In the world of sports, recruiters are constantly looking for new talent and parents want to…

In the world
of sports, recruiters are constantly looking for new talent and parents want to
identify the sport that is the most appropriate for their child. Identifying
the most plausible match between a person (characterized by a large number of
unique qualities and limitations) and a specific sport is anything but a
trivial task. Such a matching process requires adequate information about the
specific person (i.e., values of certain characteristics), as well as the deep
knowledge of what this information should include (i.e., the types of
characteristics). In other words, expert
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In the world
of sports, recruiters are constantly looking for new talent and parents want to
identify the sport that is the most appropriate for their child. Identifying
the most plausible match between a person (characterized by a large number of
unique qualities and limitations) and a specific sport is anything but a
trivial task. Such a matching process requires adequate information about the
specific person (i.e., values of certain characteristics), as well as the deep
knowledge of what this information should include (i.e., the types of
characteristics). In other words, expert knowledge is what is needed in order
to accurately predict the right sport (with the highest success possibility)
for a specific individual. It is very hard (if not impossible) to find the true
experts for this difficult matchmaking problem. Because the domain of the
specific knowledge is divided into various types of sports, the experts have
in-depth knowledge of the relevant factors only for a specific sport (that they
are an expert of), and beyond the limits of that sport they are not any better
than an average spectator. In an ideal case, you would need experts from a wide
range of sports brought together into a single room to collectively create a
matchmaking decision. Because such a setting is not feasible in the real world,
one might consider creating it in the computer world using expert systems.
Because expert systems are known to incorporate knowledge from multiple
experts, this situation seems to fit well with an expert system– type solution.
In a recent publication Papic et al. (2009) reported on an expert system
application for the identification of sports talents. Tapping into the
knowledge of a large number of sports experts, they have built a knowledge base
of a comprehensive set of rules that maps the expert-driven factors (e.g.,
physical and cardiovascular measurement, performance test, skill assessments)
to different sports. Taking advantage of the inexact representation
capabilities of fuzzy logic, they managed to incorporate the exact natural
reasoning of the expert knowledge into their advising system. The system was
built as a Web-based DSS using the ASP.NET development platform. Once the
system development was completed, it was tested for verification and validation
purposes. The system’s prediction results were evaluated by experts using real
cases collected from the past several years. Comparison was done between the
sport proposed by the expert system and the actual outcome of the person’s
sports career. Additionally, the expert system output and the human expert
suggestions were compared using a large number of test cases. All tests showed
high reliability and accuracy of the developed system.

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