Experience Stern | Faculty & Research

I attended an interesting set of talks at the most recent KDD conference in Halifax, Canada, on the use of data-driven analytics in sports. A couple of years ago, I supervised a student project with an NBA basketball team using box scores and other ancillary data for making decisions about how to match up against an opposing team, especially late in the season when game outcomes can be critical and coaches must conserve their resources. While play-by-play data existed, it seemed like a poor representation of the full richness associated with a possession, ignoring important movement details such as intent, opportunity, risk, execution, etc. I lamented that computers were unable to transform a raw video feed automatically into a structured database consisting of useful metrics such as ‘‘counter attacking speed’’ or ‘‘defensive fallback effectiveness,’’ or ‘‘risk to reward ratio’’ associated with passes and ball possessions. That has changed, and the implications are serious.

With the rapid advances of artificial intelligence (AI) in perception, machines are able to watch and analyze games at a fine-grained level that is virtually impossible for humans to record and process. Machines can now track players and calculate useful metrics without the need for laborious human labeling. They can even estimate the effectiveness of every pass along multiple dimensions, such as the risk-to-potential reward ratio associated with a pass, the ‘‘pressure’’ being applied by the opposing team before the pass, and so on. The machine is also able to infer things, like the intent of each pass, automatically by analyzing the positions and velocities of all players on the field. It can also automatically label such passes as ‘‘completed,’’ ‘‘intercepted,’’ ‘‘scored,’’ etc.

Read the full article as published by Big Data. 

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Vasant Dhar is a Professor of Information Systems.