Advances in Economics, Law and Political Sciences

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1 Human resource management from the perspective of fuzzy logic in the soccer clubs a catalyzer for facilitating the balance between sports high performance and financial high performance PhD. Student ALIN MOLCUT 1 PhD. Student LUCIAN SIRB 2 Faculty of Economics and Business Administration, West University of Timisoara, J.H. Pestalozzi, nr.16, Timisoara, ROMANIA 1 Faculty of Economics and Business Administration, West University of Timisoara, J.H. Pestalozzi, nr.16, Timisoara, ROMANIA 2 molcutalin@yahoo.com,1 luciansirb86@yahoo.com, 2 Abstract: - Especially in the last two-three decades, next to the professionalization of the management of classical business environment, within the same framework the development of a sector concerning the sports industry appeared which, similar to the companies that evolve on the market, tends to become a particularly significant element in business and its professionalization. Given this, if one would make an analysis from the standpoint of management relying on human resources and knowledge management, in order to reach the financial high performance of organizations in general, and its balancing with the sports high performance within the organizations in this domain, one would have to pay special attention to human resource management. It might very well be the most import resource an organization possesses. As regards this, the professional monitoring, selecting and training or life-long learning must become a vital objective for any company because on many occasions this aspect requires investing high quantities of money and time resources. Decisions concerning this are doubled by the uncertainty of the environment in which companies act, as well as by the subjectivity of the reasoning of decision-making human factors, which is in itself a trait of the humanoid systems. Due to this, approaching some qualitative methodologies based on the fuzzy logic and designed by the help of a software developed in the Java programming language, that would provide results as reliable and fast as possible, is an innovative efficient aspect for solving various problems the economic operators face in general, as well as the sports ones in particular, as in the case of this article. Keywords: - human resource management, soccer club, performance, fuzzy logics, Java 1 Introduction Mathematics applied to economy has become, in the last decades, a very useful tool for solving different problems encountered in practice by the decisionmakers of organizations. Looking at this issue from a broader perspective, that of the post-modern management, creating and implementing some methodologies having mathematical basis and placing them behind some IT software having userfriendly interface, such as, for example, in researching for this article, the Java programming language, is a direction towards which the management of any organization in any field should go. This is valid for the classic industry or the sports industry, in view of providing the assumptions for a professional high-performance management. 2 Problem formulation Given the ideas presented in the introduction of this research paper, in the second main chapter we desire to describe two aspects. First, there is showing some emphases from the specialty literature concerning the professionalization of sports management and second, describing the methodology relying on fuzzy logic in some aspects connected to the human resource management, mainly as regards selection and recruitment, as well as choosing the most adequate first 11 in a soccer team. It considers two directions: granting an importance weight to each criterion taken into account during the selection process, as well as assessing the sports good form or potential high performance of the players as regards these criteria. All are made while considering the assurance of a balance between the sports high performance and the financial high performance. ISBN:

2 2.1 Literature review In connection to this, the specialty literature concerning the professionalization of management within the sports organizations is rather vast. Thus, Gallardo-Guerrero et al. (2008) wanted to investigate the requirements of management technological tools for the managers of sports organizations in Spain in view of improving their activities. On the contrary, Merigo and Gil-Lafuente (2011) analyzed the use of the ordered weighted averaging (OWA) for human resource selection within the sports management. As well, Taylor and McGraw (2006) explored the practices of human resource management in the non-profit sports organizations. In that context, the results of their research proved that despite the pressure to become much more strategic in the personnel management domain, only a small part of those sports organizations held formal systems of human resource management. Dowling et al. (2014) sketched the understanding of the professionalization and formalization concept in the sports management area in view for the sports organizations to reach sports and financial high performance. More, Stmad and Guid (2010) presented, in their survey, a new model of analytical system relying on the fuzzy logic as regards the issue of making up a project team. Teodorescu and Urzeala (2013) proposed to analyze coaching as one of the management tools that can induce changes and have benefits at the time of adopting the sports strategies oriented towards reaching high performance. 2.2 The proposed fuzzy methodology As regards the research methodology approached in this article for solving the matter subject to research, it relies only on the qualitative modeling by using the fuzzy logic in the multi-criteria context of the reasoning of decision-making human factor. In this framework, two directions are essential for the human resource management within a sports organization in general and a soccer club in particular, in view of reaching the balance between sports high performance and financial high performance. They are the next: recruiting the most adequate and best players within a team and as well, but not any less important, choosing the optimum first 11 that would begin each championship match. These aspects are the advantage for reaching the high performance in general, by any sports organization. In this context, the fuzzy methodology in multi-criteria conditions is presented in the next paragraph, which actually shows a special tool used for efficiently handling the vague character of data, within which precision and their significance become sometimes incompatible. The principle of incompatibility, defined by Zadeh (1965, 1999), considered the father of fuzzy logic, converges towards vague (fuzzy) sentences and fuzzy logic tries to create a formalism for the imprecision and ambiguity that are specific to the natural language. Through fuzzification, the linguistic values can be converted into fuzzy sets, which allow an elastic and flexible mathematical modelation. As consequence, the fuzzy methodology in multi-criteria conditions used in this article, for selecting the players in a soccer team in view of making up the team, as well as for selecting the first 11 that will be used for every championship match, is the next: 1) Defining the triangular fuzzy sets related to the importance of criteria considered for the selection process, through fuzzification process, according to Fig. 1. Fig. 1-The fuzzy sets related to the importance of criteria 2) Defining the fuzzy sets related to the evaluation of performance of each player with respect to each selection criterion through the fuzzification process, according to Fig. 2. 3) Establishing the number of selection criteria (denoted by n, where i= 1,..., n). 4) Establishing the number of players participating in the selection process (denoted by m, where j = 1,..., m). ISBN:

3 Fig. 2 - The evaluation of performance of each player with respect to each selection criterion 5) Assessing the weight of performance of each criterion according to Fig. 1, so that all the weights will be transposed as a matrix with one column and n rows, denoted by I, as follows: weight of impor tance > criterion 1 weight of impor tance > criterion 2 I =, i= 1,..., n. (1) weight of impor tan ce > criterion n 6) Assessing the potential performance of each player with respect to each selection criterion according to Fig. 2, so that all the evaluations will be transposed in a matrix with m rows and n columns ( m n), denoted by P, which has on each row the performance evaluation of each player with respect to each selection criterion, so: P11 P12... P1 n P21 P22... P2 n P =, where i= 1,..., n, j = 1,..., m. (2) Pm 1 Pm 2... P mn 7) By multiplying matrices P and I above, it will result the aggregate fuzzy matrix denoted by S from solution, with 1 column and m rows, which will contain on every row the fuzzy scores corresponding to each player susceptible to be selected within the selection process. P11 P12... P1 n weight of impor tan ce > criterion 1 S1 P21 P22... P2 n weight of impor tan ce > criterion 2 S2 S = P I = =, (3) Pm 1 Pm 2... P mn weight of impor tan ce > criterion n S m where i= 1,..., n, j = 1,..., m. 8) Because every row of matrix S contains a fuzzy score as the form of a triangular fuzzy number after multiplying the matrices P and I, these scores will be converted into real, fixed numbers, through the process of defuzzification by using a common, useful and easy to use method, namely the centroid method, as it follows: let Sj = ( sj, sj, sj) from matrix S above, then sj + sj + sj DS ( j ) =, where j = 1,..., m. (4) 3 9) After comparing the real scores corresponding to each player from the every j row ( j = 1,..., m) of matrix S, the best players who have the highest scores will be selected in order to form the soccer team. 3 Problem solving In order to test the multi-criteria fuzzy methodology proposed in the previous chapter, in the selection process for making up a competitive soccer team we will consider forty players of which twenty-five will be chosen for making it up. Next, for the multicriteria selection process to be completed, several scenarios concerning the approach of any match in the championship can be made, as regards the making up of the team. Those scenarios can be made in two phases, selecting the first 18 with which the soccer team will play the championship match, as well as selecting the first 11 of the 18 players called for a championship leg, that will enter the field from the beginning of the match. Those 40 players will be named in a simple manner, by using the terms Player 1 (P.1), Player 2 (P.2),..., Player 40 (P.40), and for all three phases of multicriteria fuzzy selection, the calculation will be made by the help of an IT software designed by using the Java programming language and that is based on the multi-criteria fuzzy methodology described in the previous chapter. In this context, the selection criteria we will consider in all three phases of the multi-criteria fuzzy process indicated in above, as well as their importance weight, can be seen in Table 1 below together with the interface of the ISBN:

