10TH ANNUAL CASE COMPETITION

10th Annual Case Competition

TFA logo color

This year, we are thrilled to partner with Teach for America for our 10th
annual case competition. Student teams from across the globe are
invited to analyze real data and create analytics-driven solutions to a
real-world challenge and compete for $25,000 in cash prizes!

Registration will open in January 2023.

We would like to thank Prasad Setty for funding the Case Competition prizes. We appreciate his commitment to enhancing student opportunities and advancing people analytics as a force for good.

Predicting the Future of Work:
A Good Judgment Open Forecasting Challenge

During the Wharton Future of Work Conference, we heard from experts on the future of work. What do YOU envision for the future of work?

With that in mind, we partnered with the Good Judgment Project to run a forecasting tournament with attendees. The Future of Work Challenge on Good Judgment Open will continue to run for 12 months for conference participants alongside the 100K registered forecasters on the site.

We look forward to seeing your forecasts on the future of work!

PAST COMPETITIONS

A blue laurel wreath, symbolizing victory, achievement, or honor.

2022

The Wharton People Analytics 9th Annual Case Competition allows undergraduate and graduate students to analyze real data from a non-profit organization to solve a pressing people-related challenge. Students visualized and analyzed data to help the University of Pennsylvania’s Division of Human Resources explore the impact of the Great Resignation/Migration on their own community.
View Finalists
View Winning Team

2021

Case Competition: Students analyzed data from Teach for America. View Winning Team

White Paper: Sponsored by Google, included submissions on a wide array of topics, such as diversity and inclusion, employee productivity, and inequity in gender wage gaps. View Winning Team

2020

Our conference was cancelled due to the Covid-19 pandemic, but students completed the Case Competition. View Finalists

2019

Case Competition: Students analyzed data from Nurse-Family Partnership (NFP).

Research Competition: Researchers submitted papers on diversity, performance measurement, virtual teams, the gig economy, among others.

White Paper: Sponsored by Google’s People Innovation Lab; promoted actionable and thought-provoking insights that are grounded in research.

Startup Competition: Showcased cutting-edge startups working in the field of people analytics, from predictive hiring to employee retention.

2018

Case Competition: Students analyzed data from Global Health Corps (GHC). View Winning Team

Research Competition: Researchers submitted papers on a diverse set of research topics including advanced analytics around compensation, attitude measurement, diversity, and hiring.

Startup Competition: The second annual startup competition showcased cutting-edge startups working in the field of people analytics, from predictive hiring to employee retention. View Winning Team

2017

Case Competition: Students analyzed real data from Teach for America to understand how to best optimize their recruitment resources and implement the right strategies at schools to generate the most admits.

Research Competition: 20 researchers submitted original, unpublished papers on diverse topics, such as the dynamics of organizational culture, spatial management, and social contagion in organizations.

Startup Competition: Provides a platform for emerging and promising entrepreneurial ventures in people analytics.

2016

Case Competition: The competition was run in partnership with Doctors Without Borders (“MSF”).

Research Competition: More than 20 researchers submitted original, unpublished papers for the Research Paper Competition.

2015

Case Competition: Students were challenged to utilize data from Year Up to generate meaningful insights that the organization could use to create positive improvement in their current program.

2014

Case Competition: Students analyzed data from Teach for America (TFA) to improve the TFA’s candidate selection effectiveness.