{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_mysports-ai","slug":"mysports-ai","name":"MySports AI","type":"product","url":"https://mysports.ai","page_url":"https://unfragile.ai/mysports-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_mysports-ai__cap_0","uri":"capability://data.processing.analysis.multi.sportsbook.odds.aggregation.and.normalization","name":"multi-sportsbook odds aggregation and normalization","description":"Crawls and normalizes betting odds across multiple sportsbooks (DraftKings, FanDuel, BetMGM, etc.) in real-time, converting heterogeneous line formats into a unified data model for comparative analysis. Uses scheduled ETL pipelines to detect line movements, identify sharp vs soft books, and flag arbitrage opportunities. Normalizes American, decimal, and fractional odds into a canonical representation for downstream ML models.","intents":["I want to see all available odds for a single game across sportsbooks without manually checking each site","I need to identify which sportsbook has the best line for a specific bet type","I want to detect when a line has moved significantly and understand directional betting pressure"],"best_for":["Data-minded bettors who want to optimize line selection across multiple books","Quantitative traders building arbitrage detection systems","Casual bettors who want to avoid leaving money on the table by shopping lines"],"limitations":["Odds data freshness depends on sportsbook API rate limits and crawl frequency—real-time odds may lag by 30-120 seconds","Does not account for promotional boosts, reduced juice, or account-specific limits that vary by user","Requires legal compliance with each jurisdiction's data scraping policies; some sportsbooks prohibit automated access","Line movement detection requires historical snapshots; insufficient history window may miss early sharp action"],"requires":["Active accounts or API access with major sportsbooks (DraftKings, FanDuel, BetMGM, Caesars, etc.)","Geolocation verification for legal betting jurisdictions","Internet connectivity for real-time odds polling"],"input_types":["sport type (NFL, NBA, MLB, etc.)","event identifier (game ID, date, teams)","bet type (moneyline, spread, total, props)"],"output_types":["structured JSON with normalized odds across sportsbooks","line movement deltas (current vs historical)","arbitrage opportunity alerts"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_1","uri":"capability://planning.reasoning.machine.learning.based.outcome.prediction.with.confidence.scoring","name":"machine learning-based outcome prediction with confidence scoring","description":"Trains ensemble ML models (gradient boosting, neural networks, or hybrid approaches) on historical sports data (team stats, player metrics, weather, rest days, injury reports, public betting volume) to predict game outcomes and generate probability distributions. Models output point estimates with calibrated confidence intervals, allowing users to assess prediction uncertainty. Likely uses feature engineering pipelines to extract predictive signals from raw sports data and cross-validates on holdout test sets to estimate generalization performance.","intents":["I want an AI-generated probability for a team winning, not just a sportsbook line","I need to understand how confident the model is in its prediction so I can size my bet accordingly","I want to identify games where the model's prediction diverges significantly from the sportsbook line (potential edge)"],"best_for":["Quantitatively-minded bettors who understand model limitations and use predictions as one input among many","Bettors seeking to identify line inefficiencies by comparing model probabilities to implied sportsbook probabilities","Users comfortable with probabilistic thinking and Kelly Criterion or similar bankroll management"],"limitations":["Sports outcomes are inherently stochastic; even well-calibrated models have irreducible prediction error due to randomness and unknown variables (injuries, weather, motivation)","Model training data recency and quality are opaque—unclear whether models are retrained daily, weekly, or seasonally, or whether they account for recent roster changes","Confidence intervals may be miscalibrated if training data distribution differs from current season (e.g., rule changes, team composition shifts)","No published backtesting results, Sharpe ratios, or historical ROI metrics—users cannot independently verify prediction accuracy","Likely excludes real-time injury updates, weather changes, or late-breaking news that occur after model training cutoff"],"requires":["Historical sports data (team stats, player metrics, game results) covering multiple seasons","Real-time or near-real-time feature updates (injury reports, weather, line movements)","Computational resources for model inference (likely cloud-based, not on-device)"],"input_types":["game metadata (teams, date, sport, league)","team/player statistics (wins, points, efficiency, rest days)","contextual features (home/away, weather, injury status, public betting volume)"],"output_types":["predicted probability for each outcome (win/loss/cover)","confidence interval or uncertainty estimate","model feature importance (which factors drove the prediction)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_2","uri":"capability://planning.reasoning.bet.recommendation.generation.with.edge.detection","name":"bet recommendation generation with edge detection","description":"Compares model-predicted probabilities against sportsbook implied probabilities (derived from odds) to identify bets where the model believes the line is