{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_safebet","slug":"safebet","name":"Safebet","type":"product","url":"https://safebet.ai","page_url":"https://unfragile.ai/safebet","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_safebet__cap_0","uri":"capability://data.processing.analysis.multi.sport.matchup.analysis.and.feature.extraction","name":"multi-sport matchup analysis and feature extraction","description":"Ingests structured game data (team rosters, historical performance, injury reports, weather conditions, betting line movements) across multiple sports leagues and extracts predictive features through statistical aggregation and time-series analysis. The system likely normalizes heterogeneous data sources (ESPN APIs, official league data, weather services) into a unified feature matrix that feeds downstream ML models, handling sport-specific nuances (e.g., NBA player rest patterns vs NFL weather sensitivity).","intents":["I need to automatically collect and normalize game data across NFL, NBA, MLB, and soccer without manually scraping multiple sources","I want to identify which statistical features (team pace, injury severity, line movement velocity) are most predictive for each sport","I need a system that updates feature vectors daily as new game information becomes available"],"best_for":["sports analytics teams building proprietary models","betting platforms needing real-time game context enrichment","quantitative traders validating market efficiency in sports betting"],"limitations":["Feature engineering is sport-specific; a model trained on NFL data does not transfer to NBA without retraining","Injury report data quality varies by league and is often incomplete or delayed","Weather data accuracy degrades for games >7 days in the future, limiting long-term prediction windows","No disclosed handling of data staleness — unclear if picks are generated from EOD data or intraday updates"],"requires":["Real-time or near-real-time access to official league APIs or licensed sports data feeds (ESPN, STATS Perform, or equivalent)","Weather API integration (OpenWeatherMap, Weather.com, or similar)","Data pipeline infrastructure (Apache Airflow, Prefect, or custom scheduler) to orchestrate daily feature refresh","Historical game database with 3+ years of outcomes for model training"],"input_types":["structured game metadata (teams, dates, venues)","player roster and injury data","historical game statistics (points, yards, turnovers, etc.)","betting line data (opening line, line movement, public betting percentages)","weather forecasts"],"output_types":["feature vectors (numerical arrays)","aggregated team statistics","normalized matchup profiles"],"categories":["data-processing-analysis","sports-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_1","uri":"capability://planning.reasoning.ensemble.machine.learning.prediction.with.sport.specific.model.selection","name":"ensemble machine learning prediction with sport-specific model selection","description":"Trains and maintains separate ensemble models (likely gradient boosting, neural networks, or hybrid approaches) for each sport and bet type, selecting the appropriate model based on matchup characteristics. The system likely uses stacking or blending to combine predictions from multiple base learners (e.g., XGBoost for tabular features, LSTM for temporal patterns, logistic regression for calibration), with sport-specific hyperparameter tuning and retraining schedules. Model selection logic may route NFL games through a different ensemble than NBA games to account for league-specific dynamics.","intents":["I want to generate daily betting picks that account for sport-specific dynamics without manually tuning separate models","I need predictions that combine multiple modeling approaches (tree-based, neural, statistical) to reduce overfitting to any single technique","I want the system to automatically retrain models as new game outcomes arrive without manual intervention"],"best_for":["quantitative betting syndicates with in-house ML expertise","sports betting platforms seeking differentiated pick quality","individual bettors wanting algorithmic picks without building models themselves"],"limitations":["No disclosed model architecture, hyperparameters, or ensemble composition — impossible to audit prediction logic","Ensemble models are computationally expensive; unclear if picks are generated fresh daily or cached from previous runs","Model drift is not addressed publicly — no mention of performance monitoring or retraining triggers","Overfitting risk is high in sports prediction due to small sample sizes (e.g., ~16 NFL games per team per season); no disclosed cross-validation strategy","Black-box ensemble predictions are difficult to interpret; users cannot understand why a pick was generated"],"requires":["ML training infrastructure (GPU-enabled servers or cloud ML platforms like AWS SageMaker, GCP Vertex AI)","Historical labeled dataset with 5+ years of game outcomes and features","Model versioning and experiment tracking system (MLflow, Weights & Biases, or custom)","Inference serving infrastructure (TensorFlow