This is a guide on how to use the Big Data Tool inside the Trademate Sports software. Trademate is the ultimate tool for professional sports bettors, and with the big data tool you can analyse the results of all of the Trademate Customers in order to improve your own strategies. This article explains how it works, or you can watch the video on the right hand side.
We get a lot of questions about how we are performing on the different sports we support like: football, basketball, tennis, rugby union, rugby league, American football, baseball and ice hockey.
Or how we are performing on different bookmakers. Some even ask very detailed questions like how are you doing on football between 2.00 to 3.00 in odds, 0-1 hour before kick off and with a minimum advantage over the bookies of 1%.
In the past we used to host big data video streams to look at our user data and answer the questions our customers had or articles like “Big Data Analysis: Is Trademate Sports Profitable?”. But then we decided that it would be better if you as a customer could examine the data yourself. So to answer to questions like this we made a big data tool as a part of Trademate.
You can find the big data tool on the left hand side of the menu and once you get inside in the beginning you can see your own stats. It looks the same as the analytics tool, but the difference between this and the analytics tool is the big data tool will show you trades from all of our users.
Even though it is interesting to have a look at the overall data, you might not necessarily want to trade at a higher odds ranges. Even though an edge is still an edge, regardless of the odds, it will dramatically increase your variance, so we do not recommend it unless you have the patience to wait for the good and bad luck to even out. This article covers the concept of variance in sports betting in more detail. While this article explains how you can reduce your variance.
As an example, let’s have a look at how we have been doing on Betvictor:
The results here are pretty much what we would expect over time. We can see the green closing EV line, which is the results we expect over time and the blue line which indicates our users actual profits, which in this case very much align.
Next, there are a couple of factors related to how the data is displayed inside the tool which we should mention.
First, we had to limit the number of trades each search shows to 10,000. Our database contains more than 2.5M user trades and without this limit it would take a very long time to load searches.
Secondly, we are using flat stakes. This is done to remove the effect of different users having different bankrolls and placing trades of different sizes.
Otherwise a user who placed a bet of €10,000 on a game would skew the data a lot vs a user who only places €10 on a game. Therefore we display the results with a flat 1 unit stake size. This is independent of the currency you have selected.
So you can interpret the Total Profit shown in the Betvictor example above to mean that our users have achieved aggregate total profits of 485 units.
Third, we include all games placed by all users, so if a lot of users place the same bets, it can skew the data. For example if a lot of users place the same winning bet, it will skew the profits upwards. The opposite is true if a lot of users place the same losing bet. So keep that in mind. Particularly if you are looking at a small sample size of bets.
Let’s have a look at what happens if we look at data from Norsk Tipping and also narrow our filters. In general using narrow filters is recommended, because otherwise you won’t be able to see all of the data within that filter. Only the latest 10,000 bets.
An important thing to notice with regards to sample size, is that if we only looked at the first 3,000 bets at Norsk Tipping, we would draw the conclusion that this is something that is not profitable at all.
However, once we have the full sample size, it becomes clear that the profits are starting to align with the expected closing value, which is exactly what we would expect. Keep this in mind that over a small sample size of trades, a lot of things can happen and that even strategies which are profitable in the long run will fluctuate in the short term.
This is why it is important to get in a large sample size of bets when using Trademate. This is further explained in this article on the Law of Large Numbers in betting.
We have a lot of users from Norway, which most likely place a lot of the same edges on Norsk Tipping. This could be a potential explanation for some of the large up and downswings you can see in the graph.
When we look at an even bigger sample size of all of the trades on Norsk Tipping it becomes even clearer that the value bets we are identifying there are profitable.
I hope you can see how the big data tool is an important tool for any serious sports bettor, as it enables you to examine the results of our users. This way you can determine what has been working for our users and what’s not, and make adjustments to your own strategies and improve your results.
The Big Data Tool is available as part of the Trademate Monthly + Analytics package and the Trademate Pro subscription. Check out our pricing page inside Trademate for more details.
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