Formula One Race Analysis Using Machine Learning

Title

Formula One Race Analysis Using Machine Learning

Creator

Keertish Kumar M.; Preethi N.

Description

Formula One (also known as Formula 1 or F1) is the highest class of international auto-racing for single-seater formula racing cars sanctioned by the Fation International de automobile (FIA). The World Drivers Championship, which became the FIA Formula One World Championship in 1981, has been one of the premier forms of racing around the world since its inaugural season in 1950. This article looks at cost-effective alternatives for Formula 1 racing teams interested in data prediction software. In Formula 1 racing, research was undertaken on the current state of data gathering, data analysis or prediction, and data interpretation. It was discovered that a big portion of the leagues racing firms require a cheap, effective, and automated data interpretation solution. As the need for faster and more powerful software grows in Formula 1, so does the need for faster and more powerful software. Racing teams benefit from brand exposure, and the more they win, the more publicity they get. The papers purpose is to address the problem of data prediction. It starts with an overview of Formula 1s current situation and the billion-dollar industrys history. Racing organizations that want to save money might consider using Python into their data prediction to improve their chances of winning and climbing in the rankings. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-540, pp. 533-540.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Decision-making; Machine learning; Motorsport; Predictions; Race; Simulation; Strategy

Coverage

Keertish Kumar M., Christ University, Pune, Lavasa, India; Preethi N., Christ University, Pune, Lavasa, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981196087-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-6088-8_47

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149862381&doi=10.1007%2f978-981-19-6088-8_47&partnerID=40&md5=abfa68c37402b5bfca71f0e8d999efdf