4 software designed in the Java programming language corresponding to this phase of Figure 3. Table 1 Linguistic weight of importance of selection criteria (Crt.) Selection criterion Importance weight (Crt.) 1) Height I 2) Physical power FI 3) Effort capacity FI 4) Mobility I 5) Age I 6) Experience PI 7) Head playing I 8) Intelligence playing EI 9) Material demands I 10) Farsightedness in FI play Fig. 3 Interface of the software corresponding to granting the linguistic weight of importance of selection criteria (Crt.) Table 2 shows the assessments of the potential performance of those forty players in connection to each of the 10 criteria set out in Table 1. In Fig. 4, it can be seen the interface of the software designed in the Java programming language corresponding to this phase. Table 2 Languistic assessments (Ev.) of the potential performance of players (P.1, P.2,...,P.40) with respect to the selection criteria Crt. 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) Ev. P.1 FS E S M B S E S B B P.2 B M M E S B M B B M P.3 M B FS E M S E M FS B P.4 FS E M E B FS M E S B P.5 E M B FS E M B S E M P.6 M S B M E FS E M S B P.7 M E B FS S B M E M E P.8 FS M FS E M S E M E FS P.9 E M E M E S M E E M P.10 M E FS M S E M E M E P.11 M S E M M FS E M M S P.12 E M FS E S M M E M E P.13 M S E M E S E E S M P.14 M M S E M FS M E M E P.15 M M E S B B E M FS M P.16 S M M E S E E B M B P.17 M E S M B M S E E M P.18 E M FS S M FS E S M E P.19 B E M S B M E B S E P.20 M S B M E S M B FS E P.21 S M B E S E M B E S P.22 FS M FS B S E M E FS E P.23 E S M E M B S E M E P.24 M E E S B E M E S E P.25 FS B M E M S B M E S P.26 M E S E B M B S E S P.27 M B E M B E S E M B P.28 M B S E S E M B E E P.29 FS M E M E S E B B S P.30 B E FS S E S M E FS E P.31 E S M E M E E S B M P.32 B M S B M E S B M E P.33 M S B M S E M E FS B P.34 E M B E M S B S E E P.35 FS E FS B E FS B E M M P.36 M S E M B M S M E M P.37 B E S E M S E M E S P.38 B E FS S E FS B E FS B P.39 FS E S B M E M B E B P.40 M B FS E S B E S E B ISBN:

5 Fig. 4 Interface of the software corresponding to the language assessments (Ev.) of the potential performance of players (P.1, P.2,...,P.40) as regards the selection criteria After completing the calculation according to the fuzzy methodology described in the previous chapter, by the inference engine of the software designed in the Java programming language, the classification of players is presented in Fig. 5. Considering this, it can be easily set out the first 25 players most adequate to be selected for making up a soccer team. Fig. 5 Final classification of the players within the multi-criteria fuzzy selection process Given these aspects, by the use of the mathematical qualitative tools used in this paper, the other problems mentioned in the beginning of this chapter, selecting the first 18 and first 11 can be rather easy solved in view to approach a championship leg. Considering this, one must consider also the good form and tonus of the players at that time, next to the selection criteria already mentioned. This must be made for selecting the best soccer players while ISBN:

6 considering their opponents and the play system and tactics used. 4 Conclusion In the last decades, the sports industry has been granted gradually more attention, which has led to professionalization of the management of sports organizations. In this context, this paper is included in the constructivism domain, as epistemological current, being an innovative multi-disciplinary approach that comprises several sciences, such as mathematics, computer science, psychology or economics. By this, it is part of the post-modern management paradigm when considering the scientific approach. The methodological tools used for solving the problems subject matter of the research can be genuinely useful for the management of sports organizations or soccer clubs from the point of view of choosing or selecting the most adequate players. Thus, the necessary advantage for acquiring or catalyzing the balance between sports high performance and financial high performance is supplied in efficiency, reliability and accuracy conditions. [7] Zadeh, L.A., Fuzzy sets, Information and Control Journal,Vol. 8, No. 3, 1965, pp [8] Zadeh, L.A., Some reflections on the anniversary of Fuzzy Sets and Systems, Fuzzy Sets and Systems Journal, Vol. 100, 1999, pp References: [1] Gallardo-Guerrero, L., Garcia-Tascon, M., Burillo-Naranjo, New sports management software: A need analysis by a panel of Spanish experts, International Journal of Information Management, Vol. 28, 2008, pp [2] Merigo, Jose M., Gil-Lafuente, A. M., Decision-making in sport management based on the OWA operator, Expert Systems with Applications, Vol. 38, 2011, pp [3] Taylor, T., McGraw, P., Exploring Human Resource Management Practices in Nonprofit Sport Organisations, Sport Management Review, Vol. 9, 2006, pp [4] Dowling, M., Edwards, J., Washington, M., Understanding the concept of professionalisation in sport management research, Sport Management Review, 2014, pp [5] Stmad, D., Guid, N., A fuzzy-genetic decision support system for project team formation, Applied soft computing, Vol. 10, 2010, pp [6] Teodorescu, S., Urzeală, C., Management Tools in Sports Performance, Procedia Social and Behavioral Sciences, Vol. 81, 2013, pp ISBN: