  Sports analysis has gained paramount importance for coaches, scouts, and
fans. Recently, computer vision researchers have taken on the challenge of
collecting the necessary data by proposing several methods of automatic player
and ball tracking. Building on the gathered tracking data, data miners are able
to perform quantitative analysis on the performance of players and teams. With
this survey, our goal is to provide a basic understanding for quantitative data
analysts about the process of creating the input data and the characteristics
thereof. Thus, we summarize the recent methods of optical tracking by providing
a comprehensive taxonomy of conventional and deep learning methods, separately.
Moreover, we discuss the preprocessing steps of tracking, the most common
challenges in this domain, and the application of tracking data to sports
teams. Finally, we compare the methods by their cost and limitations, and
conclude the work by highlighting potential future research directions.
