QuantSport
Institutional-Grade Sports Probability Baselines
We compute objective mathematical probabilities for global sports markets. Monitor market divergences, isolate pricing inefficiencies, and evaluate expected value through our B2B data feeds.
The public website is a transparent audit layer. It shows processed events, settlement history, and model metrics while keeping the internal research stack private.
Building your own models? Get early access to our B2B API & Data Feeds.
Data Freshness
04/05, 11:30 AM
Processed Events
188
Published Anomalies
21
Coverage
2 sports / 7 leagues
Public coverage remains intentionally selective. New sports and leagues are added only after data quality, calibration, and live validation pass internal review.
How the quantitative pipeline works
From raw data ingestion to settlement and delivery. The site exposes the architecture of the process without disclosing internal formulas, thresholds, or proprietary weighting schemes.
Data collection
Historical matches, box-score context, lineups, schedule state, and market reference prices are synchronized into the research store.
Feature engineering
Form, home-away splits, fatigue, schedule density, opponent strength, and interaction features are recalculated on the latest archive.
Probability modeling
CatBoost models estimate event probabilities across supported markets and surface only calibrated confidence layers.
Market comparison
Internal probabilities are compared against market-implied prices to isolate probability anomalies and divergence zones.
Data delivery
Raw probability matrices and isolated EV zones are delivered via low-latency API and WebSocket-oriented distribution layers.
Settlement
After the event ends, settlement state, market implied price, and position delta are written into the archive.
Pipeline components
Exact feature formulas, model weights, and internal gating thresholds remain private. The public layer is designed to explain process integrity, not to disclose the research edge itself.
Public Performance Layer
Performance metrics use only settled positions with confirmed archived market prices.
Metrics are calculated across 188 processed events. Yield and equity curves strictly utilize confirmed historical closing lines for 337 priced positions.
The line shows cumulative position delta only for rows with a real archived market price.
Confirmed Positions
337
Historical Yield on Turnover
2.88%
Max Drawdown
-10.32u
Mean Implied Price
2.00
04/05/2026
Live Market Anomalies
No active public anomalies. New valid market divergences will appear here automatically.
Recent Settled Positions
Transparent log of the latest processed positions. If an archived market price is missing, the site leaves the field empty rather than fabricating a value.
| Date | Sport | League | Match | Market | Target Outcome | Implied Price | Model Prob. | Settlement | Position Delta |
|---|---|---|---|---|---|---|---|---|---|
| 04/05 | NHL | NHL | Seattle Kraken vs Chicago BlackhawksScore: 2:4 | Goals Match winner | Over 5.5 Away win (Chicago Blackhawks) | 1.70 2.42 | LOW LOW | +0.70u +1.42u | |
| 04/05 | NHL | NHL | San Jose Sharks vs Nashville PredatorsScore: 3:6 | Goals Match winner | Over 6.5 Home win (San Jose Sharks) | 1.96 1.85 | LOW MEDIUM | +0.96u -1.00u | |
| 04/05 | NHL | NHL | Anaheim Ducks vs Calgary FlamesScore: 3:5 | Goals Match winner | Over 6.5 Home win (Anaheim Ducks) | 1.82 1.68 | LOW LOW | +0.82u -1.00u | |
| 04/05 | NHL | NHL | Edmonton Oilers vs Vegas Golden KnightsScore: 1:5 | Goals Match winner | Over 6.5 Home win (Edmonton Oilers) | 1.92 1.90 | LOW LOW | -1.00u -1.00u | |
| 04/05 | NHL | NHL | Los Angeles Kings vs Toronto Maple LeafsScore: 7:6 | Goals Match winner | Over 6.0 Away win (Toronto Maple Leafs) | 1.88 2.67 | LOW MEDIUM | +0.88u -1.00u | |
| 04/05 | NHL | NHL | Vancouver Canucks vs Utah Hockey ClubScore: 4:7 | Goals Match winner | Over 6.5 Home win (Vancouver Canucks) | 1.91 2.91 | LOW MEDIUM | +0.91u -1.00u | |
| 04/05 | NHL | NHL | Columbus Blue Jackets vs Winnipeg JetsScore: 1:2 | Goals Match winner | Over 5.5 Home win (Columbus Blue Jackets) | 1.75 1.69 | LOW MEDIUM | -1.00u -1.00u | |
| 04/05 | NHL | NHL | Carolina Hurricanes vs New York IslandersScore: 4:3 | Goals Match winner | Over 6.0 Away win (New York Islanders) | 1.81 3.14 | LOW MEDIUM | +0.81u -1.00u |
League coverage
The platform currently covers football, NHL, basketball and tennis. New leagues are added only after model and data validation.
Bundesliga
Germany
La Liga
Spain
Ligue 1
France
Premier League
England
Serie A
Italy
UEFA Champions League
Europe
NHL
USA / Canada
Supported leagues are shown independently from whether they already have settled rows in the public archive.
B2B API & Enterprise Data Feeds
We are opening our raw probability matrices and automated edge detection for institutional testing. Enter your email to join the developer waitlist.
Quantitative Risk & Variance
Algorithmic market exposure carries inherent variance. QuantSport provides objective probability baselines, not financial advice.
Probability baselines are not certainty
The system estimates distributions and divergence zones. A single settled position can still fail even when the model is directionally correct over a large sample.
Variance is structural
Drawdowns and losing streaks are mathematically unavoidable in probabilistic market work, even when the long-run edge is positive.
Capital risk infrastructure is required
Our data feeds are designed for institutions equipped with their own capital allocation, execution, and exposure management models.
Historical transparency is not a promise
Out-of-sample curves, settlement logs, and public metrics exist for auditability, not as a guarantee of forward performance.
Core principle: historical out-of-sample performance does not guarantee future market inefficiencies. Institutions using these data feeds still require their own capital allocation, execution, and variance controls.
B2B Infrastructure
Explore our API documentation, review the public methodology, and integrate objective probability matrices into your automated pipelines.
The public interface is intentionally limited. Raw matrices, low-latency delivery, and historical data exports are reserved for enterprise evaluation.