Advanced Rating Algorithm API for Padel Applications
The first API service specifically designed for accurate, manipulation-resistant padel player ratings using advanced TrueSkill algorithms and proven statistical methods.
- 85%+
- Prediction Accuracy
- <150ms
- Response Time
- 99.9%
- Uptime SLA
- 95%+
- Manipulation Detection
Core Features - Why PadelRank is Different
Built on academic research and designed specifically for padel doubles, our API solves the fundamental flaws in existing rating systems.
- TrueSkill Algorithm. Designed specifically for team sports like padel doubles. Bayesian approach with uncertainty tracking (μ ± σ) that automatically separates individual skill from partnership performance.
- Anti-Manipulation System. Multi-layered fraud prevention using Shannon diversity index, progressive penalties for repeated matchups, and statistical anomaly detection with chi-square tests.
- Score Context Integration. Score margin bonuses for dominant victories (6-0, 6-1 sets get +10% points). Set-by-set analysis provides more accurate skill assessment than just match winners.
- Adaptive Rating System. Performance-based volatility detection prevents rating stagnation. Players showing consistent improvement get increased uncertainty for faster rating adjustments.
- Real-Time Processing. Sub-150ms response times for fast rating updates. Preview calculations before committing results. Batch processing for historical data imports.
- Complete Transparency. Detailed calculation explanations for every rating change. Mathematical transparency builds user trust. Comprehensive audit trails for every match.
Powered by PadelRank API
See how your padel application could display rich match data and player ratings powered by our API.
Recent Matches
Match Details
Match Analysis
Friday, December 1, 2023 at 03:30 PM
Match Overview
Match Results & Player Performance
Set Breakdown
Algorithm Details
• TrueSkill Bayesian rating system optimized for doubles
• Score margin bonus: +50%
• No manipulation penalties detected
• Uncertainty reduced for all players after match
Exceptional Performance
⭐ John Doe performed significantly above expected level
Click on match cards to see detailed analytics • Click on player badges to view player profiles
Benefits - Built for Developers, Loved by Players
Save months of development time while providing your users with the most accurate and fair rating system available.
For Developers
- Quick Integration. Single API call replaces complex algorithm development. No need to research and implement TrueSkill mathematics.
- Production Ready. Enterprise-grade reliability with 99.9% uptime SLA. Automatic scaling handles your user growth seamlessly.
- Cost Efficient. Pay-per-use pricing that scales with your success. Avoid hiring specialized algorithm developers and months of development time.
For Your Users
- Fair Competition. Ratings that actually reflect skill level with protection against gaming the system.
- Skill Development. Detailed performance trends and confidence intervals show rating reliability and improvement areas.
- Trust & Transparency. Mathematical transparency with detailed explanations for every rating change builds user confidence.
Pricing - Simple, Scalable Pricing
Start free and scale as you grow. No setup fees, no hidden costs.
Developer
Perfect for testing and development
Free
- 1,000 API calls/month
- Basic email support
- Sandbox environment
- API documentation
Startup
For small production apps
€25/mo
- 10,000 API calls/month
- Email support
- Production environment
Professional
Most popular choice
€199/mo
- 100,000 API calls/month
- 24/7 priority support
- 99.9% uptime SLA
- Advanced analytics
Enterprise
Custom solutions at scale
Custom
- Unlimited API calls
- Dedicated support team
- Custom SLA
- White-label options
FAQ - Frequently Asked Questions
Deep dive into the mathematics and features that make PadelRank the most advanced rating system for padel applications.
Traditional Elo systems often trap experienced players at their rating despite improvement. PadelRank uses performance-based volatility detection that monitors your recent performance vs. your published rating. When you consistently perform above your rating level (statistically measured over 20+ games), the algorithm automatically increases your uncertainty (σ value) by 1.5x, allowing for faster rating adjustments. This means genuinely improving players see their ratings climb appropriately, while the system maintains stability for players at their true skill level.
Score Context Integration goes beyond just "who won" to analyze how dominantly a team won. A 6-0, 6-1 victory indicates a significant skill gap and receives a 10% rating bonus, while a close 7-6, 7-6 match suggests evenly matched opponents with standard point allocation. The system also rewards straight-set victories with an additional 5% bonus. This approach provides 8-12% better match prediction accuracy compared to basic win/loss systems, as validated in studies with 60,000+ tennis matches.
