually and recording how many more or less C&S 3PA a team allowed than their opponent usually attempted. For example, if San Antonio held Golden State to 15 C&S 3PA, which was 6 less than they usually attempted, it was seen as a reduction. In contrast, if San Antonio allowed 15 C&S 3PA from Minnesota, which was about 5 more than they usually got, it was seen as an increase. We adjusted for pace by looking at percentages of typical opponent “shots” (FGA+0.44FTA) instead of totals. For ease of interpretation, know that 1% equates to approximately one shot per game.

The following chart plots CHAO-CHAD to the above-described percentage. There is a strong correlation. The teams that help the least (have the smallest CHAO-CHAD) are able to reduce opponents’ C&S 3PA. The leaders in this category (San Antonio) are holding opponents to 3-4 less C&S opportunities per game than they typically get. That’s a sizable chunk when teams are averaging 16.5 of these shots a game.


Preventing opponent C&S 3-point attempts has lessened opponents’ shooting efficiency. The top three defenses in opponent points per shot in 2014-15 were Golden State (0.940), Chicago (0.946) and Portland (0.952). San Antonio was a healthy 6th at 0.968. Those were the top 4 in terms of lowering opponent C&S 3PA.

The swarming defense

With 4:20 left in the 1st quarter of a January 31st, 2015 matchup between Portland and Milwaukee, Nicolas Batum feeds Lamarcus Aldridge on the block. Upon the penetration, all five Bucks sag into or around the paint. (This wouldn’t have been allowed before the NBA replaced it’s illegal defense with the defensive 3-in-the-key.) This leaves two Portland players (including Damian Lillard) open above the break. Portland is helping Milwaukee by having no players available in the corners.

Aldridge turns towards the paint, but is immediately met by Lillard’s man, Brandon Knight. Knight knocks the ball loose for a turnover.

Knight had to leave Lillard open for a C&S 3 when he trapped Aldridge. It was a risk. Most times, Knight isn’t going to get the steal. NBA players tend to see traps coming, and are poised and strong with the ball. A full court press might work well against high school players, but would be fruitless against he ball-handling and passing ability of the professionals.

Knight’s gamble wasn’t a full court press and it doesn’t have to work every time. Milwaukee’s swarming defense understood that it would give up a good amount of C&S threes, but believed that they would get enough turnovers to be an efficient defense overall. They were correct; Milwaukee had the 3rd best defensive rating that season (per

When the minimal help model prevents a 3, it’s usually exchanging that 3-point attempt for a different (and hopefully less efficient shot). When the swarming defense prevents a 3, it’s through a turnover. If the swarming defense can keep its turnover % high enough, it can offset the increased efficiency realized by the opposing offense through more C&S threes.

In 2014-15, Milwaukee, Atlanta and Philadelphia all saw at least some degree of defensive success with a version of this swarming defense. Their success is reflected in the percent of turnovers they induced. All three teams forced opponents to turnover the ball on at least 14.9% of possessions. They were the top 3 teams in this category.

Miami also employed a swarming defense. After being 1st in the league in opponent turnover % in 2013-14 (at 15.8%), Miami dropped to 8th in 2014-15 (with 14.2%). The inability to turn the swarming gamble into a turnover often enough meant the team slid to 21st in defensive rating.

If swarming defenses are consistently collapsing on penetration, we should see that in the spatial tracking data. The table of CHAO-CHAD data is reproduced below. The four teams that collapsed the most were Milwaukee, Miami, Atlanta, and Philadelphia.

RankTeamCodeCHAO-CHAD (sq. ft)

We suggested that swarming defenses will give up more C&S opportunities. Recall the scatter plot that mapped defensive stretch on penetration to percent influence on opponent C&S 3PA. The teams that collapsed the most (produced the highest CHAO-CHAD) also gave up more C&S 3PA.


Many of the C&S opportunities that swarming defenses give up come from the very efficient corner. 31.5% of Bucks’ opponents’ 3PA came from the corner. It was 28.8% from Atlanta’s opponents. Those were the two highest percentages on the season. In contrast, only 20.1% of Chicago’s opponents’ 3PA came from the corner. That was the lowest % in the league.

Be extreme

We discussed earlier how we still see great variation in perimeter shooting ability and usage among NBA offenses. The swarming defense would appear to be ideal against a team with minimal perimeter shooting since when the kick out to the open shooter is successful, the shooter will be less efficient on the shot. In contrast, it would seem that a swarming defense would struggle against a great passing and perimeter shooting team like Golden State.

