EV Calculation: Optimizing Kelly Criterion And Value Rating

Introduction

Enhancing the accuracy and practicality of Expected Value (EV) calculations and betting strategies is paramount for any football prediction system. This article delves into the optimization of the Kelly Criterion and improvements to the value rating algorithm within the FootballPrediction project. Building upon the foundational work detailed in Issue #116, which achieved 100% SRS compliance, this phase focuses on fine-tuning algorithm parameters to elevate overall system performance. The primary aim is to refine these critical components to provide more reliable and profitable betting recommendations.

🎯 Optimization Goals

Kelly Criterion Optimization

The Kelly Criterion, a cornerstone of risk management and optimal betting, requires meticulous attention to ensure its effectiveness. Several key areas have been identified for improvement:

• Fixing Boundary Condition Issues: Addressing instances where Kelly calculations produce unexpected or illogical results. This involves a thorough review of the mathematical implementation to ensure that the calculations remain within acceptable limits, preventing erroneous betting recommendations that could lead to significant financial losses.
• Optimizing Parameter Range and Precision: Refining the parameters used in the Kelly Criterion to improve its accuracy. This involves testing different parameter settings to find the optimal range that maximizes the effectiveness of the criterion. Precision is also crucial because minor inaccuracies in parameter values can lead to substantial deviations in the recommended betting fraction.
• Enhancing Adaptability Across Different Scenarios: Increasing the Kelly Criterion's ability to adapt to various betting scenarios, including those with diverse odds, probabilities, and market conditions. This involves incorporating contextual factors into the calculation to make it more responsive to real-world situations. Adaptability ensures that the Kelly Criterion provides relevant and accurate recommendations regardless of the specific circumstances of the bet.
• Establishing a Dynamic Kelly Strategy: Developing a dynamic Kelly strategy that adjusts the betting fraction based on real-time data and evolving market conditions. This involves creating a system that continuously monitors relevant factors, such as market volatility, team performance, and news events, and adjusts the Kelly fraction accordingly. A dynamic strategy allows for a more nuanced and responsive approach to betting, maximizing potential returns while minimizing risk.

Value Rating Algorithm Improvement

The value rating algorithm is vital for identifying opportunities where the perceived probability of an outcome differs significantly from the implied probability reflected in the odds. The goal is to enhance its precision and applicability through several strategic improvements:

• Redesigning and Recalibrating the Algorithm: Revamping the existing algorithm to better align with the project's objectives and recalibrating its parameters to improve its accuracy. This involves a thorough review of the algorithm's underlying assumptions and mathematical formulations. Recalibration ensures that the algorithm produces more reliable and consistent value ratings, reducing the likelihood of missed opportunities or poor investment decisions.
• Adding Dynamic Weights and Contextual Awareness: Incorporating dynamic weights that adjust based on market conditions, team form, and other relevant factors. This involves creating a system that continuously monitors these factors and adjusts the weights assigned to different inputs in the value rating calculation. Contextual awareness enables the algorithm to adapt to changing circumstances and provide more relevant and accurate ratings.
• Enhancing Accuracy and Practicality: Improving the algorithm's ability to accurately assess the value of different betting opportunities. This involves incorporating additional data sources and refining the mathematical formulas used to calculate the value rating. Enhanced practicality ensures that the algorithm's output is readily usable in real-world betting scenarios, providing clear and actionable recommendations.
• Establishing a Value Rating Verification System: Creating a system to validate the value ratings generated by the algorithm. This involves comparing the algorithm's ratings to actual outcomes and identifying any discrepancies. A verification system ensures that the algorithm remains accurate and reliable over time, providing confidence in its ability to identify valuable betting opportunities.

Comprehensive Scenario Optimization

Optimizing complex betting scenarios necessitates a multifaceted approach that considers various factors and their interactions. The following enhancements are essential:

• Improving the Handling of Complex Betting Scenarios: Refining the algorithm's ability to process intricate betting scenarios, such as those involving multiple variables or dependencies. This involves enhancing the algorithm's computational capabilities and incorporating advanced statistical techniques. Accurate handling of complex scenarios ensures that the algorithm can provide reliable recommendations even in challenging situations.
• Optimizing Combination Betting Strategies: Developing strategies for optimizing combination bets, such as accumulators or system bets. This involves identifying the optimal combination of bets to maximize potential returns while minimizing risk. Optimized combination betting strategies can significantly enhance profitability by leveraging the inherent advantages of these types of bets.
• Enhancing the Precision of Risk Assessment: Improving the accuracy of risk assessments to better understand the potential downsides of different betting strategies. This involves incorporating more sophisticated risk metrics and considering a broader range of factors. Enhanced risk assessment allows for more informed decision-making, reducing the likelihood of significant financial losses.
• Improving the Reliability of Betting Recommendations: Increasing the confidence in the betting recommendations generated by the system. This involves rigorous testing and validation to ensure that the recommendations are accurate and reliable. Improved reliability enhances user trust in the system and increases the likelihood that they will follow its recommendations.

