# The Concurrent Validity of Mobile Application for Tracking Tennis Performance
## 1 Introduction  
The volume of data on the performance of tennis players on the court, with the advancement of technology, increases over the years. There are multiple ways to collect information related to tennis performance. Modern technology provides the ability to collect and analyze a large volume of data in a short time. This type of data collection represents an objective way of recording indicators of sports performance and competitive performance in situational conditions [1]. The information obtained is of great importance for the analysis of statistical parameters and sports performance [2], as well as for improvement in technical and tactical performance [3, 4]. High-quality and precise data collection requires efficient applications for the automatic notation of performances. Newer sensors, due to their smaller size, allow data to be collected without interfering with the performance of the activity itself [5]. Increased use of different sensors induced the development of smartphones and portable devices to analyze sports performance. Modern smartphones are equipped with various sensor devices such as accelerometers, gyroscopes, and magnetometers [6] and can therefore be applied in research in the field of sports activities [7]. Sensors collecting movement data can be used in applications that are intended for the analysis of everyday activities and the automatic video notation of the performance of various sports [8]. The authors of [9] explored the possibility of using wearable sensors with gyroscopes to evaluate and improve serve performance in tennis. Furthermore, in a previous study [10], it was surmised that sensors with gyroscopes could be used to assess and acquire the skills of performing the first serve in tennis. Earlier research [11] showed that tennis players benefit from the classification of strokes and the recognition of the type of rotation based on sensors. Thisrepresents objective feedback to coaches and players, which allows for the evaluation of the training process. Tennis-specific portable sensors provide data on the external and internal load of tennis players [12]. Additionally, they can be divided into (A) those that are part of the racket, which connect to the grip of the racket, and (B) those that are worn around individual parts of the body, most often around the wrist [4; 13]. The greatest difference between this group of sensors is that the sensors in the (A) category are attached to the grip of the racket and have no contact with the tennis player's hand, while the sensors in the (B) category are fixed to the wrist or worn as a watch or bracelet. Sensors provide information on kinematic parameters, tennis-specific parameters (stroke type, speed, power, and tempo) [10; 14; 15; 16], the point of contact of the ball and the racket, the force transferred to the hand after contact [15; 17; 18], the duration of the active and passive time of the game, and the functional variables (heart rate variability and energy expenditure) of players. Due to the position and movement of the sensor itself, they are subject to measurement errors and can affect sports performance [9]. As wearable sensors can have a negative impact on players' technique, there is a need to develop systems that can track specific tasks without any influence on game parameters. In addition to sensors that enable motion detection and define the kinematic parameters of the body, racket, and ball, applications for the analysis of certain situational parameters have emerged. These applications use artificial intelligence (AI) and algorithms to identify the type and speed of impacts. Regarding the automated assessments of these parameters, it is necessary to determine whether the applications or algorithms used in the analysis accurately measure the observed variables. One such software package is the SwingVision (SV) mobile application. SV uses video analysis to automatically process and recognize specific movements in situational conditions.