Identifying the Best Coach by an Improved AHP Model
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Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2014, Article ID 971648, 7 pages http://dx.doi.org/10.1155/2014/971648 Research Article Identifying the Best Coach by an Improved AHP Model Jinming Xing,1 Chang Zhao,2 Xiao-liang Wang,3 and Nan Xiang2 1 School of Physical Education, Northeast Normal University, Changchun 130024, China 2 Faculty of Chemical, Environmental and Biological Science and Technology, Dalian University of Technology, Dalian 116024, China 3 Department of Electronics & Information Engineering, Dalian University of Technology, Dalian 116024, China Correspondence should be addressed to Jinming Xing; [email protected] Received 14 April 2014; Revised 7 June 2014; Accepted 7 June 2014; Published 17 July 2014 Academic Editor: Fuding Xie Copyright © 2014 Jinming Xing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The evaluation of coaches in college ball game is very essential, since a better choice of coaches will help get more scores fora team. In this paper, a simple, however, comprehensive model is proposed to evaluate college coaches of a century. By comparing the compressive index of different coaches in the evaluation, the top five coaches are found with their influence over time discussed either. Based on data of certain sport, a basic model is introduced. The superimposed application of the model makes it possible for the data of different levels to deliver proper evaluation. And by optimizing the data, we can provide precise evaluation items and authentic synthetic scores for each coach. Among their applications, the models of various sports are obtained in which relatively accurate results are still available. Although a number of deficiencies were disclosed by multiple expansions, this model is still simple, accurate, and valuable to select the best coaches. 1. Introduction change the question into specific conditions which can evaluate the objectives. As to those conditions of the same Volumes have explored the success of the sport teams or the kind, we make our judgments on their importance level and related competitive pattern based on simple data analysis and build matrix with them. By calculating the largest eigenvalue statistics [1, 2], however, to some extent, with the problem and eigenvector of this matrix, we get the weight of each of inaccuracy. Moreover, through such simple analysis, we evaluating condition and then achieve the appraisal of the cannot evaluate the coaches in multiple aspects accurately [3, greatest college coaches with all the work above [5]. 4]. In order to address the shortcomings above, we optimize our AHP model and build a new evaluating system [5]. After that, we define, screen out, and classify the specific As an important member of the sports team, coaches conditions for the evaluation. In practice, we build a sub- played a role of selecting outstanding athletes and drawing model firstly to test the influence of gender and time axis up the whole plans for their training [6, 7]. Thus, the quality which are both not clear yet. During our test on time axis, we of coaches is crucial to the development of the team [8–10]. screen out secondary index to build statistics model which Therefore, building appropriate models in selecting the best shows the relationship between times and team intuitively. coaches is of great importance and the process can be as Thismodelalsogivesusaclearvisionofthechanging follows. of American basketball competence. It is easy to find that Firstly, we determine the basic model as the basis of coaches who work in an environment of higher competence our work. After the analysis upon the subject, we find that tend to have higher professional level. At the meantime, we this can be evaluated by different indexes—qualitative and select index sharing the same level with time to finish the quantitative [5, 11]. Besides, we also find that it is so hard whole analysis hierarchy process. to build specific differential or algebraic formula due to the Finally, we use our model to do the appraisal of the ten failing quantization and uniform of each index. However, greatest CBC and more persuasive top five in them after more the model of analytic hierarchy process (AHP) can avoid indexes being added in the model. With the existing data, this the weakness of relation between each index [12–14]. We model can be applied to different sports to select the greatest 2 Abstract and Applied Analysis Choice of coaching legends Coaching career Winning rate Individual award Team award length Coaches Figure 1: General graph model. coaches in different fields. However, the comparison between Table 1: Variable definitions mentioned in model 1. results of the model and common sense can