###### Abstract
They concluded that the biggest challenge for video analyses in table tennis is the high speed of the ball. The ball reaches speeds of up to 160 km/h and ball rotations of 151 revolutions per second [19]. Furthermore, there are several moving objects (e.g., ball, rackets, players, cameras, umpires, crowd), which also complicate data collection and analysis. The occlusion of the ball through the racket or the player is a technical problem as it prevents any continuous tracking [17]. [PERSON] (2009) as well as [PERSON] and [PERSON] (2011) demonstrated an automated system which could help umpires to make quick and precise decisions over the validity of a table tennis serve (whether the serve is correct or incorrect) [18; 20]. They used a combination of spatial and temporal information from several real match video sequences to detect the ball. This system is effective for different players and perspectives and works with a complex and reasonably static movement, such as the serve. However, the serve is a static initial condition, and, in a rally, the actions are often too complex for the system to catch. Other conditions that make automatic detection of the ball difficult, as identified by [PERSON] and [PERSON] (2011), include the small size of the ball (which is often a small percent of the size of the frame) and the low contrast between the ball and the background [18]. For certain (isolated) game situations, [PERSON] et al. (2020) could identify the position of a (fixed) player and the (moving) ball during the serve [21]. They analyzed the starting position of the players included in their dataset, i.e., the elite European table tennis players, during their serves and they could identify the corresponding'most used zone' on the opponent's side of the table automatically. These kinds of analyses of certain game situations (focusing on the serve) can be used to make recommendations and develop courses of action for an improvement in the players. In another tournament, [PERSON] et al. (2010) calculated the ball placements using computer simulation for players in a wheelchair during singles matches [22]. They used high-speed video cameras (60 fps) in this observation to record images from the side of the table (at an angle of 15\({}^{\circ}\), lens height of 2.9 m and horizontal distance of 10 m between the table) to locate ball placements on the table. With the help of their dataset, they were able to identify particularly successful ball placements in wheelchair table tennis and identify the ball paths that have a higher likelihood of leading to winning points. The advantage of this specific tournament was that the wheelchair players use less space in the box and have fewer stroke variations simply due to their limited mobility. Other mathematical approaches to estimate ball placements employ artificial neural networks [23], data mining processes [24], or Markov chains [25]. These methods use a few manually recorded rallies to extrapolate and infer larger datasets via algorithms. [PERSON] and [PERSON] (2015) developed an algorithm to reconstruct the 3D trajectory of a ball with unsynchronized videos without the same timing information and these are still usable for match analysis [26]. They were able to provide the spatial features of shots (e.g., the position of the ball while it bounced, its maximum height and its direction and velocity). The greatest accuracy of the ball trajectory, however, could only be demonstrated for relatively static game situations (e.g., the serve). In a running rally, inaccuracies in ball recognition due to the aforesaid limitations still occurred. [PERSON] and [PERSON] (2017) placed the focus of the analysis on ball detection during an ongoing rally [27]. They demonstrated, with the help of a high-speed vision system, that they could track a table tennis ball accurately during a rally.