Sports Performance - Essay Example

?Running Head: Sports Performance Sports Performance [Institute’s Sports Performance Decision-making brings to mind two important factors: correct judgment and time limitations. One often studies about decision-making in the context of businesses, and important organizations where decisions need to be made on a daily, even hourly basis. One slightly more unorthodox context, in which one can study decision-making, is sports. In sports performance, these decisions have to be made on not even an hourly, but a second to second basis. There is a variety of decision agents, tasks, and contexts involved in playing sports (Bacharach, pp. 17-55, 1987). The agents include the players, the coaches, as well as the referees. Ball allocation and play calling are examples of the numerous tasks involved, whereas the contexts can be during play as well as timeout. These factors interact together in different ways, and create several unique combinations, each of which requires a unique decision. This results in a wide array of situations in sports where decisions are required. One important step to learning about decision-making from sports is thus to weed out the main features of sports performance which make it practical and easy to study. Locating these features provides us with an essential step in comprehending the importance of sports as an epitome of decision-making situations. Studying decision-making (Diederich, pp. 157-166, 2003) in laboratory has one making disadvantage: the results may be inaccurate due to the unnatural environment. In sports, however, the player the researchers observe is under real pressure, and is fully responsible for his or her decision, which ultimately leads to their decision resembling the decision-making found in the real world. This advantage of studying decision-making is one that has only recently come into being appreciated for its worth (Bacharach, pp. 17-55, 1987). To explore this example, consider three situations facing a forward in soccer, deciding who to pass the ball to during an actual soccer match; doing the same in a computer soccer match and selecting from amongst a set of gambles. One can venture a safe guess that the first situation would yield the most authentic results as opposed to the latter two, due to the natural settings. This feature of sports points out the danger of simulating a natural environment, and the importance of a naturalistic observation setting. The dynamics of sports decisions (Gold & Shadlen, pp. 10-16, 2001) are the second reason why they are ideal to study for the sake of decision-making research. The affect of this dynamism is twofold, to be found in both internal and external dynamics. Internal dynamics hold significance as they show that each decision is made over a course of deliberation, rather than spontaneously. Instead of simply making the decisions as they come, the player/decision-maker accumulates relevant information over a course of time and evaluates it at the time of decision-making. External dynamics, on the other hand, show that the situation itself fluctuates from time to time. At one moment, if the information is available (goalie position), it may be out of reach a second later due to obstruction in the view. These and other variables such as available options reveal the dynamic nature of sports. Research (Gold & Shadlen, pp. 10-16, 2001) finds that decisions are made either ‘online’, or on a reflective basis. This also links the nature of sports to the dynamism earlier mentioned. Online-made decisions are examples of when the decision is made during the task, or under similar shortage of time. A player will make online decisions during the game. This is as opposed to the coach making the game strategy beforehand, using reflective decision-making techniques, which make thorough use of all the relevant information available. Movement control and decision-making (Mayes, pp. 441-461, 2003) go hand in hand when studying sports performance and this can be aided by cognitive modeling. Cognitive modeling helps study the behavior side of sports and decision-making. While a related theory as opposed to model would reveal which concepts relate in behavior, the model is helpful in analyzing just how these concepts are related. That is to say, while a theory would help state the output of the black box, the model of a black box would involve the designing of the black box. Cognitive modeling gains popularity as time progresses, due to its innovative and unique approach to behavioral psychology. When choosing an appropriate model to study decision-making in sports performance with, a dynamic, probabilistic oriented approach is advisable. Attention to the selection of the model is essential, especially when initially observing the