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 TheUndefeated.com 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.

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3 Comments

  1. Anthony Yang

     /  August 1, 2016

    Hello Mr. Shea,

    My name is Anthony Yang. I am 17 years old and from Saint Paul, Minnesota. I read your article on basketball analytics and I was very impressed by it. I have always been very interested in learning more about basketball analytics ever since I heard of it.

    At my school every senior has to do a project. I wanted to do a project on basketball analytics using my own school’s basketball team.

    Do you know anyone from the twin cities here in Minnesota that can help me?

    Reply
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