mispriced. Generates ranked recommendations based on expected value (EV) calculations: EV = (model probability × potential payout) - (1 - model probability × stake). Filters recommendations by confidence threshold and minimum EV threshold to surface only high-conviction opportunities. May apply Kelly Criterion or fractional Kelly sizing to suggest bet amounts.","intents":["I want the AI to tell me which specific bets to place, not just probabilities","I want recommendations ranked by expected value so I can prioritize high-edge opportunities","I want to know the suggested bet size based on my bankroll and risk tolerance"],"best_for":["Bettors seeking actionable picks rather than raw probability estimates","Users with defined bankroll management strategies who want AI-assisted sizing","Casual bettors who want a simplified decision-making process (AI picks vs manual analysis)"],"limitations":["EV calculations assume model probabilities are well-calibrated; systematic miscalibration (e.g., overconfident on favorites) will produce misleading EV estimates","Does not account for sportsbook limits, account restrictions, or promotional restrictions that may prevent placing recommended bets","Recommendations are static snapshots; odds move constantly, so a recommended bet may become unfavorable by the time the user places it","Freemium tier likely restricts access to premium recommendations (specific picks, real-time alerts), limiting practical utility without paid subscription","No published track record of recommendation accuracy or ROI—users cannot verify whether recommendations historically generate positive returns"],"requires":["Model predictions with confidence intervals (from ML prediction capability)","Real-time odds data (from odds aggregation capability)","User bankroll and risk tolerance settings (for Kelly Criterion sizing)"],"input_types":["model predictions (probability, confidence)","current sportsbook odds","user bankroll and risk parameters"],"output_types":["ranked list of recommended bets (team, bet type, recommended odds)","expected value for each recommendation","suggested bet size (in dollars or % of bankroll)","confidence/conviction level"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_3","uri":"capability://automation.workflow.real.time.alerts.and.notification.delivery","name":"real-time alerts and notification delivery","description":"Monitors for triggering events (line movement exceeding threshold, new recommendation generated, odds at target level, injury report published) and delivers notifications via push, email, or SMS. Likely uses event-driven architecture with message queues (Kafka, RabbitMQ) to decouple alert generation from delivery. Allows users to configure alert preferences (sports, bet types, minimum EV threshold, notification channels) and quiet hours to avoid spam.","intents":["I want to be notified immediately when a high-EV opportunity appears so I can act before odds move","I want alerts when a line moves significantly, indicating sharp action or public betting pressure","I want to set up custom alerts for specific teams, leagues, or bet types without manual monitoring"],"best_for":["Active bettors who need to act quickly on time-sensitive opportunities","Users who cannot monitor the platform continuously and rely on push notifications","Bettors with specific preferences (e.g., only NFL, only spreads) who want filtered alerts"],"limitations":["Notification latency depends on alert generation frequency and delivery provider; SMS/push may lag by 10-30 seconds, missing fast-moving lines","Freemium tier likely restricts alert frequency or notification channels (e.g., email only, not SMS or push)","Alert fatigue risk if thresholds are too loose; users may ignore alerts if signal-to-noise ratio is poor","Requires user to opt-in to notifications and manage preferences; default settings may not align with individual risk tolerance","Timezone handling may be imperfect, leading to alerts at inconvenient times"],"requires":["User account with notification preferences configured","Valid contact information (email, phone number for SMS, device token for push)","Real-time event stream (line movements, new recommendations, injury reports)"],"input_types":["alert trigger configuration (threshold, sports, bet types, minimum EV)","notification channel preferences (push, email, SMS)","user timezone and quiet hours"],"output_types":["push notification","email","SMS","in-app notification"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_4","uri":"capability://data.processing.analysis.historical.performance.tracking.and.roi.analytics","name":"historical performance tracking and roi analytics","description":"Logs all user bets (placed through the platform or manually logged) and tracks outcomes (win/loss/push) against predicted probabilities. Computes aggregate metrics: win rate, ROI, Sharpe ratio, maximum drawdown, and calibration curves (comparing predicted vs actual win rates across probability buckets). Generates performance dashboards and reports to help users assess whether recommendations are generating positive returns and whether model predictions are well-calibrated.","intents":["I want to track whether the AI's recommendations are actually making me money over time","I want to see if the model's predicted probabilities match actual outcomes (calibration)","I want to compare my performance across different bet types, sports, or time periods"],"best_for":["Serious bettors who want data-driven feedback on strategy effectiveness","Users who want to audit whether the platform's recommendations are worth the subscription cost","Bettors