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

We are doing this with the help of our back-logged data. This is because the big data tool inside the software 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 June. There are no filters on this data, it is every trade placed no matter what the odds range, time placed, edge, etc.

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.

Results & Key Findings

In the month of November, 179,254 trades were placed, €24,284,358 was turned over and €811,963 was profited at an ROI of 3.34%. Here is how each sport performed, broken down into recommended leagues (popular/bigger leagues) and non-recommended leagues (lower/smaller leagues):

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Soccer

An unbeaten start to this season with Soccer traders profiting every month so far. Recommended Soccer Leagues are the standout performers returning a whopping 7.22% ROI for traders, they provided a total profit of€189,781.67. Hopefully the great form continues into a busy Christmas period for Football in Europe.

Tennis

The best month for Tennis the year! At least ROI wise with an impressive 11.09%. Beating the previous best which was april at 10.43%. I did notice a clear similarity between these months, neither contained a Grand Slam and therefore no recommended leagues. In fact a fair few of the months with no grand slam had very good results for everything else. However the sample sizes are also slightly lower so it may just be good swings in variance.

Basketball

A fairly average month for basketball and some really healthy looking sample sizes. As results go, not a great month for NBA traders returning with a tiny loss. It’s not all that concerning though, as they did manage to get a solid 3.23% closing edge on the market, so it seems it’s just a short term swing to the downside. Non-reccomended leagues performing well as usual.

Ice Hockey

Some surprise low returns for Ice Hockey in November, especially considering the high closing edge both recommended and non-recommended leagues achieved. However, some variance has to be expected and Ice Hockey has had an incredible start to the season prior. With the NHL in full swing there’s plenty more months of Ice hockey bets to come.

CS:GO (Esports)

The streak that never ends.. CS:GO once again returns traders a positive ROI, this month a healthy 6.24%. If December also returns a a positive ROI then that’s every month in 2021!

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And that’s all she wrote for my latest Trademate Sports monthly results. Although most of these results come with huge samples, it’s important to take them with a grain of salt. Just because a sport is profitable or unprofitable doesn’t mean that you trade on every single bet that comes through our software. Follow ourrecommended presets, and run some simulations on our big data tool yourself to figure out the best odds ranges, times to trade, etc.

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