Predicting tennis match outcomes mid-game using machine learning on psychological and physical data Article Swipe
Boyuan Li
,
Zihui Deng
,
Gaurav Gupta
,
Jinger Li
,
Y.Y. Miao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s40537-025-01216-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s40537-025-01216-4
Predicting game outcomes has significantly garnered the interest of researchers in recent years. The role of player performance is integral in-game analytics, impacting the interpretation and results of the analysis. Our work presents an AI for Science (AI4Sci) method to use real-time data from each game point to determine essential feature values, formulate and assess the impact of psychological momentum, and employ machine learning methodology on mid-match data for predicting the game’s victor. The data source is from Wimbledon and US Open games from 2017 to 2022, a total of 1592 games, and utilize 363 games of 2023 to evaluate their forecasting ability. We first obtained weights through information entropy and defined psychological momentum, and then 3 best classifiers, random forest, CatBoost, and Logistic Regression, were detected to assess the features. Additionally, we implemented a soft voting ensemble method integrating the Random Forest and CatBoost classifiers. All four models achieve over 90% accuracy and F1-score, with the soft voting classifier performing the best (accuracy: 97.5%, F1 score: 97.4%). These models achieve predictive accuracies above 70% using the first 25% data of a game.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s40537-025-01216-4
- https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4
- OA Status
- gold
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412103496
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- OpenAlex ID
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https://openalex.org/W4412103496Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s40537-025-01216-4Digital Object Identifier
- Title
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Predicting tennis match outcomes mid-game using machine learning on psychological and physical dataWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-08Full publication date if available
- Authors
-
Boyuan Li, Zihui Deng, Gaurav Gupta, Jinger Li, Y.Y. MiaoList of authors in order
- Landing page
-
https://doi.org/10.1186/s40537-025-01216-4Publisher landing page
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https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4Direct OA link when available
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Computer science, Computational Science and Engineering, Machine learning, Artificial intelligence, Data scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.predictive | 172 |
| abstract_inverted_index.Regression, | 125 |
| abstract_inverted_index.forecasting | 102 |
| abstract_inverted_index.implemented | 134 |
| abstract_inverted_index.information | 109 |
| abstract_inverted_index.integrating | 140 |
| abstract_inverted_index.methodology | 65 |
| abstract_inverted_index.performance | 18 |
| abstract_inverted_index.researchers | 10 |
| abstract_inverted_index.classifiers, | 119 |
| abstract_inverted_index.classifiers. | 146 |
| abstract_inverted_index.Additionally, | 132 |
| abstract_inverted_index.psychological | 59, 113 |
| abstract_inverted_index.significantly | 5 |
| abstract_inverted_index.interpretation | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.28346781 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |