Analysis of player tracking data for American football is in its infancy, since the National Football League (NFL) released its Next Gen Stats tracking data publicly for the first time in December 2018. While tracking datasets in other sports often contain detailed annotations of on-field events, annotations in the NFL's tracking data are limited. Methods for creating these annotations typically require extensive human labeling, which is difficult and expensive. We begin tackling this class of problems by creating annotations for pass coverage types by defensive backs using unsupervised learning techniques, which require no manual labeling or human oversight. We define a set of features from the NFL's tracking data that help distinguish between "zone" and "man" coverage. We use Gaussian mixture modeling and hierarchical clustering to create clusters corresponding to each group, and we assign the appropriate type of coverage to each cluster through qualitative analysis of the plays in each cluster. We find that the mixture model's "soft" cluster assignments allow for more flexibility when identifying coverage types. Our work makes possible several potential avenues of future NFL research, and we provide a basic exploration of these in this paper.
More work has been done in invasion sports regarding player tracking in determining formations of teams. @cite_3 provides an excellent overview of this topic, as well as a detailed overview of the different ways that tracking data has been used in sports over the last decade. We encourage interested readers to visit this paper for more information on this topic. Most of the work that has been done in this field has been to determine how teams as a whole attempt approach the game. In this paper, we attempt to determine how certain players on a team are behaving, in a sport where not much tracking work has been done.
@cite_3
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Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions. To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., Shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league. In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabling temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data. Tracking players in sports videos presents numerous challenges due to weak distinguishing features and unpredictable motion. Considerable work has been done to track players in such videos using a combination of appearance and motion modeling, mostly in continuous streams of video. However, in a broadcast sports video, having advertisements, replays and intermittent change of camera view, it becomes a challenging task to keep track of players over an entire game. In this work, we solve a novel problem of tracking over a sequence of temporally disjoint soccer videos without the use of appearance cue, using a Graph based optimization approach. Each team is represented by a graph, in which the nodes correspond to player positions and the edge weights depend on spatial inter-player distance. We use team formation to associate tracks between clips and provide an end-to-end system that is able to perform statistical and tactical analysis of the game. We also introduce a new challenging dataset of an international soccer game.