Route identification in the National Football League

Abstract

<jats:title>Abstract</jats:title><jats:p>Tracking data in the National Football League (NFL) is a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. In this paper, we demonstrate how model-based curve clustering of observed player trajectories can be used to identify the routes run by eligible receivers on offensive passing plays. We use a Bernstein polynomial basis function to represent cluster centers, and the Expectation Maximization algorithm to learn the route labels for each of the 33,967 routes run on the 6963 passing plays in the data set. With few assumptions and no pre-existing labels, we are able to closely recreate the standard route tree from our algorithm. We go on to suggest ideas for new potential receiver metrics that account for receiver deployment and movement common throughout the league. The resulting route labels can also be paired with film to enable streamlined queries of game film.</jats:p>

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League

“…The results presented are the first published description of how teams at the professional level are positioning their players during this phase of the game. The visualization in Figure 4 clearly showed that player positioning strategies varied between teams, and repeated structures or formations, were present in these data, similar to other sports [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Teams differed in their choice of player-type combinations in their forward lines at CBs ( Figure 7 ).…”

“…For example, it has been suggested that zone defences in AF were able to blunt the effectiveness of offensive strategies reliant on retaining ball possession [ 8 ] and that specific defensive formations in American football can reduce the offensive team’s ability to advance down the field [ 9 ]. Formations have been studied in soccer [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], American football [ 18 , 19 , 20 , 21 ], and hockey [ 22 ], reflecting the importance of formations in understanding how a sport is played. Additionally, it has been observed that soccer teams commonly switch the type of formation they use after losing a match [ 23 ], suggesting a belief among coaches that formations are a contributing factor to match outcomes.…”

Big ideas in sports analytics and statistical tools for their investigation

“…This annual competition has similarly fueled new research directions in American football and a JQAS special issue on player tracking data in the National Football League (Lopez, 2020). Successful entries and their corresponding publications (Chu et al, 2020; Deshpande & Evans, 2020; Yurko et al, 2020) have launched the careers of several of the most prominent early‐career researchers in sports analytics.…”

Markov-switching decision trees

“…To demonstrate the usefulness of the proposed method, we present a simulation experiment and a case study from sports analytics, namely American football, a sport which has seen a steady rise in statistical analyses in recent years (see, e.g., Yam and Lopez 2019;Yurko et al 2019Yurko et al , 2020Chu et al 2020;Dutta et al 2020;Lopez 2020;Reyers and Swartz 2021). In particular, we model play calls, which can either be a run or a pass.…”