Group Members: Evan, Myles, Brayden, Jake, and Faimon
Group Tag: #CarletonRecruitmentMap
Project Topic and Objectives
Our project will examine the geographic diversity of athlete recruitment across Carleton College athletic programs. We are interested in identifying where Carleton athletes come from and whether different sports recruit from similar or distinct regions of the United States and beyond.
The primary objective is to create an interactive digital map and accompanying visualizations that reveal recruitment patterns across teams, regions, and years. By visualizing athlete hometown data, we hope to answer questions such as:
- Which states and regions contribute the largest number of Carleton athletes?
- Do some sports recruit more nationally than others?
- How geographically diverse are different athletic programs?
This project combines spatial analysis, data visualization, and digital humanities methods to investigate how geography influences collegiate athletics.
Proposed Methodology
Sources
We plan to collect athlete hometown and high school information from multiple sources, including:
- Official Carleton Athletics team roster pages.
- NCAA and conference athletic websites when available.
- Direct communication with Carleton coaches and athletic program staff to gather additional recruitment information and context.
- Publicly available athlete biographies and recruiting profiles.
Using publicly available roster data is a practical starting point because most athletic websites already list hometowns and high schools. Contacting coaches and athletic programs may provide additional information that can help verify data accuracy and explain recruiting strategies that are not visible in roster data alone.
Processes
To gather and organize the data, we plan to:
- Collect roster information from athletic websites and archived team pages.
- Compile athlete hometowns and high school locations into a structured spreadsheet.
- Geocode hometown locations using ArcGIS.
- Store and manage the dataset in Excel or CSV format.
- Create feature layers and hosted datasets within ArcGIS for mapping purposes.
Tools we expect to use include:
- ArcGIS Online
- Microsoft Excel
- Google Sheets
- Flourish
- RAWGraphs
Analysis and Transformations
Once data are collected, we plan to perform several types of analysis:
- Count athletes by state, region, and country.
- Compare recruiting distributions across athletic programs.
- Calculate diversity measures, such as the number of unique states represented on each roster.
- Identify recruiting “hot spots” that consistently supply athletes to Carleton.
Potential visualizations include:
- Choropleth maps showing athlete counts by state.
- Point maps displaying hometown locations.
- Bar charts comparing team diversity.
- Interactive filters that allow users to explore specific sports, years, and regions.
Presentation
Our final project will be an interactive web-based experience centered on an ArcGIS StoryMap or ArcGIS Online dashboard.
The final product may include:
- Interactive maps of athlete hometowns.
- Filters for sport, year, and geographic region.
- Charts created in Flourish or RAWGraphs.
- Narrative sections explaining observed recruitment trends.
- Embedded visualizations that allow users to explore the data themselves.
By combining maps, charts, and written interpretation, users will be able to interact directly with the data and gain a better understanding of Carleton’s athletic recruiting landscape.
Proposed Timeline of Deliverables
Week 1: Data Collection and Organization
- Finalize research question and project scope.
- Identify target athletic programs for analysis.
- Collect roster data from Carleton Athletics websites and archived rosters.
- Contact coaches and athletic staff for additional recruitment information.
- Compile athlete hometown and high school data into a shared spreadsheet.
- Clean and organize the dataset for analysis.
Week 2: Mapping and Analysis
- Geocode hometown locations using ArcGIS.
- Create feature layers and maps displaying athlete origins.
- Analyze recruitment patterns by team, state, and region.
- Create supporting visualizations using Flourish and/or RAWGraphs.
- Identify trends, recruiting hotspots, and differences between athletic programs.
Week 3: Final Product and Presentation
- Develop the final ArcGIS StoryMap or dashboard.
- Integrate maps, charts, and written analysis into a cohesive digital project.
- Refine visualizations and improve usability.
- Write conclusions and interpretations of recruitment diversity patterns.
- Complete peer review, revisions, and final publication on the course blog.
DH Model Projects and Precedents
The following digital humanities projects provide useful inspiration for our work:
- Mapping Inequality (University of Richmond)
https://dsl.richmond.edu/panorama/redlining/
These projects demonstrate effective use of interactive maps, spatial storytelling, and data visualization to communicate geographic patterns.
Individual Interests
Evan
I am interested in combining GIS technology, data visualization, and athletics. I hope this project reveals recruitment trends that are not immediately visible and demonstrates how mapping tools can be used to tell meaningful stories through sports data.
Myles
I am interested in the visual design and presentation side of the project. I hope our final product creates interactive maps and graphics that make recruitment patterns easy to understand and explore.
Brayden
I am interested in collecting and analyzing the data. I hope the project can show how different athletic programs approach recruiting and whether certain regions consistently produce Carleton athletes.
Jake
I am interested in sports and geographic analysis. I hope this project can highlight regional influences on college athletics and provide insights into where Carleton programs find successful recruits.
Faimon
I am interested in using digital humanities methods to explore real-world questions. I hope our project produces an engaging resource that combines data, geography, and athletics in a way that is informative for both students and athletic staff.
Team Athletics Recruitment,
I like the concept for this project, and using the athletics department data is promising. One caveat: usually you want to anonymize student data when pointing to home towns, etc., but since these data are already publicly available it is an interesting question that you should ask both athletics staff and archives.
On the archives, I’d encourage you to also nominate at least one team member to reach out to the archives and see what comparable data we have for years prior to the athletics web page stats. There were big changes in student recruitment in the 60s during the civil rights era and at other points, and it would be fascinating to see how sports recruiting interacted with those developments.