# The Role of Artificial Intelligence in Sports Analytics:
## 3 Results
### Systematic Review and Meta-Analysis

Table 5: _Cont._fluence of sport-specific complexity and data type on algorithmic performance. The overall pooled estimate for classification accuracy across all studies was 87.2% (95% CI: 81.4-91.7%). However, heterogeneity was substantial, with an I\({}^{2}\) statistic of 82.6% and a between-study variance (\(\tau^{2}\)) of 0.021, indicating that much of the observed variance in performance metrics is attributable to methodological and contextual differences rather than sampling error alone. The funnel plot suggested moderate asymmetry, potentially reflecting reporting bias favoring high-performing models or selective publication. Among the included studies, 11 were peer-reviewed journal articles, while five were conference proceedings that had undergone formal peer-review. No preprints or unpublished grey literature were included. Studies with lower methodological transparency received correspondingly lower quality ratings and were not overemphasized in pooled analyses. To address the considerable methodological heterogeneity, we performed subgroup analyses based on AI methodology (classical ML vs. deep learning), sport type (individual vs. team sports), and study quality (high vs. moderate/low). As illustrated in Figure 3, studies employing deep learning architectures achieved a substantially higher pooled clas  
Figure 2: Forest plot of the effectiveness of AI methods in sports analysis. sification accuracy (92.3%; 95% CI: 89.1-95.4) compared to classical machine learning methods (78.6%; 95% CI: 73.5-83.2). Similarly, team sports studies demonstrated better model performance (89.7%; 95% CI: 86.1-92.4) than individual sports (81.2%; 95% CI: 76.8-85.6), potentially due to more complex and richer data environments. High-quality studies also exhibited greater consistency, with pooled accuracy reaching 90.5% (95% CI: 88.2-92.8), as opposed to 83.4% (95% CI: 79.3-87.2) in moderate- and low-quality studies. These results emphasize the importance of methodological rigor and AI technique selection in sports analytics. Figure 3 subgroup analysis forest plot showing pooled classification accuracy estimates across AI technique, sport type, and study quality. The violin plot presented in Figure 4 further elucidates the distributional properties of the four primary performance metrics accuracy, precision, recall, and F1-score stratified by AI technique. Deep learning approaches exhibited not only the highest median values in accuracy and F1-score but also the narrowest interquartile ranges, indicating greater consistency and robustness across datasets. In contrast, traditional machine learning methods demonstrated wider dispersion, particularly in precision and recall, suggesting susceptibility to noise and variability in training data. For instance, while some models such as LSTM achieved moderate gains in temporal sequence modeling (F1-score = 59.0%, 95% CI: 55.2-62.7%), others employing shallow classifiers like Support Vector Machines or Decision Trees frequently underperformed and yielded unstable metrics across folds. Figure 4: Violin plot of four performance metrics (accuracy, precision, recall, and F1-score) across different AI methods. Figure 3: Subgroup analysis: pooled accuracy by category. These findings underscore a key conclusion of this meta-analysis: deep learning architectures, particularly those leveraging spatiotemporal features or multimodal input, offer markedly enhanced predictive capacity and generalization when applied to performance analysis tasks in sport. At the same time, the variability observed across models especially in precision and recall highlights ongoing challenges in data preprocessing, class balance, and methodological standardization. Consequently, future research must prioritize the harmonization of evaluation metrics and validation protocols to ensure comparability across AI studies in sport.