AI Recommendations

Costs 2 credits per request

Returns AI-generated betting recommendations for individual markets. Each recommendation includes:

Confidence level (High/Medium/Low)
Statistical edge percentage
Kelly Criterion stake sizing
Detailed rationale
Risk factors
Data support

Use this endpoint to find value betting opportunities across all events and markets.

Authorization

string

default:"Bearer "

required

Your API key for authentication

Query Parameters

eventId

string

Filter recommendations for a specific event

confidence

string

Filter by confidence levelValues: High, Medium, Low

minEdge

string

Minimum edge percentageExample: 5 for 5% minimum edge

limit

string

default:"10"

Maximum number of recommendations

regionId

string

Filter by region

competitionId

string

Filter by competition

timezone

string

Timezone for date filtering

Response

Returns an array of betting recommendation objects.

string

Unique recommendation ID

eventId

string

Event this recommendation is for

marketId

string

Market ID

betItemId

string

Specific bet item ID

outcome

string

Recommended outcomeExamples: "Home Win", "Over 2.5", "BTTS Yes", "Lakers -5.5"

odds

number

Current decimal oddsExample: 2.10

confidence

string

AI confidence level

High: Edge > 5%, expected 60-65% win rate
Medium: Edge 3-5%, expected 55-60% win rate
Low: Edge < 3%, expected 50-55% win rate

edgePercentage

number

Statistical edge over market oddsExample: 6.8 means 6.8% edgeHow edge is calculated:

Edge = AI Probability - Implied Probability

Example: If AI gives 52% probability and odds of 2.10 imply 47.6%, edge is 4.4%

kellyFraction

number

Optimal stake size using Kelly Criterion (10% fractional Kelly)Example: 0.032 means 3.2% of bankrollFull Kelly formula:

Kelly % = (Edge × Odds - 1) / (Odds - 1)

We recommend 10% fractional Kelly for safer bankroll management.

rationale

array

Array of strings explaining why this bet has valueExample:

[
  "Home team's excellent form (8 wins in last 10)",
  "Away team struggling defensively (10 goals conceded in 3 games)"
]

riskFactors

array

Array of potential risks to considerExample:

[
  "Away team has key players returning from injury",
  "Weather forecast shows heavy rain"
]

dataSupport

array

Statistical data supporting this recommendationExample:

[
  "Simulation gives 52% home win probability vs 47.6% implied by odds",
  "Expected goals: Home 1.8, Away 1.1"
]

settled

boolean

Whether the bet has been settled

result

string

Settlement result (if settled)Values: won, lost, push

Example Request

curl -H "Authorization: Bearer sk_live_..." \
  "https://api.mrdoge.co/v2/ai/betting-recommendations?confidence=High&minEdge=5&limit=5"

Response

{
  "data": [
    {
      "id": "cmhqld82i0dwts201h1dcxr7a",
      "eventId": "43089384",
      "marketId": "1488408114",
      "betItemId": "6169425387",
      "analysisTimestamp": "2025-11-08T18:01:59.322Z",
      "outcome": "Mais de 3,5",
      "odds": 2.2,
      "point": 3.5,
      "confidence": "Medium",
      "edgePercentage": 8.2,
      "kellyFraction": 0.096,
      "rationale": [
        "Simulation shows a total of 4 goals (3-1)",
        "Both teams have shown the ability to score, with Liverpool scoring in 100% of away games",
        "High offensive ratings for both teams support the expectation of a high-scoring match",
        "Data support: Simulated total of 4 goals aligns with historical scoring patterns; City's average goals per match at home is high, supporting the Over 3.5 bet"
      ],
      "riskFactors": [
        "Potential for a lower-scoring game if Liverpool fails to capitalize on chances"
      ],
      "settled": false,
      "result": null,
      "settlementDate": null,
      "actualReturn": null,
      "createdAt": "2025-11-08T18:01:59.322Z",
      "updatedAt": "2025-11-08T18:01:59.322Z",
      "bookmakerSource": "Novibet",
      "dataSourceId": "novibet"
    }
  ]
}

When no recommendations match your filters, the endpoint returns an empty array: {"data": []}. Try adjusting your filters (lower minEdge, remove confidence filter, or increase limit) to see more results.

Understanding Confidence Levels

High Confidence (Edge > 5%)

What it means: Strong statistical advantage over market oddsExpected accuracy: 60-65% win rateWhen it happens:

Clear form advantage
Market inefficiency detected
Strong tactical mismatch
High data quality

Recommendation: These are our best bets. Consider using suggested Kelly stake.

