INSTRUCTIONS:

Write a minimum of 250 words for each of the discussion questions below.

Perform a google scholar (Links to an external site.) search to identify Capstone project relevant research articles published in the most recent years, e.g., 2015-present. Think creatively about what phrases to use in the scholar search to find articles.

NOTE: The Welcome module has a page listing reference books on Sports Analytics(attached below). You can also find relevant journal articles cited in these books.

NOTE: Do not review user-written web pages/ reports on sports analytics projects.

Review three articles of your choice each written by different authors and published in different years and in different journals.

Post a summary of these three articles and how it informs your capstone project.

[NOTE: Please do not reproduce the content of the articles including the abstract. You must write your own sentences to reflect the information contained in the article and your opinions about the article on how it informs your Capstone project]

Capstone Sponsor(Capstone Project)*

Georgetown Analytics and Technology (Links to an external site.) is an IT consulting firm with a focus on Big Data implementation, with Data Scientists serving the Washington DC Metro area commercial market and federal government, is looking for students to use AI models to help in sports development and performance.

Capstone Background and Deliverables

Develop Predictive AI Models and Data Sets for Sports Development

Currently there are Increasing demands on sport development coaches, trainers, and health and medical personnel to identify talented individuals for specialist development across all sports. Talent identification results in the streamlining of resources to produce optimal returns from a sports investment. However, talent identification for individual and team sports is complex, and success prediction is imperfect. The aim is to develop predictive artificial intelligence models and data sets to qualify the impact of sports physiology, nutrition, psychology, and applied biomechanics impact of maturity-related differences on the long-term outcomes, particularly for track and field and female participants.

Maturation is a primary confounding variable in talent identification. Furthermore, for talent identification programs to succeed, valid and reliable data and intelligence must be qualified and accepted and implemented in a range of performance-related categories. Limited success in scientifically based talent identification is evident in a range of team sports. Genetic advances challenge the ethics of talent identification in adolescent sport. However, the environment remains a significant component of success prediction in sport.

Students will develop artificial intelligence and data models to support the following area:

• Applied Biomechanics,
• Sport Physiology,
• Sport Nutrition, and
• Sport Psychology