🔍 Current Status Analysis

✅ Completed Core Functionality

• Accurate implementation of EV calculation mathematical formulas (100% accuracy).
• 100% compliance with SRS requirements.
• Implementation of the basic Kelly Criterion.
• Four betting strategy frameworks.
• Complete RESTful API interface.

⚠️ Issues Requiring Optimization

1. Kelly Criterion Issues (75% Accuracy)

• Boundary Conditions: Some test cases show Kelly calculations outside the expected range. This can lead to incorrect betting recommendations, potentially causing financial losses. Addressing this requires a thorough review of the mathematical implementation to ensure that calculations remain within acceptable limits.
• Parameter Accuracy: More precise parameter handling is needed for Kelly fraction calculations. Minor inaccuracies in parameter values can lead to substantial deviations in the recommended betting fraction, affecting profitability. Refining the parameter settings is essential for maximizing the effectiveness of the Kelly Criterion.
• Adaptability: A fixed maximum Kelly ratio may not be suitable for all scenarios. Different betting situations require varying levels of risk tolerance. Enhancing adaptability involves incorporating contextual factors into the calculation to make it more responsive to real-world scenarios.

2. Value Rating Algorithm (0% Accuracy)

• Underrated Values: The current algorithm is too conservative, rating most EV opportunities as low value. This results in missed opportunities to capitalize on potentially profitable bets. Recalibrating the algorithm to better identify valuable opportunities is crucial.
• Fixed Weights: There is a lack of dynamic weight adjustment mechanisms. Market conditions, team form, and other relevant factors can significantly impact the value of a betting opportunity. Incorporating dynamic weights allows the algorithm to adapt to changing circumstances and provide more relevant and accurate ratings.
• Missing Context: The algorithm does not consider market conditions and betting scale. The value of a bet can be influenced by factors such as market liquidity, public sentiment, and the size of the bet relative to the market. Incorporating contextual awareness enhances the algorithm's ability to assess the true value of a betting opportunity.

3. Comprehensive Scenario Handling (50% Accuracy)

• Complex Scenarios: Handling of evenly matched and underdog situations is not precise enough. Accurately assessing these scenarios requires a more nuanced approach that considers various factors and their interactions. Refining the algorithm's ability to process intricate betting scenarios is essential.
• Combination Optimization: Risk assessment for multiple betting combinations needs improvement. Optimizing combination bets, such as accumulators or system bets, requires identifying the optimal combination of bets to maximize potential returns while minimizing risk. Improving risk assessment for these types of bets is critical.
• Strategy Selection: The betting recommendation strategy needs more intelligent selection logic. Different betting strategies may be more suitable for different situations. Incorporating intelligent selection logic allows the system to choose the most appropriate strategy based on the specific circumstances of the bet.

📊 Current Test Results

{
  "overall_accuracy": 0.75,
  "critical_scores": {
    "ev_calculation": 1.0,
    "kelly_criterion": 0.75,
    "srs_compliance": 1.0,
    "risk_assessment": 1.0
  },
  "individual_tests": {
    "value_rating": { "accuracy": 0.0, "status": "failed" },
    "comprehensive_scenarios": { "accuracy": 0.5, "status": "failed" }
  }
}

✅ Completion Criteria

Kelly Criterion Optimization

• [ ] Kelly Criterion accuracy reaches 95%+
• [ ] Boundary condition handling is complete
• [ ] Dynamic Kelly strategy is implemented
• [ ] Support for different risk preferences

Value Rating Algorithm Improvement

• [ ] Value rating accuracy reaches 90%+
• [ ] Dynamic weight adjustment mechanism is implemented
• [ ] Context awareness capability is established
• [ ] Rating calibration and verification are completed

Comprehensive Scenario Optimization

• [ ] Complex scenario handling accuracy reaches 85%+
• [ ] Combination betting strategy optimization is complete
• [ ] Risk assessment accuracy is improved
• [ ] Betting recommendation reliability is verified

Performance and Availability

• [ ] Algorithm performance optimization (response time < 10ms)
• [ ] Memory usage optimization
• [ ] Cache strategy improvement
• [ ] Monitoring and alerting established

🛠️ Technical Implementation

Phase 1: In-Depth Kelly Criterion Optimization (Estimated 2 Days)

1.1 Parameter Accuracy Optimization

# Current Implementation
def calculate_kelly_fraction(self, ev: float, odds: float, probability: float, max_fraction: float = 0.25):
    b = odds - 1
    p = probability
    q = 1 - probability
    kelly = (b * p - q) / b
    return min(kelly, max_fraction)

# Optimized Implementation
def calculate_kelly_fraction_optimized(self, ev: float, odds: float, probability: float,
                                     market_volatility: float, confidence: float,
                                     risk_profile: str = "balanced"):
    # Dynamic Parameter Adjustment
    b = odds - 1
    p = probability
    q = 1 - probability

    # Basic Kelly Formula
    kelly = (b * p - q) / b

    # Market Volatility Adjustment
    volatility_adjustment = 1.0 / (1.0 + market_volatility)

    # Confidence Adjustment
    confidence_adjustment = min(confidence, 0.95)  # Confidence Upper Limit Protection

    # Risk Preference Adjustment
    risk_multipliers = {
        "conservative": 0.5,
        "balanced": 1.0,
        "aggressive": 1.5
    }

    adjusted_kelly = kelly * volatility_adjustment * confidence_adjustment * risk_multipliers[risk_profile]

    # Safety Boundary
    max_safe_fraction = 0.30 if risk_profile == "aggressive" else 0.20
    return max(0, min(adjusted_kelly, max_safe_fraction))

1.2 Dynamic Strategy Implementation

• Market volatility awareness
• Confidence adjustment
• Customized risk preferences
• Safety boundary protection

Phase 2: Value Rating Algorithm Reconstruction (Estimated 3 Days)

2.1 Multi-Factor Rating Model

def calculate_value_rating_enhanced(self, ev: float, probability: float, odds: float,
                                   market_context: Dict, bet_size: float) -> float:
    """
    Enhanced Value Rating Algorithm
    """
    # Basic EV Score (0-8 Points)
    base_score = min(max(ev * 20, 0), 8.0)

    # Probability Bonus (0-2 Points)
    prob_bonus = min(probability * 2.5, 2.0)

    # Market Efficiency Bonus (0-2 Points)
    market_efficiency = self._calculate_market_efficiency(market_context, odds)

    # Betting Scale Bonus (0-1 Point)
    size_adjustment = self._calculate_size_adjustment(bet_size, market_context)

    # Time Sensitivity Bonus (0-1 Point)
    timing_bonus = self._calculate_timing_bonus(market_context)

    # Risk Adjustment (-1 to 0 Points)
    risk_penalty = self._calculate_risk_penalty(probability, odds, market_context)

    total_score = base_score + prob_bonus + market_efficiency + size_adjustment + timing_bonus + risk_penalty

    return max(0, min(total_score, 10.0))

2.2 Dynamic Weight System

• Market efficiency assessment
• Betting scale influence
• Time sensitivity analysis
• Risk dynamic adjustment

Phase 3: Comprehensive Scenario Optimization (Estimated 2 Days)

3.1 Complex Scenario Handling

• Evenly matched situation analysis
• Underdog opportunity identification
• Combination betting optimization
• Market sentiment awareness

3.2 Strategy Selection Optimization

• Intelligent strategy switching
• Multi-condition decision tree
• Risk-return balance
• Short-term and long-term strategy combination

📁 Related Files

Core Algorithm Files

• src/services/betting/ev_calculator.py
• src/services/betting/betting_service.py
• src/api/betting_api.py
• test_betting_core.py

Test Files

• test_betting_ev_strategy.py
• test_betting_core_report.json

📊 Verification Criteria

Algorithm Accuracy Verification

python test_betting_core.py
# Expected: overall_accuracy ≥ 0.95

Performance Testing

• Kelly Criterion calculation < 1ms
• Value rating calculation < 2ms
• Combination optimization < 10ms
• Memory usage < 100MB

Functional Verification

• All test cases pass
• Boundary conditions are handled correctly
• Complex scenarios are handled completely
• SRS compliance remains at 100%

🎯 Expected Benefits

Algorithm Accuracy Improvement

• Kelly Criterion accuracy: 75% → 95%+
• Value rating accuracy: 0% → 90%+
• Comprehensive scenario accuracy: 50% → 85%+
• Overall accuracy: 75% → 90%+

Betting Effect Improvement

• Betting recommendation reliability is improved
• Risk control ability is enhanced
• Income stability is improved
• User experience is optimized

Business Value

• Betting success rate is improved
• Risk management ability is enhanced
• System intelligence level is increased
• Competitive advantage is established

📅 Timeline

• Phase 1: Kelly Criterion Optimization (2 Days)
• Phase 2: Value Rating Algorithm Reconstruction (3 Days)
• Phase 3: Comprehensive Scenario Optimization (2 Days)
• Total: 7 Days

🔗 Related Links

• Quality Assessment Report
• Issue #116
• EV Calculation Documentation
• Kelly Criterion Principles

🚨 Risks and Mitigation Measures

Technical Risks

• Algorithm Complexity: Implement in phases, test thoroughly
• Performance Impact: Performance benchmark and optimize
• Parameter Sensitivity: Establish parameter tuning framework

Business Risks

• Betting Recommendation Changes: Provide A/B testing mechanism
• User Acceptance: Gradually release and collect feedback
• Compliance: Maintain 100% SRS compliance

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Priority: 🟡 Medium (Algorithm optimization, improve system intelligence level) Complexity: 🟡 High (Involves complex mathematical algorithms and parameter tuning) Impact Range: Betting strategy module, API service Dependencies: None

For further reading and a deeper dive into the mathematics behind the Kelly Criterion, explore resources available on Investopedia's Kelly Criterion page.

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