building personal betting systems and using MySports AI as one input"],"limitations":["Requires manual bet logging if bets are placed outside the platform (most sportsbooks don't have direct API integration); manual logging introduces human error and selection bias","Sample size bias: early performance metrics may be unreliable with small sample sizes (e.g., 10-20 bets); statistical significance requires 100+ bets minimum","Survivorship bias: users who lose money may stop using the platform, making aggregate performance metrics appear better than they are","Does not account for sportsbook promotions, bonuses, or juice changes that affect true ROI","Freemium tier likely restricts access to detailed analytics; premium tier may be required for full performance dashboards"],"requires":["User bet history (placed through platform or manually logged)","Bet outcomes (win/loss/push) and final odds","Sufficient sample size (100+ bets recommended for statistical reliability)"],"input_types":["bet details (team, bet type, stake, odds, date)","bet outcome (win/loss/push)","model prediction (if available)"],"output_types":["win rate, ROI, Sharpe ratio, maximum drawdown","calibration curves (predicted vs actual win rates)","performance dashboards and reports","performance by sport, bet type, or time period"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_5","uri":"capability://automation.workflow.freemium.tier.access.with.premium.feature.gating","name":"freemium tier access with premium feature gating","description":"Implements a freemium business model with tiered access: free tier provides limited predictions and odds data (likely delayed or aggregated), while premium tier unlocks real-time alerts, specific pick recommendations, advanced analytics, and priority support. Uses feature flags and API rate limiting to enforce tier boundaries. Likely uses subscription management (Stripe, Paddle) to handle billing and tier upgrades.","intents":["I want to test the platform's prediction accuracy before paying for a subscription","I want access to premium features like real-time alerts and specific picks","I want to upgrade or downgrade my subscription based on my usage and ROI"],"best_for":["Casual bettors exploring AI-assisted picks without financial commitment","Serious bettors willing to pay for real-time alerts and premium recommendations","Users who want to evaluate platform effectiveness before committing to a subscription"],"limitations":["Free tier likely provides limited utility (delayed odds, generic predictions) to drive conversion to paid tier","Premium tier pricing may be prohibitive for casual bettors; ROI must exceed subscription cost to justify","Feature gating may create artificial scarcity (e.g., real-time alerts only on premium) rather than reflecting technical limitations","Subscription churn risk: users who lose money may cancel, creating revenue volatility","No clear pricing transparency in editorial summary; users cannot compare value proposition across tiers without signing up"],"requires":["User account creation","Payment method for premium tier (credit card, PayPal, etc.)","Subscription management system (Stripe, Paddle, or custom)"],"input_types":["user tier (free, premium)","subscription status and billing cycle"],"output_types":["feature access (predictions, alerts, analytics)","API rate limits","subscription confirmation and billing receipts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_6","uri":"capability://data.processing.analysis.injury.report.and.roster.change.integration","name":"injury report and roster change integration","description":"Ingests injury reports, roster transactions, and player status updates from official sources (ESPN, NFL.com, NBA.com, etc.) and integrates them into the ML prediction pipeline as real-time features. Updates model inputs when key players are ruled out, downgraded, or return from injury. May use NLP to parse unstructured injury reports and extract player status (out, questionable, probable, day-to-day). Triggers re-prediction when material roster changes occur.","intents":["I want predictions to account for key player injuries and roster changes","I want to be alerted when a major injury or roster move significantly changes the odds","I want to understand how much a specific injury impacts the model's prediction"],"best_for":["Bettors who understand that injuries are major prediction drivers and want models that account for them","Users betting on player props or team totals where injury status is critical","Serious bettors who want to exploit slow-moving lines after injury announcements"],"limitations":["Injury report parsing is imperfect; NLP may misinterpret status (e.g., 'questionable' vs 'probable') or miss nuanced updates","Injury impact modeling is difficult; the same injury affects different players differently (star QB vs backup), and models may not capture this nuance","Injury reports are released on varying schedules (some teams report Friday, others Sunday morning); timing mismatches may lead to stale predictions","Does not account for injury severity or expected return timeline; a player ruled out for 1 week vs season-ending injury should have different impact","Requires real-time data feeds from official sources; delays in injury report publication lead to delayed model updates"],"requires":["Real-time injury report feeds (ESPN API, official league sources, or web scraping)","NLP model for parsing unstructured injury reports","ML model retraining or feature update pipeline to incorporate injury status changes"],"input_types":["injury report (player name, status, expected return date)","roster