<Maximizing Profits: Leveraging Bookie Errors with Python>
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When it comes to sports wagering, bookmakers establish the odds with the intent of minimizing their losses, irrespective of the outcome or the number of successful bets. However, they are not infallible! There exists a strategy known as value betting that capitalizes on these errors to enhance profitability. This article will guide you on how to identify value bets manually using free betting resources, as well as automating the process with Python coding.
# Understanding Value Betting
In any endeavor, the goal is to enhance our returns, and betting is no different. Given a choice, we would all prefer to double or triple our initial investment—wouldn’t we? This principle forms the basis of value betting, which is how bettors can earn over time.
A value bet is identified when the “real odds” of a match's outcome exceed the odds provided by bookmakers. This notion is rooted in the mathematical concept of expected value. Every time a wager is placed, it’s crucial to seek out value. For instance, let's examine the “Over 2.5 goals” odds gathered from seven bookmakers for the upcoming match between Liverpool and Manchester City on February 7th.
Although we have yet to compute the actual odds, it’s evident that the bookmakers offering higher odds (listed first) present more lucrative opportunities, making them potential candidates for value bets.
The concept is straightforward, yet the challenge lies in accurately calculating the real odds and identifying value bets efficiently. Before delving into coding solutions, I’ll illustrate how this can be accomplished manually with betting tools to help you grasp the underlying mathematics.
Note: Value betting is a mathematically sound system supported by statistics, but it carries risks. If you’re interested in a no-risk method, consider my previous articles on “surebets.” While finding surebets requires more time, it can yield quick returns. Conversely, value betting is less time-intensive but can lead to profits over time. Assess which option suits you best.
How Does It Function?
In this scenario, we’ll search for value bets related to the Liverpool vs. Manchester City match. We need to ascertain both the actual probabilities and the bookmakers' odds for “1x2” (1: Liverpool wins, x: Draw, 2: Manchester City wins).
For simplicity, let’s assume we already have the true probabilities of the match (I’ll explain how to derive them shortly). According to FiveThirtyEight, Manchester City (47%) has a higher probability of winning compared to Liverpool (28%).
To identify a value bet, we need to convert these probabilities into decimal odds. The odds are simply the inverse of the probability.
odds = 1/probability
From this, we determine that Liverpool’s odds are 3.57, Manchester City’s are 2.12, and a draw is at 4.0. These are considered “real odds” as they reflect the true likelihood of an event occurring (though based on estimates, they are more accurate indicators than those set by bookmakers).
Next, to uncover a value bet, we compare these three “real odds” with the bookmakers’ odds using the following formula:
Value = (Bookies Odds / Real Odds) - 1
We’ve identified a value bet if value > 0.
Here’s a set of “1x2” odds I sourced from Odds Portal just two days prior to the match. Let’s make a comparison.
Using the “value” formula, we discovered value bets for “Manchester City Wins” with MarathonBet, Pinnacle, and Unibet. The odds provided by these bookmakers exceed what they should be (2.12), indicating these are indeed value bets.
Suppose we wager $100 on a draw. Based on our calculated real odds, this should yield $400 (odds of 4.0). However, if we place our bet with the bookmaker offering the highest odds (3.8) and win, we would fall short by $20 of our expected return ($400). Conversely, if Manchester City wins, our $100 bet would yield an extra $7 compared to our expected return ($212).
But this is merely one match! Value bets can be identified across numerous betting markets in various sporting events. By placing value bets, seasoned bettors enhance their potential earnings. However, locating them individually can be tedious; thus, many opt to pay for tools that streamline the process. Fortunately, if you possess basic coding knowledge, you can create your own solution.
# Discovering Value Bets Using Python
Experienced bettors often invest in costly yet effective betting tools that assist in swiftly identifying value bets among the myriad of football matches occurring daily. However, coding skills can eliminate the need for such expenses.
In fact, I developed a profitable betting tool using Python’s Selenium and Pandas. Below, I provide the complete code and explain my methodology.
I Once Paid $180/yr for a Profitable Betting Tool. This Year I Built One in Python
Full code to create a football betting tool with Pandas and Selenium.
The following three steps outline a simplified version of the betting tool I constructed in Python. They will provide insight into the value betting concept.
Step 1: Acquire the Data to Support Your “Real Odds.”
To identify value bets, we first need to calculate the “real odds.” While FiveThirtyEight offers match outcome probabilities, calculating your own real probabilities is preferable, as they do not cover all betting markets. This is why I derive “real odds” using historical data in Python.
You can find historical data for 27 football leagues spanning the last decade on this website. Instead of downloading each dataset manually, execute the following lines of code to obtain them within minutes.
The data encompasses numerous football statistics, including goals scored, conceded, corners, shots on target, etc. With this information, you can compute your own probabilities for any market you desire. For this example, we’ll utilize data from four football leagues.
Step 2: Compute the Real Odds.
In this instance, we’ll calculate real odds for the “over 2.5 goals” market, though you can apply this to any market—just select the relevant column for your analysis. Since I wish to analyze goals, I’ll focus on the FTHG (home goals) and FTAG (away goals) columns. The formula to calculate the real odds is straightforward.
The underlying mathematics is uncomplicated: it’s the number of favorable outcomes divided by the total number of events. For ‘over 2.5 goals’, this would be represented by the following formula:
Real Odds = (Team 1 and Team 2 Matches Over 2.5) / Total Matches
So, if you input in Python:
calculate_real_odds('Liverpool', 'Man City', dict_historical_data['English Premier League'], 'Over/Under')
You will get 1.67 as real_odds. This value will vary depending on the sample you choose for analysis. In Step 1, I downloaded data from five seasons (from 15/16 to 20/21), but you can adjust the seasons as needed.
You can retrieve the real “over 2.5 goals” odds for any match by inserting any pair of teams into the formula.
Step 3: Contrast Real Odds with the Bookmakers’ Odds.
Now that you possess the actual odds for any match, you must compare them against the bookmakers' odds. As previously mentioned, websites like Odds Portal can help determine whether the odds are inflated or undervalued.
Here are the bookmakers’ odds for the ‘Over 2.5’ market in the match between Liverpool and Manchester City, sourced from OddsPortal.
Thus, applying the formula:
Value = (Bookies Odds / Real Odds) - 1
We find that only William Hill fails to offer a value bet.
This example covers just one market in a single match. Numerous inefficiencies exist across various sporting events and different bookmakers. You can even scrape odds from your preferred bookmaker or from OddsPortal for automatic retrieval instead of analyzing each match manually as demonstrated in Step 3.
# Conclusion
Bookmakers can err, and we can take advantage of these mistakes. The key is to identify value bets that can yield profits over time. Today, you learned how to locate value bets manually using free betting tools and automate the process through Python coding.