中文体育类核心期刊

中国人文社会科学期刊AMI综合评价(A刊)核心期刊

《中文社会科学引文索引》(CSSCI)来源期刊

美国《剑桥科学文摘》(CSA)收录期刊

中国高校百佳科技期刊

CBA运动员赛季前体能测试成绩与赛季效率状态的关联分析

Correlation Analysis Between Pre-season Physical Fitness Test and Season Efficiency Status for Chinese Basketball Association Players

  • 摘要:
      目的  探讨利用赛季前体能测试成绩预测中国男子职业篮球联赛(Chinese Basketball Assuciation,CBA)运动员赛季效率状态和综合表现的可行性,研究影响综合表现的关键因素,为干预和强化特定的体能和技能状态提供参考。
      方法  选择2015年CBA运动员赛季前体能测试(折返跑、强度投篮、负重深蹲、负重卧推)数据,并在CBA官方网站查询当赛季相应的效率状态值。通过多因素logistic回归分析建立以赛季前体能测试和基线数据为基础,预测CBA运动员赛季效率状态的模型。
      结果  经逐步回归分析,将强度投篮命中率、负重深蹲、年龄纳入最终预测模型,模型的χ2=34.014(P < 0.010),多变量模型拟合良好(Hosmer-Lemeshow检验,χ2=5.433,P=0.710)。强度投篮命中率比值比(OR)=1.041,95%置信区间(95% CI)1.007~1.075,P=0.017和负重深蹲(OR=1.084,95% CI 1.013~1.160,P=0.020)是影响CBA运动员赛季效率状态、综合表现的独立因素。模型的曲线下面积为0.753(95% CI 0.679~0.828,P < 0.050)。
      结论  模型可用于预测CBA运动员整个赛季的状态和表现,并可通过对投篮能力和下肢力量的训练,提高运动员的整体表现水平。

     

    Abstract:
      Objective  The feasibility of predicting the comprehensive performance of Chinese Basketball Association(CBA)professional players was explored with pre-season physical fitness test results and the key factors influencing the comprehensive performance, which may provide reference to intervening and strengthening certain physical and skill status.
      Methods  Pre-season physical fitness test data of CBA players in 2015, including shuttle run, deep squat, bench press and field-goal percentage were selected, and the corresponding efficiency rating of the current season on CBA official website was checked. By multivariate regression analysis based on the test and baseline data, the model was established to predict the season efficiency rating of CBA professional players.
      Results  After the strategy of stepwise regression analysis, fieldgoal percentage, deep squat, and age were included into the final predicting model, χ2=34.014(P < 0.010), and the model was fitted well(Hosmer-Lemeshow test, χ2=5.433, P=0.710). Field-goal percentage(OR=1.041, 95%CI: 1.007-1.075, P=0.017)and deep squat(OR=1.084, 95%CI: 1.013-1.160, P=0.020)were independent affecting factors for season efficiency rating of CBA players. AUC of the model was 0.753(95%CI: 0.679-0.828, P < 0.050).
      Conclusion  The model can be used to predict the status and performance of CBA players in the whole season, and adjust the development trend of players'season performance by intervening and strengthening the shooting level and lower limb strength.

     

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