transactions (trades, signings, releases)"],"output_types":["updated predictions reflecting injury status","injury impact analysis (how much did this injury change the prediction?)","alerts for material roster changes"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mysports-ai__cap_7","uri":"capability://data.processing.analysis.comparative.odds.analysis.and.line.movement.tracking","name":"comparative odds analysis and line movement tracking","description":"Maintains historical snapshots of odds across sportsbooks and computes line movement metrics: point spreads moved by X points, totals moved by Y points, moneyline odds shifted by Z percentage points. Identifies directional movement patterns (sharp money moving one direction, public money moving another) by correlating line movement with betting volume. Generates visualizations showing line history and movement velocity to help users understand betting pressure and identify late-breaking information.","intents":["I want to see how a line has moved since opening and understand what caused the movement","I want to identify games where sharp bettors are moving the line in one direction and public bettors in another","I want to know if a line is at an extreme or historical outlier that might indicate mispricing"],"best_for":["Quantitative bettors who use line movement as a signal for sharp action or public sentiment","Bettors seeking to exploit slow-moving lines by identifying when the market hasn't fully adjusted to new information","Users who want to understand betting pressure and market dynamics"],"limitations":["Line movement interpretation is ambiguous; movement can reflect sharp money, public money, sportsbook risk management, or random variation—difficult to distinguish without volume data","Historical snapshots require sufficient data retention; if snapshots are taken infrequently (e.g., hourly), fast intraday movement may be missed","Does not account for sportsbook-specific factors (e.g., one book moving a line to balance liability while others don't), leading to misinterpretation of market consensus","Requires correlation with betting volume data to distinguish sharp vs public movement; volume data is often unavailable or proprietary","Line movement velocity is noisy; small movements may be random variation rather than signal"],"requires":["Historical odds snapshots across multiple sportsbooks (requires continuous data collection)","Betting volume data (optional but improves signal quality)","Time-series analysis and visualization tools"],"input_types":["game identifier","bet type (spread, total, moneyline)","time range for analysis"],"output_types":["line movement history (opening line, current line, movement magnitude)","movement velocity (points moved per hour)","directional movement analysis (sharp vs public)","line movement visualizations"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Active accounts or API access with major sportsbooks (DraftKings, FanDuel, BetMGM, Caesars, etc.)","Geolocation verification for legal betting jurisdictions","Internet connectivity for real-time odds polling","Historical sports data (team stats, player metrics, game results) covering multiple seasons","Real-time or near-real-time feature updates (injury reports, weather, line movements)","Computational resources for model inference (likely cloud-based, not on-device)","Model predictions with confidence intervals (from ML prediction capability)","Real-time odds data (from odds aggregation capability)","User bankroll and risk tolerance settings (for Kelly Criterion sizing)","User account with notification preferences configured"],"failure_modes":["Odds data freshness depends on sportsbook API rate limits and crawl frequency—real-time odds may lag by 30-120 seconds","Does not account for promotional boosts, reduced juice, or account-specific limits that vary by user","Requires legal compliance with each jurisdiction's data scraping policies; some sportsbooks prohibit automated access","Line movement detection requires historical snapshots; insufficient history window may miss early sharp action","Sports outcomes are inherently stochastic; even well-calibrated models have irreducible prediction error due to randomness and unknown variables (injuries, weather, motivation)","Model training data recency and quality are opaque—unclear whether models are retrained daily, weekly, or seasonally, or whether they account for recent roster changes","Confidence intervals may be miscalibrated if training data distribution differs from current season (e.g., rule changes, team composition shifts)","No published backtesting results, Sharpe ratios, or historical ROI metrics—users cannot independently verify prediction accuracy","Likely excludes real-time injury updates, weather changes, or late-breaking news that occur after model training cutoff","EV calculations assume model probabilities are well-calibrated; systematic miscalibration (e.g., overconfident on favorites) will produce misleading EV estimates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"ecosystem":0.25,"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":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=mysports-ai","compare_url":"https://unfragile.ai/compare?artifact=mysports-ai"}},"signature":"yhRaHMOLd54+TzLKn+im/mnKMfQzQCxel9Lc12/ymybe1mlFsi2cMcqnj6NOjMzWelsX9l7NztUaSZqaadHHCQ==","signedAt":"2026-06-21T00:56:13.363Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mysports-ai","artifact":"https://unfragile.ai/mysports-ai","verify":"https://unfragile.ai/api/v1/verify?slug=mysports-ai","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"}}