ConductVision Sports
Give scouts consistent data on your players
During an academy pilot, ConductVision measures each player the same way every session and builds a player profile the academy can share with scouts.
Even a defender 5 feet away lowered the chance that a shot close to the hoop went in. Drawn to scale on a college court.
The research
Why scouts want video data
NBA teams lack detailed tracking data on college players, because installing tracking systems in more than 300 Division I arenas is impractical.1 Tracking data pulled from college broadcast video predicted which players would reach the NBA better than play-by-play data did.1
One published method, using video from a single moving camera like those used in sports broadcasts, placed players within a foot of their true location 94.5% of the time in early tests.2
Defense
How closely each shot was guarded
In 15,835 college shots with player tracking, even a defender 5 feet away substantially lowered the chance that a shot close to the hoop went in.3 If your scrimmage video covers the full court, a pilot can also measure how closely the shooter was guarded on each shot. As with every other measure, a ConductVision engineer checks a sample by hand.
What the profile contains
Profile contents
- Shooting mechanics and shot-to-shot consistency
- Athletic test results and season trends
- How closely the player was guarded on each shot, where the camera covers the full court
- The dates, drills and camera setup behind every number
References
- Patton A, Scott M, Walker N, et al. (2021). Predicting NBA Talent from Enormous Amounts of College Basketball Tracking Data. MIT Sloan Sports Analytics Conference.
- Johnson N (2020). Extracting Player Tracking Data from Video Using Non-Stationary Cameras and a Combination of Computer Vision Techniques. MIT Sloan Sports Analytics Conference.
- Tenan MS, Rezai AR (2023). Player Tracking Facilitates Valid Causal Inference: The Average Treatment Effect of Defender Proximity on Scoring. MIT Sloan Sports Analytics Conference.
- Needham L, Evans M, Cosker DP, et al. (2021). The accuracy of several pose estimation methods for 3D joint centre localisation. Scientific Reports.
- Mundt M, Colyer S, Wade L, et al. (2024). Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis. Scandinavian Journal of Medicine & Science in Sports.
Plan a recruiting profile pilot
Tell us about your players and the scouts you work with. A ConductVision engineer replies with a proposal.
