## 6 Conclusions and Practical Applications  
The present study has allowed the characterization of the kinematic and neuromuscular external load demands of professional basketball players during official competition by establishing load distribution ranges. The traditional speed thresholds established in the literature, specifically those related to very-high-intensity and/or sprint actions, should be modified. Data higher than 24 km/h are not usually observed, so it seems appropriate to establish new ranges, encompassing maximum intensity actions from 17.5 km/h and sprints at 19 km/h. In relation to accelerations or decelerations, the data obtained reflect the same controversy. Accelerations or decelerations exceeding 2 m/s\({}^{2}\) are barely observed, not being in accordance with the traditionally established ranges. The most recent results lead us to understand that these criteria were not adequate for basketball, and that it is necessary to establish ranges in a much more personalized way for each team and/or player. This research overcomes the traditional limitation observed in the literature specific to the thresholds of kinematic variables in basketball and offers a useful tool for the coach and physical trainer, in such a way that allows to have a closer knowledge of the reality when designing training sessions. In relation to the neuromuscular variables recorded (impacts and Player Load), the results lead us to consider basketball as a fundamentally neuromuscular sport, where high-intensity impacts or high- and very-high-intensity Player Load represent a high percentage. In view of the results obtained, and noting that, in most of the literature, basketball is defined as an intermittent sport, mainly from the analysis of kinematic variables, this definition should be reconsidered, being more accurate to understand basketball as a sport with a high neuromuscular load. The methodology of data analysis and classification (cluster analysis of the variables recorded during the competition) allows for a more precise knowledge adapted to the specific circumstances of each team and each player. In relation to the clustering methods used, two-step clustering and k-means clustering, different results are obtained for both kinematic variables and impacts, but not for the Player Load analysis. The use of these clustering techniques will depend on the objective of the study, using a mathematical criterion to classify or a criterion based on expert judges. While two-step clustering may offer a more robust model, from a practical point of view, the groupings proposed by this model may not discriminate enough for the work of the trainers, making it necessary to use k-means clustering. The classification system based on expert judgment, defining five thresholds, allows a greater discrimination of the type of tasksby always establishing five groups. The work thresholds derived from the mathematical cluster offers the coach a more uniform type of tasks. Based on these conclusions, the following practical applications can be established:  
1. The traditionally established thresholds of kinematic variables, especially those referring to maximum intensity, do not really reflect the reality of competitive basketball. Therefore, it is necessary to establish new speed thresholds, based on the data obtained in competition, which allow a more concise customization of the different ranges of work, depending on the characteristics of each team (competitive level, sex, age) and each player. 2. Since it is known that the competitive activity experienced by players is highly conditioned by different factors (gender, age, competitive level, etc. ), future research on this topic could allow the establishment of speed ranges according to specific positions, gender, competition, etc., and thus, significantly improve the training processes. 3. The high demands for accelerations and decelerations identified in these athletes suggest that specific lower-body strength training should be implemented to minimize the risk of injuries.