The ideal defense might be one that can employ both defensive strategies. However, the 82-game regular season provides little opportunity for teams to prepare for specific opponents. Often teams won’t have a real practice between games.

Since the analysis averages CHAO-CHAD across the season, a team that alternated styles could appear as central and non-distinct.  We did study the game-by-game numbers, and as the difficulty of this strategy would suggest, no team altered strategies in a way that correlated with the 3-point shooting of the opponent.  Yes, teams do scheme for particular 3-point threats, such as Curry or Korver, but otherwise swarming defenses swarm.   Any adjustments for individual players were not significant enough to alter team stretch totals.

The defenses on the extreme in CHAO-CHAD outperformed those in the middle.  The bottom 8 teams in defensive rating were ranked between 13 and 26 in CHAO-CHAD (where the minimal difference was ranked 1st). The skew here suggests that not collapsing is generally better than swarming. The minimal help model also wins when we look at the top of the defensive rating board.

For the sake of this argument, suppose that a CHAO-CHAD < 285 indicates a minimal help model. No minimal help model finished in the bottom 12 in defensive rating.

There is a difference between system and execution. The minimal help model will only be successful if it actually reduces opponents’ C&S 3PA%. Consider the model effective if it reduced opponents C&S 3PA by at least 1% (of the total offense). There were 5 effective minimal help models (out of the 6), and those 5 teams were in the top 11 in defensive rating. Two of these models (GSW and SAS) were the top 2 in defensive rating.

If we consider a team as swarming when CHAO-CHAD>300, we have 5 swarming models. This model is effective only if the team is able to induce a high amount of turnovers. Let’s make that cutoff 14.5%. We then have 3 effective swarming models. Two of them (Milwaukee and Atlanta) were in the top 6 in defensive rating, while the third (Philadelphia) was 13th.

The bottom 17 teams in defensive rating qualified as neither an effective swarming nor an effective minimal help model. In other words, all 8 effective modern defensive models were in the top 13 in defensive rating.


Team Takeaways

What actions should a team take now with the above information in hand?

On Offense

This article is about defensive strategy, but we can’t help but again suggest that modern offenses need to be a threat both inside and out. Lineups need at least one player that is dangerous around the hoop. With modern hand-checking fouls and the typically superior free-throw shooting of perimeter players, this is often a guard that can drive (perhaps off a screen).

During penetration, offenses need to force defenses to make tough decisions. A player that is efficient attacking the hoop begs help defenders to collapse. Spacing the floor with 3-point threats (and we recommend at least 3) makes it dangerous to leave the corner unmanned.

We suspect that as offenses get better at shooting 3s, teams will help less on penetration. In other words, the minimal help model will be become the most popular. Thinking ahead, what does this mean for offenses? It’s possible that this opens the door for the return of the dominant post center (in the mold of Olajuwon or Shaq). More likely, NBA offenses will be able to counter with stronger and more athletic driving ball handlers that won’t be as affected by one defender on their shoulder. LeBron is the ideal, but this might also be Ben Simmons in Philadelphia or Giannis in Milwaukee (as examples).

On Defense

Teams need to decide on a defensive strategy. Ideally, teams would have the flexibility to play both modern models described above. However, it’s not realistic to expect young players to be able to switch from one helping extreme to the other on the fly. The middle ground defensively, which can happen through indecision, hesitation and confusion, is the worst. Thus, it probably makes sense for teams (and especially younger teams) to commit to predominantly one style for the regular season.

As teams trend towards better perimeter shooting, we suspect that the minimal help model will surpass the swarming defense in effectiveness. The swarm will have it’s role, but in small doses like a blitz in football.

Length and athleticism in defenders is remarkably helpful regardless of system. A perimeter shot contested by Kawhi Leonard is different than a perimeter shot contested by Jason Terry. And if the perimeter defender is left with little help when his player penetrates, a paint shot contested by Leonard is different than a paint shot contested by Terry.

In addition, length and athleticism translates to positional versatility. It allows players to switch screens without creating major mismatches in speed or size. Switching screens cuts off the space that offensive players use to get a step to the hoop or launch a 3.

Final Thoughts

We scan the NBA landscape and see elite offenses with 3-point shooting at their core. The natural reaction is to expect NBA defenses to be designed with preventing the 3 as a core principle.

Certainly some teams have adapted quickly. San Antonio and Golden State both employ effective minimal help models (and not surprisingly, are very successful franchises). Coach Tom Thibodeau pioneered the model as an assistant coach for the 2008 Champion Celtics. He then employed his defensive model with great success for years in Chicago.