efficacy (Mayes, pp. 441-461, 2003). The behaviorist point of view from half a century ago now see rivalry from the new method of studying the information processing that is behind human behavior. Due to this reason, the correct model applied can be beneficial to the application of decision-making theories to sports. Static, deterministic outcome models are not suited for this particular situation, since they are unable to account for the humanistic factors present in decision-making. For example, the deterministic weighting and integration of static cues may adequately model the tennis coach’s choice of a starting service. The suggested model is now studied further in the following sections. In recent history, researchers (Diederich, pp. 157-166, 2003) used sequential sampling models, also known by their nickname ‘horse race’ models, to study decision-making and judgment. They make use of simple assumptions of about the fundamental challenges of information processing. However, their results often indicate complex behaviors. This model initiates by assuming selective and limited attention. This is both a classical and effective method for modeling. For example, ‘if one consider a playmaker faced with an allocation decision (e.g., a point guard in rugby running a play). He has several cues to consider for this decision (e.g., defender distances, teammate shooting percentages, etc) (Johnson & Busemeyer, pp. 19-25, 2005).’ This is where ‘bounded rationality’ comes into consideration. The objective of the decision is to score a goal, while the decision consists of a choice to receive a pass. Next, decision field theory is brought as a specific sampling model of this playmaker’s consideration procedure. Further, decision-field theory makes precise suppositions regarding all the essential procedures discussed above. It allows for non-neutral choices, like a player displaying favoritism for a specific player, in spite of the condition’s specifications (Johnson & Busemeyer, pp. 19-25, 2005). DFT assumes that each piece of information consider results in a different effect on the decision and the opinion of the player, largely due to experience. If the player believes defender distances, and one other player of the team is closely protected this may create an unenthusiastic response towards passing ball to this player based on remembered instances of turnovers (Osborne, pp. 34-40, 2004). Furthermore, these responses are understood to be scaled related to the responses across the whole set of alternatives. This is to say, that if the decision maker has a strong positive reaction to one option, while a different one to another, than she would be adamant on choosing the former. If, for example, one player ranks lower than the other as far as shooting averages is concerned, the former will be considered unfavorable, even if he too has a reasonable average. Another critical assumption (Johnson & Busemeyer, pp. 19-25, 2005) is as follows: one’s preference for an option is affected by the preferences of others. That is to say, that if a player starts to prefer one strategy to others, they will automatically start to consider other options inferior, and this process will be spiral. If they are used to and comfortable with making passes to one teammate, their hesitance to choose a different teammate to pass to will increase over time. The decision is still made under a time constraint and a final choice needs to be made. DFT (Nevill & Balmer, pp. 261-272, 2002) introduces a threshold at which the player will consider an option reasonable to choose. The team player will eventually have to decide whom to pass the ball to, and his accumulating preferences will help make this decision. This generation of possible courses of action is one of the very important aspects of decision-making in sports (Schmidt & Lee, pp. 44-60, 2005). A way one can further apply theory to decision making would be to take the assistance of the game theory (Colman, pp. 287-308, 2004). One can expect that unavoidability in team sports could be understandable as an asymmetric, chronological, non-zero-sum sport. Every individual holds a different level of important in the game, and payoffs are always either negative or positive. Moreover, once can view the game as a field of imperfect information, where each player is aware of certain action in the game from the past, but not all the moves possible in a sport situation (Johnson & Raab, pp. 215-229, 2003). The player cannot make any decisions solely based on the performance of his opponents, but also the performance of his own teammates. Using both these factors, he needs to make a decision, which will derive the highest possible utility considering each side of the coin (Colman, pp. 12-35, 1995). So, to eliminate the disequilibrium, as