Analytics

This article is republished with permission. It was written by DoSicko and originally appeared on HoopCoach.org.

Football coaches have long analyzed their team’s offensive and defensive efficiencies inside the red zones.  The thinking is simple, “We got inside the 20; we better damn well score.”  Teams have special red zone plays and red zone practice time.  And well they should; the effort to move the ball there and the relatively low number of possessions makes it incumbent on teams to come away with points once the ball is that deep in enemy territory.

Speaking of possessions, one of the inherent difficulties of coaching basketball is getting players to understand the value of a possession.  The high number of possessions in a basketball game at any level lends itself to thinking like this, “Big deal, we’ve got a zillion more possessions in this game; who cares if we turned it over or took a bad shot?”  This lack of understanding is not restricted to young players but is certainly more prevalent among lesser experienced players.  I remember asking players at camps how many possessions there were in a game.  Of course, the number depends on the playing tempo of both teams.  But many campers didn’t even have a clue.  Getting these players to first realize that possessions are finite is a start.

With that said, any device one can use to break the game down into smaller pieces for teaching purposes is extremely valuable.  One such breakdown is an analysis of how one’s team performs in basketball’s “red zone”.

It shouldn’t be difficult to designate a basketball red zone.  For me, it would clearly start with the key and most likely include the low blocks and the high post.  One could add the elbows and the mid-post areas, if one so chooses.  No matter.  It’s your analysis, so you make the call.

Before we get ahead of ourselves, it’s probably wise to dispel the importance of the concept of “points in paint”.  For years, this stat has been quoted like it’s totally conclusive.  Nothing could be farther from the truth.  It merely indicates the number of points scored on field goals in the paint and ignores several other important types of points scored in addition to just field goals:

1. It does not include free throws scored directly on shooting fouls in the paint or at the low block.  For example, over the course of a game, if Team A makes 9 FT’s on shooting fouls “in the paint”, those points need to be added to the “points in the paint” tally.
2. It does not account for the FT’s made after the one-and-one kicks in-that were directly the result of a foul made before the bonus because of offensive penetration in the paint off the bounce or by pass.  For example, let’s say that 3 non-shooting fouls were committed because the ball entered the “red-zone” before the one-and-one kicked in and that the bonus kicked in at 7 fouls.  Theoretically, every “points in the paint” FT scored after the bonus is 3/7 or 42.9% attributable to fouls in the paint before the bonus.  If 12 FT’s were made after the bonus, then 12 X 42.9% is 5 points that needs to be added to the “points in the paint” tally.

So, let’s say that a game stat sheet said Team A scored 24 points in the paint.  If one adds the 9 FT’s scored on “in the paint shooting fouls” and the 5 FT’s scored on one and one FT’s scored after the bonus kicked in, the ACTUAL points in the point tally is 38 points (24+9+5).

Now, let’s go back to “red zone” analysis.  To accurately analyze red-zone efficiency, one must also add 2 and 3 point FG’s that were scored as a result of getting the ball into the “red zone”, as the defense adjusted to the penetration and the offense kicked the ball back out to the perimeter for a shot.  Let’s say that Team A makes 4- 2 point FG’s (8 points) and 5-3 point FG’s (15 points) attributable to red zone penetration, the total number of “red zone” points for Team A is 61 (24+9+5+8+15).

It’s obvious that the points in the paint total of 24 points on the stat sheet and the 61 “red zone” points tell two completely different stories and that “red zone” points is a far more telling stat than “points in the paint”.

To analyze “red zone” efficiency then, one needs to compute “red zone” penetrations and divide the points by penetrations. (perhaps in our hypothetical example 61 points divided by 47 penetrations or 1.290.  Obviously, this number, in of itself, only tells us something when compared to other games.  But the biggest advantage is that it helps coaches first understand patterns of success and failure. The bottom line is that the coach is constantly assessing these two questions, “How and why did we score when we get the ball in red zone and how and why didn’t we score?”  But, perhaps just as important as the success/failure ratio of red zone efficiency is just the simple concept of getting the ball there.  If players are totally cognizant of the importance of getting the ball there, their notion of the importance of each possession will also improve.

Then too, one can assess defensive “red zone” efficiency and the questions become, “How and why did we stop our opponent from scoring or how and why did our opponents score when they get the ball into the red zone.  The simple concept of preventing opponents from getting the ball in the red zone will also serve to help players realize the importance of each defensive possession.

Originally titled “The 40/60/80 Club”

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

Editor’s note from Brian: Yes you have to play to your individual players’ strengths, and some of your individual player’s strengths might be long 2s. The data is presented to stimulate some thought as to what types of skills you want to work on to develop in your players, and how you want to structure your offensive and defensive philosophy and tactics.