help us find that Items Characters the results do not seem to be so accurate which means more analysis is needed to contribute to the optimization. Year Team quantity Average team quantity in near a century 2. Model Building Participation times in middle year of coach’s career The goal of our team is quite clear which is to look for “the Winning rate best of all time college coach” of both male and female for the previous century. This paper introduces a quantifying model with factors that have already been precisely classified. The 350 model can be used to evaluate coaches on their excellence directly. It contains impersonal data such as time, winning 300 rate, and game award and, at the meantime, personal factors 250 such as experiment experience which works in importance classification process. 200 Figure 1 shows our model intuitively. Teams 150 2.1. Assumptions. Other factors such as individual personali- 100 ties do not influence our results [11]. The assuming ranking of the factors is accurate while 50 creating and using the model [15]. 0 2.2. Factors. In this part, we consider as many as possible 0 20 40 60 80 100 120 direct factors that influence coaches’ professional level and Time career awards, among which choose four as key indexes, which are coaching career length, winning rate, individual Figure2:Curveofyearandteams(basketball). award, and team award [16]. Winning rate refers to the ratio between NACC team quantity in middle year of objective’s career [17]andaverage quantity of nearly a century to judge the average intense of indexes of the same class. The model we introduced adds levelofcompetenceinthisfield[18, 19]. Optimization of the more persuasion to index of high level with integrative use of winning rate is achieved with algebraic expression, fitness of indexes of lower level and reference to objective law. In this winning rate, and average intense level of competence using way, we fully utilize the existing data and optimize high level a certain coefficient [20]. We find that time axis plays an index to make our model closer to reality. important role in this process. With the passage of time, the increase of team quantity indicates the increase in winning rate under objective coaches. As for this question, analysis 3. The Proposed Model hierarchy process does not only split practical and abstract problems into certain specific evaluating index but also show 3.1. Description of Timeline. First we assign different letters to howindexoflowlevelinfluencesthechaintransitionthrough the variables in favor of the later modeling (Table 1). computing index of different level. By searching quantity of NCAA participating teams of Moreover, we can achieve the comparability between each different years and conducting simple regression analysis, we decisive factor by quantizing and sequencing the importance get the curve graph in Figure 2 (Table 2). Abstract and Applied Analysis 3 Table 2: Coefficients in Figure 2. Unstandardized Coefficients Standardized Coefficients Std. Error Beta Sig. (Constant) −3729.584 116.622 0.954 −31.980 0.000 Wecanfindfromthisgraphthatquantityofparticipating Table 3: Relevant data in optimization. teams first experiences a fast rising section and then gradually decreases: Attached list one Coach Year () =+. (1) John Wooden 1961 0.804 221.831 0.783 Adolph Rupp 1951.5 0.822 202.689 0.731 By applying Matlab, we get a model that basically fits this trend: Jim Calhoun 1992.5 0.697 285.304 0.873 Mike Krzyzewski 1995 0.764 290.341 0.973 = 2.015 − 3729.584, (2) Bob Knight 1987 0.776 274.221 0.934 Dean Smith 1979.5 0.793 259.109 0.902 where is regression variable, that is, time. Then we compute and find the fitting formula of par- Rick Pitino 1996.5 0.706 293.364 0.909 ticipating teams, in which 2.015 is regression coefficient and Billy Donovan 2004.5 0.710 309.484 0.964 other factors that influence are contained in random error Branch McCracken 1952 0.750 203.696 0.670 −3929.584. Denny Crum 1986.5 0.666 273.214 0.799 After this, we optimize the winning rate, one of the Hank Iba 1950 0.731 199.666 0.641 indexes, by using relation between teams. It is easy to Roy Williams 2001.5 0.696 303.439 0.927 understand that competence of each team ascends with Jim Boeheim 1995.5 0.756 291.349 0.967 the participating team and so is level to win. With the Tubby Smith 2003 0.790 306.461 1.062 data of winning rate of related team as reference and the Tom Izzo 2005 0.774 310.491 1.055 optimization, now we can give better evaluation to coach. Gary Williams 1995 0.688 290.341 0.877 We assume the winning rate after optimization is (model Jud Heathcote 1983.5 0.740 267.169 0.868 1) Jerry Tarkanian 1986 0.711 272.206 0.849 − =+∗ . (3) John Calipari 2001.5 0.649 303.439 0.864 Jim Harrick 1991.5 0.656 283.289 0.816 Take John Wooden as an example.