Oliver’s four-factor model: Validation through causality

While there has been a growing interest in sports analysis in recent years, much research first focused on a classical statistical approach and later on an artificial intelligence approach. This article aims instead to propose a causal inference approach to sports analysis. In particular, the present article intends to review the famous four-factor model proposed by Dean Oliver for assessing the winning ability of National Basketball Association (NBA) teams through a causal inference approach. A structural equation model is used to validate Oliver’s model. The present paper considers the winning percentage and the factors’ statistics over entire seasons from [Formula: see text] to [Formula: see text]. The statistics for the [Formula: see text] season are considered only on a subset of the games. This is because the games played in the Orlando bubble under the particular COVID-19 situation have been regarded as outliers compared to the games played in the other NBA seasons, hence they have not been taken into account. The second goal of the article is to analyse if the fitting ability of the four-factor model changes when it is fitted over the pre[Formula: see text] and post[Formula: see text] basketball eras datasets, considering the year [Formula: see text] as the turning point for the NBA playing style.

Formation of Vocational Competence of Future Specialists in Physical Education and Sports

The objective of the study is to determine the components of the formation of vocational competence of future specialists in physical education and sports. Analysis of components of students in the specialties “Management in sports activities” and “Secondary education (Physical education)” was made. Comparative characteristic of partial readiness for the professional and pedagogical self-development of the first and fourth-year students was conducted.

Criminal Liability for the Use of Substances and (or) Methods Prohibited in Sports in Respect of a Sportsman

Purpose: assessment of criminal law and criminological problems of combating crime in the field of sports related to doping. Research method: doctrinal analysis of criminal legislation, conducting surveys of specialists in the field of justice and non-such citizens interested in sports, analysis of survey results. Conclusions: amendments to the criminal legislation providing for liability for acts related to doping is justified and timely. Scientific and practical significance: the significance of surveys for assessing decisions made in the field of criminal liability for acts related to doping is shown; the point of view is substantiated according to which, when doping is used against an athlete by taking pills or powder inside, injection, etc., regardless of his consent, the respective athlete should be recognized as a victim of this illegal act.

Soccer Ball Spatial Kinematics and Dynamics Simulation for Efficient Sports Analysis

The intelligent sports analysis requires exactly modeling the kinematics and dynamics of a soccer ball in a three-dimensional (3D) space. To address this problem, a 3D dynamic model of the soccer ball is developed to simulate the motion and capture the kinematics and dynamics performance. The model consists of three sub-models governed by the classic mechanics and formulated as time-dependent ordinary differential equations (ODEs). The simulations involve visualizing the ball traveling trajectory, which contains the instantaneous force information; and plotting the time-varying displacement and force curves. The model is validated by comparison of the results from this simulation and another theoretical calculation. A case study is presented to simulate the projectile motion of a soccer ball in a virtual environment. The spatial kinematics and dynamics results are obtained and analyzed. The results show the max projectile height and range, and kick force increase with the increase of the initial velocity. This research is significant to simulate the soccer ball motion for promoting the planning, evaluation, and optimization of trajectory.