Milwaukee and Atlanta have found defensive success with a modern swarming model. Their success is in part due to a focus on bringing in long and positionally-versatile wings that can switch screens and occupy passing lanes.

Yet, we still see a number of teams seemingly unsure of what to do in response to the 3-point revolution. To understand why teams appear so stubborn, we have to understand where today’s coaches and managers came from.

Many of the executives and coaches in today’s NBA have been involved in high-level basketball for 30 years or more. The first 25 of these years, these individuals never encountered a team like Golden State. Furthermore, these coaches and managers are where they are because they were so successful in the past. We have individuals that have seen decades of success with certain systems and philosophies. Why would we expect them to change so quickly?

We can’t ignore the practical challenges of finding the right personnel for a modern defensive system. When the 3-point shooting giants first presented, it was also new for NBA players. A minimal help model might be nice in theory, but how successful would a team be at implementing it if all of its players have no experience in anything similar?

Changing defensive systems can also require a significant revision to the roster. Teams might have players under contract for several years whose value would diminish tremendously if the team changed defensive strategy.

While we sympathize with the challenges NBA decision makers face when trying to counter the 3-point revolution, the challenges do not negate the reality that teams must adapt.

Basketball Analytics: Still Misunderstood

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

June 1, 2016


We take one step forward and then two steps back. Every time I think our world is beginning to understand analytics, an article like that from Michael Wilbon for comes out to destroy my optimism.

Wilbon brings up a very important point that analytics could be “a new path to exclusion, intentional or not” in NBA front offices. That’s a topic worth discussing, but not one that I’ll be addressing today.

Rather, Wilbon’s commentary, some of the quotes he pulled from those around the league, and the subsequent chatter on Twitter and through the media have left me (once again) concerned that fans, the media, coaches, front offices, and players still don’t know what basketball analytics is or its proper role in an NBA organization.

Here is my attempt to correct the major misconceptions surrounding basketball analytics.

1. Basketball analytics begin with an understanding of and feel for the game.

There is a misconception that basketball analysts experience the game as a series of numbers streaming across their computer screen. That couldn’t be further from the truth.

Long before I cared about eFG% (or even FG%), I picked up a ball and walked to the local court just like every player in the NBA. In those pick-up sessions that could last for several hours, never once did I think about my assist-to-turnover ratio or whether I had an unhealthy obsession with mid-range jumpers. I just played.

I still spend more time playing and watching basketball than I do querying a database or examining numbers, and that experience with the game greatly influences what I do when I am behind a computer. Let me walk through an example.

In 2013, I watched intently as the Spurs and Heat squared off in the NBA Finals. I immediately noticed that the Spurs opened the series with a unique defensive strategy. They backed off of LeBron, begging him to take perimeter shots. In addition, they exploited the below-average perimeter shooting of Wade and Haslem to bring more help defense into the lane and further dissuade LeBron from attacking the hoop.

LeBron was not a terrible shooter, but he was more dangerous going to the hoop than he was on the perimeter. The Spurs were choosing the lesser of two evils, and it worked. San Antonio won 2 of the first 3 games in the series.

Then Miami adjusted. Mike Miller (an excellent 3-point shooter) replaced Haslem in the starting lineup. In addition, Ray Allen (who is arguably the greatest 3-point shooter of all time) played over 33 minutes off the bench in game 4. Miami’s offensive adjustments appeared to stretch San Antonio’s defense. This meant more room for LeBron and Wade to attack the hoop.

Miami’s adjustments worked. They won 3 of the last 4 games to take the series in 7.

I didn’t have to be an analyst to notice the Spurs’ defensive strategy and Miami’s adjustment, and through the conclusion of the series, I didn’t run any numbers. Actually, it was my years of experience surveying the help defense before considering driving to the hoop or being forced to decide when to leave my man and help on an opponent’s shot that were percolating in my brain, not some algorithm.

However, this experience started nagging at my mathematical side. I began to wonder if Miami’s offensive adjustment truly stretched the Spurs’ defense, and if so, how much? Heck, I was intrigued simply by the question of how to quantify defensive stretch.

I wondered how much more efficient Miami’s offense became after the adjustment. Was this improvement simply a product of making more 3s or was stretching the defense improving Miami’s 2-point efficiency? Again, how much?

I enlisted the help of Chris Baker, and we began working through the details. There was tedious data gathering, programming, mathematics, and statistics, but also a lot of basketball. Every step of the process was heavily influenced by our experience with and feel for the game.

When I do analytics, there is one question I ask more than any other. What would I do in the player’s position?

