Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting play calls in the National Football League using hidden
  Markov models   In recent years, data-driven approaches have become a popular tool in a
variety of sports to gain an advantage by, e.g., analysing potential strategies
of opponents. Whereas the availability of play-by-play or player tracking data
in sports such as basketball and baseball has led to an increase of sports
analytics studies, equivalent datasets for the National Football League (NFL)
were not freely available for a long time. In this contribution, we consider a
comprehensive play-by-play NFL dataset provided by www.kaggle.com, comprising
289,191 observations in total, to predict play calls in the NFL using hidden
Markov models. The resulting out-of-sample prediction accuracy for the 2018 NFL
season is 71.5%, which is substantially higher compared to similar studies on
play call predictions in the NFL.

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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: cs
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn