9 datasets found

  1. Stats-Bomb Football Data

    • kaggle.com
    Updated Mar 12, 2025
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    Saurabh Shahane (2025). Stats-Bomb Football Data [Dataset]. https://www.kaggle.com/datasets/saurabhshahane/statsbomb-football-data/versions/1045
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Saurabh Shahane
    Description

    StatsBomb Open Data

    StatsBomb are committed to sharing new data and research publicly to enhance understanding of the game of Football. We want to actively encourage new research and analysis at all levels. Therefore we have made certain leagues of StatsBomb Data freely available for public use for research projects and genuine interest in football analytics.

    StatsBomb are hoping that by making data freely available, we will extend the wider football analytics community and attract new talent to the industry. We would like to collect some basic personal information about users of our data. By giving us your email address, it means we will let you know when we make more data, tutorials and research available. We will store the information in accordance with our Privacy Policy and the GDPR.

    Whilst we are keen to share data and facilitate research, we also urge you to be responsible with the data. Please register your details on https://www.statsbomb.com/resource-centre and read our User Agreement carefully.

    Terms & Conditions By using this repository, you are agreeing to the user agreement.

    If you publish, share or distribute any research, analysis or insights based on this data, please state the data source as StatsBomb and use our logo, available in our Media Pack.

    Getting Started The data is provided as JSON files exported from the StatsBomb Data API, in the following structure:

    Competition and seasons stored in competitions.json. Matches for each competition and season, stored in matches. Each folder within is named for a competition ID, each file is named for a season ID within that competition. Events and lineups for each match, stored in events and lineups respectively. Each file is named for a match ID. StatsBomb 360 data for selected matches, stored in three-sixty. Each file is named for a match ID. Some documentation about the meaning of different events and the format of the JSON can be found in the doc directory.

  2. StatsBomb: Events data WSL and La Liga (2020/2021)

    • kaggle.com
    Updated Jan 16, 2023
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    Aleksandr Bormotin (2023). StatsBomb: Events data WSL and La Liga (2020/2021) [Dataset]. https://www.kaggle.com/datasets/alexandrbormotin/statsbomb-events-data-wsl-and-la-liga-20202021
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 16, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aleksandr Bormotin
    Description

    This is StatsBomb events data for the following matches:

    • 131 games of Women's Super League 2020/2021
    • 35 games of La Liga 2020/2021

    Quote from StatsBomb:

    Whilst we are keen to share data and facilitate research, we also urge you to be responsible with the data. Please register your details on https://www.statsbomb.com/resource-centre and read our User Agreement carefully. By using this repository, you are agreeing to the user agreement. If you publish, share or distribute any research, analysis or insights based on this data, please state the data source as StatsBomb and use our logo.

  3. f

    3-Cluster Outcome

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    • +1more
    xlsx
    Updated Oct 29, 2024
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    Iyán Iván-Baragaño (2024). 3-Cluster Outcome [Dataset]. http://doi.org/10.6084/m9.figshare.27325734.v1
    Explore at:
    xlsxAvailable download formats
    Unique identifier
    https://doi.org/10.6084/m9.figshare.27325734.v1
    Dataset updated
    Oct 29, 2024
    Dataset provided by
    figshare
    Authors
    Iyán Iván-Baragaño
    License

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

    Description

    Pass Eventing - Cluster Outcome

  4. h

    xg-thesis

    • huggingface.co
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    Fadhil Raihan Akbar, xg-thesis [Dataset]. https://huggingface.co/datasets/fadhilra101/xg-thesis
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    Authors
    Fadhil Raihan Akbar
    Description

    expected-goals-thesis

    A repository for analysis on Expected Goals using StatsBomb and Wyscout data.

      StatsBomb data

    This repository assumes that the StatsBomb open-data has already been cloned to a local directory.

      Versioning

    The original thesis was run from a particular version of the data and mplsoccer (my football plotting library). The original code is here:… See the full description on the dataset page: https://huggingface.co/datasets/fadhilra101/xg-thesis.

  5. 2-Pass Events to Cluster FWWC23

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    xlsx
    Updated Oct 29, 2024
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    Iyán Iván-Baragaño (2024). 2-Pass Events to Cluster FWWC23 [Dataset]. http://doi.org/10.6084/m9.figshare.27325719.v2
    Explore at:
    xlsxAvailable download formats
    Unique identifier
    https://doi.org/10.6084/m9.figshare.27325719.v2
    Dataset updated
    Oct 29, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Iyán Iván-Baragaño
    License

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

    Description

    Pass Events to Cluster FWWC23

  6. FIFA World Cup 2018 (complete teamwise data)

    • kaggle.com
    zip
    Updated Aug 1, 2022
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    tom (2022). FIFA World Cup 2018 (complete teamwise data) [Dataset]. https://www.kaggle.com/datasets/blessontomjoseph/fifa-world-cup-2018-complete-teamwise-data
    Explore at:
    zip(42549917 bytes)Available download formats
    Dataset updated
    Aug 1, 2022
    Authors
    tom
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    Description

    Here we have folders with country names on them- representing each country that participated in the 2018 FIFA WC Each folder contains:


    1. TeamLineup: Lineup of the particular team under consideration 2. OppLineup: Opposition Lineup Lineup-attributes explained


    3. MatchEvents:Home and away events happened in the game Match_Events-attributes explained


    4. MatchesPlayed:A file containing information on all the different matches the country played in the tournament Matches_Played-attributes explained ~please do upvote if you download it or find it useful.

  7. Raw data and cleaned data

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    xlsx
    Updated Oct 29, 2024
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    Iyán Iván-Baragaño (2024). Raw data and cleaned data [Dataset]. http://doi.org/10.6084/m9.figshare.27315459.v1
    Explore at:
    xlsxAvailable download formats
    Unique identifier
    https://doi.org/10.6084/m9.figshare.27315459.v1
    Dataset updated
    Oct 29, 2024
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Iyán Iván-Baragaño
    License

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

    Description

    Clustering statsbomb

  8. u

    eventing_statsbomb_fwwc2023

    • portalcientifico.universidadeuropea.com
    • figshare.com
    Updated 2024
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    Iván-Baragaño, Iyán; Iván-Baragaño, Iyán (2024). eventing_statsbomb_fwwc2023 [Dataset]. https://portalcientifico.universidadeuropea.com/documentos/67321c93aea56d4af04836da?lang=de
    Explore at:
    Dataset updated
    2024
    Authors
    Iván-Baragaño, Iyán; Iván-Baragaño, Iyán
    Description

    Full Dataset eventing Statsbomb FWWC2023

  9. w

    Global Sports Video Analysis Software Market Research Report: By Sports Type...

    • wiseguyreports.com
    Updated Aug 6, 2024
    + more versions
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Sports Video Analysis Software Market Research Report: By Sports Type (Team Sports, Individual Sports), By Video Type (2D Video, 3D Video, Motion Capture), By Deployment Mode (On-premises, Cloud-based), By Function (Player Performance Analysis, Team Performance Analysis, Scouting and Recruitment, Injury Prevention and Rehabilitation), By End User (Professional Sports Teams, Amateur Sports Teams, Coaches, Athletes, Sports Scientists) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/sports-video-analysis-software-market
    Explore at:
    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 8, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 2023546.01(USD Billion)
    MARKET SIZE 2024590.18(USD Billion)
    MARKET SIZE 20321100.0(USD Billion)
    SEGMENTS COVEREDSports Type ,Video Type ,Deployment Mode ,Function ,End User ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSIncreased adoption of AI and machine learning Growing demand for player performance analysis Rise in popularity of wearable technology Expansion of cloudbased solutions Government initiatives to promote sports
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDHudl ,Veo ,Sportscode ,XOS Digital ,SportsTec ,Coach Logic ,Catapult ,Wyscout ,Zone 7 ,GPSports ,Dartfish ,InStat ,KlipDraw ,StatsBomb ,Kinduct
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIES1 AIpowered player performance analysis 2 Cloudbased software for remote collaboration 3 Integration with wearable and IoT devices 4 Motion capture for detailed injury prevention 5 Videobased scouting and recruitment
    COMPOUND ANNUAL GROWTH RATE (CAGR) 8.09% (2025 - 2032)

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Click to copy link
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Saurabh Shahane (2025). Stats-Bomb Football Data [Dataset]. https://www.kaggle.com/datasets/saurabhshahane/statsbomb-football-data/versions/1045
Organization logo

Stats-Bomb Football Data

Stats-Bomb Football Analytics Data

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 12, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Saurabh Shahane
Description

StatsBomb Open Data

StatsBomb are committed to sharing new data and research publicly to enhance understanding of the game of Football. We want to actively encourage new research and analysis at all levels. Therefore we have made certain leagues of StatsBomb Data freely available for public use for research projects and genuine interest in football analytics.

StatsBomb are hoping that by making data freely available, we will extend the wider football analytics community and attract new talent to the industry. We would like to collect some basic personal information about users of our data. By giving us your email address, it means we will let you know when we make more data, tutorials and research available. We will store the information in accordance with our Privacy Policy and the GDPR.

Whilst we are keen to share data and facilitate research, we also urge you to be responsible with the data. Please register your details on https://www.statsbomb.com/resource-centre and read our User Agreement carefully.

Terms & Conditions By using this repository, you are agreeing to the user agreement.

If you publish, share or distribute any research, analysis or insights based on this data, please state the data source as StatsBomb and use our logo, available in our Media Pack.

Getting Started The data is provided as JSON files exported from the StatsBomb Data API, in the following structure:

Competition and seasons stored in competitions.json. Matches for each competition and season, stored in matches. Each folder within is named for a competition ID, each file is named for a season ID within that competition. Events and lineups for each match, stored in events and lineups respectively. Each file is named for a match ID. StatsBomb 360 data for selected matches, stored in three-sixty. Each file is named for a match ID. Some documentation about the meaning of different events and the format of the JSON can be found in the doc directory.

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