Do advanced metrics redefine the role of foreign players? A positional breakdown of domestic vs. foreign players in the Chinese Basketball Association

The aim of the study was to (i) use an clustering analysis method to classify and identify native and foreign basketball players into similar groups based on game-related statistics; (ii) use the Pearson’s Chi-square test to identify the key clusters that affect whether a team enters the playoffs; and (iii) use the classification tree analysis to stimulate the prediction of team ability and the construction of the team roster. The sample consisted of 422 foreign players and 1,775 native players across 9 seasons from 2011 to 2019. The clustering process allowed for the identification of nine native and six foreign player performance profiles. In addition, two clusters (p < 0.001, ES = 0.33; p < 0.001, ES = 0.28) of native players and one cluster (p < 0.05, ES = 0.16) of foreign players were identified that had a significant impact on team ability. These results provide alternative references for basketball staff concerning the process of evaluating native and foreign player performance in the Chinese Basketball Association.

Established in 1995, Chinese Basketball Association (CBA) is the first-tier professional men's basketball league in China. CBA opening doors to foreign players has brought a lot of changes to the Chinese professional men's basketball league. By reviewing previous research, this paper conducts further analysis on the impact of the introduction of foreign players on CBA from the perspective of the sports economy, and gives suggestions on the future development of CBA. Finally, this paper draws a conclusion that the introduction of foreign players increased salary expenditure, ticket income, and the number of sponsors. However, it may also create obstacles to the balanced development of Chinese players and different CBA teams, as well as weaken their advertising value. Therefore, apart from foreign players, there is also a need for CBA to add more talents for better management in team operation and intangible assets, thus establishing a stable system and promoting the maturity of the Chinese basketball market.

Nationality Effects on the Allocation of Playing Time in the Chinese Basketball Association: Xenophilia or Xenophobia?

This study aimed to (1) use the clustering method to build a classification model based on the play-type data of basketball players, to classify native and foreign players into different offensive roles; (2) use the clustered offensive role model to investigate how different offensive roles influence team performance. The sample was drawn from 20 teams spanning five seasons (2017-2021) in the Chinese Basketball Association, comprising 823 native and 228 foreign players. The clustering results obtained fourteen offensive roles for native players and five for foreign players. Subsequent analyses revealed that the offensive roles of two native player clusters, namely N6 Spot-up Wings who Attack (OR = 3.281, p < 0.05) and N13 Bigs who Cut to the Rim (OR = 4.272, p < 0.05), significantly influenced team performance. Conversely, no significant impact was observed for foreign players. The findings of this study offer novel insights into player dynamics and offer coaches a fresh perspective on team composition.

Since 1995, the Chinese Basketball Association (CBA) has made a huge improvement in business development, hiring foreign players and basketball fans' engagement. However, comparing with the NBA (National Basketball Association), CBA still faces impressive challenges in financial scale, influence in the market and operating efficiency. This paper will analyze the difference between two leagues, by using the date-driven and SWOT analysis, which is able to offer CBA some advice to develop CBA into an advanced and more commercial accessible basketball league.

Air pollution and indoor work efficiency: Evidence from professional basketball players in China

This paper uses 2011–2019 data from the Chinese Basketball Association to assess the determinants of playing time with a focus on the effects of players’ national origin. Playing time is explained by an array of standard performance variables as well as each player's characteristics (such as age, height, and weight). Controlling for these factors, we test for whether there is any evidence of preferential treatment for foreign players over Chinese players. Our findings, using both a fixed effects model and the Oaxaca–Blinder decomposition approach, offer consistent support for discrimination in favor of U.S. players and other foreign nationals. Intriguingly, Chinese coaches discriminate against Chinese players even more than non-Chinese coaches. We argue that foreign players draw attendance and hence receive more playing time than is justified by their performance alone.

BackgroundThe application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle’s milk production into low (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<$$\\end{document}4500 kg), medium (4500–7500 kg), and high (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:>$$\\end{document}7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).ResultsThe comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA’s 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.ConclusionThe findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables. The LDA on these PC’s may provide better classification accuracy in clinical diagnostics than on usual measurements. Methodology: Two datasets were utilized for demonstration: one from a Sudden Sensorineural Hearing Loss (SSNHL) case-control study and the other from a Gall Bladder (GB) case-control study. Linear Discriminant Analysis (LDA) was conducted on the actual correlated measured variables for group classification, as well as on the derived principal component variables, to compare their classification accuracies. Performance metrics including Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Classification Accuracy, and F1 Score were assessed. For validation, a third simulated dataset was employed. Additionally, LDA was performed on each dataset using eigenvectors of the control group applied to the cases and vice versa, revealing a strong agreement in classification as measured by the kappa statistic. Results: When LDA was applied to the actual lipid measurements in the SSNHL dataset, the classification accuracy was 57.2%, and the F1 score was 39.7%. However, when LDA was performed using principal components (PCs), the classification accuracy markedly improved to 99.2%, with an F1 score of 98.5%. Similarly, for the GB cancer dataset, the classification accuracy and F1 score were initially 77.2% and 77.3%, respectively. Upon applying LDA with the PCs, these metrics were significantly enhanced to 98.4% and 98.3%, respectively. For the simulated dataset, both the classification accuracy and F1 score were 99.1%. The study also demonstrated that the classification accuracy and F1 score remained consistent regardless of whether the eigenvectors from the cases or controls were used to classify new subjects (Kappa Statistic = 0.962, P &lt; 0.001). Conclusion: In group separation, utilizing principal components significantly improves classification accuracy and overall performance metrics, outperforming the use of the original correlated predictors.