Most analysts don’t have professional playing experience, and they couldn’t make the plays that the pros make look easy, but that doesn’t mean the analysts can’t tap into the perspective of the player at some basic level or that they don’t have a feel for the game.

(You can read more about our work on floor spacing in Chapter 5 of Basketball Analytics: Spatial Tracking, or at the blog here, here, or here.)

2. Basketball analytics are creative

There are no statistics textbooks that tell you how to measure the defensive value of a basketball player. There is no standard way to measure the impact J.J. Redick has from the weak side when Chris Paul and DeAndre Jordan run the pick and roll.

An analyst’s value isn’t in the programming languages she or he knows or in the statistics degrees she or he holds. As mentioned above, an analyst must have an understanding of the game, but beyond that, there is a requirement for creativity at the intersection of a number of disciplines.

When trying to run an objective draft model that projects the future pro impact of prospects, a good analyst will recognize that a statistical regression that identifies statistical markers, which are linked to the success of prospects in the past, might not always predict success in future prospects. Why? One reason is that the game is evolving.

NBA teams have learned to adjust their defense post the abolishment of illegal defenses. As a result, offenses have had to rely more on 3-point shooting to help space the floor. Also, as the pool of available players became more adept at shooting 3s, it became a more efficient shot in and of itself. This opens up the opportunity for 3-point shooting to be a better predictor of a future prospects’ pro impact than it was for past prospects.

But, what exactly are defenses doing differently? What does this mean for team needs in terms of defensive personnel? How are offenses adjusting? Have teams found near optimal strategies for the current set of rules or is there still significant room for growth? If there is significant room for growth, how quickly will NBA teams catch on and adjust? The answers to these questions require an odd blend of basketball knowledge, technical expertise and psychology.

Basketball analytics is not a dry subject. We aren’t simply button pushers that operate fancy software. Good basketball analytics require a great amount of creativity. There is no one approach to a given problem (and as a result, analysts often disagree).

3. The appropriate output is conversations

If you are an NBA general manager, coach, trainer or player, and the majority of the analytics information you get is through numbers in a PDF or portal, you are doing it wrong.

If a team is considering taking Utah center Jakob Poeltl with the 7th overall selection in the 2016 NBA draft, an analyst can provide all sorts of numbers that might help influence that decision. That analyst can tell you all about Poeltl’s college production, box score and other (such as efficiencies as the roll man in pick and rolls). An analyst could approximate how much a comparable player will cost in free agency in 2018, 2019 and 2020.

An analyst could also run numbers on how often NBA teams play a true “big” center in Poeltl’s mold and how efficient those lineups are in comparison to “smaller” groups.

An analyst could run those numbers and lots more, but the most value an analyst could provide is to be part of a conversation with the decision makers of the organization on how Poeltl will and should fit within the organization in the coming years.

As mentioned above, the NBA game is evolving. The modern “pace and space” offenses mean defenses have to “chase.” There is a lot of value in a big man that can switch screens and guard other positions. There is a lot of value today, but there will be even more value in future years as more teams adapt. (For example, you can expect Luke Walton to run a different offense in L.A. than Byron Scott ran.)

Ultimately, the most value an analyst can provide is not in a spreadsheet filled with percentages, but a conversation on how Poeltl’s role in the NBA 3 years from now will be significantly different than what a similar player’s role was 3 years ago.

4. The goal is collaboration

Above, I suggested that the appropriate output for analytics is a conversation. When I referred to a conversation on how the role of the traditional center is changing in the NBA, I meant a true two-way conversation with both sides listening, sharing information, and asking questions.

The conversation shouldn’t start after the analysis is complete. Let’s obliterate the “go-for-coffee” model where the general manager puts in the order, and the analyst makes the run.

I’ve had the pleasure of visiting several front offices in different professional sports. Almost always, I come out wondering, “Where are the white boards?” Coaches use them to scheme. I know sports teams bring them in for draft preparations, but I saw too many offices and too many conference rooms without big writing spaces.

Front office decisions can be challenging puzzles, and often, those puzzles have an undeniably quantitative component. Every offseason, a team has to consider how it will fit returning players, free agent targets, trade targets, and draft picks into a competitive roster and under the cap. So, order some food, gather the bright minds in the organization and head to the boards to brainstorm.

And while we’re on the topic, let me emphasize that communication is part of collaboration, but collaboration requires more than communication. Communication can mean that the general manager asks the analytics team to create a draft model that ranks prospects, and then the analytics team produces a clear presentation of the results.

