Market landscape — what edge tools already do (and where we can differentiate)
This is the market-research companion to our decision packet. It keeps us honest about what’s already built, and it highlights where a truth-layer + transparent evaluation loop can still create real novelty.
Why these products exist
Sports betting markets generate high-volume, time-sensitive data (odds, props, injuries, lineups). That makes “data + workflow” a product category on its own: bettors want line shopping, alerts, tracking, and quick research tools.
Most commercial products optimize for speed and convenience. Our project is intentionally different: it starts with auditable truth and uses that to produce edges we can measure (EV, CLV, calibration) rather than vibes.
Representative edge tools (benchmarks, not direct competitors yet)
| Product | What it does | Notes |
|---|---|---|
| Unabated | Odds screen + line shopping + tools for bettors. | Paid subscription (pricing varies by plan). |
| OddsJam | Line shopping, arbitrage, positive EV tools. | Subscription product; not a raw data feed. |
| Props.Cash | Player prop research dashboards. | Consumer tool; useful benchmark for UX + metrics. |
| Action Network | Content + picks + odds tools. | Subscription tiers; strong media layer. |
| Pikkit | Bet tracking + analytics. | Great example of “workflow” value beyond raw odds. |
| Betstamp | Bet tracking + odds screen products (ProphetX). | Shows expectations for CLV, alerts, and book connectivity. |
What most tools do well
- Line shopping and identifying best available prices across books
- Alerts for line movement and market opportunities
- Projections / “prop models” that output a number and flag +EV
- Bet tracking, ROI summaries, and sometimes CLV
Common gaps we can exploit
- Opaque data lineage: users can’t reproduce results or audit where a number came from.
- Weak backtesting rigor: survivorship bias, missing line history, and unclear timestamp alignment.
- Under-modeled “role” shifts: lineups/injuries → usage/minutes changes are often handled heuristically.
- No “truth warehouse” mentality: raw feeds aren’t stored in a replayable, canonical form with QA.
Our differentiation thesis (works regardless of which vendor is chosen)
Truth-first
- Canonical warehouse with versioned ingests
- Idempotent backfills and coverage reports
- Cross-source reconciliation (vendor odds vs our snapshots)
Evaluation-first
- CLV tracking as a first-class outcome
- Calibration + error decomposition by market type
- “Why we won/lost” explanations linked to raw truth
Decision-variable note: if Jeffrey selects an enterprise stack, we’ll expand into in-play edges earlier; otherwise we prioritize pregame props and build the line-movement history ourselves via scheduled snapshots.
Past releases
Release 2025-12-21 (bundle v4) — this page did not exist
New in v5.