Nylon Questions: How can we predict performance for incoming NBA players?

Sports analytics is a constantly evolving field and keeping up can be a challenge, especially with so much work being divided between the public and private spheres. As we head into the 2018-19 season, Nylon Calculus wanted to take stock of where we are and of what’s coming next.

This project is a throwback to work Keith Woolner did for Baseball Prospectus nearly two decades ago, and an update to Kevin Pelton’s basketball-specific version from five years ago. Our staff compiled 10 questions whose answering will likely guide the next few years of public analytic work. Not knowing what has been accomplished in private by NBA teams and consulting firms, we focused on questions that could be worked on in the public sphere, wouldn’t have to be answered with existing datasets (we don’t want to imagine the data we have now is all we’ll ever have) and things that would theoretically have an effect on how teams operate on and off the court.

Hopefully, these questions will help spark, refocus, and recalibrate conversations and lead to collaborative progress here at Nylon and everywhere else sports analytic work is being done.

1. How can we predict performance for incoming NBA players?

Predicting future performance in any occupation is hard, especially in professional sports. Evaluating and selecting the best players is the quickest route to success. In the NBA, player selection is more important than in other professional sports leagues because of the way the game is structured.

First, basketball is played with only five people on the court which is the fewest of any major professional sport. Baseball has nine, football and soccer have 11, and hockey plays with six. With fewer players on the court, the best players have more of an impact in basketball compared to other sports. Two All-Stars on a basketball team constitutes an average of about 14 percent of an NBA roster and 40 percent of the players on the court at a time. In the NFL, two All-Pros only makes up about four percent of the roster and 18 percent of the players on the field at a time. Picking better players is more important in the NBA because it has more of an impact.

A second reason for the importance of stars is that basketball players are required to play both offense and defense. Most sports have specified positions for offense and defense, the most obvious is football. Unless your name is Deion Sanders, Bo Jackson, or Charles Woodson, football players rarely play both sides of the ball. Even soccer and hockey have specified offensive and defensive players. In basketball, every player has to contribute on both ends of the court.

If talent is so important what can we do about improving our ability to predict the performance of incoming NBA players? With the growing popularity of analytics in sports and data science in many business operations, our ability to predict performance should only increase. More and more people are realizing the potential of machine learning and working to understand how to implement algorithms to help solve problems. But analytics alone cannot solve this problem, the interweaving of analytics with traditional scouting practices will lead to better results.

Terms such as “Augmented Intelligence” and Human-in-the-Loop machine learning are methods used to improve typical machine learning. The basic premise is to allow complicated algorithms to do most of the work and let humans sort out the rest. Imagine a scenario where an algorithm can take all the data provided in pre-draft tests and performances to select the top prospects. From there NBA scouts and front office personnel can perform their own due diligence by watching film, scouting games, and interviewing players. Combining machine learning with typical scouting efforts can improve efficiency and ability to gain more insights into incoming players.

Using math, machines, and human knowledge to select players is bound to improve the process, but we must emphasize the correct data. To help understand areas of improvement in data collection we can use ideas from Fergus Connolly, a leading sport and human performance expert. Connolly promotes an idea he calls the “Four Coactives of Athletic Performance”. These coactives can be thought of as related and dependent skills that work together to define an athlete’s performance.

The four coactives of physical, psychological, technical, and tactical skills act in unison during an athletic performance and together they influence the performance of teams and players. The physical skills include height, length, strength, and leaping ability. Technical attributes would include a player’s fundamental abilities such as shooting, passing, ball handling, and footwork. Tactical skills, also known as basketball I.Q., typically show up more in traditional scouting and includes on-court awareness, proper positioning, and court vision. Finally, the psychological attributes are concerned with the player’s mind and can include things such as grit, motivation, leadership and even sleep habits.

In most sports, and the NBA is no exception, we focus most of our attention on the physical attributes. Think about it, the NBA combine is a multiple day event promoting the physical attributes of incoming players. Players’ height, vertical leap, quickness, and other skills are measured and analyzed. Players, if they choose to, then perform many “game like” shots from various positions on the floor, giving NBA personnel the opportunity to analyze the technical skills of each player. Finally, the players play in a multitude of pick up style games against their fellow prospects. These game-like situations offer a limited view of the players’ tactical ability. You’ll notice there is no time or method to measure a player’s psychological frame of mind.

One way we could improve the prediction of incoming players is to gather more data from all four coactives. First off, we could benefit from having a more even focus on all four aspects of a player’s ability. We focus on physical skills in abundance followed closely by technical skills for prospects. On the other hand, tactical skills are a vital part of evaluating players but we do not have an easy way to quantify it from an analytics point of view. If there were a way to quantify the tactical skills of a player and how they benefit their team it would go a long way. Tactical skills such as movement without the ball or help defense and how it benefits their team are important parts of the game.

Almost everyone in the NBA will be on nearly the same level in terms of physical, technical, and tactical skills. There might be some players that stand out in certain areas such as lethal 3-point shooting or out of this world wingspan. But for the most part, prospects have at least a common baseline set of skills required to play in the NBA. The difference between a productive long-term career and being forced out of the league in a few years can, in part, be traced to their mental abilities. If there were a way to measure their grit, motivation, ability to handle stress or even their sleep habits we might get a better idea of the types of players that can handle stresses of the long NBA season.

Predicting future performance is hard, especially for 19-22-year-old men. There is so much variability in performance of draft picks but if analytics can make even small incremental improvements then it will be considered a success. By focusing on new ways of quantifying other aspects of the game besides just physical and technical skills we might be able to make enough small improvements. If we make continuous small improvements over the long run, our decisions will eventually improve enough to make a difference in quantifying player selection.