Automated performance tracking in basketball is reaching a new level of maturity through rapid advances in computer vision. Swish Basket, a sports technology startup originating from the NBA Launchpad 2026 program, announced on August 24, 2026, the completion of a 3.2 million US dollar pre-seed financing round. The investment round was led by venture capital firm Aristagora VC. The startup was founded by Joel Bar-El, widely known as the co-founder of retail analytics company Trax.
The technical approach of Swish Basket focuses on capturing player performance entirely without physical body sensors or wearable devices. The system utilizes a specialized hardware unit mounted directly above the basketball rim. By combining optical computer vision with LiDAR sensors, it tracks shooting mechanics, ball trajectories, and shooting percentages in real time. An integrated facial recognition engine automatically attributes the collected metrics to individual players on the court.
In parallel with hardware improvements for training and amateur courts, tactical video analysis in elite basketball is also advancing. The Sports Analytics Lab at the University of Florida, in collaboration with Paris-based tracking specialist SkillCorner, introduced an automated classification model for tactical court decisions. Their joint research focuses specifically on quantifying offensive rebounding decisions, known as offensive crashing.
SkillCorner's underlying technology operates without in-stadium optical setups, relying solely on standard television broadcast signals. Operating at 25 frames per second, neural networks reconstruct player coordinates and predict the positions of occluded athletes. The system classifies offensive rebound patterns with 97 percent accuracy and has been directly incorporated into the Florida Gators scouting workflow.
Eliminating the need for dedicated venue hardware and wearable tags expands the scope of talent evaluation and player development. While elite programs historically depended on expensive optical camera arrays installed in arenas, computer vision models now derive positional and tactical insights from standard video streams. Concurrently, modular units like Swish Basket deliver granular tracking metrics directly to practice facilities and grassroots programs.