## 1 Introduction
In addition to these studies that identify ranges for the kinematic variable of speed, in the literature, there are other studies that establish ranges for distances traveled [34], accelerations and decelerations [8], impacts [8], or Player Load [35], which show reference values for individualization of training and adaptation to the demands of competition. In the evolution of basketball research, ref. [36] presented a relationship between the four phases through which data analysis in sport evolves proposed by [PERSON] et al. [37] (Descriptive analysis, Diagnostic analysis, Predictive analysis and Prescriptive analysis) with the techniques for data analysis. Clustering techniques would be found within the Diagnostic phase, whose objective is to establish the performance profiles of athletes. Within the clustering techniques, two of the methods that are commonly used for the grouping of cases are two-step clustering and k-means clustering. These two techniques provide very relevant information but differ in their approach and underlying principles. Two-step clustering involves a hierarchical process consisting of two stages. The first stage involves grouping the data using a less complex clustering algorithm (cluster analysis), followed by a more refined clustering analysis (logistic regression analysis). Two-step \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Sex** & **Level** & **Standing** & **Walking** & **Jogging** & **Running** & **Sprinting** \\  & & & **km/h** & **km/h** & **km/h** & **km/h** & **km/h** \\ \hline [PERSON] et al. [30] & Female & U’18 & \(\leq\)3.6 & 3.6–6.5 & 6.5–10.2 & 6.5–10.2 & \(>\)14.4 \\ [PERSON] et al. [31] & Male & U’18 & \(\leq\)6.0 & 6.0–12.0 & 12.1–18.0 & 18.1–24.0 & \(>\)24.1 \\ [PERSON] et al. [32] & Male & U’18 & \(<\)7.0 & 7.0–14.0 & 14.0–18.0 & \(\geq\)18.0 \\ [PERSON] et al. [33] & Male & Senior non-professional & \(\leq\)6.0 & 6.1–12.0 & 12.1–18.0 & 18.1–24.0 & \(\geq\)24.0 \\ [PERSON] et al. [8] & Female & Senior Professional and semi-professional & \(\leq\)2.31 & 2.31–5.33 & 5.34–9.32 & 9.33–13.12 & 13.13–17.08 \\ [PERSON] et al. [6] * & Male & Senior Professional and semi-professional & \(\leq\)3.6 & 3.96–10.8 & 11.16–25.2 & \(>\)25.2 \\ \hline \hline \end{tabular}  
* Values converted from m/s to km/h.  
\end{table} 
Table 1: Studies using speed ranges in basketball. clustering is based on a combination of distance and probability measures. K-means clustering is one of the most popular and widely used methods in cluster analysis. This algorithm seeks to group objects into k clusters, where k is a predetermined number [38]. The results obtained with two-step clustering offer more robust results, as its approach is based on logistic regression and distance measures, whereas k-means clustering is based on minimizing the sum of squared distances within each cluster. The number of clusters in two-step clustering is not set in advance and is determined optimally during logistic regression, whereas in k-means clustering, the number of k clusters is set in advance by the researcher. The use of these clustering techniques will depend on the objective of the study. Although two-step clustering may be a better model, sometimes the clusters it proposes may not discriminate sufficiently for the work of the trainers. Therefore, it is necessary to experiment with the different solutions provided by each model. As far as is known, there are few studies that define the work ranges in objective external load (OEL) variables in professional basketball players during official competition. Likewise, there is very little research that, based on a mathematical model, establishes the ranges, as well as confronts different models to better define these ranges. This type of information is necessary for coaches to be able to adapt training processes to the real demands of competition.