With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.

A low-roughness ultra-thin copper foil was prepared by pulsed electrodeposition on titanium substrate. The influence of sodium 3,3'-dithiodipropane sulfonate (SPS), hydroxyethyl cellulose (HEC), gelatin and collagen additives on the microstructure, mechanical properties and electrochemical behavior of electrolytic copper foil was explored. Furthermore, the reaction mechanism of SPS and collagen additives on electrodeposited copper was discussed. The results showed that at 0.08 g/L collagen concentration, the lowest thickness, the highest microhardness and the optimal surface roughness were achieved to be 5.12 µm, 279.63 HV0.05 and 1.885 µm, respectively. X-ray diffraction results confirmed that electrolytic copper foils prepared by SPS was introduced into the blank solution had a preferred orientation of (220) texture, which benefitted from the synergistic effect of copper ions and additives. The intermediates formed by the additive and Cu+ occupied the active sites on the cathode surface that increased nucleation sites for deposition. Besides, the formed complexes can act as a barrier to narrow ion deposition channels and inhibit the growth of Cu ions.

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