SynthesisPloS one2025
The application of artificial intelligence techniques in predicting game outcomes of professional basketball league: A systematic review.
Synthesis in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- NeuroPlayNet: a multimodal AI framework for real-time cognitive-aware strategy optimization in professional basketball.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting basketball game outcomes is a critical area in sports science and data analysis, providing concrete benefits for optimizing coaching strategies, improving team management, and informing betting decisions.
objectiveThis methodological review systematically evaluates the effectiveness of specific artificial intelligence technologies in predicting professional basketball game outcomes over the past five years from 2019 to 2024, providing detailed insights into current methodologies and identifying emerging trends and challenges in this domain.
methodsFollowing PRISMA-SCR guidelines, a comprehensive keyword search was conducted across four electronic bibliographic databases: PubMed, Web of Science, Scopus, and EBSCO. Studies were included if they utilized artificial intelligence techniques, focused on professional leagues, and aimed to predict game outcomes.
resultsThis review incorporated 34 studies that met the predefined eligibility criteria, examining various artificial intelligence techniques used to predict professional basketball game outcomes over the past five years. The findings reveal that artificial intelligence models, particularly the multilayer perceptron neural network, achieved a high prediction accuracy of 98.90%. The random forest model, based on four factors, reached an accuracy of 93.81%, while the voting regression ensemble model achieved 93.3%. The studies underscore the importance of effective data processing and feature selection in enhancing model performance. Additionally, dynamic prediction models that adapt to real-time changes in the game were shown to be particularly useful for tactical decisions and betting strategies.
conclusionsArtificial intelligence significantly improves the accuracy of predicting outcomes in professional basketball games. Future research should include diverse basketball leagues and employ more advanced validation techniques to enhance model robustness and applicability. Integrating real-time data and exploring transfer learning will likely improve prediction accuracy and decision-making support.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.