Real-Time Football Match Prediction Platform
The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting footba...
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04003.pdf |
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| _version_ | 1825206542218035200 |
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| author | An Zhongqi |
| author_facet | An Zhongqi |
| author_sort | An Zhongqi |
| collection | DOAJ |
| description | The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-friendly interface is also developed to facilitate easy access and analysis of this data. The platform’s real-time data updating mechanism ensures prediction accuracy, while the integration of multiple models through a Stacking method further enhances reliability. The platform’s innovative design addresses key challenges in sports analytics by providing a robust tool for data-driven decision-making. Future work will focus on enhancing model algorithms and incorporating more complex data sources, such as social media sentiment analysis, to further improve prediction accuracy. |
| format | Article |
| id | doaj-art-0e37e676ae244ca49d3fe2ae98597732 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-0e37e676ae244ca49d3fe2ae985977322025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700400310.1051/itmconf/20257004003itmconf_dai2024_04003Real-Time Football Match Prediction PlatformAn Zhongqi0Zibo International Academy at Hi-tech ZoneThe integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-friendly interface is also developed to facilitate easy access and analysis of this data. The platform’s real-time data updating mechanism ensures prediction accuracy, while the integration of multiple models through a Stacking method further enhances reliability. The platform’s innovative design addresses key challenges in sports analytics by providing a robust tool for data-driven decision-making. Future work will focus on enhancing model algorithms and incorporating more complex data sources, such as social media sentiment analysis, to further improve prediction accuracy.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04003.pdf |
| spellingShingle | An Zhongqi Real-Time Football Match Prediction Platform ITM Web of Conferences |
| title | Real-Time Football Match Prediction Platform |
| title_full | Real-Time Football Match Prediction Platform |
| title_fullStr | Real-Time Football Match Prediction Platform |
| title_full_unstemmed | Real-Time Football Match Prediction Platform |
| title_short | Real-Time Football Match Prediction Platform |
| title_sort | real time football match prediction platform |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04003.pdf |
| work_keys_str_mv | AT anzhongqi realtimefootballmatchpredictionplatform |