Serving, Triton, or REST API wrapper)","Automated retraining pipeline triggered daily or weekly"],"input_types":["feature vectors from matchup analysis capability","historical game outcomes (binary: win/loss or continuous: point spread outcomes)","betting line data for calibration"],"output_types":["probability predictions (e.g., 0.58 probability of team A covering the spread)","confidence scores or uncertainty estimates","pick recommendations with implied odds"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_10","uri":"capability://automation.workflow.subscription.management.and.billing.integration","name":"subscription management and billing integration","description":"Manages user subscriptions, billing, and access control through a subscription management system (likely Stripe, Paddle, or custom) that handles recurring payments, plan tiers, and feature access. The system likely supports multiple subscription tiers (e.g., free trial, basic, premium) with different feature access levels (e.g., basic users see only top picks, premium users see all picks with detailed reasoning). Billing is likely monthly or annual with automatic renewal, and the system handles failed payments, cancellations, and refunds.","intents":["I want to subscribe to Safebet and pay monthly for access to daily picks","I need to upgrade or downgrade my subscription based on my betting activity","I want to cancel my subscription if I'm not satisfied with pick quality"],"best_for":["casual bettors seeking a low-commitment subscription","professional bettors willing to pay for premium features","Safebet's business model (recurring revenue from subscriptions)"],"limitations":["Subscription tiers and pricing are undisclosed; unclear what features are included at each tier","No disclosed free trial or money-back guarantee; users cannot evaluate pick quality before committing","Billing terms and refund policy are undisclosed","No disclosed support for alternative payment methods (crypto, bank transfer, etc.)","Churn analysis is undisclosed; unclear what percentage of users cancel after 1 month, 3 months, etc."],"requires":["Payment processor integration (Stripe, Paddle, or similar)","Subscription management system (custom or third-party like Supabase, Firebase)","User authentication and authorization (role-based access control)","Billing database (subscription status, payment history, plan tier)","Email notifications for billing events (payment received, renewal reminder, failed payment)"],"input_types":["user payment information (credit card, PayPal, etc.)","subscription plan selection (tier, billing frequency)"],"output_types":["subscription confirmation and receipt","access tokens or feature flags enabling/disabling features","billing history and invoice data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_2","uri":"capability://automation.workflow.daily.automated.pick.generation.and.delivery","name":"daily automated pick generation and delivery","description":"Orchestrates a scheduled workflow that runs model inference on upcoming games, ranks picks by confidence or expected value, filters picks based on configurable thresholds (e.g., minimum probability, maximum implied odds), and delivers results to users via web dashboard, email, or API. The system likely uses a task scheduler (cron, Airflow, or Lambda) to trigger inference at a fixed time (e.g., 8 AM ET) to align with betting market opening, then formats predictions into human-readable pick cards with reasoning (e.g., 'Team A favored due to home-field advantage and superior defensive metrics').","intents":["I want to receive daily betting picks automatically without manually checking the platform","I need picks delivered before market open so I can place bets at optimal line prices","I want to filter picks by confidence level or bet type (spread, moneyline, over/under) to match my risk tolerance"],"best_for":["casual bettors seeking hands-off pick consumption","professional bettors using Safebet as one input to a larger decision framework","betting syndicates integrating Safebet picks into automated wagering systems"],"limitations":["No disclosed pick filtering logic — unclear what thresholds are used to decide which games to recommend","Delivery timing is not customizable; users cannot request picks at specific times (e.g., 6 AM for early games)","No A/B testing or user preference learning — all users receive the same picks regardless of historical performance","Reasoning explanations are likely templated or generated from feature importance scores, not genuine causal analysis","No disclosed mechanism to handle games with missing data (e.g., last-minute injury announcements)"],"requires":["Task scheduler (cron, Apache Airflow, AWS Lambda, or equivalent)","Inference serving endpoint (REST API or direct model loading)","Email service (SendGrid, AWS SES, or in-house SMTP)","Web dashboard or API for pick delivery","User notification preferences storage (database or config file)"],"input_types":["model predictions from ensemble capability","user preferences (sports, bet types, confidence thresholds)","game schedule and metadata"],"output_types":["pick recommendations (text, JSON, or HTML email)","confidence scores or win probability estimates","reasoning summaries or feature importance explanations"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_3","uri":"capability://data.processing.analysis.multi.sport.league.coverage.and.bet.type.support","name":"multi-sport league coverage and bet type support","description":"Extends pick generation across multiple sports leagues (NFL, NBA, MLB, soccer/MLS, likely others) and multiple bet types (spread, moneyline, over/under, parlays, props) by maintaining league-specific data pipelines, feature engineering logic, and model ensembles. The system abstracts league differences (e.g., NFL has 16 games/season, NBA has 82) through a configurable league registry that specifies data sources, feature definitions, and model parameters, allowing new leagues to be added without rewriting core prediction logic.","intents":["I want to receive picks across multiple sports without managing separate tools for each league","I need picks for different bet types (spread, moneyline, over/under) so I can diversify my wagering strategy","I want to compare pick quality across leagues to identify which sports the AI predicts best"],"best_for":["diversified bettors who wager across multiple sports","betting platforms seeking comprehensive pick coverage","users wanting to exploit league-specific model strengths"],"limitations":["Multi-sport coverage increases operational complexity; data quality and pick accuracy likely vary significantly by league","No disclosed per-league performance metrics — impossible to know if NFL picks are more accurate than NBA picks","Bet type support (props, parlays) is undisclosed; unclear if all bet types are equally well-modeled or if some are afterthoughts","Seasonal sports (NFL, MLB) have different data availability patterns; off-season handling is not described","International soccer data quality is likely lower than US-based sports, but this is not acknowledged"],"requires":["League-specific data source integrations (ESPN, STATS Perform, official league APIs, international soccer data providers)","Configurable league registry (YAML, JSON, or database) defining data sources, feature definitions, and model parameters per league","Separate feature engineering pipelines per league","Separate model ensembles per league and bet type","Unified inference and delivery orchestration layer"],"input_types":["league-specific game data","league-specific historical outcomes","bet type specifications (spread, moneyline, over/under, props)"],"output_types":["league-specific pick recommendations","bet type-specific predictions","unified pick feed across all leagues"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_4","uri":"capability://data.processing.analysis.real.time.betting.line.monitoring.and.value.detection","name":"real-time betting line monitoring and value detection","description":"Continuously monitors betting lines from multiple sportsbooks (DraftKings, FanDuel, BetMGM, etc.) and compares model predictions against current market odds to identify 'value' opportunities where the model's implied probability diverges from the sportsbook's implied probability. The system likely polls sportsbook APIs or scrapes line data at regular intervals (e.g., every 5-15 minutes), calculates expected value (EV) for each pick using the formula EV = (Model Probability × Payout) - (1 - Model Probability), and ranks picks by EV to surface the most profitable opportunities.","intents":["I want to know which picks offer the best expected value relative to current market odds","I need to identify line movements that suggest sharp money is moving against the public consensus","I want to place bets at optimal times when lines are most favorable relative to my model's predictions"],"best_for":["professional bettors optimizing bet sizing and timing","value-focused bettors seeking positive expected value opportunities","betting syndicates with capital to exploit small EV edges across many bets"],"limitations":["Line monitoring requires real-time data access; unclear if Safebet monitors lines continuously or only at pick generation time","EV calculations assume model probabilities are well-calibrated; if the model is overconfident, EV estimates will be misleading","Sportsbook API access is restricted and rate-limited; scraping is against terms of service and may be blocked","Line movement is extremely fast in modern betting markets; by the time a user sees a pick, the line may have moved significantly","No disclosed mechanism to handle line discrepancies across sportsbooks (e.g., DraftKings offers -110 while FanDuel offers -105)"],"requires":["Real-time or near-real-time access to sportsbook APIs (DraftKings, FanDuel, BetMGM, Caesars, etc.) or licensed line data feed","Line data storage (time-series database like InfluxDB or TimescaleDB) to track historical line movements","EV calculation engine with configurable payout structures (standard -110 odds, alternative odds, parlays)","Line comparison logic to identify value across multiple sportsbooks","Alert system to notify users when high-EV opportunities appear"],"input_types":["model