These are NBA data and the NBA 3 point arc is constructed differently than college and high school. I still believe that there are applications of this information to those levels.

Analytics have had no more obvious influence on the game of basketball than on shot selection, and the influence extends beyond the suggestion to take more threes.

The best shots are from behind the arc, at the hoop and at the free-throw line.

(The points per shot for free throws is for a 2 shot free throw situation)

Even though high school does not have a “restricted area,” you can still use the visual from college and professional games to get an idea where those shots are taken, even on a court without that marking.

As a result, NBA teams are taking half as many mid-range jumpers as they did 20 years ago. And there’s no sign of that trend slowing down.

If teams are strategizing to take more shots at the hoop, from three and from the free-throw line, then it’s only natural that they should want the players that are the most efficient from those regions.

We introduce the 40/60/80 Club, an exclusive group of go-to NBA scorers that shoot better than 40% from three, 60% from the restricted area and 80% from the free-throw line.

Editor’s note from Brian: Just an idea that you might be able to apply to your players. In my opinion, looking at overall field goal percentage (combining 2s and 3s as field goal attempts is not a very helpful statistic. Breaking shots into restricted area, other 2’s, 3’s and free throws give you a much better idea of where you are strong and where you need to improve–both from an offensive and defensive point of view.

Dr. Shea has co-authored two books on the subject of utilizing analytical data in basketball. You can find out more about both books by clicking on the links or images of the book covers below.

Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win

Basketball Analytics: Spatial Tracking

By Stephen Shea, Ph.D. (@SteveShea33) and published on his blog:  Basketball Analytics.  You can find out more about Dr. Shea and his work in the field of Basketball Analytics below the article.

Editor’s Note from Brian The purpose of this post is to offer some ideas about applying these analytics to what you currently do and improve how you evaluate your offensive execution.  You probably won’t be able to apply all of this, but hopefully you can use parts of it to help your players understand and measure how you want your team to play on offense.  I have included screenshots of part of the tables as a way to add context to the points that Dr. Shea makes.

If you want to view the entire article including sortable data tables for all NBA teams, you can click here: What if Your Team had Houston’s Shot Selection I realize that the shorter distances for different 3 point shots does not apply to high school and college, but I also believe that there are specific spots on the arc that your players shoot better from, or at least favor as spots for 3 point attempts.

And, of course, we have to coach to our player’s strengths, but if we develop and play players whose strength is mid range, then we will be limited in the effectiveness of our offense, just as we would be limiting our offense if our primary ball handler could only dribble towards their strong hand.

I also realize that free throw shooting is even more efficient than field goal shooting, and that you are going to get to the line more frequently by attacking the basket. That has to be factored in. Free throw scoring efficiency is tied to the ability of the free throw shooter. A 70% free throw shooter will score, on average, 1.40 points per 2 shot free throw possession, which is better tan any of these. To me, that still points out that paint shots outside the restricted area and mid range shots are the worst ways to attempt to score.

As always, my goal is to provide food for you and your staff to use to work to improve your program. I do believe that analytics have a place in the decision making processes for basketball coaches, but that it is not the only tool to use.

End of Editor’s Note

What if your team had Houston’s shot selection?
Stephen Shea, Ph.D.

There are 5 major shooting zones on an NBA court: the restricted area (at the hoop), the paint (but not in the restricted area), mid-range, corners, and above the break. Among the zones, the paint and mid-range shots are, by far, the least efficient.

One team has leveraged this information to design a strategy that attempts to greatly reduce paint and mid-range shots. This season, just 7.6% of Houston’s field goal attempts have come from the paint and just 5.8% have come from mid-range. Both percentages are league lows.

Houston’s shot selection is far from the norm. While mid-range attempts are on the decline, many teams are still taking 20% or more of their shots from this inefficient region. What if they didn’t?

As a thought exercise, let’s suppose every team had Houston’s shot selection. We’ll keep each team’s field goal percentages from each zone the same. For example, Sacramento has shot 36.6% from mid-range this season and taken 28.1% of their shots from that region. We’ll assume Sacramento maintains their 36.6% but that they only take 7.6% of their FGA from mid-range (Houston’s percentage).

We’ll measure the team’s shooting efficiency by points per shot (PPS). The table below contains each team’s current PPS, their hypothetical PPS with Houston’s shot selection (labeled NewPPS), the difference between the hypothetical and actual PPS, and the additional points per game the team would score with Houston’s shot selection.

Shot selection can impact shooting efficiency, and so, it wouldn’t be fair to suggest that a team could radically alter their shot selection tomorrow and maintain their shooting efficiencies from each zone. Still, when we see that a team like Sacramento would produce 12.6 more points per game with their current field goal percentages and Houston’s shot selection, we have to ask, why aren’t they trying?

