Advanced Soccer Analytics: Insights and Performance Analysis
Explore a comprehensive dataset of soccer events to analyze player performance, spatial distribution, velocity, and more. Enhance your understanding of player roles, movements, and tendencies in the game. Uncover valuable insights to optimize strategy and improve player performance.
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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
