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We turn game-level data into organizational decisions.
We classify sports organizations by their competitive architecture, predict where they're headed before the standings confirm it, and translate what the data reveals into the organizational decisions that determine whether a team wins a championship or stays stuck in the middle tier for a decade.
Most sports analytics firms stop at the descriptive — here's what happened, here's what the numbers say. We go one level deeper: connecting on-ice and on-field performance to the leadership and organizational decisions that separate sustained winners from the permanent middle.
Data-Driven Intelligence for Sports Organizations
Data-Driven Intelligence for Sports Organizations
We gain novel insights by treating game performance, competitive trends, and organizational history as data. We use statistical modeling and AI to analyze this information and translate the findings into the decisions that determine whether a program contends or stays stuck.
Our vision
Our vision
We believe most sports organizations know more about their opponents' box scores than they know about their own competitive trajectory — and that gap is where championships are won and lost.
Every program's story looks different on the surface. Dig deeper and they share the same structure: a season that matters, a roster and a record that don't always tell the same story, and a leadership group that needs to trust the answer before they can act on it.
ORRO exists because we find all of it interesting — the pattern behind the standings, the model that reveals it, and the harder work of translating what we found into a decision a GM, coach, or ownership group can actually stand behind.
We bring rigorous analytical methods and organizational fluency to every engagement, and we stay until the answer is both right and understood.
About us
About us
ORRO was founded by Jessica Pearson and Liz Goldstein, graduates of the University of Virginia Darden School of Business. Together they bring strategic leadership, deep technical expertise, and over 10 years of experience applying AI and statistical modeling to complex, real-world problems — including sports analytics research presented at LINHAC 2026.
Featured speakers at DataConnect 2024 in Columbus Ohio, DataConnect 2025 in Columbus Ohio, and LINHAC 2026 at Linkoping University in Linkoping Sweden
Our services
Our services
From a single-season diagnostic to an ongoing analytics partnership, we offer a range of engagements built around where your organization is right now — including custom research for the specific question keeping your front office up at night.
From a single-season diagnostic to an ongoing analytics partnership, we offer a range of engagements built around where your organization is right now — including custom research for the specific question keeping your front office up at night.
Sample dashboard from our NHL Organizational Map
Our approach
Our approach
Every engagement starts with genuine curiosity about your organization's specific situation — not a template waiting to be filled in.
We've applied our methods across professional and college programs, in multiple sports. The competitive context changes. The commitment to building something that actually fits your organization's question doesn't.
Our work typically moves through four phases:
Our work typically moves through four phases:
- Deep discovery to understand what you really need to know;
- Exploratory analysis to find the right method;
- Rigorous modeling and insight development;
- A final deliverable built for the people who will act on it.
who we work with
who we work with
We work with professional sports organizations and college athletic programs that want their competitive position and trajectory understood with the same rigor they bring to scouting and player evaluation. Our broader analytical foundation spans workplace strategy, consumer brands, and academic research — the same commitment to rigorous, bespoke analysis, applied to your sport.
© 2026 ORRO AI Genius, LLC. All rights reserved.
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