The COVID-19 pandemic is a potential threat to professional sporting events when they eventually return to their usual calendar with spectators’ capacity of football stadiums usually exceeding 40,000 seats for important events. Hence, a strategy for safe return to sporting events is needed in the COVID-19 pandemic to pave the way towards a new normalcy. We reviewed the guidelines and policies implemented in organising the Amir Cup Football Final of Qatar, which hosted about 20,000 fans. The authors evaluated the publicly available information on the official websites of the various organizations involved and highlight the importance and usefulness of the Covid-19 Rapid Antigen Assay-Kit as a tool for screening sports spectators as well as the importance of a rigorous spectator pathway, including their accurate traceability thanks to a specific mobile phone application. Despite the surging of COVID-19 all over the world, a big football event with around 20,000 spectators in the same stadium has been hosted under strongly controlled preventative measures. These preventative measures show that it is possible to organize a major football match held outdoors, with the presence of thousands of supporters. This article is a call for action for the organisers of such events where the supporters’ health status is traceable to provide the scientific community with actual data of post-event infection rates. Therefore, it is suggested to consider using procedures like the ones described in the present article as a potential model in the process of organizing big sporting events with spectators in times of COVID-19.

Background: Quantifying soccer players' performance using different types of technologies helps coaches in making tactical decisions and maintaining players' health. Little is known about the relation between the performance measuring technologies and the metrics they measure. The aim of this study is to identify and group the different types of technologies that are used to track the health-related performance metrics of soccer players. Methods: We conducted a systematic search for articles using IEEE Xplore, PubMed, ACM DL, and papers from the Sports Medicine Journal. The papers were screened and extracted by two reviewers. The included papers had to fall under several criteria, including being about soccer, measuring health-related performance, and using technology to measure players' performance. A total of 1,113 papers were reviewed and 1,069 papers were excluded through the selection process. Results: We reviewed 44 papers and grouped them based on the technology used and health-related metrics tracked. In terms of technology, we categorized the used technologies into wearable technologies (N=27/44) and in-field technologies (N=14/44). We categorized the tracked health-related metrics into physiological metrics (N=16/44) and physical metrics (N=44/44). We found out that wearable technologies are mainly used to track physical metrics (N=27/27) and are also used to track physiological metrics (N=14/27). In-field technologies are only used to track physical metrics (N=24/24). Conclusion: Understanding how technology is related to players' performance and how it is used leads to an improvement in the monitoring process and performance outcomes of the players.

As football (soccer) is one of the most popular sports worldwide, winning football matches is becoming an essential aspect of football clubs. In this study, we analyzed football players' performance in a total of 864 football matches of the Qatar Stars League (QSL) between the years 2012 and 2019. For each match, the collective performance of the players in key playing positions was analyzed to understand their effectiveness in winning games. We formulated this study as a classification framework in the machine learning (ML) context to distinguish the winning team from the losing team in a match. This allowed us to check the effectiveness of different performance metrics considered a feature vector for ML models. Different ML models were considered for this classification task, and the logistic regression-based model was considered the best performing model, with more than 80% accuracy. Multiple feature selection methods were leveraged to identify players' performance metrics that could be considered as contributing factors to determine the match result. The proposed ML model identified several features, including (a) shots on target by forwarders (b) distance covered by forwarders and midfielders at very high speed (c) successful passes, that can be considered as effective performance metrics for winning a football match in QSL. Interestingly, we revealed that the defenders' role could not be ignored for match results, and playing fair games improves the chance of winning matches in QSL. We also showed that players' performance metrics from the last five seasons would provide sufficient discriminative power to the proposed ML model to predict the match-winner in the upcoming season. The proposed ML model will support the players, coaching staff, and team management to focus on specific performance metrics that may lead to winning a match in QSL.