Two Circles / Fan Intelligence
NFL fan segmentation, Cricket Australia matchday intelligence, and cross-sport revenue drivers. A self-directed project exploring how sponsorship pricing actually flows from fan behavior data, modeled on how leading sports analytics agencies structure their work.
RoleSelf-directed project
Duration1 weekend
Year2026
StatusShipped
The brief.
Sports analytics is one of the most interesting applied data spaces — sponsorship valuation, fan LTV, in-venue behavior, and matchday optimization all have to come together in one operator view. I built this project to work through how I'd structure that kind of analysis end-to-end, using NFL and Cricket Australia as the lens.
The work.
FAN_SEGMENTATION_HEATMAP / NFLLIVE
← CasualEngagement intensitySuperfan →
SEG_A
2.4M fans · $42 LTV
2.4M fans · $42 LTV
SEG_B
820K fans · $187 LTV
820K fans · $187 LTV
SEG_C ★
94K fans · $1.2K LTV
94K fans · $1.2K LTV
How I approached it.
- Built three tabs: NFL fan segmentation (casual to superfan with LTV bands), Cricket Australia matchday intelligence (concession spend, in-venue behavior), and cross-sport revenue drivers.
- Modeled segments around engagement intensity rather than demographics — the more useful axis for sponsorship pricing.
- Used a heatmap-style segmentation grid that visually anchors the LTV story without being a stale pie chart.
The outcomes.
- Translated abstract fan segments into concrete sponsorship pricing implications.
- Cross-sport pattern recognition: same engagement curve shape across NFL and Cricket Australia.
- Reusable framework for any sports property looking at LTV-driven segmentation.
The stack.
Tableau
Industry standard at sports analytics agencies
Python
Synthetic data generation modeled on public benchmarks
HTML/CSS/JS
Final deliverable was an interactive HTML demo