With the international break in full swing I decided to take most of the week off, only placing 63 trades from Monday to Sunday. But those 63 trades proved to be valuable, moving back above my closing EV after spending a couple of weeks below the green line. This week I’m going to take a deeper look into the analytics and big data tool elements of the software.
I wanted to see how I’m performing with each bookie in terms of my average closing edge and closing EV against my profits. Here is a summary of my data (photos are Betvictor stats):
After picking up on a slightly low closing edge with BetVictor, I thought I would take a look to see if this was a similar trend for all Trademate Sports users over the past ten weeks. Here’s what I found:
People aren’t having the same problem as me, with an average closing edge of 3.2%. Customers are placing trades at an average time of 4 hours before kick off, so I think this could be an area where I could improve. This can fixed my editing my presets from 0-8 hours before kick-off to something less. I could also set my BetVictor presets to a higher minimum edge. But overall, my big data search was only based on just over 3,200 trades from Trademate uses, so it’s not a large population.
This kind of investigative work is really useful in getting an idea of how to improve your strategy. Even though I’m profiting with BetVictor, my investigation showed that my strategy can improve. Like I said earlier, I could now make a few adjustments and edit my presets. But in general, 99% of the time, Sport X, Bookie Y or Bet Type Z not being profitable, is explained by random variance. People cut a bookie, or sport after 100 trades, because they were not profitable there while running at some crazy numbers like -15% ROI and still having a positive closing edge. This is a common trap that people fall into, making a change based on only 10 weeks worth of data and a small sample size is a mistake. So, if you’re noticing something isn’t working in your value betting journey, take a look at the analytics and big data tool tabs to see if you can find anything interesting, but give it time and only make changes if it’s obvious. Ideally you should split test the big data over multiple time periods and look at whether the closing edge and ROI is consistently positive or negative or whether it varies from period to period. Only if there is a consistent trend, over a big sample size of trades both in the big data and in your personal results should you consider doing something like skipping a particular sport.
Below are some screenshots of my biggest edge and biggest win, along with my biggest negative edge and biggest losing trade respectively.
Check out my Week 0 video, showing how I got set up with my Trademate and betting accounts here!
Check out how I went in Week 1- 9 in My Value Betting Journey here.
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