# The Role of Artificial Intelligence in Sports Analytics:
## 3 Results  
### Quality Assessment  
Assessment revealed variable methodological rigor across the included studies. Below the summarized data are presented in tables for each study group using tailored tools. For the experimental studies [9, 17, 30, 31, 32, 33, 34, 35], we applied both the Mixed Methods Appraisal Tool (MMAT) and the JBI Critical Appraisal Checklist for Quasi-Experimental Studies (when applicable). Table 2 displays the MMAT scores (based on 5 criteria) and the JBI scores (for quasi-experimental designs, where applicable). Overall, while several studies demonstrated high quality, e.g., [17, 31], others exhibited moderate quality, often due to limitations in intervention clarity or sample handling. Notably, studies employing wearable sensors or motion tracking systems, e.g., IMUs in [9, 30], tended to score higher in methodological rigor, possibly due to the controlled experimental conditions ensuring replicability. In contrast, studies relying on traditional statistical techniques without advanced AI integration exhibited more inconsistencies in performance evaluation, e.g., [35]. While experimental studies generally provided well-controlled environments for AI testing, their findings may not fully capture real-world variability in athletic performance. Future studies should focus on hybrid approaches that integrate controlled experimental setups with real-world game data to improve external validity. As summarized in Table 2, studies rated as high quality demonstrated rigorous intervention protocols, consistent data handling, and clear reporting of AI model development. In contrast, studies with moderate quality ratings often suffered from less detailed methodological descriptions, smaller sample sizes, or limited generalizability beyond controlled laboratory conditions. These factors may have introduced minor biases affecting performance outcomes. Observational studies [18; 37] were evaluated using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. As shown in Table 3, [PERSON] [18] achieved a higher score compared to [PERSON] [37]. While observational studies contribute valuable insights into real-world AI applications, limitations such as potential biases in manual annotation and a lack of standardization in data collection remain key concerns [18]. Computer Vision and Predictive Modeling Studies  
For studies emphasizing performance prediction using computer vision and deep learning [7; 12; 15; 36; 38], we employed the Prediction model Risk Of Bias ASessment Tool (PROBAST) alongside considerations of TRIPOD-ML adherence. As summarized in Table 4, studies such as [7] exhibited low risks of bias and high reporting standards, whereas others demonstrated moderate concerns that may impact the interpretability of their performance metrics. Computer vision-based approaches displayed a clear advantage in action recognition accuracy (e.g., [7], achieving 98.6% classification accuracy in tennis). However, some models exhibited inconsistencies in their validation procedures, particularly when dealing with occlusion challenges or limited training data [15]. These discrepancies highlight the ongoing need for standardized evaluation protocols to enhance reproducibility across different sports domains. An important consideration in computer vision models is their dependence on high-quality video inputs. Variability in camera angles, lighting conditions, and occlusion effects can introduce biases in action recognition, which may explain the performance gaps observed between studies.