Medium Confidence (Edge 3-5%)

What it means: Moderate statistical advantageExpected accuracy: 55-60% win rateWhen it happens:

Slight market inefficiency
Minor form advantage
Moderate data quality

Recommendation: Good value bets. Consider using 50% of suggested Kelly stake.

Low Confidence (Edge < 3%)

What it means: Marginal edge, higher riskExpected accuracy: 50-55% win rateNote: We rarely recommend Low confidence betsRecommendation: Only for experienced value bettors. Use minimum stakes.

Kelly Criterion Staking

The kellyFraction field tells you the optimal stake size for each bet.

Example Calculation

Your Bankroll: $1,000 Recommendation:

Odds: 2.10
Edge: 6.8%
Kelly Fraction: 0.032 (3.2%)

Stake Calculation:

Full Kelly: $1,000 × 0.032 = $32
10% Fractional Kelly: $32 × 0.10 = $3.20 ← Recommended

Start Small: If you’re new to value betting, consider using 5% fractional Kelly (half our recommendation) until you’re comfortable with variance.

Filtering Strategies

High Confidence Only

?confidence=High&minEdge=5

Best for conservative betting

Specific Event

?eventId=123456

Get all recommendations for one match

Premier League

?competitionId=1&confidence=High

Focus on specific league

High Edge Opportunities

?minEdge=7&limit=3

Find the biggest edges

Credit Cost

2 credits per request - More affordable than Mr. Doge Picks for getting multiple individual recommendations.

Use Cases

Find value bets across all events
Filter by confidence and edge
Get recommendations for specific matches
Build your own betting strategies
Track AI performance

Value Opportunities

Get only the highest-edge opportunities

Mr. Doge Picks

Get curated picks (singles or parlays)

AI Performance

See historical recommendation performance

Query Parameters

eventId

string

Filter by event ID

confidence

enum<string>

Filter by confidence level

Available options:

High,

Medium,

Low

minEdge

string

Minimum edge percentage

Example:

"5"

limit

string

default:10

Maximum recommendations

regionId

string

Filter by region

competitionId

string

Filter by competition

timezone

string

Timezone for date filtering

Response

Betting recommendations

string

Example:

"rec_123456"

eventId

string

Example:

"123456"

marketId

string

Example:

"mkt_123456"

betItemId

string

Example:

"bet_123456"

outcome

string

Example:

"Home Win"

odds

number<float>

Example:

2.1

confidence

enum<string>

Available options:

High,

Medium,

Low

Example:

"High"

edgePercentage

number<float>

Statistical edge over market odds

Example:

6.8

kellyFraction

number<float>

Optimal stake size using Kelly Criterion (10% fractional Kelly recommended)

Example:

0.032

rationale

string[]

Example:

[
  "Home team's excellent form (8 wins in last 10)",
  "Away team struggling defensively (10 goals conceded in 3 games)"
]

riskFactors

string[]

Example:

[
  "Away team has key players returning from injury"
]

dataSupport

string[]

Example:

[
  "Simulation gives 52% home win probability vs 47.6% implied by odds"
]

settled

boolean

Example:

false

result

enum<string> | null

Available options:

won,

lost,

push

Sports Endpoints

AI Endpoints

Core Utilities

Authorization

Query Parameters

Response

Example Request

Response

Understanding Confidence Levels

Kelly Criterion Staking

Example Calculation

Filtering Strategies

High Confidence Only

Specific Event

Premier League

High Edge Opportunities

Credit Cost

Use Cases

Value Opportunities

Mr. Doge Picks

AI Performance

Query Parameters

Response

Sports Endpoints

AI Endpoints

Core Utilities

​Authorization

​Query Parameters

​Response

​Example Request

​Response

​Understanding Confidence Levels

​Kelly Criterion Staking

​Example Calculation

​Filtering Strategies

High Confidence Only

Specific Event

Premier League

High Edge Opportunities

​Credit Cost

​Use Cases

​Related Endpoints

Value Opportunities

Mr. Doge Picks

AI Performance

Query Parameters

Response

Authorization

Query Parameters

Response

Example Request

Response

Understanding Confidence Levels

Kelly Criterion Staking

Example Calculation

Filtering Strategies

Credit Cost

Use Cases

Related Endpoints