BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation
This dataset addresses the problem of automated player skill assessment for basketball scouting and development, though it is incremental as it primarily provides new data rather than novel methods.
The authors introduced BASKET, a large-scale basketball video dataset with 4,477 hours of footage covering 32,232 players worldwide, to tackle fine-grained skill estimation across 20 skills, finding that current state-of-the-art video models significantly underperform compared to human baselines.
We present BASKET, a large-scale basketball video dataset for fine-grained skill estimation. BASKET contains 4,477 hours of video capturing 32,232 basketball players from all over the world. Compared to prior skill estimation datasets, our dataset includes a massive number of skilled participants with unprecedented diversity in terms of gender, age, skill level, geographical location, etc. BASKET includes 20 fine-grained basketball skills, challenging modern video recognition models to capture the intricate nuances of player skill through in-depth video analysis. Given a long highlight video (8-10 minutes) of a particular player, the model needs to predict the skill level (e.g., excellent, good, average, fair, poor) for each of the 20 basketball skills. Our empirical analysis reveals that the current state-of-the-art video models struggle with this task, significantly lagging behind the human baseline. We believe that BASKET could be a useful resource for developing new video models with advanced long-range, fine-grained recognition capabilities. In addition, we hope that our dataset will be useful for domain-specific applications such as fair basketball scouting, personalized player development, and many others. Dataset and code are available at https://github.com/yulupan00/BASKET.