With the rapid advancement of China’s sports sector and the increasing professionalization of the Chinese Basketball Association (CBA), the market value of elite basketball athletes has drawn wide attention in both scholarly and commercial fields. Existing studies, however, have mostly emphasized sporting performance and public visibility, while paying less attention to the significance of online influence and sponsorship outcomes in shaping players’ overall value. Taking CBA athletes as the research sample, this paper develops a multi-dimensional assessment framework that integrates sporting indicators, social influence, and brand partnership effectiveness. Methodologically, the research adopts a mixed approach combining literature review, factor and regression analysis, along with case comparisons of influence-oriented and performance-oriented players. The findings indicate that athletes with stronger online influence secure more collaborations and broader exposure, whereas technically skilled players show higher efficiency in premium endorsements and stable value accumulation. Moreover, a significant correlation exists between online presence and sponsorship numbers, as well as a moderate positive link between performance indicators and brand premium capacity. The results demonstrate that incorporating social and brand dimensions yields a more comprehensive picture of athlete market value, offering theoretical refinement and practical guidance for agencies, brands, the league, and players themselves in strategic planning.

Logistic, linear, and quadratic discriminant analyses were compared in their ability to differentiate hypercalcemic patients with primary hyperparathyroidism from those with malignancy. Linear and quadratic discriminant analyses were performed by use of both untransformed and logarithmically transformed data. Application of principal components analysis with varimax rotation was helpful in revealing the underlying relationships between variables. All discriminant methods identified serum albumin as the best single discriminating test, with the log-quadratic discriminant analysis classifying 81% of patients correctly. The combination of albumin, carboxy-terminal parathyroid hormone, and chloride improved classification accuracy (92% by use of log-quadratic discriminant analysis). Logistic discriminant analysis, using all 20 variables, gave a classification accuracy of 100%. Quadratic discriminant analysis gave better classification than linear discriminant analysis, and both methods performed better when log-transformed data were used. Logistic discriminant analysis followed by discrimination procedures using log-transformed data yielded the highest classification accuracy and reliability of the methods used.

در این مقاله شیوه‌ای برای طبقه‌بندی اناردانه‌های خارج شده از میوه انار بر مبنای کیفیت رنگ ارائه شده است. هدف از این مقاله استخراج و استفاده از ویژگی‌های مبتنی بر تصویر برای درجه‌بندی اناردانه‌ها در سه گروه رنگی (قرمز، صورتی، سفید) و غشاء می‌باشد. طی مراحل پیش پردازش با ارزیابی کیفی فیلترهای رنگی برای بخش‌بندی بر مبنای آستانه، مناسب‌ترین فیلتر ارائه گردید. در این مطالعه از مجموع 26 ویژگی مورد استفاده، 10 ویژگی ریخت‌شناسی و شکلی، 10 ویژگی طیفی و رنگی مستخرج از فضاهای RGB, HIS, Lab و 6 ویژگی بافتی مستخرج از ممان‌های آماری تصویری استفاده گردید. برای طبقه‌بندی و شناسایی چهار گروه تعریف شده از روش تحلیل تفکیک خطی (Linear Discriminant Analysis) استفاده گردید. دقت طبقه‌بندی بر مبنای ویژگی‌های شکلی و اندازه برای جداکردن اناردانه و غشاء 3/ 96%، بر مبنای ویژگی‌ها رنگی RGB، HSI، Lab بیشترین دقت به ترتیب 87 %، 84 %، 1/89 % همچنین نتایج دقت طبقه‌بندی بر مبنای ویژگی‌های بافتی 3/93 % بدست آمد. در پایان با استفاده از روش ترکیب ویژگی‌ها بعنوان ورودی مدل طبقه‌ بند، دقت طبقه‌بندی 99 % بدست آمد.

The main objective of the study is to evaluate the prediction and the classification accuracy of two Supervised Machine Learning Techniques which are linear discriminant analysis (LDA) and logistic regression analysis (LRA) using real data of Type II diabetes. The classification accuracy for both models was determined by the classification accuracy rate. LRA and LDA correctly classified 78.70%and 80.00% of the Type II diabetes mellitus (diabetics and non-diabetics) respectively. The LRA has sensitivity and specificity was 64.38% and 85.35% respectively and the LDA had a sensitivity and specificity of 70.88% and 84.77% respectively. Both algorithms had a good overall classification rate. In terms of proper classification rate, the LDA model slightly outperformed the LRA approach. In general, the findings of this study revealed that the LRA model appears to be appropriate for prediction accuracy while the LDA model appears to be appropriate for classification procedures.

Classification of fresh and spoiled Japanese dace (Tribolodon hakonensis) fish using ultraviolet–visible spectra of eye fluid with multivariate analysis

Classification of Fritillaria using a portable near-infrared spectrometer and fuzzy generalized singular value decomposition