Evaluating the Impact of Equipment Investments on Olympic Medal Probabilities for Australian Professional Cyclists

AusCycling is the National Sporting Organization for cycling in Australia. Their oversight includes all aspects of the sport, including the high-performance program. As AusCycling begins preparations for the 2028 LA and 2032 Brisbane Olympics, they look to invest in new cycling equipment to boost their expected medal counts. This research takes a three-phased approach at selecting an efficient equipment investment portfolio for AusCycling that results in high medal probability. Using machine learning techniques, we first build a CATBoost prediction algorithm that classifies future Olympic performance into the categories of “earn a medal”, “close to earning a medal”, and “not close to earning a medal”. The model predicts Olympic performance from performance at competitions prior to an Olympic Games with an overall accuracy of 96.3%. In the second phase of the research, a methodology is built to compute the percentage in race time an athlete needs to improve between two World Championship competitions in order to meet a probability threshold for earning a medal at the next Olympics. The final phase of this research combines the models and methodology of the first two sections by creating a Mixed-Integer Nonlinear optimization model which selects optimal equipment investments to maximize predicted Olympic performance while minimizing cost. When used on synthetic data similar to that available to AusCycling, the optimization selects an investment portfolio that yields an expected number of medals of 2.15 across men’s and women’s Team Pursuit and men’s Team Sprint for the 2028 Olympics. This methodology may help AusCycling determine which equipment investments to make ahead of future Olympic competitions.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Kostecki, Katherine E.

Advisor dc:contributor.advisor

• Hosoi, Anette

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162725

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162725