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The current study was conducted to identify the impact of artificial intelligence on sports marketing (applications and limitations). This research is a sequential mixed-methods study (qualitative-quantitative). The participants in qualitative phase consisted of 15 sport management experts who were interviewed. In the quantitative section, the statistical population included members of the sports management academic community that was selected through random sampling approach. Kolmogorov-Smirnov test, confirmatory factor analysis, and path analysis were used in the quantitative part of the study. The structural equation method with partial least squares method was used to measure the validity of the model, and the research hypotheses. According to the results of the study, there are 8 effective factors of artificial intelligence applications in sports marketing, including predicting sales and prices of sports products/services, identifying revenue growth opportunities, automating sales processes of sports products/services, personalized recommendation systems through analyzing customer behavior of sports products/services, tracking satisfaction with sports products/services, and advertising. Additionally, there are 5 factors identified as limitations of artificial intelligence in sports marketing, including limited understanding of human elements, concerns about data privacy, costs, and technical issues. The findings can help sports marketers to better utilize artificial intelligence for better delivery and customer satisfaction of sports products and services.

This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the gameplay and the direction it takes. We propose to break down the gameplay into Events. At this junction, we observe two kinds of events – ones that follow sequential logic and ones that do not. Our focus is pertained to sports that comprise sequential events. To examine these events further, to understand how a winner emerges, we take the help of Finite State Automaton from theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as Finite State Machines. We depict these Finite State Machines as State Diagrams. We examine these State Diagrams to observe how a team/player reaches the final states of the sport, with special focus on one final state – the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman, Weightlifting (Snatch). Based on our observations of how this final State of winning is achieved, we propose a categorization of sports.

Purpose: the etiology of children obesity can be varied. Adipose tissue is an active tissue that secretes proteins such as TNF-α, IL-6, leptin, and adiponectin. The purpose of this study was to investigate the effect of short-term sprint interval training on serum levels of adiponectin, leptin, TNF-α, IL-6, insulin, and lipid profile in overweight boys. Method: a number of 30 overweight male students (with a mean age of 9.93±0.2 and BMI of 27.73±0.2) were randomly assigned to the control group (n=15) and sprint interval training (n=15). The subjects of the experimental group did the training for eight weeks, as the control group was engaged in doing their daily activities without intervention. Measurement of anthropometric indices and fasting blood collection were performed (to measure insulin, leptin, adiponectin, TNF-α, IL-6, cholesterol, LDL, HDL and TG) one day before and one day after the training program. Results: the results of the present study indicated that 8 weeks of sprint interval training significantly decreased weight and BMI in overweight children (P<0.001). In addition to weight and BMI reduction, levels of TNF-α, IL-6, leptin, insulin, cholesterol, triglyceride, and LDL significantly reduce the following eight weeks of the sprint interval workout (P< 0.001 for all), while adiponectin and HDL levels increase (P<0.001, P<0.01, respectively). Conclusions: in general, the present study demonstrate that sprint interval training which is a new way of physical activity exerted a positive effect on most of components contributing to children obesity.