SciSports: Learning football kinematics through two-dimensional tracking data

doi:doi:10.57702/gw8ypw3d

SciSports: Learning football kinematics through two-dimensional tracking data

Two-dimensional positional data during entire matches. The data provided to us was two-dimensional positional data during entire matches. We developed methods based on Newtonian mechanics and the Kalman filter, Generative Adversarial Nets and Variational Autoencoders.

BibTex:

@dataset{Anatoliy_Babic_and_Harshit_Bansal_and_Gianluca_Finocchio_and_Julian_Golak_and_Mark_Peletier_and_Jim_Portegies_and_Clara_Stegehuis_and_Anuj_Tyagi_and_Roland_Vincze_and_William_Weimin_Yoo_2024,
    abstract = {Two-dimensional positional data during entire matches. The data provided to us was two-dimensional positional data during entire matches. We developed methods based on Newtonian mechanics and the Kalman filter, Generative Adversarial Nets and Variational Autoencoders.},
    author = {Anatoliy Babic and Harshit Bansal and Gianluca Finocchio and Julian Golak and Mark Peletier and Jim Portegies and Clara Stegehuis and Anuj Tyagi and Roland Vincze and William Weimin Yoo},
    doi = {10.57702/gw8ypw3d},
    institution = {No Organization},
    keyword = {'football', 'kinematics', 'two-dimensional tracking data'},
    month = {dec},
    publisher = {TIB},
    title = {SciSports: Learning football kinematics through two-dimensional tracking data},
    url = {https://service.tib.eu/ldmservice/dataset/scisports--learning-football-kinematics-through-two-dimensional-tracking-data},
    year = {2024}
}