Written by Alex Vella

To give you a bit more insight into the performance of our users and software, we have just started writing an article every month where we go through our overall results, as well as taking a deeper look into our performance with each individual sport.

We are doing this with the help of both our big data tool and back-log data. The back-log data was used to find out the number of trades placed, flat ROI and actual ROI. This is because the big data tool has a cap of 10,000 trades to speed up loading time, so for some of our sports it will not show results of every trade placed in November. But for the average edge placed and closing edge data we have used the big data tool. So for some sports it is only a sample size of 10,000.

Using the big data tool to see our edge data, we tried to narrow down the trades we would analyse. Here are the types of trades we calculated the edge data for; bets placed 0-8 hours before kick-off, all odds types, odds ranges 1.0 - 4.0 and 1.5% - 15% edges.

The reason we used those filters is because they are the presets that we recommend to our users. See our recommended presets article here. For the back-log data, there are no filters, it is every trade placed no matter what the odds range, time placed or edge.

Limitations

Before we get into how we performed in November, it’s important to note some of the limitations of the data we will show you:

1. For the Flat ROI calculations, stake sizing of €1 per trade is used to remove the effect of users with large bankrolls skewing the results. Also, this way we can see whether our edge is based on beating the market, rather than the Kelly criterion.
2. There is a chance that some of the bets in this data are duplicated as multiple customers could be placing the same bet. But the chances of this happening regularly are low as our user-base is quite geographically-diverse, which means they have to use different bookmakers.
3. Something else that is possible but even more rare is customers logging their own bets manually within our software. Those bets are not recommended by us, but they will still appear in this data, win, lose or void.
4. Some of the sample sizes will be very low, meaning no conclusions should be taken from those data-sets. The bigger the sample size the more accurate the data. Generally, a sample size of under 2,000 trades will include a lot more randomness and needs to be taken with a grain of salt or completely ignored.

In the month of November, 183,421 trades were placed, €10,300,613 was turned over and €410,373 was profited at an ROI of 3.98%! Here is how each sport performed, broken down into recommended leagues (popular leagues) and non-recommended leagues (lower leagues):

Key Findings

Soccer

What a month for Soccer traders once again! The flat ROI data shows that we have an edge no matter what our staking system is. But you can see, by using theKelly Criterion, with a sample size of nearly 100,000 bets, you can really ramp up your ROI. There is absolutely no tipster or software that can beat our results on Soccer over the sample size we have (if there is I’m sorry, please let me know). And the best news is, the Soccer will keep on coming until June of next year.

Tennis

No grand slams this month (only the grand slams are included in recommended leagues), so all our data this month has come from every other Tennis tournament in the world. A much better ROI than last month which is promising, over a healthy sample size of 6,298 bets. The only problem is, I’m sure our customers would have had a hard time keeping their accounts after this great month of Tennis. Bookmakers don’t like taking winning bets on these smaller ATP/WTP tournaments!

Basketball

On the back of a 6.11% ROI in October for recommended leagues and 8.39% for non-recommended leagues, Basketball took a substantial turn in results in November. We still maintained a positive ROI (1.77%) in non-recommended leagues over a humongous sample size but it is our results in the Euroleague (recommended league) which don’t look pretty. The sample size of 4,257 is pretty decent, so it will be something to keep an eye on heading into December, but my guess would be we were unlucky in November, and lucky in October.

American Football

To see whether you have an edge in any sport, tracking your flat ROI is the best measurement as it doesn’t take into account how successful your staking strategy is. So with that in mind, let’s take a look at our NFL results. We have an edge, but it was very small this month. In October the flat ROI was -1.3% with a sample of 3,426. So after two months of data, we can so far see that we haven’t been very successful. I think this warrants a closer look into what might be going wrong and I’ll keep you updated on any changes we might make. As for non-recommended leagues, for now, I’m going to put those results down to variance as we achieved a flat ROI of 3.1% in October.

Baseball

Such a small sample size so it doesn’t warrant any attention at all. Some healthy edges for sure though.

Ice Hockey

With no NHL on in November, there were only trades for the non-recommended leagues. And once again they proved to be quite profitable over a huge sample size. Data looks nice to me, with a similar look to our Soccer results.

Rugby

Not big enough samples to make any definitive conclusions but interesting to see the difference in results between recommended & non-recommended leagues. I think in December I will delve a bit deeper into Rugby results once we get some more bets registered.

Rugby League

Meh. 54 trades doesn’t warrant a comment from me!

CS:GO (Esports)

As always, another stellar month on CS:GO - it really is a gold mine the ol’ Esports. But I’ll take you back to my comments from last month which might keep you away from betting CS:GO:

“I would be very cautious betting Esports when it comes to keeping your accounts from being limited though, from my personal experience it has led to me losing quite a few bookmakers. It could be smart to only place an Esports trade every now and then to make it look like it’s not your main sport. But maybe not. My experiences are just a very small size of the population!”

Having now talked to various users of Trademate, it seems this has been the scenario for them too. So I have stopped betting CS:GO myself. But, if you talk to Pro Sports Bettor, Neel Shah, video series here, for him the edges are simply too big for him to ignore.

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And that’s all she wrote for our second look at Trademate Sports’ monthly results. Please let myself or the team know if you have any queries about this data and whether you would like us to add some other data in for December.

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