# The Concurrent Validity of Mobile Application for Tracking Tennis Performance
## 4 Discussion
In other analyzed shots (backhand, serve, FH volley, BH volley, and overhead), the results also revealed excellent validity. In a previous study [23], where they used a portable sensor that monitored data on the total number of shots, the validity of the results was very good (ICC = 0.90). Comparing all the results that most sensors can offer [4; 13], in this parameter, the results are the most reliable. In the tested variable of spin shots, a good validity (ICC = 0.76) could also be observed. For example, VA detected a total of 234 shots with topspin rotation, while SV recognized a total of 213 shots. Of all the observed shots (topspin, flat, slice, and kick), the detection of the topspin shot was the least valid (ICC = 0.76), which represents good validity results. As the rotations of the player and racquet are very extreme, individual characteristics in some conditions can highly affect the detection of the technique and type of shot. Accurately obtained data for these parameters are extremely important. The ability to play shots with different rotations represents the quality of an individual tennis player. The above is important for developing technical and tactical assumptions of the game for tennis players. This is crucial to follow through different age categories, to gain insight into the total number of strokes and type of spin shots during training or match. Based on these findings, we can conclude that the SV application can predict the actual number of shots during a tennis match with good-to-excellent validity. Following this information, one of the possible uses of the SV app can be to define the total number of individual shots played in tennis during a game, which can help coaches plan and program the entire training load. In addition, this application can be used for testing procedures and as a practical tool for tennis coaches. In addition, SV can use tennis umpires in lower-level tournaments as a helping tool to check the results if there is a miscalculation in counting or if players do not know the current results, which can be checked on the application. Tennis players during a match perform numerous movements, such as stopping, picking up, or tapping the ball [24]. This application does not recognize these movements, which is its advantage, because it only recognizes tennis shots during the play. Comparing the other devices mentioned in the introduction of this article, this is one of the greatest practical benefits for the player, as the sensor has no contact with the player's body or racket. In a conversation with players who conducted tests with portable devices on their wrists or rackets, disturbance during playing was cited as the greatest drawback of the observed device. In the future, an increasing number of such applications are expected to emerge, which will offer even more parameters and higher accuracy of the obtained data due to the overall development of the mobile industry. One of the study limitations is possible measurement errors that can exist during the visual review of the video of the play. The small number of variables and parameters is one of the limiting factors that cannot give a complete analysis of players' performance. Furthermore, different weather conditions could affect the detection of different strokes. Additionally, the application is currently only compatible with iOS mobile devices, which narrows the range of potential users. In addition, the problem of accurate shot recognition is one of the most important features of automated systems for quantifying external hitting load in tennis [16]. The external load that SwingVision can monitor consists of the parameters of the total number of shots played and the duration of the practice or match. Obtaining precise data on external load parameters represents a great benefit of the application for practical purposes.