A few months ago, I developed a Machine Learning algorithm to predict the Major Leagues' results using a simple Poisson process. The track record of the program was more impressive than I thought it would be, correctly predicting the results for 8/10 matches in the first Matchweek.

## The algorithm against an Expert

One of the difficulties of testing an algorithm is to find a good benchmark for its performance. Say, if my prediction has an accuracy of 70% over 200 matches, is it good, bad or mediocre? It surely outperforms random guessing of mybetting tips.

Here, I compared the results between 315 matches Merson predicted on his all leagues. He achieved a 54.9% accuracy, while my Poisson process algorithm achieved a surprising 78.7% accuracy.

## From predictions to Betting

The result startled me. A 10% edge over an expert’s opinion itself is huge. But 23.8% is a breakthrough. And I did not even have to do much besides asking the beloved Poisson processes to chunk out numbers.

This is when I started looking into sports mybetting tips. And I enter a new game against a new opponent.

## The Little Secret

But things are not always nice and simple. In reality, to increase profit, bookmakers invest in teams of data scientists to analyze decades of sports data and develop highly efficient and accurate models for predicting the outcome of sports events and giving odds to their advantage.

Let’s assume that the bookmakers’ odds are a perfect reflection of the probability of the various teams winning, drawing or losing. So, for that Liverpool vs Man City clash, since the odds BetWinner gave to Liverpool winning are 2.4, the probability of them winning is simply 1/2.4 = 41.6%, surprisingly close to my prediction of 45%. Similarly, the probability of Man City winning is 1/3.0 = 33.3%, and the probability of a draw is 1/3.6 = 27.8%.
41.6% + 33.3% + 27.8% = 102.7%! That’s odd

The reason the probabilities don’t add up to 100% is that the odds aren’t fair. That extra 2.7% is the bookmaker’s advantage. To get the real probabilities, we need to correct for the profit by dividing through by 102.7. So the bookmakers’ true probability of a Liverpool win is 41.6/102.7 = 40.5%, the probability of a City win is 33.3/102.7 = 32.5%, and for a draw, it is 27.8/102.7 = 27.06%. For a perfectly efficient bookmaker, these are the probabilities of each outcome.

## My Strategies

This understanding does not stop me from trying to exploit any potential inefficiencies in the market. At first, I devise the general bet strategies.
1. I set out a budget of \$1000, divided equally to 30 previous rounds of the Premier League. So each weekend I have roughly \$33 dollars to bet.
2. For each match, a prediction will be made by one of the three methods: (a) Paul Merson’s prediction, (b) my Poisson process algorithms and (c) a random assignment of equal probability to win, draw and lose.
3. With the prediction, I find the highest odds among 6 online betting houses. This means if I win, I get the highest profit possible. This will be the odds at which I place my bet.

## Conclusion

IF You had a large starting capital (I simulate with 10,00,000₹ but every match I have only 5000₹ to bet), a lot of patience and a cool head. Reach out to me I will give you similar more bets to make you a hell lot of money.

If anything, this article is an example of what you could potentially do. But the bookmakers have made it extremely difficult for anyone to gain sustainable profits. If the bookie thinks the probability of a win is 1/6, then he will guarantee that his expected intake minus payout is positive by setting the odds to be less than 5, maybe something like 4.6. If there are still a lot of people placing a bet at 4.6 odds, then the bookie surely realizes that the probability of a win must be higher than his own estimation and will adjust the odds to say 4. Chances are that by the time the code infers the most optimal odds, it has been changed.