predictions (probability estimates)","real-time betting lines from multiple sportsbooks","payout structures and odds formats"],"output_types":["expected value scores per pick","ranked pick lists sorted by EV","line movement alerts","sportsbook comparison data"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_5","uri":"capability://data.processing.analysis.user.performance.tracking.and.historical.pick.analytics","name":"user performance tracking and historical pick analytics","description":"Maintains a database of all generated picks, tracks outcomes (win/loss/push), calculates per-user and aggregate performance metrics (win rate, ROI, units won/lost, hit rate by sport/bet type), and surfaces this data via dashboard or API. The system likely stores picks with timestamps, model confidence scores, actual outcomes, and user action (whether the user placed the bet), enabling post-hoc analysis of pick quality and user decision-making patterns. Performance tracking may include attribution analysis to identify which features or model components drive successful picks.","intents":["I want to see my historical win rate and ROI to evaluate whether Safebet picks are profitable for me","I need to compare pick performance across sports and bet types to identify where the AI excels","I want to understand why certain picks succeeded or failed to improve my betting strategy"],"best_for":["analytical bettors who track detailed statistics","users evaluating whether to continue their Safebet subscription","professional bettors using Safebet as one input to a larger system"],"limitations":["No disclosed public performance metrics — users cannot see aggregate win rates or ROI before subscribing","Performance tracking is only as good as user reporting; if users don't log actual bets placed, metrics are incomplete","Survivorship bias: users who lose money may cancel subscriptions, inflating reported performance metrics","Attribution analysis is undisclosed — unclear if Safebet explains which features drove successful picks or just reports raw win rates","No disclosed handling of partial bets (e.g., user places a bet on only 50% of recommended picks) or bet sizing variations","Historical data may be cherry-picked or backfilled; no disclosed methodology for how picks are retroactively assigned outcomes"],"requires":["Pick database with timestamps, model confidence, and metadata","Outcome tracking system (manual user input, API integration with sportsbooks, or web scraping)","Analytics engine to calculate win rate, ROI, hit rate by sport/bet type, and other metrics","Dashboard or API to surface performance data to users","Data retention policy (how long historical picks are stored)"],"input_types":["generated picks with timestamps and confidence scores","actual game outcomes","user betting actions (whether they placed the bet, bet amount)"],"output_types":["win rate and ROI metrics","hit rate by sport, bet type, and confidence level","performance trends over time","attribution analysis (which features drove successful picks)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_6","uri":"capability://automation.workflow.web.dashboard.and.mobile.friendly.pick.consumption.interface","name":"web dashboard and mobile-friendly pick consumption interface","description":"Provides a user-facing interface (web dashboard, likely mobile-responsive) that displays daily picks, historical performance metrics, and user account settings. The interface likely uses a modern frontend framework (React, Vue, or Angular) to render pick cards with team logos, confidence scores, reasoning summaries, and action buttons (e.g., 'View on DraftKings'). The dashboard may include filtering and sorting options (by sport, bet type, confidence level) and integration with sportsbook links to streamline bet placement.","intents":["I want to view today's picks in a clean, easy-to-read format without navigating multiple websites","I need to quickly place bets on recommended picks without manually entering team names and odds","I want to track my performance over time and compare results across sports"],"best_for":["casual bettors seeking a frictionless pick consumption experience","mobile-first users who bet primarily on smartphones","users who value UI/UX polish over analytical depth"],"limitations":["Dashboard design is undisclosed; unclear if it prioritizes clarity or information density","No disclosed mobile app; web-only interface may be suboptimal for on-the-go betting","Sportsbook integration is likely limited to link generation; true one-click betting would require OAuth integration with each sportsbook","Real-time line updates on the dashboard are undisclosed; users may see stale odds","No disclosed accessibility features (screen reader support, high-contrast mode, keyboard navigation)"],"requires":["Frontend framework (React, Vue, Angular, or similar)","Backend API to serve picks, performance metrics, and user data","Responsive design framework (Bootstrap, Tailwind CSS, or custom)","Sportsbook link generation logic (URL templates for DraftKings, FanDuel, etc.)","User authentication and session management"],"input_types":["daily