Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting and classifying moments in basketball matches using sensor
  tracked data   Data analytics in sports is crucial to evaluate the performance of single
players and the whole team. The literature proposes a number of tools for both
offence and defence scenarios. Data coming from tracking location of players,
in this respect, may be used to enrich the amount of useful information. In
basketball, however, actions are interleaved with inactive periods. This paper
describes a methodological approach to automatically identify active periods
during a game and to classify them as offensive or defensive. The method is
based on the application of thresholds to players kinematic parameters, whose
values undergo a tuning strategy similar to Receiver Operating Characteristic
curves, using a ground truth extracted from the video of the games.

19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex