2 datasets found
  1. f

    Approaches and included features.

    • plos.figshare.com
    xls
    Updated Oct 30, 2024
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    Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova (2024). Approaches and included features. [Dataset]. http://doi.org/10.1371/journal.pone.0312278.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 30, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In association football, predicting the likelihood and outcome of a shot at a goal is useful but challenging. Expected goal (xG) models can be used in a variety of ways including evaluating performance and designing offensive strategies. This study proposed a novel framework that uses the events preceding a shot, to improve the accuracy of the expected goals (xG) metric. A combination of previously explored and unexplored temporal features is utilized in the proposed framework. The new features include; “advancement factor”, and “player position column”. A random forest model was used, which performed better than published single-event-based models in the literature. Results further demonstrated a significant improvement in model performance with the inclusion of preceding event information. The proposed framework and model enable the discovery of event sequences that improve xG, which include; opportunities built up from the sides of the 18-yard box, shots attempted from in front of the goal within the opposition’s 18-yard box, and shots from successful passes to the far post.

  2. f

    Event sequences and their mean xG.

    • plos.figshare.com
    xls
    Updated Oct 30, 2024
    Share
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    Click to copy link
    Link copied
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    Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova (2024). Event sequences and their mean xG. [Dataset]. http://doi.org/10.1371/journal.pone.0312278.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 30, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In association football, predicting the likelihood and outcome of a shot at a goal is useful but challenging. Expected goal (xG) models can be used in a variety of ways including evaluating performance and designing offensive strategies. This study proposed a novel framework that uses the events preceding a shot, to improve the accuracy of the expected goals (xG) metric. A combination of previously explored and unexplored temporal features is utilized in the proposed framework. The new features include; “advancement factor”, and “player position column”. A random forest model was used, which performed better than published single-event-based models in the literature. Results further demonstrated a significant improvement in model performance with the inclusion of preceding event information. The proposed framework and model enable the discovery of event sequences that improve xG, which include; opportunities built up from the sides of the 18-yard box, shots attempted from in front of the goal within the opposition’s 18-yard box, and shots from successful passes to the far post.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova (2024). Approaches and included features. [Dataset]. http://doi.org/10.1371/journal.pone.0312278.t002

Approaches and included features.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Oct 30, 2024
Dataset provided by
PLOS ONE
Authors
Ishara Bandara; Sergiy Shelyag; Sutharshan Rajasegarar; Dan Dwyer; Eun-jin Kim; Maia Angelova
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

In association football, predicting the likelihood and outcome of a shot at a goal is useful but challenging. Expected goal (xG) models can be used in a variety of ways including evaluating performance and designing offensive strategies. This study proposed a novel framework that uses the events preceding a shot, to improve the accuracy of the expected goals (xG) metric. A combination of previously explored and unexplored temporal features is utilized in the proposed framework. The new features include; “advancement factor”, and “player position column”. A random forest model was used, which performed better than published single-event-based models in the literature. Results further demonstrated a significant improvement in model performance with the inclusion of preceding event information. The proposed framework and model enable the discovery of event sequences that improve xG, which include; opportunities built up from the sides of the 18-yard box, shots attempted from in front of the goal within the opposition’s 18-yard box, and shots from successful passes to the far post.

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