picks from generation capability","user performance metrics from analytics capability","user preferences and account settings"],"output_types":["rendered HTML/CSS/JavaScript for web browser","JSON API responses for mobile or third-party integrations","sportsbook deep links"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_7","uri":"capability://automation.workflow.email.and.push.notification.delivery.with.customizable.preferences","name":"email and push notification delivery with customizable preferences","description":"Sends daily pick summaries to users via email or push notifications, with configurable delivery times, sports filters, and notification frequency. The system likely uses an email service provider (SendGrid, AWS SES) to send templated emails containing pick cards, reasoning, and dashboard links, and may support push notifications via Firebase Cloud Messaging or similar. Users can customize which sports they receive picks for, what time picks are delivered, and whether they prefer email, push, or both.","intents":["I want to receive daily picks via email without logging into the dashboard","I need picks delivered at a specific time (e.g., 7 AM) so I can place bets before market open","I only want to receive picks for sports I bet on (e.g., NFL and NBA, not soccer)"],"best_for":["busy users who prefer passive pick consumption","users who want to integrate Safebet picks into their email workflow","users with strong time-of-day preferences for betting"],"limitations":["Email delivery is not real-time; picks may arrive after lines have moved significantly","No disclosed support for SMS or other notification channels","Email templates are likely static or minimally personalized; no disclosed A/B testing of email designs","Push notifications require app installation; web-only users cannot receive push alerts","Delivery time customization may be limited to preset times (e.g., 7 AM, 9 AM, 11 AM) rather than arbitrary times"],"requires":["Email service provider (SendGrid, AWS SES, Mailgun, or in-house SMTP)","Email template engine (Handlebars, Jinja2, or similar)","Push notification service (Firebase Cloud Messaging, OneSignal, or similar)","User preference storage (database table for notification settings)","Scheduled task to send emails/push at user-specified times"],"input_types":["daily picks from generation capability","user notification preferences (sports, delivery time, channels)","user email address and push notification tokens"],"output_types":["formatted email with pick cards and reasoning","push notification with pick summary and deep link to dashboard"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_8","uri":"capability://safety.moderation.model.performance.monitoring.and.drift.detection","name":"model performance monitoring and drift detection","description":"Continuously monitors model prediction accuracy, calibration, and feature importance to detect performance degradation (model drift) and trigger retraining or alerts. The system likely calculates metrics like log loss, Brier score, or calibration error on a rolling window of recent predictions and compares against historical baselines. If performance drops below a threshold, the system may automatically retrain the model, alert data scientists, or roll back to a previous model version. Feature importance monitoring may detect when previously predictive features lose signal, indicating market regime changes or data quality issues.","intents":["I want to know if the AI's pick quality is degrading over time so I can adjust my betting strategy","I need to be alerted if the model is making systematically biased predictions (e.g., consistently overestimating team A's win probability)","I want to understand if model performance varies by season, league, or bet type"],"best_for":["professional bettors who need to monitor model reliability","Safebet's internal data science team managing model quality","users evaluating whether to continue their subscription based on recent performance"],"limitations":["Model monitoring is entirely internal; no disclosed public metrics or alerts for users","Drift detection thresholds are undisclosed; unclear what level of performance degradation triggers action","No disclosed mechanism to distinguish between genuine model drift and natural variance in sports outcomes","Retraining triggers are undisclosed; unclear if the model is retrained daily, weekly, or only when drift is detected","Feature importance monitoring is undisclosed; users cannot see which features drive predictions or how feature importance changes over time"],"requires":["Metrics calculation engine (scikit-learn, custom Python) to compute accuracy, calibration, log loss, etc.","Time-series database (InfluxDB, Prometheus, or similar) to store historical metrics","Drift detection logic (statistical tests, threshold-based alerts, or anomaly detection)","Automated retraining pipeline triggered by drift detection","Model versioning and rollback capability","Alerting system (email, Slack, PagerDuty) to notify data scientists of issues"],"input_types":["model predictions with confidence scores","actual game outcomes","feature