## 4 Discussion  
This work aimed to analyze the agreement and reliability of a new algorithm to measure angles during changes of direction at different angulations and speeds. The WIMU Pro device algorithm showed a high agreement and reliability to detect angles during changes of direction at different angles and speeds, in a standardized context, in relation to the video criteria. The algorithm studied showed a favorable validation level (ES < 0.31) in 50% of the recorded angulations, while it overestimated the angulations of 90R, 135R, 180L, and 180R with respect to the video. However, it must be said that at a practical level the impact that these differences may have on decision making in sport seems irrelevant. The results on the validity of the algorithm shown in the present study were very similar to those previously found by [PERSON] et al. (2019) [19], where the most significant mean difference was with 180L (mean \(\pm\) SD = 4.9 \(\pm\) 3.7\({}^{\circ}\); ES = 1.36), while in the current work it was recorded at around 135R (mean \(\pm\) SD = 5.7 \(\pm\) 0.5\({}^{\circ}\); ES = 1.0). At the same time, our results agree with other studies where the fusion of various inertial sensors was used to analyze angulation in sports, for example, to quantify the angulation and rate of turn during turns in swimming [31], to analyze the individual technique of skiers during a propulsive cycle in cross-country ski, to measure the knee and trunk inclination during the descent in alpine skiing [32], or to analyze the rotation of the canoe in rowing [33]. Regarding the accuracy of the algorithm, our work, as in [19], showed relative differences of less than 5% in all angulations between the algorithm and the video recording. It is interesting to observe how the percentage difference did not increase, even after adding the 128 CoDs corresponding to the 18 km/h speed to the analysis. On the other hand, the results showed a high degree of reproducibility of the algorithm at all angulations, except at 45L and 45R, where they were \"poor\" both at 13 km/h and 18 km/h (CV > 10%). This decrease in reproducibility could be related to the pre-CoD speed at 45\({}^{\circ}\), especially at 45R, where the recorded speed was the highest of the entire test (Figure 7). These results coincide with those presented by [PERSON] et al., 2019 [19], where the highest pre-CoD speeds at 45R were recorded (14 km/h) as in this work (13.3 km/h); in both studies, there was also an upward trend of the CV (%) in relation to the rest of the angulations. More specifically, at 45\({}^{\circ}\) and at a speed of 18 km/h, the greatest variability of the algorithm was recorded (CV > 16%). This could be due to being left or right footed. In the present study, the dominant side of the five participants was the right. This was also the case in the study carried out by [PERSON] et al. (2022) [24], where 75% of the participants were right dominant, and it was observed that they veered more to the right than to the left during various small-sided games. In this way, there could be a relationship between laterality and experience or motor richness, and the need to brake less to turn towards their dominant side [34], and particularly towards angulations that are not very tight. This naturally coincides with the duration of the CoD (see Figure 7c), which increases progressively as the angle becomes greater. Only the 45L, 45R, and 90R angulations were performed below 470 ms, with 180L being the slowest, at an average of 745 ms; these data corroborate the hypothesis. In relation to the differences in the agreement and reliability depending on the starting speed, it should be said that the algorithm was able to register angles with a similar precision, regardless of speed (13 and 18 km/h), and only showed significant differences at 45L (mean bias = 2.6 \(\pm\) 1.1\({}^{\circ}\)). We can conclude that the speed variable did not \"negatively\" influence the ability of the algorithm to detect angulations, as in previous works [35].