The objective of the current study is to identify how victory or defeat is affected by the game-related statistics among men’s national teams in the Basketball World Cup 2019. The sample consisted of 76 games, during which 22 relevant indicator variables were directly recorded and analysed using “SportScout” video-analysis program. For each round, univariate analysis was used, in order to examine the differences among winning and losing teams. Linear discriminant analysis was implemented, which revealed the variables that had the greatest association with the outcome of the game. Assists were found to effectively predict the outcome of the game in the first and final round of the tournament. During the second round, the discriminant analysis showed that free-throw attempts, successful free-throws and defensive rebounds were associated with winning the game. On the contrary, 2-point field-goal attempts were negatively associated with the desired outcome. In the final round, 3-point successful shots and the scores of the 1^st and 4^th quarter had a positive relationship with winning the game. By analysing the tournament’s statistics, a coach can determine the trends that are noticed, evaluate the performance of his team and thus plan the corresponding strategies for the success of the game.

Contagious diseases are constantly affecting more and more people every day, resulting in widespread health crises especially in developing nations. Previous studies have developed deterministic and stochastic mathematical models to investigate the spread of epidemics. In the present study, a hybrid particle filtering epidemiological model is proposed, which combines the elements of a deterministic susceptible-exposed-infectious-recovered-deceased model with the inclusion of stochastic and penalty factors, in order to efficiently evaluate the dynamics of the disease. The inclusion of penalty factors stands out as the main novelty of the proposed methodology, guaranteeing estimations that align with the unique aspects of the examined natural phenomenon. The model is applied to the monkeypox data of the United States from 25 June to 21 November 2022. Our approach is compared to four alternatives, corresponding to deterministic and stochastic approaches that are associated with either fixed or time-varying parameters. In all cases, the particle filtering models displayed better characteristics in terms of infectious cases and deaths compared to their deterministic counterpart. The final version of the proposed epidemiologically informed particle filtering model exhibited significant potential and provided the best fitting/predictive performance compared to other examined methodologies. The predictive effectiveness of the proposed methodology has been thoroughly evaluated across various time intervals. Moreover, the inclusion of additional penalty factors in the weight computation procedure, assists in reducing fitting and prediction errors while simultaneously providing increased likelihood estimates. This modeling approach can be readily applied to other epidemics, both existing and emerging, where uncertainties in system dynamics and real-time observations hinder the accurate capture of the epidemic’s progression.

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

Background: Previous studies have used Benford’s distribution to assess the accuracy of COVID-19 data. Data inaccuracies provide false information to the media, undermine global response and hinder the preventive measures taken by authorities. Methods: Daily new cases and deaths from all the countries of the European Union were analyzed and the conformance to Benford’s distribution was estimated. Two statistical tests and two measures of deviation were calculated to determine whether the reported statistics comply with the expected distribution. Four country-level developmental indexes were included, the GDP per capita, health expenditures, the Universal Health Coverage (UHC) Index and the full vaccination rate. Regression analysis was implemented to examine whether the deviation from Benford’s distribution is affected by the aforementioned indexes. Results: The findings indicate that Bulgaria, Croatia, Lithuania and Romania were in line with Benford’s distribution. Regarding daily cases, Denmark, Ireland and Greece, showed the greatest deviation from Benford’s distribution. Furthermore, it was found that the vaccination rate is positively associated with deviation from Benford’s distribution. Conclusions: The findings suggest that overall, official data provided by authorities are not confirming Benford’s law, yet this approach acts as a preliminary tool for data verification. More extensive studies should be made with a more thorough investigation of countries that showed the greatest deviation.

In basketball, the offensive movements on both strong and weak sides and tactical behavior play major roles in the effectiveness of a team’s offense. In the literature, studies are mostly focused on offensive actions, such as ball screens on the strong side. In the present paper, for the first time a second-order Markov model is defined to evaluate players’ interactions on the weak side, particularly for exploring the effectiveness of tactical structures and off-ball screens regarding the final outcome. The sample consisted of 1170 possessions of the FIBA Basketball Champions League 2018–2019. The variables of interest were the type of screen on the weak side, the finishing move, and the outcome of the shot. The model incorporates partial non-homogeneity according to the time of the execution (0–24″) and the quarter of playtime, and it is conditioned on the off-ball screen type. Regarding the overall performance, the results indicated that the outcome of each possession was influenced not only by the type of the executed shot, but also by the specific type of screen that took place earlier on the weak side of the offense. Thus, the proposed model could operate as an advisory tool for the coach’s strategic plans.