# A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash
## 4 Discussion
Players spent an average of 53.7% (unfiltered and filtered coordinates) of the time on the left side of the T which concurs with the findings of 56.5% from [PERSON] et al. [24]. Since the left side wall camera view was not analyzed, these percentages are expected to be higher. This aligns with [PERSON] et al. [34] who recorded an average of 64.6% of shots coming from the left side of the court for 10 matches played at the men's World Team Championship in 2003. As both players were right-handed, a higher percentage spent on the left (backhand) side was expected since at the elite level, a common tactic is to play to your opponent's backhand which is considered weaker and more difficult [24]. An overwhelming 86.4% (unfiltered and filtered coordinates) of the time was spent behind the T, agreeing with the manual tracking average of 89.7% from [PERSON] et al. [24]. This is similar to the previous studies of [PERSON] et al. who found 74.5% of shots coming from behind the T at the same 10 matches recorded at the men's World Team Championship in 2003 as stated above [34]. The tendency to favor the left and situate yourself behind the T typically occurs when a player returns to center to anticipate the next shot. The lower percentages calculated using the proposed method compared to the reference is likely since most missing frames due to player occlusion occur near the T during their return to the ideal position. The average speeds calculated by the filtered coordinates (overall 1.90 m/s) are much closer to [PERSON] et al. 's results (1.85 m/s) [24] than the unfiltered coordinates (2.23 m/s). This supports the need for filtering of coordinates and is once again likely due to the variation in foot detection, causing increased distance traveled and in turn higher reported speeds between consecutive frames. The results of the filtered coordinates align with previous studies where [PERSON] et al. [24] recorded a maximum average speed of 2.04 m/s over 5 matches of elite players from 2012-2014 and [PERSON] and [PERSON] [17] recorded a maximum mean speed of 1.98 m/s, while the maximum average speed was 1.99 m/s using filtered coordinates. As the average walking speed is around 1.4 m/s and the walk-to-run transition speed has noted to occur below 2 m/s, our results of 1.90 m/s as the overall average speed reflects the idea that squash comprises of shifts between walking and running [35; 36; 37; 38]. Removing speeds below 1 m/s is argued by [PERSON] et al. [24] to provide a more realistic idea of how fast players move to return shots. Speeds that fall under 1 m/s are primarily identified when a player is at center court waiting for their opponent to play a shot, during the pause for accuracy and power before a player makes their shot, and when players change directions. With this selection, our results show that for 70.2% of the time during active match play, players moved at an average speed of 2.44 m/s and only spent 29.8% of the time moving less than 1 m/s. This is reflective of [PERSON] et al. 's analysis of 5 matches [24] as mentioned above, which found the mean speed of players as 2.52 m/s (excluding speeds less than 1 m/s), 69.6% of the time during active match play. These speeds represent the incredible level of conditioning and endurance elite squash players must possess to compete. A limitation of this study is the inability to analyze the entirety of active match play (83.32% analyzed on average, Table 3). Another constraint is the assumption that players slide horizontally across the plane of the court when converting video coordinates into court coordinates, meaning that vertical movement of a player due to jumping is considered as distance traveled. In addition, the conversion does not take into account any lens warping. Our future work will focus on continuing to develop the reliability of this method, add the analysis of additional camera angles, refine the model to reduce/handle missing frames, and to gather data on recent PSA matches. Further research opportunities include analysis of upper body and arm kinematics.