  Like many team sports, basketball involves two groups of players who engage
in collaborative and adversarial activities to win a game. Players and teams
are executing various complex strategies to gain an advantage over their
opponents. Defining, identifying, and analyzing different types of activities
is an important task in sports analytics, as it can lead to better strategies
and decisions by the players and coaching staff. The objective of this paper is
to automatically recognize basketball group activities from tracking data
representing locations of players and the ball during a game. We propose a
novel deep learning approach for group activity recognition (GAR) in team
sports called NETS. To efficiently model the player relations in team sports,
we combined a Transformer-based architecture with LSTM embedding, and a
team-wise pooling layer to recognize the group activity. Training such a neural
network generally requires a large amount of annotated data, which incurs high
labeling cost. To address scarcity of manual labels, we generate weak-labels
and pretrain the neural network on a self-supervised trajectory prediction
task. We used a large tracking data set from 632 NBA games to evaluate our
approach. The results show that NETS is capable of learning group activities
with high accuracy, and that self- and weak-supervised training in NETS have a
positive impact on GAR accuracy.
