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Badminton Men's Singles Match Final Racket Motion and Scoring Rate Regression Analysis id515122375 pdfMachine by Broadgun Software - a great PDF writer! - a great PDF creator! - http://www.pdfmachine.com http://www.broadgun.com BBiiooTTISSNe e: 097cc4 - h7h435 nnoolVlolouome 1gg0 Issyuye 5 An Indian Journal FULL PAPER BTAIJ, 10(5), 2014 [1132-1137] ’ Badminton men s singles match final racket motion and scoring rate regression analysis Jinchao Gao1, Yu Huang1, Qiaoxian Huang2* 1School of Sports Leisure, Beijing Normal University Zhuhai, Zhuhai 519087, Guangdong, (CHINA) 2School of Physical Education & Sports Science, South China Normal University, Guangzhou 510631, Guangdong, (CHINA) ABSTRACT KEYWORDS ’s Badminton match final racket motion is very crucial to badminton match Badminton; result. Look for relations between badminton final racket motion and Scoring rate; badminton match gains and losses are surely very important to athlete Simple linear regression model; ’ matches, players’ technical training. Especially for comparable players Least square method. final racket motions will become the focus. The paper takes world top ’s badminton singles players as research objects. Adopt simple linear men regression model, and use SPSS software to make research on final racket motion and scoring rate relationships. Utilize least square method solution ’s on simple linear regression equation parameters to calibrate solved model parameters. Therefore it gets badminton final racket motion and badminton match gains and losses relationship, which has important significances in ’ technical and tactics training. future badminton players 2014 Trade Science Inc. - INDIA INTRODUCTION most beneficial one for winning the match is the key to research problem. The problem has attracted attentions Modern badminton was originated from Britain. In of numerous experts and scholars, for example, as ear- “Women’s badminton the 80s, Chinese badminton has already reached world lier as 1989, Lin Jian-Cheng in ”; he advanced level. With Beijing Olympic Games hosted in match initiative techniques and regularly discussion 2008, China has gradually been among world sports had ever highlighted final racket motion success rate powers. Meanwhile, Chinese badminton performance impacts on winner ability[1]. Zhou Zhi-Hui selected five ’s is moving towards higher targets. But Chinese men world top badminton singles players they were respec- ’s singles performance is not stable. Therefore, Chinese tively Gade, Li Zong-Wei, Lin Dan, Park Sung-hwan, ’s singles still need to reinforce the internal work, men and Taufik. In final racket motions, Li Zong-Wei used and further promote tactics levels. smashing technical efficiency was the highest, while In badminton competitions, final racket motion blocking higher used proportion were successively plays crucial roles in match result. Final racket motions Gade, Li Zong-Wei, Lin Dan, while Park Sung-hwan ’s driving used proportions were higher. totally have twenty kinds, which motion on earth is the and Taufik BTAIJ, 10(5) 2014 Qiaoxian Huang et al. 1133 FULL PAPER ’s singles players’ final racket Made statistics of five men defined value, Y has its distribution, if Y exists some technique used rates, it got following result. Smashing numerical features, then their values are defined with was highest motion in used proportions, scoring rate X extracting, it can use a group of samples x1, y1 , and losing rate were 37.72% and 12.64%. Secondly …, was lifting used proportion, scoring rate and losing rate x2 , y2 , xn , yn to estimate numerical features. were 4.12% and 14.84%. Blocking used proportion For two variables with linear relations, due to interfer- was 9.31%, scoring rate and losing rate were 2.26%, ence of random factors, in two variables linear rela- 14.46%. Intercepting used proportion was basically the tions, it should include random error term , that simple same as lobbing that were respectively 6.91%, 6.86%, linear regression model: the two scoring rate were respectively 9.83%, 6.51%. y a bx (1) Hooking used proportion was 6.17, its scoring and los- Among them, a is called constant term, b is called ing rate were respectively 4.78%, 7.22%, and rushing, regression coefficient, is called random error. pushing, driving as well as other motions use propor- Original data tions were little. The paper on the basis of previous research re- The data is from Gade, Li Zong-Wei, Lin Dan, Park ’s singles players’ per- sults, it analyzes