Collaboration begins with the general manager, scouts, coaches, and analytics team in a room addressing questions like, “What are the short and long term objectives of the organization?” and “What characteristics do we want to prioritize in a prospect?” Collaboration continues with questions like, “How will we help Cheick Diallo continue to develop?” or “What would Buddy Hield’s role within the organization be this year? in 3 years?” or “What do we believe are Henry Ellenson’s defensive limitations?”

The goal of analytics is not a hostile takeover of front offices. Analytics thrive on basketball wisdom, and NBA teams are stacked with tremendous basketball minds. So, let’s collaborate!

I like peanut butter. That doesn’t imply I don’t want jelly. In fact, a sandwich with both is far better than eating either one alone. I like analytics, but that doesn’t mean I wouldn’t drop everything to learn from a great basketball mind like Phil Jackson, Larry Bird or Danny Ainge.

I’ve championed for years that every team should have a member of their analytics group travel with the team. The individual’s role is not to provide information to the team (although that’s a possibility too). Rather, the purpose would be for the analyst to see how the team interacts, the culture in the locker room, how the coaches and players communicate, what the players are focused on from the bench and on the court, the toll the season takes on the players’ bodies and minds, etc. The analyst should travel with the team to learn.

Basketball analytics won’t negotiate a contract with an agent, won’t run the drills in practice, and won’t make the shots in the game. The role of analytics is to support all of the talented basketball people in their current roles, not to replace them. The goal is collaboration, not competition.

Final Thoughts

I’m an analyst, but I’m not a robot. I play, watch, feel, and “smell” the game too. I talk about how a player is a leader, plays with intensity, or otherwise has characteristics “that can’t be measured.” I have gut feelings and instincts. I’m drawn to players that can make the spectacular dunk or block. I’m wowed by players that simply “look good” playing the game.

Analytics can’t come close to telling us everything that’s important in the game of basketball. When analytics does provide good information, it often agrees with traditional thinking. However, sometimes, it suggests that something we believed were true wasn’t actually so, that maybe our years of experience with the game has tinted the lens through which we evaluate performance, and that we may not have completely solved the best way to adjust to the new talents of current players and the recent modifications to the rules.

I’m thankful for the times that analytics have proved me wrong.

College Prospect Ratings (CPR) 2016

By Steve Shea, Ph.D. (@SteveShea33)
April 6, 2016

College Prospect Ratings (CPR) is a formula that uses NCAA players’ on-the-court performance to quantify their NBA potential. Below, I will present the CPR ratings for 105 of the top prospects in the 2016 draft. Before I get to this year’s ratings, I go through some of the aspects of the model and present some of the model’s successes and failures among recent draft classes.

A Performance-based Model

CPR uses each player’s performance on the court (as measured by box-score stats) to approximate his pro potential. There can be a number of reasons a prospect does not perform well on the floor. Many of these are an indication that the prospect will not be a great pro. However, there are other reasons for a lack of performance that may not suggest lesser potential. The most extreme example is an injury that takes the player off the court entirely. This happened for high-profile prospects Kyrie Irving and Nerlens Noel in recent seasons. When an injury takes a player off the court, it’s going to hurt his CPR. In these cases, it’s important to understand that a lower CPR does not reflect a lower talent level for these prospects.

As a performance-based model, CPR will not like players like Skal Labissiere and Cheick Diallo. These players did not perform well this season. Any team that drafts them will be doing so based on indicators besides their on-the-court performance this past season.

Quality of Opposition

CPR does not adjust for quality of opposition. It’s true that certain players face different contexts, but I have not yet seen an appropriate way to measure this context.

Often, quality of opposition is factored into a model by measuring the quality of the teams the individual faced. For example, Andrew Harrison’s Kentucky team in 2014-15 faced tough competition. That Kentucky team had a strength of schedule score (according to of 8.67. In contrast, Steph Curry’s 2008-09 Davidson team did not face very difficult competition. Davidson had a strength of schedule score of -3.33.

In 2008-09 Curry shot 38.7% on 3s. In 2014-15, Andrew Harrison shot 38.3%. Since Harrison’s team faced a tougher schedule, should the model be more impressed by Harrison’s 3P%? I’d argue the exact opposite. Harrison played on a loaded Kentucky team. How often was the defense focused on stopping Harrison? How often was Harrison double-teamed? Almost never.

Curry was the offense in Davidson. Opponents schemed specifically for Curry. Curry may have been playing mid-major competition, but they were draped all over him, and he still managed to shoot an amazing percentage. If we were going to adjust for quality of opposition, I’d argue that Curry’s numbers should be inflated as opposed to Harrisons.

