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Predicting wide receiver trajectories in American football

Namhoon Lee, Kris M. Kitani
2016 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)  
Predicting the trajectory of a wide receiver in the game of American football requires prior knowledge about the game (e.g., route trees, defensive formations) and an accurate model of how the environment  ...  We validate our technique on a video dataset of American football plays.  ...  Acknowledgement This research was supported in part by the JST CREST grant and Jeongsong Cultural Foundation. We thank Wen-Sheng Chu for valuable comments on the manuscript.  ... 
doi:10.1109/wacv.2016.7477732 dblp:conf/wacv/LeeK16 fatcat:wvkedejutbgnnp2hk5j3xisoeu

Development and Verification of a Highly Accurate and Precise Passing Machine for American Football

Bernhard Hollaus, Christian Raschner, Andreas Mehrle
2020 Proceedings (MDPI)  
Passing a ball is a central aspect in the game of American Football. However, current passing machines do not fulfill the high quality standards for adequate catch training.  ...  Additionally, a pass prediction model was developed to determine where the pass went to and which height to catch it by least squares fit of 225 sample points with a second order function (R2>0.99).  ...  Acknowledgments: The authors are grateful to Jan Eisenbraun, the main pass receiver during the testing phase of the machine.  ... 
doi:10.3390/proceedings2020049094 fatcat:5jekwsoejrgglpxkdnqqgeaaza

A Topic Model Approach to Representing and Classifying Football Plays

Jagannadan Varadarajan, Indriyati Atmosukarto, Shaunak Ahuja, Bernard Ghanem, Narendra Ahuja
2013 Procedings of the British Machine Vision Conference 2013  
We address the problem of modeling and classifying American Football offense teams' plays in video, a challenging example of group activity analysis.  ...  We define a football play as a unique combination of player trajectories. We develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model.  ...  An offense consists of 11 players of which six play a critical role: quarterback (QB), running back (RB), wide receiver left (WR-L), wide receiver right (WR-R), tight end left (TE-L) and tight end right  ... 
doi:10.5244/c.27.64 dblp:conf/bmvc/VaradarajanAAGA13 fatcat:puowirsnunh5lj3wwo57upzcey

Using Opponent Modeling to Adapt Team Play in American Football [chapter]

Kennard R. Laviers, Gita Sukthankar
2014 Plan, Activity, and Intent Recognition  
To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator  ...  Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this  ...  The views expressed in this document are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government.  ... 
doi:10.1016/b978-0-12-398532-3.00013-0 fatcat:obft3365vrhvnkmi6ho75ycpwm

Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football with Tracking Data [article]

Ronald Yurko, Francesca Matano, Lee F. Richardson, Nicholas Granered, Taylor Pospisil, Konstantinos Pelechrinis, Samuel L. Ventura
2019 arXiv   pre-print
In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level.  ...  In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data.  ...  Framework for Continuous-Time Modeling in American Football As the first step towards building a continuous-time valuation framework for American football, we model the expected end-of-play yard line.  ... 
arXiv:1906.01760v3 fatcat:m6soqaizizfaxmnqny2qcc2gem

Play type recognition in real-world football video

Sheng Chen, Zhongyuan Feng, Qingkai Lu, Behrooz Mahasseni, Trevor Fiez, Alan Fern, Sinisa Todorovic
2014 IEEE Winter Conference on Applications of Computer Vision  
This paper presents a vision system for recognizing the sequence of plays in amateur videos of American football games (e.g. offense, defense, kickoff, punt, etc).  ...  The system is aimed at reducing user effort in annotating football videos, which are posted on a web service used by over 13,000 high school, college, and professional football teams.  ...  Figure 2 . 2 Two frames showing two wide receivers (in red box) running from right to left.  ... 
doi:10.1109/wacv.2014.6836040 dblp:conf/wacv/ChenFLMFFT14 fatcat:vgqcrcnlovbebcxs7brxvfdjoy

Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data

Ronald Yurko, Francesca Matano, Lee F. Richardson, Nicholas Granered, Taylor Pospisil, Konstantinos Pelechrinis, Samuel L. Ventura
2020 Journal of Quantitative Analysis in Sports (JQAS)  
In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level.  ...  In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data.  ...  Framework for continuous-time modeling in American football As the first step towards building a continuous-time valuation framework for American football, we model the expected end-of-play yard line.  ... 
doi:10.1515/jqas-2019-0056 fatcat:m7pfysrndjdtfevdtqi76jja3i

The physics of kicking a football

Peter J. Brancazio
1985 The Physics Teacher  
An American football has a roughly ellipsoidal shape. It is 11.1 in. (28.2 cm) in length along its major axis and has a maximum diameter of 6.8 in. (17.3 cm).  ...  T he game of American football is filled with illustrations of the principles of physics.  ... 
doi:10.1119/1.2341866 fatcat:vujoqw2rozbprgpattuahwypuu

Unsupervised Methods for Identifying Pass Coverage Among Defensive Backs with NFL Player Tracking Data [article]

Rishav Dutta, Ronald Yurko, Samuel Ventura
2020 arXiv   pre-print
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.  ...  Importantly, this does not mean that the patterns of motion of defensive backs in man coverage will follow well-defined trajectories, as is the case for wide receivers.  ... 
arXiv:1906.11373v3 fatcat:ashnogljffgidaevdgbisfr6xi

Game Plan: What AI can do for Football, and What Football can do for AI

Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steel, Pauline Luc, Adria Recasens (+24 others)
2021 The Journal of Artificial Intelligence Research  
We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for  ...  The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision  ...  For example, predictive models of football players at the trajectory-level (H. H. M. Le et al., 2017; C.  ... 
doi:10.1613/jair.1.12505 fatcat:klaw7alkzrhp7kd7ebdefpxh7e

Spatio-Temporal Analysis of Team Sports

Joachim Gudmundsson, Michael Horton
2017 ACM Computing Surveys  
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  ...  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  ...  This is often the case in sports where there are practical difficulties in capturing player trajectories, such as rugby and American football.  ... 
doi:10.1145/3054132 fatcat:h44jjaiw6ngcfnd7lmb7c4taxq

Game Plan: What AI can do for Football, and What Football can do for AI [article]

Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adria Recasens (+24 others)
2020 arXiv   pre-print
We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for  ...  The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision  ...  For example, one might train a trajectory prediction model on league data (e.g., as done in H.  ... 
arXiv:2011.09192v1 fatcat:wydr55wiz5h6lhuhhjajho4k3i

The collection, analysis and exploitation of footballer attributes: A systematic review

Edward Wakelam, Volker Steuber, James Wakelam
2022 Journal of Sports Analytics  
Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes.  ...  There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers.  ...  of South American, African, European, Asian and North American footballers.  ... 
doi:10.3233/jsa-200554 fatcat:kerf3jkppnhh3nhmyepvxhdpja

Automated Player Selection for Sports Team using Competitive Neural Networks

Rabah Al-Shboul, Tahir Syed, Jamshed Memon, Furqan Khan
2017 International Journal of Advanced Computer Science and Applications  
This will help decide which team of 11 football players is best to play against a particular opponent, perform prediction of future matches and helps team management in preparing the team for the future  ...  We argue in favour of a semi-supervised learning approach in order to quantify and predict player performance from team data with mutual influence among players, and report win accuracies of around 60%  ...  Some play narrow, some wide -some use a diamond in the midfield, while some use a double-pivot.  ... 
doi:10.14569/ijacsa.2017.080859 fatcat:4zfujgzwu5gzff6pyhxmp7n6s4

Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Bernhard Hollaus, Sebastian Stabinger, Andreas Mehrle, Christian Raschner
2020 Sensors  
In American football many things are logged, but there is no wearable sensor that logs a catch or a drop.  ...  Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.  ...  American football training is multifaceted in its exercises. Offensive plays in an American football match can be executed in various ways.  ... 
doi:10.3390/s20236722 pmid:33255462 fatcat:dtptyohmtrfv7b3auqvqhfj764
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