badminton men Sung-hwan, and Taufik five world top men badminton ——final racket ’ video formance important influence factor players. By all twenty-one important competitions statistics and analysis, it gets following data information motion, discusses linear regression algorithm, and pro- “World excellent badminton TABLE 1. Data is from vides theoretical basis for it, simple linear regression ’s singles players’ technical and tactics features re- model is applying linear regression into sports, it makes men ’ performance and has re- ”. well evaluation on players search markable advantages in all aspects of sports. TABLE 1: Hitting technique scoring and losing rate status Used Scoring Losing Hitting technique MODEL ESTABLISHMENT percentage% rate% rate% Smashing 23.43 37.72 12.64 With China gradually moving towards sports pow- Lifting 10.23 4.12 14.84 ers, badminton is also required to be constantly rose. ’s singles performances Blocking 9.31 2.26 14.64 Due to Chinese badminton men Net receiving 7.6 2.66 11.33 are not stable, and final racket motion is crucial to match Intercepting 6.91 9.83 4.71 (As Figure 1). High lobbing 6.86 6.51 7.12 Simple linear regression model Hooking 6.17 4.78 7.22 Rushing 4.8 7.97 2.41 Propose that random variable Y and variable X Pushing 4.8 6.51 3.51 have some correlations, is a variable that can be X Driving 4.63 3.59 5.42 controlled and precisely observed, as age, testing mo- Driving clear 3.83 2.52 4.81 ment temperature, time and pressure so on. It can ran- ’s Net blocking 3.14 6.24 0.8 domly take X n pieces of values x1, x2 ,, xn , X is Placing 2.86 0.66 4.51 regarded as non-random variable and common inde- Service 2.63 2.52 2.71 pendent variable. Because Y is random, to X every Backhand dropping 0.69 0 1.2 High point placing 0.63 1.2 0.2 Killing 0.51 0.8 0.3 Backhand clearing 0.51 0 0.9 Low dropping 0.29 0 0.5 Clearing 0.11 0 0.2 Moving 0.06 0.13 0 Figure 1 : The icon of badminton action Total 100 BiioT10e0c hnol1lo0g0 y An Indian Journal ’ 1134 Badminton men s singles match final racket motion and scoring rate regression analysis BTAIJ, 10(5) 2014 FULL PAPER Final racket motion and losing score relations cise is not so high, it needs to choose higher precise Model preliminary establishment model, each model precise is as following TABLE 2. By above TABLE 2, it is clear that reciprocal equa- Number the hitting techniques, corresponding num- ber 1-21, numbers amount is X , score is Y , use SPSS software to make simple linear regression analysis of two groups of data, it gets final racket motion and los- ing scores function relations, (to easy compare, we ex- pand score result by a hundred times when calculating): 1 y 0.744 7.674 (2) x According to SPSS analysis result, it can get func- tion image as Figure 2. Figure 2 : Relations with batting technique and the scoring SPSS software generated mathematical model pre- rate TABLE 2 : Model summary and parameter estimated value Model summary Parameters values estimation Equation R square F df1 df2 Sig. Constant b1 b2 b3 Linear 0.367 10.424 1 18 0.005 13.792 -0.848 0 0 Logarithm 0.573 24.156 1 18 0.000 21.220 -7.716 0 0 Reciprocal 0.786 66.267 1 18 0.000 -1.025 32.870 0 0 Quadratic 0.470 7.532 2 17 0.005 20.59 -2.685 0.087 0 Cubic 0.546 6.422 3 16 0.005 28.516 -6.761 0.561 -0.015 Compounda 0 0 0 0 0 0 0 0 0 Powera 0 0 0 0 0 0 0 0 0 Sa 0 0 0 0 0 0 0 0 0 Growtha 0 0 0 0 0 0 0 0 0 Indexa 0 0 0 0 0 0 0 0 0 Logistica 0 0 0 0 0 0 0 0 0 tion precise is the highest, so we select reciprocal equa- 21 21 2 2 tion, and make further calibration on its parameters. Q a,b i y i a bx i (4) i 1 i 1 Model parameters calibration a bxi i 1,2,,21 is regression value, In the following, we further define parameters a , b . In the problem, it totally has 21 groups of data Q a,b represents samples observed value yi and re- ’s deviation status, gression value Q a,b gets smaller, x1, y1 , x2 , y2 ,, xi , yi , i 1,2,21, according ÿ to formula(1), it has: deviation will be smaller Q a,b Gets bigger, devia- tion will become bigger. To solve regression model, it y i a bxi i should let deviation value to be the minimum one. E 0, D 2 i 1,2, ,21 i i (3) BecauseQ a,b is a andb function, a and b values i should let Q a,b to arrive at the minimum. Therefore, By formula (2), it has y a bx , i i i regarding and values solution problems are trans- a b i 1,2, ,21, record: BiioTechnollogy formed into solving binary function Q a,b minimum An Indian Journal BTAIJ, 10(5) 2014 Qiaoxian Huang et al.
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