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Carvalho, Adolfo (2022) Using Historical Data to Identify the Best Driver in Formula 1 History. Undergraduate thesis, Dublin, National College of Ireland.
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Abstract
Sports are a core aspect of human society. As a species we’re not only content in playing, we enjoy the competition. In fact, we are the only species on the planet that play games with specific sets of rules while other animals just play randomly to test their physical skills and/or to assess their hierarchical position in their society (Fagen, 1974). As civilization evolved and modernised, so did sports. New tools and new artefacts opened the possibilities for new types of competitions.
Formula 1, in the media and this project referred to as F1, is one of the most technical sports in existence and it is an attraction for sports fans, car lovers, technology enthusiasts and people eager for adrenaline. Being so attractive to fans and bringing an enormous sum of money every year makes F1 the most successful motorsport competition in the world.
Apart from the competition, an aspect of following a sport that any fan enjoys is having arguments and discussions about the sport they follow. Since Muhammad Ali claimed to be “The GOAT”, The Greatest of All Time, among every sports fan around the globe there’s a discussion of who was the greatest in that sport and such debates are responsible for a considerable amount of media coverage.
As we phase through the Artificial General Intelligence stage of AI (Shevlin, Vold, Crosby and Halina, 2019) it is very important to be able to interpret subjective concepts in objective terms. The term GOAT is very subjective and every pundit will bring some emotional baggage to their argumentation. This project hopes to shine a light on how to properly identify what and how objective parameters can be used by an AI to construct a subjective concept.
Although the aim of this project is mainly academic, it is possible to extrapolate the knowledge gained from it into multiple commercial areas for example:
➢ Media and publicity: being able to rank competitors would allow the leagues to focus the coverage on the best performer athletes;
➢ Gaming: sports games are a huge industry and to achieve a realistic competitiveness gaming companies spend much of their efforts on ranking the characters;
➢ Betting houses: data analysis, DA, and AI are already vastly used by betting houses, this project might bring more ideas on how to apply statistical analysis to odds prediction.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports |
| Divisions: | School of Computing > Bachelor of Science (Honours) in Computing |
| Depositing User: | Clara Chan |
| Date Deposited: | 30 Aug 2022 09:51 |
| Last Modified: | 30 Aug 2022 09:51 |
| URI: | https://norma.ncirl.ie/id/eprint/5719 |
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