  Classifying player actions from soccer videos is a challenging problem, which
has become increasingly important in sports analytics over the years. Most
state-of-the-art methods employ highly complex offline networks, which makes it
difficult to deploy such models in resource constrained scenarios. Here, in
this paper we propose a novel end-to-end knowledge distillation based transfer
learning network pre-trained on the Kinetics400 dataset and then perform
extensive analysis on the learned framework by introducing a unique loss
parameterization. We also introduce a new dataset named SoccerDB1 containing
448 videos and consisting of 4 diverse classes each of players playing soccer.
Furthermore, we introduce an unique loss parameter that help us linearly weigh
the extent to which the predictions of each network are utilized. Finally, we
also perform a thorough performance study using various changed
hyperparameters. We also benchmark the first classification results on the new
SoccerDB1 dataset obtaining 67.20% validation accuracy. Apart from
outperforming prior arts significantly, our model also generalizes to new
datasets easily. The dataset has been made publicly available at:
https://bit.ly/soccerdb1
