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.
In work done in soccer, @cite_0 have done work in using player tracking data in order to identify a team's playing style. They are able to identify a team just from the player's position data on the field by gathering features regarding reducing entropy of role-specific occupancy maps. In doing so, the authors assign some formation-based roles to individual players, and then create occupancy maps based on these roles that are assigned.
@cite_0
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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. Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach. Abstract Identifying players in soccer videos is a challenging task, especially in overview shots. Face recognition is not feasible due to low resolution, and jersey number recognition suffers from low resolution, motion blur and unsuitable player pose. Therefore, a method to improve visual identification using spatial constellations is proposed here. This method models a spatial constellation as a histogram over relative positions among all players of the team. Using constellation features might increase identification performance but is not expected to work well as a single mean of identification. Thus, this constellation-based recognition is combined with jersey number recognition using convolutional neural networks. Recognizing the numbers on a player’s shirt is the most straight-forward approach, as there is a direct mapping between numbers and players. Using spatial constellation as a feature for identification is based on the assumption that players do not move randomly over the pitch. Players rather follow a tactical role such as central defender, winger, forward, etc. However in soccer, players do not strictly adhere to these roles, variations occur more or less frequently. By learning constellation models for each player, we avoid a categorical assignment of a player to one single tactical role and therefore incorporate each player’s typical behaviour in terms of switching positions. The presented player identification process is expressed as an assignment problem. Here, an optimal assignment of manually labeled trajectories to known player identities is calculated. Using an assignment problem allows for a flexible fusion of constellation features and jersey numbers by combining the respective cost matrices. Evaluation is performed on 14 different shots of six different Bundesliga matches. By combining both modalities, the accuracy is improved from 0.69 to 0.82 when compared with jersey number recognition only. 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.