This post was written by Andy Rochon. He is the boys JV coach and Varsity Assistant at Ocoee High School in west Orlando. His site is about his system of Symmetrics. He has several other posts on the site with ideas for quantifying ball movement and player decision making. Click the link to see more of his concepts: Symmetrics

Editor’s Note from Brian. I realize that many coaches reading this blog do not have the time or the resources to apply all of these ideas. However, I hope that you might be able to apply some of the concepts on a smaller scale. If you have a player or two that you are trying to get to react quicker or be more aware, you could apply some of these ideas to help them. Or, you could time just a few possessions just to identify where the sweet spot is for your team.

**Pete Carril said, “be good at all things that happen a lot.” I truly believe that is the cornerstone of what Symmetrics is about!**

Now it is time to take a look into HOW we track Tempo. The 3 parts of Tempo are Pace(total number of possessions), Player Movement(average speed at which players cut/sprint while performing decision/action), and Average Length of each possession on offense/defense). When I was an assistant at State College of Florida I had one player that these three numbers helped tremendously, especially on the defensive side of the ball. It was a neat process to watch him go from being inactive/disinterested to a bouncy/active defender who averaged 2.8 blocks per game at 6’6.

Here is how you can apply the numbers to your coaching philosophy/game plan. Knowing the Pace or how many possessions your team creates in a game is important. This lets you know if you are getting enough offensive opportunities. Opportunities are vital for teams, while the amount of good opportunities may be important for others. Either way my colleagues has expressed know how many possession their team averages a game is a stat they want to know.

Next, is Player Movement and this is a little bit tougher to track each instance an action/decision is committed. This is why we have each player perform 5 trials of a given action/decision, add each individual score and divide people total number of teammates who performed the action/decision, and that is how we get a verage speed of each action/decision. These numbers are very similar to average speed numbers the NBA is tracking on the SportVu cameras(average speed while sprinting up/down floor, total miles ran, ect). These numbers let a coach know if their players are are putting in the EFFORT or playing with ENERGY you want your players to play with.  In Symmetrics we call this the   JUICE INDICATOR and will be solely based on how much energy you expend while playing the game(aka the faster you move the more energy you produce).

The last and most important part(my opinion)to tracking Tempo is the average time spent on offense vs average time spent on defense. Why is this important you might ask? Back in ‘07 when the Phoenix Suns 7 seconds or less uptempo style was popular it was important for them know how many times they got a shot in 7 seconds or less, how many possessions did this take place, and stats showing how efficiently they performed in these situations(makes/misses, turnovers, fouls, ect). Knowing that information allows the staff to know whether or not to tell them to perform the action more or less based on stats look. For example, when I was at SCG our offensive style was similar to the Spurs. We wanted to come up the floor, move the ball, drive and kick to get 3 or best shot in the paint. If our time on offense was only averaging 10 seconds I would urge our players to make a few more passes, foot fight with their defender when they catch, or enter the ball to the post. All three of these decisions that turn to actions allowed us to possess the ball just a few seconds longer. Which gives more time for the defense to breakdown or make a mistake. May seem minor, but ball/player movement makes all the difference possession by possession when wearing down an opponent. Defensively, to wear down opponents we would really get out and put pressure on the ball. Our secondary defenders aka help side would sit in gaps to protect against dribble penetration(pack line principles w/pressure on the ball). This didn’t allow for ball handle to be comfortable and make direct line passes, and it didn’t up as many uncontested shots because we closed out to everything like pack line teams do.

Here is how Raheem and I would go over these numbers. On the bus ride home(it’s juco so usually 2+ hour trips)we would sit and just talk hoops. He would tell me about his life living between Canada and the United States, the players he has had the opportunity to play with and against, as well as his overall knowledge of the game. Somewhere in between him talking about his Juco stint in Casper, Wyoming he said something that I’ll never forget. Raheem said, “Coach I feel like in my head I know what to do, but I can’t get my teammates to do it or sometimes I mess it up.” To me this is a kid who is trying to be a leader for a team full of players who were trying to get theirs. This is when we started talking about Player Decision-Making.

After, each game we got on the bus and sat down and talked about the good, the bad, and the downright horrific!! Yes, there were times where we would have to agree to disagree. That is the beauty of Symmetrics, it is all about tracking your decisions/actions and you do not have to change if you do not want to(Your playing time may though). A typical night riding home on a big charter bus we would watch college basketball on our phones and I would go through the team report. For example, I’d say, “Heem as a team we were 1st help defender was Late to help on dribble penetration 18 times, we gave up 14 points on 4/6 shooting(2 3pt FG), they drew 5 fouls, and our 2nd help wasn’t there 11 of 18 times our 1st help was late. I’d explain to him what a contested shot looked like, how many he attempted vs how many he made, and other actions/decisions he could do more of to score a few extra basketball because of his great athleticism(Offensive Rebounds/Tip ins).

