Big Data 10 min read

How to Model Used Sailboat Prices and Rethink the Future of the Olympics

These COMAP MCM problem statements challenge teams to develop statistical models for pricing used sailboats using a large 2023 dataset and to propose innovative strategies for the Olympic Games, evaluating regional effects, data sources, and policy recommendations for sustainable hosting.

Model Perspective
Model Perspective
Model Perspective
How to Model Used Sailboat Prices and Rethink the Future of the Olympics

Problem Y: Understanding Used Sailboat Prices

Like many luxury goods, sailboats vary in value as they age and as market conditions change. The attached "2023_MCM_Problem_Y_Boats.xlsx" file includes data on approximately 3,500 sailboats (36–56 ft) advertised for sale in Europe, the Caribbean, and the USA in December 2020. The data may contain missing values or other issues that require cleaning before analysis.

The Excel file has two tabs—one for monohulled sailboats and one for catamarans—with columns for Make, Variant, Length (ft), Geographic Region, Country/Region/State, Listing Price (USD), and Year of manufacture. Teams may supplement this dataset with additional sources (e.g., beam, draft, displacement, rigging, sail area, hull material, engine hours, sleeping capacity, headroom, electronics, etc.) but must retain the original data and fully document any supplemental sources.

A sailboat broker in Hong Kong (SAR), China has commissioned a report on used sailboat pricing. The required tasks are:

Develop a mathematical model that explains the listing price of each sailboat in the spreadsheet, including useful predictors and a discussion of estimate precision for each variant.

Use the model to assess the effect of geographic region on listing prices, discussing consistency across variants and practical versus statistical significance.

Explain how the regional modeling can be applied to the Hong Kong market by selecting an informative subset of sailboats (both monohulls and catamarans), finding comparable Hong Kong listings, and modeling any regional effect specific to Hong Kong.

Identify and discuss any other interesting inferences or conclusions drawn from the data.

Prepare a one‑ to two‑page report for the Hong Kong broker, including a few well‑chosen graphics.

Problem Z: The Future of the Olympics

The International Olympic Committee (IOC) faces a declining number of bids to host the Summer and Winter Games. Historically, hosting was highly competitive and prestigious, but recent host cities/nations have experienced various short‑ and long‑term negative impacts. Innovators are exploring options such as permanent locations for each season or splitting the Olympics into four smaller games (Winter, Spring, Summer, Fall) to reduce the hosting burden.

COMAP’s Interdisciplinary Committee on Modern Games (ICMG) seeks creative strategies, policies, and metrics to ensure the Olympics remain successful and globally unifying. Teams must recommend options, evaluate feasibility, implementation timelines, and impacts on metrics such as economic effects, land use, human satisfaction (athletes and spectators), travel, future improvement opportunities, host prestige, and any additional criteria they identify. The deliverable is a one‑page memorandum to the IOC describing the strategy and policy recommendations.

Source: https://www.contest.comap.com/undergraduate/contests/mcm/contests/2023/problems/

data modelingstatistical modelingprice analysisOlympicsregional effects
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Model Perspective

Insights, knowledge, and enjoyment from a mathematical modeling researcher and educator. Hosted by Haihua Wang, a modeling instructor and author of "Clever Use of Chat for Mathematical Modeling", "Modeling: The Mathematics of Thinking", "Mathematical Modeling Practice: A Hands‑On Guide to Competitions", and co‑author of "Mathematical Modeling: Teaching Design and Cases".

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