# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion
### AI Performance  
The statistical analysis of performance metrics across the reviewed studies reveals noteworthy trends and variability in the application of AI to sports performance analysis. Experimental studies reported accuracies ranging from approximately 47% to 59%, with \begin{table}
\begin{tabular}{c c c c c} \hline
**Domain/Outcome** & **Classification Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Risk of Bias** & 0 & 0 & 0 & 0 \\ \hline
**Inconsistency** & 1 & 1 & 1 & 1 \\ \hline
**Indirectness** & 0 & 0 & 0 & 0 \\ \hline
**Imprecision** & 0 & 0 & 0 & 0 \\ \hline
**Publication Bias** & 0 & 0 & 0 & 0 \\ \hline
**Total Downgrades** & 1 & 1 & 1 & 1 \\ \hline
**Score (4-downgrades)** & 3 & 3 & 3 & 3 \\ \hline
**Grade** & Moderate & Moderate & Moderate & Moderate \\ \hline
**Effect Estimate** & 87.8\% (95\% CI 82.7-92.9\%) & 48–97\% & 47–96\% & 45–95\% \\ \hline
**Number of Studies** & 16 & 13 & 13 & 13 \\ \hline \end{tabular}  
A single “serious” concern in any of the five GRADE domains—risk of bias, inconsistency, indirectness, imprecision, or publication bias—was assigned one downgrade point (no serious concern = 0 points; “possible” publication bias did not incur a downgrade). Starting from a baseline score of 4 (“high”), each outcome’s total downgrades were subtracted to yield a final score (4 = high; 3 = moderate; 2 = low; 1 = very low). The effect estimates are presented as pooled percentages (with 95 % confidence intervals for classification accuracy) or as the observed range across studies, and the number of studies reflects how many independent investigations contributed data to each metric.  
\end{table} 
Table 6: GRADE assessment. deep learning models, such as LSTM architectures, consistently achieving the higher end of this spectrum. For example, the LSTM model in running performance analysis attained an accuracy and F1-score of 59%, which represents an 8-12% improvement over several traditional machine learning classifiers that clustered around the 48-51% range. These differences suggest that deep learning techniques may have the potential to better capture complex temporal patterns, although no formal statistical comparison was conducted [13; 30]. However, the benefits of deep learning are highly dependent on data quality and preprocessing techniques, which can significantly impact final performance outcomes. In the domain of computer vision and predictive modeling, performance improvements were even more pronounced. Studies utilizing advanced frameworks in tennis and basketball reported accuracies exceeding 95%, with one study in tennis achieving 98.60% classification accuracy [7], and another in basketball reporting 98.8% accuracy alongside mean average precision (mAP) values in the mid-90% range [15]. Such high performance metrics suggest that computer vision approaches may offer robust solutions in movement-intensive settings, though these findings should be interpreted cautiously due to methodological heterogeneity. Nevertheless, these models remain sensitive to data acquisition variables, such as video resolution, camera positioning, and occlusion effects [12]. The lack of standardized datasets across studies further complicates direct performance comparisons. Across the reviewed studies, 11 studies reported accuracy rates above 90%, demonstrating that AI models are highly effective in many instances. In contrast, three studies reported accuracies between 80-90%, while two studies fell in the 70-80% range. Notably, 19 studies did not report specific accuracy information, which limits direct comparative analysis. This variability in reported accuracies underscores the diverse performance outcomes achievable with different AI techniques and datasets [33; 35]. The need for standardized AI benchmarking procedures is evident, as inconsistency in reporting methods can obscure meaningful performance trends.