AI in Basketball: A Wealth of Possibilities
The game of basketball is constantly changing and evolving. The NBA today is nothing like the NBA of 10 years ago. Yet how can the game continue to grow into the future? Many teams, developers, and players have turned to data analysis and artificial intelligence technologies to answer this question.
Noah Basketball is a company founded by a father who only wanted to help his daughter improve her shot. He attached a computer to a camera and put it in his driveway to track and record the angle of the ball’s trajectory for each shot. Today, Noah Basketball has become an established technology used by half of the NBA teams, multiple top college programs, and hundreds of individuals throughout the U.S. Similar technologies such as HomeCourt, which tracks advanced statistics such as launch angle, reaction time, body position, vertical jump, and leg angle, have been used to share this technology with the general public. Making this technology available to everyone is a big step in promoting the evolution of the game.
Yet the uses of such technology in basketball extend beyond shooting. At its highest levels, the outcome of a basketball game depends on things such as team chemistry and fatigue; the game is a complex set of interactions and reactions. To respond to the intricacies and provide more accurate analysis, companies like Stats are developing software that simulates how a team would respond to a given play. To do this, Stats’ deep learning algorithm represents a team as a single unit, instead of 5 individual players. In addition, the software takes into account different styles of play and personnel to make the simulations as accurate as possible.
Machine learning has allowed unprecedented advances for in game analysis, enabling coaches to more accurately predict and quantify player/team performances. Machine learning was quickly found to be the perfect tool for game analysis because it is perfectly suited to identify trends. This makes analyzing the innumerable variations of plays such as pick-and-rolls significantly easier, and as more and more data is collected, programs can more effectively identify variants of these kinds of plays within data that they have never seen before. In essence, machine learning makes real-time analysis and trend identifications possible and efficient.
Of the numerous domains of machine learning and AI, Natural Language Processing (NLP), Computer Vision (CV), and Time Series (TS) might be among the most useful in basketball in the near future. NLP, which tries to replicate the human ability to process words and generate meaning, could play a significant role in the globalization of basketball and international diversification within professional and college-level sports. As more and more recruits, both in college programs and the NBA come from abroad, language barriers can render communication difficult, or even put international players at a disadvantage in negotiations. By enabling instantaneous translations from one language to another, NLP technology could help break language barriers between coaching staffs and players, as well as promote an even more involved international audience.
Computer Vision, which involves computers gaining the ability to gather and analyze visual data to replicate human understanding, has already begun to prove its worth in quickly generating predictions and information to justify crucial coaching decisions. For instance, by analyzing speed and performance data, these programs can identify when a player is fatigued, which represents an indication that a substitution should be made. Such programs also help coaches determine strategic matchups and effective substitution times. As a whole, the programs enable coaches to make more informed judgement calls that could potentially change the outcome of a game.
Finally, Time Series takes chronological data to find patterns, statistics, characteristics, and insights among data points in order to produce predictions or descriptions of data. As a result, Time Series provides endless potential in identifying peaks and valleys in players’ careers and developments. These programs could potentially help produce more accurate predictions about player injuries and team performances.
AI technology and data analysis has already begun to usher the game of basketball into the future. Machine learning will allow for better and faster player development, provide coaches with more precise and useful information, and help audiences experience the game in new, exciting ways. There is no doubt that AI and machine learning will help basketball grow and expand both its potential and global reach in the near future.
Written by Paul Luu Van Lang, Edited by William Turchetta & Alexander Fleiss
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