Since the beginning, Trademate's bread and butter has been Soccer/Football betting. With roughly 40% of our trades placed coming from the 'world game'.
As it says in the title, our users have placed 1.37 million Soccer trades at an ROI of 2.37%. So we thought it would be beneficial to take a deep dive into our Soccer performance and answer some of our most asked questions.
A great feature of Trademate that you may be underusing is the big data tool. In this article, we take a look at how Trademate is performing in the Soccer markets and get the big data tool to try and answer some of our burning questions.
We hope this can serve as a useful introduction to some of the things this powerful software can do. This article is a fairly simple example as the person writing it is a relative simpleton themselves. If I can do it, then anyone can!
Here are some caveats to begin with on how we ran these data simulations and why we chose these parameters.
10,000 trade sample
10,000 trades is a big enough sample size to give a reasonably accurate picture of how the software is performing. Of course, 10,000 is still ‘small’ in the sense that there have been 1.37 million football trades and will be millions more in the future. So this 10,000 trades still only captures a period of time (on average 3-6 months of football).
Edges of 3%+
Although any edge above 0% will theoretically be profitable in the long run, over 10,000 trades, small edges of 1% or less might be subject to some standard deviation from the mean (i.e. some freak results). I have also chosen an edge of 3% because the Betfair Exchange has a commission rate of 2% on winning trades. I thought 3% would make a good starting point and one that I personally use myself to give myself the best possible chance of beating the market.
Trademate recommends Kelly Staking to maximise the edges available to you. However, for the purposes of this data analysis we wanted to give a simple example that is easy to compare and easier for you to make sense of. So the numbers you see for EV and profit are all given in GBP (as it is my currency), but you can easily convert this to euros or the currency of your choice. Even though flat staking is still profitable as shown in the examples in the article, in practice, if you are able to use kelly staking, you will grow your bankroll faster.
Again, because of the sample size, there are not as many opportunities to bet on football games at higher than odds of 4. The variance on bets like these can be extremely high and you may experience long losing runs on these too. So we don’t recommend these unless you are an experienced bettor and have the bank and temperament to withstand long losing runs. The image below also shows the number of trades in proportion to the odds. The bigger bubbles show a larger number of bets in that odds range and therefore these are the most common bets you will find in your Trademate feed when you are looking yourself.
Here we’ve run an analysis of all football markets with a 3% edge and odds of 1-4 and compared them closer to kick off.
Is there anything that strikes you as interesting from the data above?
Immediately, you can see that the closing EV gradually increases as you get closer to kick off time. For further visual clarity, you can see this on the graph below.
Note that I have focused on the closing EV and not the actual profit. The reason for this is that over 50,000/100,000/1 million trades, you should expect that actual profit will be closer in line to the closing EV. In this example, you would be more profitable betting 2 hours before kick off than 1 hour, but that might not always be true.
So why does it matter how close to kick off you are?
I have some speculative theories on this and if any readers have alternative suggestions then please let us know. Some ideas I thought might be:
We’ve established that betting closer to kick off can be a more profitable angle to take. Our last analysis was quite basic though. We looked at all football markets but there are 3 main markets that Trademate can help you find an edge in. Are these equally profitable or can we find a bigger edge in one over the other?
Why is there a difference between the market types?
Let us know if you can think of any other suggestions. My theories are the following:
Something we suggest to limit account restrictions is to stick to recommended leagues. However, if you were wondering whether the potential risk/reward ratio is worth considering, we ran some analysis for you, for games 2 hours from kick off.
What captured your attention here?
Although the closing EV is clearly higher on non-recommended leagues, in practice, more profit was made on match odds and asian handicaps on the recommended leagues. Regardless, a solid ROI% can be found on recommended leagues which makes them a viable option. Even on restricted accounts, you will be able to stake reasonable amounts on recommended games in high liquidity leagues.
So how do we explain what’s happening here?
Some speculative theories here but perhaps the match odds in the recommended leagues are more predictable than more obscure leagues. The lower the quality of the football, the more erratic results can be. This can work to your advantage, as you can see in the closing EV, but often the results may not match what the closing line suggests. There might be a lack of information on the part of the bookmaker and the lines may have been incorrectly priced initially.
On the over/under lines, bookmakers will have gaps in their knowledge and will look mostly at statistics to price up those markets. Compare this to big leagues where the traders will be watching those games and factor in other variables into their pricing. This might mean that one bookmaker could have someone with specialist knowledge in a particular league and other bookmakers are either slow to react or lazy in their pricing of those particular markets as someone is covering those games just to make up their workload.
Our final question is a more topical one. COVID has affected all of us but in terms of sports betting, it’s effects have been widely debated. Some analysts believe it has made a significant difference to the betting markets and made it harder to profit from sports events. Others have said the opposite and said that there is more value to be found betting on underdogs and finding value if you know where to look for it.
We wanted to see on a big scale, whether there has been any difference in football betting before COVID and after COVID.
For the purposes of this analysis, Pre-covid analysis was from August 2019 to March 2020. Post Covid data is from the middle of March (when the English premier league was suspended) until the 1st of November. This factors in the gap in the football calendar in most leagues and the new conditions where teams were playing behind closed doors and had a busy schedule to finish their seasons, as well as the new seasons that have just started.
Interestingly, the closing EV is fairly similar and only slightly smaller than it was Pre Covid. However, profit to flat stakes and ROI% is more than DOUBLE what it has been Post Covid. Regardless, profit has still been made on football in this strange new world we are living in.
With access to the big data tool, you can run your own analyses on football and a range of other sports. Has the home crowd advantage eradicated some of the edges in that sport post-covid? Do bets placed closer to kick off play a bigger role now than they did before because of impacts on team news? The possibilities for analysis are endless and we would love to hear from you and any trends in the data you’ve found interesting!
Written by Neel Shah. Neel is a full time sports bettor and trader. He has documented his trademate journey over the past few months which has taken him into profits of over 10,000 GBP. You can find his articles on the trademate website, his blog (mybettorlife.com) and reach him via twitter @mybettorlife.
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