Value Betting Tested: 30-Day Results & ROI — Full Breakdown & Transcript

[00:00] For 30 days I recorded over £2,000 worth of bets to put Valuebet in through that ultimate test. Every single win, loss and price change was meticulously tracked. The goal? To find out if Valuebet really works or if it's just another system that's doomed to fail.

[00:16] Now, the results were a surprise, but not for the reasons that you might think. But here's where it gets really interesting. Not only did I track my personal experience, but halfway through the experiment, I gained exclusive access to the performance analytics of Odds Monkey's entire user base.

[00:32] Now in total, I tracked 149 bets with stakes of £2,282.50 on a mixture of football and racing events. But how did my performance compare to the average value betting user?

[00:45] How many won? How many lost? And were the tools actually effective? In this video, I'll reveal everything, provide some critical analysis, and highlight the biggest problems I've found en route. But before we begin, and so everyone understands, what is value betting?

[01:01] Value betting is a math-based strategy that focuses on betting over the price of. By betting odds that are higher than the true probability, it provides the bettor with a long-term advantage over the bookie. Now, OzMonkey claims that their new value betting tools can identify these bets in real time, which is why I set up this rigorous 30-day experiment.

[01:20] On day one, I cracked out Excel and set some ground rules because each tool specialises in different types of betting. To keep things fair, I focused on the key metrics that truly matter in betting.

[01:34] The sample of bets, stakes, profit, loss and return on investment. I did this because I knew that the metrics would provide a complete picture of whether value betting delivers consistent long-term results.

[01:46] Now also, to make this as transparent as possible, I used a broad selection of their value betting tools across multiple sports to generate the selections. I then tracked the outcomes manually, logging critical details like the tool used, the bets odds, stake, expected value percentage that

[02:02] had been suggested, and of course the profit or loss for the actual bet. I did it like this so it's clear for us to see which tool is most effective. After all, each of these tools find value in different areas using different methods. Now I know there's been some skeptical

[02:16] comments out there too so I'll address them directly as we go. I want to be totally transparent here there is an affiliate link in description down below if you want to test these tools yourself whilst supporting the channel however that won't stop me from being critical about my experience

[02:32] or the broader user basis results later on in the video. So by day seven I tracked a total of 38 beds and was starting to feel a little bit frustrated.

[02:44] For those first three days it was bad news and while the post portion of some bets won, the wins didn't. Every day for the first three days was a net loss which highlights an important point. Value betting isn't guaranteed profit in the short term. Variance in results plays a huge

[03:01] role for short results that can fluctuate significantly Now one key observation from the the first week was around the availability of those odds Were the odds actually available In most cases I was able to place bets on

[03:14] the prices recommended by the tools. However, timing was critical. When it comes to the very best high margin value bears, the price sometimes shifted within a few minutes, meaning it was important to act fast to lock in that value once it had been identified. If I missed

[03:29] it didn't matter too much because I just simply ignored it and found another result. Now using the software itself was simple, the dashboards were easy to navigate and the tools provided plenty of opportunities. However I did notice that some of the sportsbooks had less results because

[03:44] of course they offer more bad value bets than some of the others. Now on the bookmaker side I didn't encounter major restrictions early in the experiment but I know from previous experience that frequent betting on value horses, particularly Irish horse racing, is likely to get your account

[03:59] Now, let's talk numbers. So, on day 4 and 7 the results changed with a couple of decent winners. From those 38 bets that week, my return on investment across the four tools

[04:11] averaged out at 2.96% which might seem pretty poor, although it was a very limited sample. Looking at the public report that OzMonkey put out that week, their users reported a

[04:23] return on investment of 16% so there's a drastic difference. Now, the fact they're willing to share this publicly says a lot. In 15 years I've never seen transparency from a software vendor like it. Now here's a quick look at how the tools perform individually. At 24.4% the extra place

[04:40] EV tool was particularly strong but perhaps that's because they recommend betting only on outcomes over 110%. Now overall value betting offers a logical path to profit for disciplined bettors but, and this is important, it's not a get rich quick thing in the short term. With such a small sample

[04:57] it was too early to draw any firm conclusions, although I'd deeper into the trends that were starting to form as we progressed throughout the month, because by day 14, the sample size had almost doubled.

[05:09] At 71 bets, there were a few minor patterns starting to form. Again, there were quite a few losing bets, although one or two large winners more than made up for those losers. As expected, the lucky 15 tool was the least consistent in terms of strike rate,

[05:23] although when good win connected, it really turned things around. It seems to be a trend that other users have also reported in the OddsMonkey Facebook group. Now, for the second week, the overall return on investment across the bets that I recorded was 44.48%,

[05:38] really bringing up that average. However, at this point, I also gained deeper access into that data from OddsMonkey of their aggregated user base. Now, for clarity, around only 30% of their users actually used the log set feature.

[05:51] It included insights into how many entries had been logged, the amount of stakes, expected profit, return on investment, and of course, most importantly, the actual profit and return on investment the user base had actually achieved after each bet had been settled.