opposed to the sub game-perfect equilibrium (complexity of the teams sport’s performance can induce player’s partial analysis of some aspects of sport tactical performance), a player could select a trembling hand equilibrium (Johnson & Raab, pp. 215-229, 2003). In addition, when a team is reasoning, they may end up selecting an array of playoffs, which maximize the collective benefit from all the players. In addition, strategic dominance has an important role to play in this circumstance, as the player chooses strategies, which dominate other strategies (Szymanski, pp. 467-477, 2003). One can also use the game theory (Colman, pp. 287-308, 2004) to deduce that the abilities of a sportsman are limited and scarce. If one task is such that it takes up all the effort of the player, there will be less attention and effort left to give the other tasks, which may also be equally significant in the game (Colman, pp. 12-35, 1995). This can refer to even simple things. Like in tennis, the unskilled player may be trying so hard to keep the ball from hitting the ground that they may forget to stay within the allowed area of the court. If the player contains the ability to process information quickly, they will have a more coordinated motor action in the game, and this will automatically facilitate their decision-making. For this reason, the data processing model holds much significance in determining the ability of a player in an individual sport to make quality decisions during the game (Lipshitz & Klein, pp. 331-352, 2001). If many questions need to be answered in a short period, the quality of the decision will also decrease. This is what distinguishes a skilful sportsman from a less skilled sportsman (Raab, pp. 29-35, 2005). Research has proven that as the player learns more and more with time, the attention requirements from the brain decrease and the decision can be made faster, thus, allowing time and mental capacity to evaluate other information alongside (Lipshitz & Klein, pp. 331-352, 2001). However, at the same time, this cannot be used when the player is confronted with unusual situations. Here, only tactical and technical training comes into use, which helps the player think on her feet. This can be seen in tennis, sometimes, when even Roger Federer sometimes performs poorly. This is the reason why even the greatest sportsmen fall short of their expectations sometimes, despite experience and skills (Zucchini, pp. 41-61, 2000). To conclude, this paper briefly examines the advantages of applying cognitive models to sports decisions as well as movement control and behavior. In addition, the paper applies the game theory (Colman, pp. 287-308, 2004) to the different aspects of sports, while stating examples from football as a team sport and tennis as an individual sport. References Bacharach, M. 1987. 'A theory of rational decision in games.' Erkenntnis. Volume 27, pp. 17-55. Colman, A. M. 1995. Game Theory and Its Applications in the Social and Biological Sciences. Routledge. Colman, A. M. 2004. 'Reasoning about strategic interaction: Solution concepts in game theory.' Psychology of reasoning. Psychology Press, pp. 287-308. Diederich, A. 2003. 'MDFT account of decision making under time pressure.' Psychonomic Bulletin and Review. Volume 10, Issue 1, pp. 157–166. Gold, J. I., & Shadlen, M. N. 2001. 'Neural computations that underlie decisions about sensory stimuli.' Trends in Cognitive Neuroscience. Volume 5, pp. 10–16. Hayes, R. M. 2003. 'Cooperative game theoretic models for decision-making in contexts of library cooperation.' Library Trends. Volume 51, Issue 3, pp. 441-461. Johnson, J. G., & Busemeyer, J. R. 2005. Rule-based Decision Field Theory. Lawrence Erlbaum Associates. Johnson, J. G., & Raab, M. 2003. 'Take the first: Option generation and resulting choices.' Organizational Behavior and Human Decision Processes. Volume 91, Issue 2, pp. 215–229. Lipshitz, R., Klein, G. 2001. 'Taking stock of naturalistic decision making.' Journal of Behavioral Decision Making. Volume 14, pp. 331–352. Nevill, A. M., Balmer, N. J. 2002. 'The influence of crowd noise and experience upon refereeing decisions in football.' Psychology of Sport & Exercise. Volume 3, Issue 4, pp. 261–272. Osborne, M.J. 2004. An Introduction to Game Theory. Oxford University Press. Raab, M. 2005. Think SMART, a review of teaching decision making in sports from an ecological rationality perspective. Springer. Schmidt, R. A., & Lee, T. D. 2005. Motor control and learning: a behavioral emphasis. Human Kinetics. Szymanski, S. 2003. 'The assessment: the economics of sport.' Oxford Review of Economic Policy. Volume 19, Issue 4, pp. 467-477. Zucchini, W. 2000. 'An introduction to model selection.' Journal of Mathematical Psychology. Volume 44, Issue 1, pp. 41–61. Read More