Data Mining Model of Internet of Things based on Blockchain Technology

Hong-ping, Cao; Cao, Hai-Xia; Liang, Guiping

doi:10.1145/3544109.3544400

Cited by 1 publication

(2 citation statements)

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“…In addition, this research study aimed to correlate NBA player age with salaries and advanced basketball performance analytics. Figure 5 presents a matrix with the attributes of age, salary and advanced basketball performance analytics contained by PTS, REBS, AST, TRB%, TOV%, USG%, TS%, AST%, STL%, BLK%, NetRtg, PER, WS, WS per_48, BMP or Plus/Minus or +/− and VORP which used, as the most appropriate rating, basketball analytics in the background [1,19,[74][75][76] (RQ2 and RQ3). As illustrated in Figure 6, the green highlighted cells are those which demonstrate players reaching their peak performance within each age group.…”

Section: Resultsmentioning

confidence: 99%

“…Biometric characteristics (such as speed, agility, height, weight, and body mass), demographics (such as age, country, and college), nutrition, training pace, psychology, marketing strategy and social life are significant in terms of performance and salary expectations. Uncertainty minimization in every sport can lead to cost reduction and efficiency and productivity optimisation [19].…”

Section: Introductionmentioning

confidence: 99%

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Sports Analytics: Data Mining to Uncover NBA Player Position, Age, and Injury Impact on Performance and Economics

Sarlis,

Tjortjis

2024

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In the intersecting fields of data mining (DM) and sports analytics, the impact of socioeconomic, demographic, and injury-related factors on sports performance and economics has been extensively explored. A novel methodology is proposed and evaluated in this study, aiming to identify essential attributes and metrics that influence the salaries and performance of NBA players. Feature selection techniques are utilized for estimating the financial impacts of injuries, while clustering algorithms are applied to analyse the relationship between player age, position, and advanced performance metrics. Through the application of PCA-driven pattern recognition and exploratory-based categorization, a detailed examination of the effects on earnings and performance is conducted. Findings indicate that peak performance is typically achieved between the ages of 27 and 29, whereas the highest salaries are received between the ages of 29 and 34. Additionally, musculoskeletal injuries are identified as the source of half of the financial costs related to health problems in the NBA. The association between demographics and financial analytics, particularly focusing on the position and age of NBA players, is also investigated, offering new insights into the economic implications of player attributes and health.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%