Now I’m telling you we did this almost after every game, except for the really hard loses or the few major wins we had that year. He truly changed as a player, not because I am a genius, but because he was willing to listen and grow as a player. I wanted to observe to see how well he would understand terminology, how quickly he was able to apply the information, and Symmetrics was the reason he became aware enough to ACT instead of REACT to situations on the floor.

Over the last three seasons I have tracked Tempo in this way and have found it extremely useful for our coaching staff and players.

Now I want to share Symmetrics with as many coaches possible who are looking to have an immediate impact on their team! This is not to reinvent your philosophy or revamp your style of play. Symmetrics is just to categorize and organize your philosophy into simple cues, in your terminology, that players and your coaching staff can easily remember and apply to practice or games right away!

Thanks again for reading this if you stuck around until the end. Spend some time this weekend thinking about which decisions/actions you value or do not value. Then begin plugging them into the various Risk categories. Is the decision which leads to an action low in risk and high in reward? Then it belongs in the Low Risk category. Is the decision high in risk and low in reward? Then it needs to be added to the High Risk category. If the action is something that depends on the player’s skill set, time/situation, and/or positive only outweighs negative by a little or vise versa then it belongs in the Mid Risk + or – category pending on which one carries more weight.(ie: 50/50 ball= Mid Risk+ because there are more positive outcomes by diving for a loose ball instead of trying to dribble a loose ball).

About the Author of This Post

This post was written by Andy Rochon. He is the boys JV coach and Varsity Assistant at Ocoee High School in west Orlando. His site is about his system of Symmetrics. He has several other posts on the site with ideas for quantifying ball movement and player decision making.

This article was written by By Stephen Shea, Ph.D and published on his blog: Basketball Analytics. You can find out more about Dr. Shea and his work in the field of Basketball Anayltics at the end of this article.

This is an edited version of the original article. For the entire article, click on: The Defensive 3 Point Revolution.

Even though the data is derived from the NBA, I feel that the implications are similar for high school and college. I know that coaches cannot get the data for your teams, but I feel the NBA results are worth considering as you work on your defense.

The Defensive 3 Point Revolution

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.

Red Auerbach once proclaimed, “Basketball 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 hand offs 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.

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.

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.

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 minus the defense’s stretch. The teams with the smallest difference are the ones that help off the perimeter the least. Here are the results

Rank Team Offense Spacing Minus Defensive Stretch (sq. ft)
1 CHI 273.1
2 POR 275.7
3 CLE 275.7
4 SAS 281.0
5 GSW 282.8
6 WAS 283.8
7 UTA 286.6

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.

he 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.

Knight’s gamble 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 Basketball-Reference.com).

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. he four teams that collapsed the most were Milwaukee, Miami, Atlanta, and Philadelphia.

Rank Team Offense Spacing Minus Defensive Stretch (sq. ft)
30 MIL 326.0
29 MIA 306.0
28 ATL 305.1
27 PHI 304.3
26 TOR 302.9
25 OKC 299.4

The teams that collapsed the most (produced the highest Offense Spacing Minus Defensive Stretch) also gave up more C&S 3PA.

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 Offense Spacing Minus Defensive Stretch (sq. ft) 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 spacing minus stretch outperformed those in the middle. The bottom 8 teams in defensive rating were ranked between 13 and 26 in spacing minus stretch (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 spacing minus stretch < 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 spacing minus stretch >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?

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.

About the Author, Stephen Shea

Stephen Shea is an associate professor of mathematics at Saint Anselm College in Manchester, NH. He earned a Ph.D. in mathematics from Wesleyan University in Middletown, CT, and a B.A. in mathematics from The College of the Holy Cross in Worcester, MA. His mathematical expertise and publication record is in the areas of probability, statistics, dynamical systems, and combinatorics. For years, he has been applying his abilities in these areas to study professional and amateur sports. Stephen is a managing partner of Advanced Metrics, LLC, a consulting company that provides analytics solutions to basketball and hockey organizations. At Saint Anselm College, he runs a course on sports analytics. His sport writing has been featured in the Journal of Quantitative Analysis in Sports, Psych Journal, the Expert Series at WinthropIntelligence.com, and the Stat Geek Idol Competition for TeamRankings.com. In 2013, Stephen coauthored the book, Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win, and co-created the accompanying blog BasketballAnalyticsBook.com. In 2014, he authored Basketball Analytics: Spatial Tracking.