PadelRank achieves 85%+ match prediction accuracy compared to 65% for basic Elo implementations. TrueSkill was specifically designed for team games like padel doubles, using Bayesian inference to separate individual skill from team dynamics. The algorithm calculates team strength as the sum of individual player ratings (μ values) while properly handling uncertainty propagation through the team's combined σ values. This mathematical approach is far superior to treating doubles as individual games.
PadelRank employs multiple mathematical safeguards: (1) Shannon Diversity Index requires a minimum 0.7 threshold, meaning you must play a variety of opponents to maintain rating validity. (2) Progressive penalties apply diminishing returns for repeated matchups (10% reduction on 3rd game vs same opponent, up to 25% on 5th+). (3) Statistical anomaly detection using chi-square tests identifies suspicious patterns with 95%+ accuracy. (4) Monthly penalty resets maintain fairness for social play. These measures are mathematically rigorous, not arbitrary rules.
Confidence intervals represent the statistical uncertainty around your rating. A rating of 1200 ± 50 means we're 95% confident your true skill lies between 1150-1250. New players start at 1000 with high uncertainty (σ = 350) that decreases as they play more games. Players with consistent performance have narrow intervals, while those showing improvement or inconsistency have wider intervals. This transparency helps you understand rating reliability - a 1400 ± 25 player is much more established than a 1400 ± 75 player.
Each match goes through a multi-step calculation: (1) Team strengths are calculated using individual μ and σ values. (2) Win probability is computed using the difference between team strengths. (3) Base point changes are determined by the TrueSkill update equations. (4) Score margin bonuses are applied based on set scores. (5) Anti-manipulation penalties are calculated and applied. (6) New uncertainty values are computed based on the match outcome vs. expectation. The entire process is deterministic and auditable.
Beating higher-rated opponents yields more points, losing to lower-rated opponents costs more points. This is automatically handled by the TrueSkill mathematics - the algorithm computes win probability based on the rating difference between teams. An upset victory (low probability outcome) results in larger rating adjustments for all players involved. The system also considers uncertainty levels - beating a 1700 ± 25 player (established) is worth more than beating a 1700 ± 100 player (uncertain).
Absolutely. The GET /v1/rating/explanation/{match_id} endpoint provides complete transparency: base points earned, score margin bonuses applied, any penalties, TrueSkill parameters used (beta=175, tau=1.5), pre/post match uncertainties, and even the calculated win probability. This level of detail builds trust and helps players understand exactly how their ratings are computed. No 'black box' algorithms here.
New players start at 1000 ± 350 (high uncertainty) and experience rapid rating changes as the algorithm learns their skill level. Established players have lower uncertainty and more stable ratings. The tau parameter (1.5) adds small amounts of uncertainty over time to account for skill evolution. When new and experienced players meet, the system appropriately weights the encounter - an experienced player beating a newcomer provides minimal rating change, while an upset by the newcomer creates significant adjustments.
Traditional Elo was designed for individual games like chess and has fundamental flaws when applied to team sports like padel doubles. TrueSkill was specifically created by Microsoft Research for team games and offers several advantages: (1) It properly calculates team strength by combining individual player ratings and uncertainties. (2) It uses Bayesian inference to track uncertainty, giving new players faster rating adjustments while keeping established players stable. (3) It achieves 85%+ match prediction accuracy vs 65% for basic Elo. (4) It handles the complexity of team dynamics where individual skill must be separated from partnership performance. (5) Most importantly, Elo treats each player independently even in doubles, while TrueSkill understands that team performance is the combination of individual skills.
Country-specific and regional rating systems are currently in development and will be available soon. This feature will support isolated rating pools for different regions, countries, or organizations while maintaining the same mathematical rigor. You'll be able to configure separate rating ecosystems where players only compete within their region, or implement a hybrid system where players have both local and global ratings. This will be particularly useful for national federations, regional leagues, or private clubs that want their own contained rating system. The API will support multiple rating contexts per player, allowing for country-specific tournaments while still enabling cross-border competition when desired. Each rating pool will maintain its own anti-manipulation tracking and statistical parameters, ensuring fair play regardless of geographic or organizational boundaries.
Ready to build fair, accurate ratings?
Join developers building the future of padel applications with PadelRank.