Identifying Value Bets#

A “value bet” is the cornerstone of any successful betting strategy. It’s a bet where you believe the true probability of an outcome is higher than the probability implied by the bookmaker’s odds. Placing value bets consistently is the key to long-term profitability.

The identify_value_bet Function#

This is the core function for value analysis. It takes bookmaker odds and your own estimated probabilities, and returns a comprehensive analysis of the betting opportunity, including expected value and a recommended Kelly stake.

penaltyblog.betting.identify_value_bet(
    bookmaker_odds: Union[float, List[float], NDArray],
    estimated_probability: Union[float, List[float], NDArray],
    kelly_fraction: float = 1.0,
    min_edge_threshold: float = 0.0,
) -> Union[ValueBetResult, MultipleValueBetResult]

Parameters#

  • bookmaker_odds: A single decimal odd, or a list/array of odds.

  • estimated_probability: Your estimated true probability (from 0 to 1) for the corresponding outcome(s).

  • kelly_fraction (default=``1.0``): The fraction of the Kelly Criterion to recommend (e.g., 0.5 for a more conservative “Half Kelly” stake).

  • min_edge_threshold (default=``0.0``): The minimum required “edge” (your probability minus the implied probability) for a bet to be flagged as a value bet.

Returns#

The function intelligently returns one of two detailed data objects:

  • ValueBetResult: If you provide a single odd and probability.

  • MultipleValueBetResult: If you provide a list of odds and probabilities.

Understanding the Results#

For Single Bets (ValueBetResult)#

When analyzing a single bet, you get a detailed breakdown:

  • is_value_bet (bool): True if the bet has a positive edge above your threshold.

  • expected_value (float): The amount you expect to win or lose per unit staked. A positive EV indicates a profitable bet in the long run.

  • edge (float): The difference between your probability and the bookmaker’s implied probability.

  • recommended_stake_kelly (float): The optimal fraction of your bankroll to stake, according to the full Kelly Criterion.

  • recommended_stake_fraction (float): The Kelly stake, adjusted by the kelly_fraction you provided.

For Multiple Bets (MultipleValueBetResult)#

When analyzing a list of bets (e.g., a weekend’s fixtures), you get a portfolio-level summary:

  • individual_results (List[ValueBetResult]): A list containing a detailed ValueBetResult object for each bet you provided.

  • total_value_bets (int): The number of bets in the list that were identified as having value.

  • average_edge (float): The average edge across all identified value bets.

  • kelly_stakes (List[float]): A list of the recommended (fractional) Kelly stakes for the entire portfolio of bets.

Usage Examples#

Single Bet Analysis#

import penaltyblog as pb

# We think a team has a 50% chance to win, but the odds are 2.5
result = pb.betting.identify_value_bet(2.5, 0.50)

if result.is_value_bet:
    print("This is a value bet!")
    print(f"Edge: {result.edge:.2%}")
    print(f"Expected Value (per £1 staked): £{result.expected_value:.2f}")
    print(f"Recommended Full Kelly Stake: {result.recommended_stake_kelly:.2%} of bankroll")
else:
    print("This is not a value bet.")

This is a value bet!
Edge: 10.00%
Expected Value (per £1 staked): £0.25
Recommended Full Kelly Stake: 16.67% of bankroll

Multiple Bet Analysis#

import penaltyblog as pb

# Analyzing three different bets from a weekend
odds = [2.1, 3.5, 1.8]
my_probs = [0.5, 0.25, 0.6] # Our estimated probabilities

results = pb.betting.identify_value_bet(odds, my_probs)

print(f"Found {results.total_value_bets} value bets out of {len(odds)}.")
print(f"The average edge on these value bets is {results.average_edge:.2%}")

# You can also inspect each individual result
for bet_result in results.individual_results:
    if bet_result.is_value_bet:
        print(f"- Bet at odds {bet_result.bookmaker_odds} has an edge of {bet_result.edge:.2%}")

Found 2 value bets out of 3.
The average edge on these value bets is 3.41%
- Bet at odds 2.1 has an edge of 2.38%
- Bet at odds 1.8 has an edge of 4.44%