Expansion

Post-mainnet, SportsPerp expands across three axes: more leagues, more sports, and more primitives. This page documents the direction without committing to specific quarters — post-launch prioritization depends heavily on what the World Cup window teaches us.

Axis 1: More leagues

The OBV Index design is league-agnostic. Adding a new league is a data-onboarding exercise, not a protocol change.

Near-term candidates (H2 2026 → H1 2027)

League	Why	Data-partner coverage
La Liga (Spain)	Second-largest European football revenue, strong global brand recognition	Full coverage, canonical OBV post-match
Bundesliga (Germany)	Deep fan engagement, high-quality data, shorter season = more markets/year	Full coverage
Serie A (Italy)	Historic brand value, returning to competitive tier	Full coverage
Ligue 1 (France)	PSG-anchored storyline, strong player-market appeal	Full coverage
MLS (USA)	Growing North American base, aligns with crypto’s strongest user demographic	Full coverage

Per-league launch requires:

A new section in roster.json with competition_id + season_id.
A dedicated ID range (e.g., 200–299 for La Liga teams + top players).
Insurance fund allocation (scaled to expected liquidity).
Confidence interval calibration if the league’s data has systematic differences from EPL.
A marketing window — leagues launch in their respective seasons.

No on-chain program changes. The same 30 instructions, same 7 PDA types, same math.

UEFA competitions (Champions League, Europa League)

A cross-league tournament dimension. Players from already-covered leagues play a different competition — the OBV computed against the tournament’s event stream is distinct from their league OBV. Technically tractable; product decision pending on whether to launch separate “tournament” markets or fold tournament OBV into existing player composites.

Axis 2: More sports

Soccer is the strongest fit — it has the deepest per-event data, the most mature OBV-style methodology, and the largest global audience. Other sports are interesting but require more custom work.

NFL (American football)

Data: our current data partner does not cover NFL at event-action granularity; alternative providers (Pro Football Focus, Next Gen Stats) exist but with different frameworks.
Index concept: per-play EPA (Expected Points Added) is the NFL analogue of per-event OBV. A composite of EPA + recent-game performance + snap counts (for players) is structurally similar.
Windows: 18-week regular season + playoffs = concentrated market activity from September to February. Complementary to EPL’s August-to-May calendar.
Timeline: realistic target is mainnet 2027 NFL season.

NBA (Basketball)

Data: per-possession efficiency metrics (ORTG, DRTG) are the established analogue; provided by several vendors.
Index concept: composite of efficiency + recent form + usage rate for players.
Timeline: if NFL ships first, NBA is the natural second. 2027–2028 season window.

Cricket

Data: CricViz provides ball-by-ball event data with probabilistic models similar to OBV.
Addressable market: India-centric. Dominant sport in SportsPerp’s largest potential non-Western audience.
Timeline: 2027+, contingent on a data partnership.

Tennis, Golf, MMA

Individual-athlete sports where “performance” is definable but the event-density and traceability differ significantly from team sports. Possible but lower priority than team sports with better data substrates.

Axis 3: More primitives

Beyond leagues and sports, the on-chain program itself gains primitives post-mainnet.

Limit order book

Today: market orders + trigger orders. Next: a true CLOB with limit orders, depth-of-book, and maker/taker dynamics. The maker fee (currently reserved at 5 bps) activates.

Cross-market portfolio margin

Today: margin is per-position per-market. Next: margin shared across a user’s full book — a profitable long in one market offsets margin requirement on a losing short in another. Requires significant new risk math; 2027+ target.

Sub-accounts and isolated / cross-margin modes

Today: one position per (trader, market) pair. Sub-accounts let a single owner control multiple isolated books — useful for strategy compartmentalization and treasury separation.

Permissionless LP deposits

Today: the single global liquidity pool is seeded by protocol treasury (admin via seed_pool / withdraw_pool_lp). Next: open the same pool to permissionless LP deposits, with a share of fee revenue and liquidation allocations and a defined risk profile. The model matches Hyperliquid’s HLP shape — one aggregated pool — by design; the per-market segregated-vault model that earlier versions of SportsPerp used was retired pre-mainnet specifically to enable this. See On-Chain Program → Capital model.

Yield-bearing collateral

USDC is the only accepted collateral today. Post-mainnet, supporting wrapped yield-bearing stablecoins (e.g., sUSDC, JLP) so deposited collateral continues earning yield. Requires oracle integration for the yield asset and margin-ratio math adaptation.

Cross-chain bridges

Solana-native is the right default. Longer-term, letting users deposit from Ethereum / Base / Arbitrum via standard bridges (Wormhole, Mayan, DeBridge) broadens the addressable capital pool. Bridging complexity means this is 2027+ at earliest.

Prioritization principles

Three rules we apply when deciding what ships next:

Follow capital efficiency. A feature that lets existing users trade more, hold larger positions safely, or deposit less to achieve the same exposure wins over a new-market addition.
Follow proven data. A new league or sport requires multi-season backtest validation of the composite index. No launch without a published ρ-style result.
Respect the KISS invariants. No feature that compromises the zero-bad-debt guarantee, the permissionless-keeper model, or the open-source SDK parity. These are load-bearing.

What we probably won’t build

Prediction markets. Binary outcomes are a different product category. Polymarket does it well.
Spot trading. SportsPerp is a perp protocol. Spot trading of performance indices doesn’t obviously improve on existing DEXes.
Live betting on single events. Discrete-outcome betting is a sportsbook product; see above.
Traditional finance assets. The architecture could price anything with an oracle, but that’s not the focus. Sports performance is the wedge.