In my experience, using measures such as strength of schedule in a draft model grossly underappreciates the context players like Steph Curry, Damian Lillard and C.J. McCollum played in, and thus, grossly underrates these players.

No Physical Measurements

CPR does not include height, weight, wingspan, or any other physical measures of the prospect. These measurements are important information, but mashing physical characteristics with on-the-court performance into one metric can be difficult to interpret.

Speaking about Providence’s Ben Bentil, a scout said, “He’s not going to be a power forward in the pros. He’s not 6-9. I’m hoping he’s 6-8 with a pair of sneakers on, so that means he’s going to have to be some form of a small forward.”

It sounds like what scouts said about Draymond Green in 2012. “The consensus is that Green won’t be able to guard either forward position because true small forwards will be quicker and true power forwards taller and able to post him and shoot over him.”

First, guarding in the post has far more to do with a player’s footwork, anticipation, awareness, athleticism, grit, length, etc. than it does an inch in his height. Second, the traditional five positions is an antiquated notion. In an NBA where the ability to switch screens is incredibly important, players like Green shouldn’t be labeled “tweeners.” They are versatile.

The scouts can determine whether an inch or two in height is important. The teams can decide to pass on Karl Towns because he doesn’t have a big enough ass. CPR will focus on basketball performance.

CPR’s Successes

CPR has correctly identified numerous 2nd round picks that eventually went on to have pro careers that far exceeded the expected value of a 2nd-rounder. For example, in 2012, Draymond Green (CPR=5.0), Jae Crowder (CPR=4.7) and Will Barton (CPR=6.2) all went in the 2nd round, but CPR rated all 3 in the top 10 for the class. In retrospect, all three would have been great 1st round selections. CPR had Kyle Korver (CPR=5.2) as a first round talent in 2003, and thought Hassan Whiteside (CPR = 14.6) was a ridiculous steal when he went 33rd overall in 2010.

CPR has made the right choice when many teams have missed. Here are just a few examples. In 2009, Minnesota selected Johnny Flynn (CPR=4.3) ahead of Steph Curry (CPR=10.6). In 2010, Golden State took Ekpe Udoh (CPR=4.8) when they could have had Paul George (CPR=8.9). In 2011, Phoenix took Markieff Morris (CPR=2.1), and Houston drafted Marcus Morris (CPR=2.3) right before Indiana drafted Kawhi Leonard (CPR=5.7). In 2012, Cleveland drafted Dion Waiters (CPR=1.8) 4th overall when Damian Lillard (CPR=4.8) went two spots later.

CPR correctly identifies superstar talent. Kevin Durant (CPR=38.6), Anthony Davis (CPR=24.1) and Carmelo Anthony (CPR=14.9) are the top 3 overall scores (among an incomplete run of recent draft classes). The “above 10” class also includes Blake Griffin (CPR=10.1), Tim Duncan (CPR=12.7), DeMarcus Cousins (CPR=10.9), and Kevin Love (CPR=14.5) among others.

CPR’s Failures

CPR doesn’t always find the late-round steals. CPR thought Chandler Parsons (CPR=1.7) was a 2nd round pick in 2011. That’s where he went, but his performance in the NBA has made that pick look great in retrospect.

CPR has missed at the top. CPR had Greg Oden (CPR=10.2) as the 2nd best prospect behind Kevin Durant in 2007. Oden went 1st overall. Unfortunately, Oden’s career was derailed by injuries.

In a poorly rated 2013 class, CPR thought Anthony Bennett was a top 3 pick (CPR=7.6). Bennett went 1st overall, but has been a complete bust thus far in his brief career.

2016 CPR

CPR offers a perspective that differs from traditional scouting. When CPR agrees with scouts, it provides added assurance on the prospect. When CPR disagrees with scouts, it should prompt teams to ask why and to take a second look at the player. With that in mind, here are the 2016 scores.

