###### Abstract  
(1) Background: Computer-based analyses have been widely used to study aspects of various team and racket sports. However, such analyses have so far eluded table tennis, except in special competition forms, under fixed laboratory conditions or have only tracked the ball. The aim was to detect a basic, global, positional behavior, independent of the score. (2) Methods: We investigated the player position of professional male table tennis players with respect to handedness (right/left), playing system (offensive/defensive) and racket holding (shakehand/penholder) to determine the applicability of automated analysis systems. We used existing video data of competitive matches (N = 198 sets; 2006-2020) and transliterated them into an x-y coordinate system. From this, we were able to conduct a heatmap analysis for different types of players. (3) Results: The comparison between right- and left-handed players resulted in a significant difference in the positioning of the x coordinate (D = 0.5663; \(p\) = 0.001). Both groups positioned themselves on average in their own backhanded half of the table (Re: x = \(-\)0.22 m, Li: x = 0.39 m). (4) Conclusions: Our results have yielded valuable insights into the importance of analyzing positional behavior in a differentiated manner depending on handedness, playing strategy and racket holding posture. heatmap analysis; table tennis; footwork; movement analyses; playing style; handedness; computer-based analysis; track players +
Footnote †: journal: Applied Sciences  
tracking. For example, the net often occludes players on the far side of the court, which can only be bypassed by using multiple, synchronized cameras [6]. [PERSON] and [PERSON] (2017) used a camera position from vertically above the court to avoid the players blocking each other while shifting their positions variably [7]. They compared the position data of players in squash generated by an automatic acquisition system with the positions of these players evaluated manually. Their findings indicated a similarity between automated and manual acquisition of 72.47%, so the comparison of manual evaluations almost coincides with the automated ones. The aim of this study was to propose a methodological approach to analyze the table positions of players in table tennis games through an automated tracking system using certain predefined factors for assessment. In table tennis, match analyses go back to the 1970s when the coaches used manual notations to identify strengths and weaknesses or specific tactics of individual players (e.g., [8; 9]; for a review, see [10]). Modern'manual' analysis--partly computer-assisted, partly pen-and-paper-based--uses predefined markers to shorten analysis time and evaluation steps (e.g., [11; 12; 13]). Regardless, these steps are still rather time-consuming and, therefore, usually can only be used in the analysis of a few individual competitions or players. For a more global overview of the players' movement behavior, automated systems are critical to draw valid conclusions and elucidate training implications from larger datasets. To the best of our knowledge, this study is the first automated technical approach to track the positions of table tennis players in a real game situation and to recognize tactical structures for making recommendations thereof. The process of professionalization of table tennis has been accompanied by an increasingly detailed analysis of individual players [14]. However, so far, there have been no fully automated systems for the real-time analysis of a table tennis match. Most studies in table tennis have focused on tracking the ball. This usually involves analyzing ball trajectories and their associated tactical placements in a competitive game [15; 16; 17]. There are a wide variety of solely technical problems, which exist in the automated analysis of table tennis matches. [PERSON] and [PERSON] (2011) summarized the problems of an automated (computer) system to track the ball and the player during a table tennis match [18].