Hopefully, you have read our Ultimate Guide to Value Betting. The practice of Value Betting involves finding betting opportunities where the odds available are greater than what they ‘should’ be according to the True Probability of an event.

At Mercurius, we utilise Big Data Analysis; another way of saying that we look at billions of data points. Through this detailed research of selected football leagues, we can determine whether or not there are Value bets to be had in the home/away/draw markets. If there is such a bet, we place it on your behalf.

At this point, you are doubtless hoping we get to the point and outline the two Value Betting strategies you can implement! They are:

1. Taking advantage of favourable market odds.
2. A thorough analysis of the event.

1 – Finding ‘Value’ in Market Odds

How to Reduce the Overround

Unless the bookmaker makes a serious blunder, the ‘book’ on every market will exceed 100% to ensure the bookie has an ‘edge’. One way to aid your Value Betting cause is to trim this overround.

In this Finland versus Greece game, the bookmaker’s overround is as follows:

• 100/2.20 = 45.45
• 100/3.10 = 32.26
• 100/4 = 25
• 45.45 + 32.26 + 25 = 102.71
• Edge is 2.71%

This figure assumes that the three options are evenly bet, of course. Remember, a bookmaker’s margin is never constant because it depends on risk as competition. High-level games often have a smaller edge than lower-level matches for this reason.

Finding the ‘True’ Overround

As we don’t know if the overround is distributed equally, we have to use an adjustment model. The multiplicative model (or normalisation model) offers a proportionate distribution of the overround. In case you were wondering, the formula looks something like this:

True Probability of Home Win = Implied Probability of [Home Win] / (1 + Overround Total)
TP Home Win = 0.4545 / (1 + 0.0271) = 0.4426

In this case, the true overround of a home win is 2.26% (100/0.4426). Do the same for the away win and draw outcomes.

Other options include the Additive, Power, and Shin methods, all of which are significantly more complicated than the multiplicative model, and beyond the scope of this guide.

Wisdom of the Crowd

This notion goes back to the early 1900s when Francis Galton asked 800 people to guess the weight of an ox. The average guess of the crowd was just 1% short of the true weight! As a result, this led to the belief that a large crowd is more likely to be correct than a select few.

In 2014, Pinnacle famously put the theory to the test by inviting people to guess the number of chocolate balls in a video. The right answer was 636, and after a couple of days, the mean guess was seldom more than a few percent away from the correct answer.

In betting, bookies provide the initial ‘tissue’ price, which is akin to the early inaccuracy of the chocolate balls experiment. It is at this point where value is arguably at its highest. However, liquidity, or the lack of it, can cause an issue with this tactic.

Ultra-popular markets such as the English Premier league may have decent liquidity up to a week before a game but in general, you need to wait to have the chance to place major wagers. At Mercurius, we place bets closer to the time of kick-off to ensure we can take advantage of excellent liquidity, but retain a positive Expected Value in the process.

Soft & Sharp Bookmakers

As the name implies, ‘sharps’ are bookmakers with excellent track records of understanding the True Probability of an outcome. These bookies have fast-moving odds that closely reflect true odds; bad news for value seekers!

As bettors, we want to find ‘soft’ bookmakers who change their odds slowly and appeal to casuals. Softs usually take longer to change their odds than sharps, and as a result, there is a greater chance of finding value. As the majority of bookmakers want non-professional players, they are typically ‘soft’ but don’t allow experts to bet for long before restrictions hit.

In the above Champions League game, you can see that while SkyBet offer odds of just 1.28 on Inter Milan to win, Coral and a couple of others offer 1.36. This may not seem like a big difference but it can be significant, especially when looking at short-priced favourites.

2 – Analysis of the Event

Sharps Seldom Restrict Profitable Punters – Here’s Why

While it is undoubtedly true that you can make money by merely following odds data and finding ‘value’ in that manner, it certainly behoves you to gain an understanding of your chosen sport. This is precisely what ‘sharp’ punters do to try and defeat ‘sharp’ bookmakers.

A savvy bettor will swiftly earn a profit from ‘soft,’ promotion-driven, bookies but will be restricted just as quickly. Ultimately, it is all about Return on Capital rather than Return on Investment. Your ability to make a decent passive side income is greatly diminished if you are restricted to a couple of pounds per bet.

While ‘sharp’ bookmakers won’t give you as much as an edge, they are also far less likely to restrict or ban you. As the Pinnacle head of trading, Marco Blume, said, by not banning professionals, they are ‘hiring’ them in a sense. As a result, Pinnacle does not need in-house statisticians. Therefore, if you are a ‘sharp’ bettor, you can win from a ‘sharp’ bookie like Pinnacle without fear of restriction.

Statistical Modelling

Knowledge is power! These days, we know that statistical modelling is essential for long-term profit. However, this process has been used for decades. Bill Benter earned hundreds of millions of dollars from ‘beating’ the notoriously Sharp Hong Kong racecourse.

From the thousands of pieces of information related to horse racing, Benter focused on about twenty to incredible effect. He found that by using public odds as a starting point, it was possible to alter them with his proprietary algorithm. In essence, Benter found a method of uncovering Value Bets at the famous Happy Valley racecourse.

Using Information to Your Advantage

Haralabos Voulgaris is a man who has used a combination of statistical modelling and ‘scouting’ to great effect. He has made millions of dollars beating sharps at basketball. He also proved that even sharps make mistakes with his huge win on the LA Lakers to win the 1999-2000 NBA Championship. His $80,000 bet at odds of 7.50 helped launch his career.

Experts bemoaned the Lakers’ ‘poor’ start to the season even though they won 5 of their opening 7 games. Their odds drifted from 5.00 to 7.50, and ‘Bob,’ as Voulgaris is known, swooped as he knew the Lakers were overpriced.

While the sharp bookmakers gave the Lakers a 13% chance of winning the title, Bob’s calculations told him the True chance was 25%; a clear value bet. Even though he stood to earn $520,000, he saw a ‘theoretical’ profit of $70,000 from the bet, which meant it had value from the very beginning.

Bob uses a program to simulate the outcomes of NBA games, but only follows the data when the pick is supplemented by other information. In this instance, Bob even checks out the Twitter accounts of NBA players. For example, if one tweets about heading to a nightclub, it may suggest he isn’t too serious about the following day’s game.

Weaknesses in Using Statistical Modelling & Information Advantage to Profit from Sharps

• Overfitting – The process of using far too many parameters in a statistical model than is justified by the data. This is one reason why Benter used relatively ‘few’ parameters.
• Operational – When relying on computers, there is always the danger of a malfunction.
• Incorrect Data – There is a real chance that your data is inaccurate; the only way to find out is to test it!
• Risk of Ruin – In theory, a bettor with a limited bankroll will always go bankrupt when up against an opponent with limitless funds. You can use Kelly’s Criterion to mitigate this risk, but you need to calculate your edge in a market correctly.
• Quality of Information – Knowing which information is valuable, and which is worthless can take on a ‘trial and error’ method. In some cases, it can be very expensive to get your hands on high-quality information