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Interconnectedness of Badminton Athletes--论文代写范文精选

2016-04-01 来源: 51due教员组 类别: Paper范文

51Due论文代写网精选paper代写范文:“Interconnectedness of Badminton Athletes” 对于目前的生活而言,提出了历史相对性能指标,以定量的分数达到提取信息,该指数被视为权重之间的竞争。这篇社会paper代写范文还提出了构建基于经验的网络,为了运动员之间的相对指标,没有人类现代生活的其他方面更有竞争力。然而,体育运动的发展与一定的规则和公平相关。在我们的年龄变得越来越好,所有改进是为了开放的可能性,需要一个非常复杂的团队合作,而不是仅仅由个人技术赢得比赛。

然而,其他体育游戏都包含了团队合作。在这种游戏中,球员是非常重要的,从运动员的表现可以了解。体育比赛,如羽毛球、网球、壁球等等。然而,仍然许多分析方法,提取任何有用的信息,下面的paper代写范文进行论述。

Abstract 
  The paper proposes the Historical Relative Performance Index in order to quantitatively extract information in the scores hit in the sets of head‐to‐head game in badminton tournaments. The index is treated as the weights of the directed networks built between competing athletes. The paper also proposes the way to build the fully connected network based on the empirically found network in order to have relative index between athletes that have never nor will be met in series of games. Some further directions as well as implementation to small amount of data is described for advanced analysis. 
Keywords: badminton tournament, athlete’s performance, network of athletes

 Introduction 
  Probably, there is no other aspect of human modern life that is more competitive than the world of sport. However, sport grows with certain rules and with the invigorating of the fairness in its development from one tournament to another. However, the tournament system in our age has grown better and better, all improvement was made for the sake of open possibility that only the best athlete would become the champion. From the large varieties of sport, some of them are played as a collective game and it needs a very complex teamwork and not solely by individual skill in order to win a game. However, other large varieties of the game of sports are played individually or if it incorporates teamwork, it did in doubles or very limited number of players. In this kind of game, the head‐to‐head meets among opposite players is very important not to mention the importance to see the performance of an athlete from her historical meetings with other players.   

  Sports tournament, like badminton, tennis, squash, et cetera, records the meetings of players in a large number of tournaments. However, there are still not many analytical approach being proposed to extract any useful information that might exist in them. This is the motivation of the paper. We realize that the head‐to‐head game results are emerged from the complexities of player’s condition at the exact moment of the game. This complexity comes from a  lot of sources, psychological, social, even sometimes political. Simply speaking, to win a game in this kind of sport, the aspect of health fitness, intelligence, emotions, strategy, and techniques must be in the excellent state.   

  This paper reports an endeavor to extract the information in the historical chart of badminton tournament by proposing the Historical Relative Performance Index calculated from the scores gained by any players in sets of games in tournaments. This is followed by building the network of athlete based upon the meeting in various tournaments. In order to complete the network of index, we apply a model for relatively completing the unconnected athletes to build a fully connected network of athlete. Several further directions are also included as well as implementation to small amount of data of badminton scores in two seasons of Olympic Games.   

 Historical Relative Performance Index 
  The model is proposed by constructing a full‐connected network between players in the dataset. The full‐connected network is comprised by vertices that are representing the corresponding players and the edges between players that are representing the relative performance index, reflecting the aggregate results of any historical games between them. For instance, a tournament will yield a matrix of the head to head game results on each player. Of course not all players will have chance to meet with all other players in a single tournament, and it is possible that in a single tournament two players meet twice (or more) in the series of the games. A game might result two or three sets of games, and the scores each player gains are the parameter denoting the result of the game: who wins or loses.  

  It is tempting to have the hypothetical relative performance network that is defined as the fully connected network by filling the empty edges by certain algorithmic calculations based on the existing historical relative performance network. We do this by adopting equation of the chain chemical enzymatic reactions as theoretically proposed by biochemists Michaelis‐Menten (cf. Mathews, et. al., 2000). From figure 1, we see that the red‐dashed lines are the hypothetical edges made up from our calculations.  The question is how we can ‘predict’ the more likely values of the red‐dashed lines. To answer this, we refer to the previous section that the historical network was comprised by the sets of matrices of which elements are the aggregate performance index between two players x and y ( ) α xy . We must realize, at long enough periods most players should play in many series of tournaments and events, and thus most players have ever met each other. 

  Thus, by using these transformative calculations, we could fill most of the zeros in the Historical Relative Performance matrix and we easily measure the strength of certain teams as a tournament is coming along. From the eventually yielded fully connected graph, we could find two qualitative measurements, i.e.:  the likeliness relative qualities of the players in our game. This can be measured respect to time (the degrading or upgrading trends of each player in one team or simply by the comparative of relative performance among players in the opposing teams. Nonetheless, the proposed algorithm might also be used to becoming an alternative index reflecting an athlete’s performance over a series of competitions or tournaments. This interesting issue is left for interested readers.  

 Indonesian Badminton Teams 
  Badminton is a kind of sport that is very interesting in the perspective of Historical Relative Performance Index. Indonesia has been well known for her world class players in many international stages of badminton tournaments, e.g.: All England Tournaments, Sudirman Cup Tournament, including universal sport seasons like Asian Games and the Olympic Games. The single male or woman of a badminton game somehow shows the quality of the players and the emergent results are more likely to reflect the quality of players at the exact moment of the game. From the head‐to‐ head data of Summer Olympic Games 2000 and 20042 , we apply the algorithmic steps as described in the previous section and see the results of the game in a view of historical chart of complex network. This is shown in figure 2. The famous and tight competitions between Indonesian and Chinese teams are shown as the highlighted vertices of the network.

 Ending notes and Further Directions   
  We propose the use of the Historical Relative Performance Index of athletes in badminton tournaments as a way to extract valuable information from the historical data of games among athletes. The idea is to construct an athlete network comprised by the graph of which vertices representing athletes participating in certain tournaments and edges connecting them to those with the corresponding head to head games. The model is constructed along with the capability for the updating of the data purposes as new tournaments are coming. The inspiration from the catalysts‐ inhibitors in biochemical enzymatic reactions are also incorporated in the model in order to build the fully connected network among athletes.   (paper代写)

  From the built model, we can observe a lot of things, for instance the relative strength between an athlete with another that can be adapted to give dynamic evaluative information of the relative performance throughout time and the comparative analysis of one player with another from other teams or country.  The implementation is adapted to the Olympic Games results of badminton tournaments which the head to head games and complex individual capabilities are playing very vital role for the teams in the sustainability in particular tournaments.   However, further directions of this approach can also be directed to the statistical properties of the athlete’s network as it has been pioneered in a lot of works e.g.: the network of scientific collaboration network (Newman, 2000), film actor network (Amaral, et. al., 2000), and a lot more growing researches on complex social network.  This opens a big door for further directions originated in this report. (paper代写)   

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