Soccer Data
Soccer Data
The dataset contains data
of
soccer games
Each game is a JSON file of soccer logs
Soccer logs describe each ball-related event that occurs on
the field (e.g, passes, shots,
tackles,
…
)
You will have an aggregation of such data (i.e. players
positions, total number of events, depending on the task)
L
ink:
(password for the zip file: AlvaroRecoba19)entshttp://data.d4science.org/ctlg/ResourceCatalogue/soccer_ev
Soccer data
Soccer data
pass
simple pass
accurate
given a player, verify whether the spatial distribution of his events is
Gaussian
1) Distribution of events
Locations visited by a single player on a single game, reported
on a 1x1 grid. Dot size is according to visits number.
given a player, verify whether the spatial distribution
of his events, aggregated by shorter
time slice w.r.t an
entire game,
is Gaussian.
Is there a minimal time threshold where the spatial
distributions are the same as considering the whole
game?
2
) Distribution of events over time
Infer the movements from “close” events and
How this enrichment affects results of points 1-2?
Insigne
Insigne
Hamsik
3
) Fill trajectories
Red line represents locations visited by a player but not retrievable from the dataset.
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Compute the velocity and accelleration of a sequence of
forward events and which players participate and in which
moment (start, middle, end)
Derive a feature about the propensity of a soccer player to
participate into a fast forward sequence of events.
Insigne
Hamsik
4
) Performance velocity
Albiol
Starting from our role detection algorithm (PClustering)
investigate the tendency of players to change role
during the season or during the single game
5
) Versatility of players
Given a set of
requirements
(either technical or spatial)
specified by a user, find
players that better satisfy such
requirements
6
) Querying of players
The dataset contains soccer game logs, each as a JSON file detailing ball-related events on the field. With a focus on soccer events, this dataset offers valuable insights for sports analysts, researchers, and enthusiasts to analyze game dynamics, player performance, and strategic plays. By exploring this rich dataset, users can uncover patterns, trends, and outcomes to enhance their understanding of soccer games. Whether for statistical analysis, machine learning models, or performance evaluation, this dataset provides a comprehensive resource for studying the nuances of soccer competitions.
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Presentation Transcript
Soccer Data The dataset contains data of soccer games Each game is a JSON file of soccer logs Soccer logs describe each ball-related event that occurs on the field (e.g, passes, shots, tackles, ) You will have an aggregation of such data (i.e. players positions, total number of events, depending on the task) Link: http://data.d4science.org/ctlg/ResourceCatalogue/soccer_ev ents (password for the zip file: AlvaroRecoba19) Contacts: paolo.cintia@gmail.com, luca.pappalardo1984@gmail.com
Soccer data pass simple pass accurate
1) Distribution of events given a player, verify whether the spatial distribution of his events is Gaussian Locations visited by a single player on a single game, reported on a 1x1 grid. Dot size is according to visits number.
2) Distribution of events over time given a player, verify whether the spatial distribution of his events, aggregated by shorter time slice w.r.t an entire game, is Gaussian. Is there a minimal time threshold where the spatial distributions are the same as considering the whole game?
3) Fill trajectories Infer the movements from close events and How this enrichment affects results of points 1-2? Insigne Insigne Hamsik Red line represents locations visited by a player but not retrievable from the dataset. See https://arxiv.org/abs/1603.05583 (Analyzing In-Game Movements of Soccer Players at Scale)
4) Performance velocity Compute the velocity and accelleration of a sequence of forward events and which players participate and in which moment (start, middle, end) Derive a feature about the propensity of a soccer player to participate into a fast forward sequence of events. Insigne Albiol Hamsik
5) Versatility of players Starting from our role detection algorithm (PClustering) investigate the tendency of players to change role during the season or during the single game
6) Querying of players Given a set of requirements (either technical or spatial) specified by a user, find players that better satisfy such requirements