vectors used for prediction"],"output_types":["accuracy metrics (win rate, log loss, Brier score)","calibration metrics (expected calibration error, reliability diagrams)","feature importance scores","drift alerts and retraining triggers"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_safebet__cap_9","uri":"capability://tool.use.integration.api.access.for.programmatic.pick.consumption.and.integration","name":"api access for programmatic pick consumption and integration","description":"Exposes a REST or GraphQL API that allows third-party developers and professional bettors to programmatically fetch daily picks, historical performance metrics, and user account data. The API likely supports filtering by sport, bet type, and confidence level, and may include webhooks for real-time pick notifications. Authentication is likely via API keys or OAuth, with rate limiting to prevent abuse. The API enables integration with betting bots, portfolio management tools, and custom analytics platforms.","intents":["I want to integrate Safebet picks into my automated betting bot without manually scraping the website","I need to fetch picks programmatically so I can combine them with other data sources in my analysis","I want to receive real-time pick notifications via webhook so my bot can place bets immediately"],"best_for":["professional bettors and betting syndicates with engineering resources","third-party developers building tools that consume Safebet picks","users integrating Safebet into larger betting or analytics systems"],"limitations":["API documentation is undisclosed; unclear what endpoints are available or what data is returned","Rate limiting is undisclosed; unclear if the API supports high-frequency polling for real-time picks","Webhook support is undisclosed; unclear if real-time notifications are available","API stability and uptime SLAs are undisclosed","No disclosed support for batch operations (e.g., fetching picks for multiple users in one request)"],"requires":["REST or GraphQL API server (Flask, FastAPI, Node.js, or similar)","API authentication (API keys, OAuth 2.0, or JWT)","Rate limiting middleware (Redis-based token bucket or similar)","API documentation (OpenAPI/Swagger, GraphQL schema, or custom)","Webhook infrastructure (event queue, delivery retry logic, signature verification)"],"input_types":["API requests with filters (sport, bet type, confidence level, date range)","user authentication credentials"],"output_types":["JSON or GraphQL response with pick data (teams, odds, confidence, reasoning)","performance metrics (win rate, ROI by sport/bet type)","webhook payloads for real-time pick notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Real-time or near-real-time access to official league APIs or licensed sports data feeds (ESPN, STATS Perform, or equivalent)","Weather API integration (OpenWeatherMap, Weather.com, or similar)","Data pipeline infrastructure (Apache Airflow, Prefect, or custom scheduler) to orchestrate daily feature refresh","Historical game database with 3+ years of outcomes for model training","ML training infrastructure (GPU-enabled servers or cloud ML platforms like AWS SageMaker, GCP Vertex AI)","Historical labeled dataset with 5+ years of game outcomes and features","Model versioning and experiment tracking system (MLflow, Weights & Biases, or custom)","Inference serving infrastructure (TensorFlow Serving, Triton, or REST API wrapper)","Automated retraining pipeline triggered daily or weekly","Payment processor integration (Stripe, Paddle, or similar)"],"failure_modes":["Feature engineering is sport-specific; a model trained on NFL data does not transfer to NBA without retraining","Injury report data quality varies by league and is often incomplete or delayed","Weather data accuracy degrades for games >7 days in the future, limiting long-term prediction windows","No disclosed handling of data staleness — unclear if picks are generated from EOD data or intraday updates","No disclosed model architecture, hyperparameters, or ensemble composition — impossible to audit prediction logic","Ensemble models are computationally expensive; unclear if picks are generated fresh daily or cached from previous runs","Model drift is not addressed publicly — no mention of performance monitoring or retraining triggers","Overfitting risk is high in sports prediction due to small sample sizes (e.g., ~16 NFL games per team per season); no disclosed cross-validation strategy","Black-box ensemble predictions are difficult to interpret; users cannot understand why a pick was generated","Subscription tiers and pricing are undisclosed; unclear what features are included at each tier","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=safebet","compare_url":"https://unfragile.ai/compare?artifact=safebet"}},"signature":"42b42qPwnpubt2Ks3rFPpDh6oQ4UD/SIB5+slNDLgixbKPbghKSjr6AOpA7vA2qiDP2YO+F8KZ+biRuYONpJCw==","signedAt":"2026-06-21T00:15:03.465Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/safebet","artifact":"https://unfragile.ai/safebet","verify":"https://unfragile.ai/api/v1/verify?slug=safebet","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}