Brandon IngramDuke9.0
Ben SimmonsLSU8.8
Henry EllensonMarquette8.4
Jamal MurrayKentucky8.2
Jakob PoeltlUtah7.5
Kay FelderOakland7.5
Patrick McCawUNLV7.3
Benjamin BentilProvidence6.7
Dejounte MurrayWashington6.4
Buddy HieldOklahoma6.3
Pascal SiakamNew Mexico St.5.6
Denzel ValentineMichigan State5.6
Grayson AllenDuke5.6
Isaiah WhiteheadSeton Hall5.3
Dillon BrooksOregon5.3
Daniel HamiltonUConn5.1
Marquese ChrissWashington4.7
Tyler UlisKentucky4.5
Diamond StoneMaryland4.4
Malik BeasleyFlorida State4.2
Kris DunnProvidence4.2
Shawn LongLouisana3.9
Bryant CrawfordWake Forest3.9
Melo TimbleMaryland3.7
Jarrod UthoffIowa3.7
Malachi RichardsonSyracuse3.7
David WalkerNortheastern3.6
Domantas SabonisGonzaga3.6
Taurean PrinceBaylor3.3
Bennie BoatwrightUSC3.3
Gary Payton IIOregon St.3.2
Georges NiangIowa St.3.2
Dwayne BaconFlorida State3.2
Kyle WiltjerGonzaga3.2
Joel BolomboyWeber St.3.2
Jaylen BrownCalifornia3.0
Jameel WarneyStony Brook3.0
Dorian Finney-SmithFlorida3.0
Stephen ZimmermanUNLV2.9
Chinanu OnuakuLouisville2.8
Michael GbinijeSyracuse2.8
Brice JohnsonUNC2.8
A.J. HammonsPurdue2.7
Aaron HolidayUCLA2.7
Wade BaldwinVanderbilt2.7
Edmond SumnerXavier2.6
Antonio BlakeneyLSU2.6
Deandre BembrySt. Joseph's2.5
Michael CarreraSouth Carolina2.5
Yogi FerrellIndiana2.4
Ivan RabbCalifornia2.4
Anthony BarberN.C. State2.4
Demetrius JacksonNotre Dame2.4
Chris BoucherOregon2.4
Shake MiltonSMU2.3
Allonzo TrierArizona2.3
Josh HartVillanova2.3
James Webb IIIBoise St.2.2
Jaron BlossomgameClemson2.2
Nigel HayesWisconsin2.1
Isaac CopelandGeorgetown2.1
Malcolm BrogdanVirginia2.0
Monte MorrisIowa St.1.9
Ron BakerWichita St.1.9
Daniel OchefuVillanova1.9
Justin JacksonUNC1.8
Jake LaymanMaryland1.7
Alex CarusoTexas A&M1.7
Malik NewmanMississippi St.1.7
Fred VanVleetWichita St.1.7
Perry EllisKansas1.6
Thomas BryantIndiana1.6
Devin RobinsonFlorida1.6
Damion LeeLouisville1.5
Robert CarterMaryland1.5
Danuel HouseTexas A&M1.5
Mathew Fisher-DavisVanderbilt1.5
Troy WiliamsIndiana1.4
Tim QuartermanLSU1.4
Marcus PaigeUNC1.4
Luke KornetVanderbilt1.3
John EgbunuFlorida1.3
Moses KingsleyArkansas1.3
Sheldon McClellanMiami1.3
Isaiah BriscoeKentucky1.3
Isaiah TaylorTexas1.3
Tyler HarrisAuburn1.2
Caris LeVertMichigan1.2
Deyonta DavisMichigan State1.1
Damian JonesVanderbilt1.1
Zach AugusteNotre Dame1.1
Tyrone WallaceCalifornia1.0
Devin ThomasWake Forest1.0
Wayne SeldenKansas1.0
Shevon ThompsonGeorge Mason1.0
Skal LabissiereKentucky0.9
Kaleb TarczewskiArizona0.8
Alex PoythressKentucky0.7
Tonye JekiriMiami0.7
Sviatoslav MykhailiukKansas0.7
Amida BrimahUconn0.6
Carlton BraggKansas0.5
Prince IbehTexas0.4
Marcus LeeKentucky0.3
Cheick DialloKansas0.3

Updated 2016 CPR

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

College Prospect Ratings (CPR) are an objective measure of an NCAA prospect’s NBA potential. They are generated from a player’s projected position, his years experience in college and the box score production captured in his game logs.

Below, I will present the updated ratings (as of March 29th), which for many players (including Ben Simmons) are their final CPR ratings.

Flashback to 2009

As Steph Curry is destroying the NBA, teams that passed on him in 2009 have to be wondering if they missed something pre-draft that would have provided some insight that Steph would develop into such an exceptional player. In particular, the Wolves, who drafted 2 point guards 5th and 6th overall and right before Curry went to the Warriors, have to wonder if their draft strategy was flawed.

No one saw Steph Curry becoming the all-time elite player that he is today, but there were reasons to suspect that he would be great. A draft model like CPR would have been one of them.

Here are the top 7 picks from the 2009 draft with their CPR scores (excluding Rubio).