[06:06] Now in terms of the bet types the majority of bets were either lucky 15s or each way bets via the extra place matchup Looking at it there were plenty of lucky 15s on very low stakes that had either lost or partly won via the place portion of their bet although a handful

[06:22] of outliers had huge returns, sometimes into the thousands. When I compared my results to the odd monkey's average user data, I found that it was slightly ahead of the average user, although there's no doubt that it was due to the limited amount of bets that I tracked

[06:37] in comparison. For context, there are a total of 3,992 recorded bets for the user data at this point compared to my lowly 71. Now, I'll dig deeper into their data in a moment, but

[06:50] experiencing such variance in results is normal in value betting, particularly over the shorter sample. Looking at the user-based data, even over a larger sample, we can see that the Lucky 15 tool's actual profit was in line with its expected profit at this point. Now,

[07:05] Now, some of you highlighted a big problem with the Lucky 15 tool earlier in the month, and it's a fair point. If hundreds of users are betting on Lucky 15s at similar odds with the same selections,

[07:17] it could attract bookmakers' attention and lead to odds shortening faster, or eventually account restriction. Now, to be sure, I reached out towards Monkey and asked about this. Each time the refresh result button is hit on the Lucky 15 tool,

[07:31] it presents a randomised result with four different value selections. So in short, very few people get the exact same result. Now companies like Bet365 boast over 100 million customers too.

[07:43] So it's unlikely they're working hard to link up a few people placing 20P lucky 15s. So overall, when compared to the first, the second week was a blinder. However, this was largely down to a few individual results.

[07:56] So week three would be extremely important to the results. And as it turned out, it was one of the most challenging. Now by this point I'd tracked 107 bets in total at stakes of £1,650 whereas the user

[08:14] data had 5,981 entries with a total stake of around £50,000. Rather frustratingly it was a losing week for my record with a total return on investment of minus 12.54%.

[08:26] The tools were accurate in identifying value bets it was seeing but they just didn't fall my way. On one or two occasions I noticed that some of the odds dropped significantly before I could place the bet, so perhaps I missed out on a couple of good opportunities there.

[08:40] However, in most cases the discrepancies were small, usually just a fraction of the point. Now it's important to note that I don't believe this is a flaw in the software. OzMonkey tracked bookmaker prices in real time and bookmakers don't exactly make it

[08:54] easy for them. They're actively working to protect their margins and react quickly to high value burns. Given this dynamic, I'd say the software performs pretty well under the circumstances. It repeatedly flagged plenty of genuine value beds, but the price is crucial, so if something

[09:10] changed I just found another one. The user results highlighted a big uptick with the 2UP Tools data here. Now the user results profit surged beyond the expected profit margin I suspect the data was more volatile and less representative for this particular tool due to this time of year With a large data sample and more quality football it will probably

[09:29] smooth out. Now despite the bad run of variance the results I tracked over the entire three week span were still positive. With just one week to go I was eager to see how the final results would pan out.

[09:42] So following 30 days of sports betting results, it's time to reveal all and make a judgement. Does value betting really work for anyone?

[09:55] Based on this experiment, I'd say the answer is yes, but with some important caveats. Over the course of the month, I tracked a total of 149 bets on football and racing, and averaged almost 5 bets a day.

[10:07] The total bet volume was £2,282.50, although that wasn't the total at risk because winnings were being continually reinvested. Along the way, there were some challenges, such as the limited amount of two-up games available and the occasional price change.

[10:23] However, despite these hurdles, the tools consistently provided me with value selections. Compared to the user base, my sample of 149 bets was far lower, so the results fluctuated a hell of a lot more.

[10:36] So, let's dive into the final figures for that too. Looking at the user-based data across all of the tools, there was a total of 8,187 bet logs with a total stake of £104,429.

[10:53] For the Lucky Finder EV tool, the total return on investment was 13.62%. The short term results were way more volatile, although as some of you have inevitably found out, you can land a massive win from a small stake when an outlier connects.

[11:08] Advised use for the actual place matcher secured an actual return of 11.82%. However I should mention this figure doesn't include any additional perks like the enhanced odds that are available with some bookies.

[11:20] And lastly, the two ups all came in at 1.77%, although as I previously highlighted, there there was a lot less data and it isn't the best time of year for it. In contrast, after 30 days of wins, losses and near misses,

[11:33] I tracked a bottom line profit of £320.11, representing a total return on investment of 14.02%. Those bigger wins in week two edged me ahead.

[11:46] So, is it actually worth it? Well, if we assume the same ROI and stakes are maintained, it means that we would need to pay 464 bets per year or just 38 bets per month to cover the cost of the subscription at the full price.

[12:02] Sourcing the bets each morning only took me around 5-10 minutes, so I think it's fair to say it's a good deal if you're serious about making discipline data-driven bets. The main drawback is the inevitable swings in short-term barriers and potential bookmaker restrictions.

[12:16] Now, with OzMonkey soon moving to a monthly subscription model, it's certainly worth trying it in my opinion. I'll show you exactly why in this next video here in the end screen where I break down the Lucky 15 strategy in detail.

[12:28] Don't forget to subscribe and turn on notifications so you don't miss it.