1Blake Griffin10.1
2Hasheem Thabeet6.4
3James Harden5.5
4Tyreke Evans7.2
5Ricky Rubio-
6Johnny Flynn4.3
7Steph Curry10.6

On average, about 1 player per draft will rate above 10 in CPR. Without a doubt, a rating above 10 suggests a top 3 pick.   In 2009, both Blake Griffin and Curry rated above 10 with Curry slightly edging out Griffin for the high score. The general rule of thumb is that integer differences matter in CPR, while decimal differences aren’t that significant. Griffin and Curry were rated close enough that the model wouldn’t object to the selection of Griffin over Curry.

In contrast, CPR strongly favors Curry over the other NCAA players drafted above him (especially Johnny Flynn), and in retrospect, the model was right.

It’s also important to note that players can score well in CPR and not develop into solid NBA players, and players can score low and surprise. Hasheem Thabeet’s rating of 6.4 suggest that he should be a mid to late lottery selection and that he could develop into not a star, but a functional center. To date, he hasn’t done it. In contrast, Harden’s rating of 5.5 suggests he’s a late lottery selection, but he’s developed into the type of player that justifies his top 3 selection.

2016 Draft

Below are the updated CPR Rating for 35 of the top NCAA prospects.

Brandon Ingram9.0
Ben Simmons8.8
Henry Ellenson8.4
Jamal Murray8.2
Jakob Poeltl7.5
Dejounte Murray6.4
Buddy Hield6.1
Denzel Valentine5.6
Grayson Allen5.6
Marquese Chriss4.7
Tyler Ulis4.5
Diamond Stone4.4
Malik Beasley4.2
Kris Dunn4.2
Melo Trimble3.7
Domantas Sabonis3.6
Taurean Prince3.3
Jaylen Brown3.0
Stephen Zimmerman2.9
Brice Johnson2.8
A.J. Hammons2.7
Wade Baldwin2.7
Thomas Bryant2.5
DeAndre Bembry2.5
Ivan Rabb2.4
Demetrius Jackson2.4
Nigel Hayes2.1
Malcolm Brogdon2.0
Malik Newman1.7
Caris LeVert1.2
Deyonta Davis1.1
Damian Jones1.1
Wayne Selden1.0
Skal Labissiere0.9
Cheick Diallo0.3

Ben Simmons dropped significantly from his midseason CPR of about 15.6. There are reasons for the drop that aren’t due to him performing poorly necessarily.

First, LSU concluded its season after their last conference tournament game on March 12. This meant that Simmons had less opportunity to demonstrate his pro potential and to improve his CPR score. Had LSU made the NCAA tournament, it’s likely Simmons’ CPR score would be higher.

The second reason for the drop in CPR score is a technical reason and suggests that he was overrated in the midseason report. CPR uses a player’s 3P% in its formula. At season’s end, there are a minimum number of 3-point attempts needed for this 3P% to factor positively into the formula. (We don’t want a player that went 1 for 2 on 3s to profile as an excellent 3-point threat.) Early in the season, I usually don’t require any minimum number of 3-point attempts since CPR is dealing with small sample sizes everywhere. Later in the season, I usually require some prorated minimum, but I neglected to do this with Simmons in the above-reference ratings.

On the season, Simmons was 1 for 3 (33.3%) on 3-pointers. Obviously, that’s not a large enough sample, and so CPR considers Simmons to not have demonstrated college 3-point shooting ability. This is a significant blow to Simmons’ rating.

CPR looks for excellence in statistical production, whether it’s steals, blocks, rebounds, 3P% or elsewhere. The final output’s growth is exponential with regards to the accumulation of “excellence.” So, a player that has not profiled as excellent in anything would only get a small bump in CPR if he were a good 3-point shooter. In contrast, a player that has demonstrated excellence in 6 stats already would get a huge boost for adding 3-point shooting. Ben Simmons has demonstrated excellence in a number of categories. That’s why he has a CPR of 8.8, which is usually good enough to be in the top 3 in the draft class. If he had also demonstrated solid (but not exceptional) 3-point shooting, he would be about a 12 in CPR. In other words, CPR suggests that a Ben Simmons that could shoot 3s would be a better prospect than Blake Griffin was.

Ingram is now the top-rated 2016 prospect.  Jamal Murray, Hield and Poeltl saw significant improvements in their CPR scores since midseason. CPR likes Dejounte Murray as a late first round sleeper. There is nothing in the box score production to suggest Skal Labissiere or Cheick Diallo is going to be a great pro. Finally, CPR suggests Jaylen Brown is overrated by scouts that have him in the top 7.