I’m deep into developing this slot game with 3×5 reels, and I’m aiming for an RTP of 99%. But here’s the kicker: during my testing, I’m getting some wild RTP numbers. Initial tests show a starting RTP of around 1500%, and while it tapers off after hitting about 20,000 spins, it still hovers around 1300%. It feels way off from where I want to be!
I’ve got the reel data in YAML format and the game has 20 win lines that I check after selecting random symbols from the reels. The paytable is set, and I’m calculating the RTP using the formula `(total_wins / total_bets) * 100`. I know I should probably be running way more spins—like over 300,000—to really see the RTP normalize, but I’m curious about a few things.
First off, how can I ensure that my measurements are accurate in terms of reflecting that 99% RTP I’m targeting? I’ve read that randomness can impact results, and I’ve been using the default random function from Go’s runtime. Considering the stakes, should I switch to something like the Mersenne Twister or `dev/random`? Does the source of random numbers really make that big of a difference in measuring RTP accurately, or is it more about sheer volume of spins?
Also, given that I have a lot of complexity with the win lines and the payouts, is there a risk that my calculation method could be throwing things off? I want to make sure I’m properly accounting for all possible wins, especially since I haven’t even incorporated scatters into this testing phase yet.
I’m not a slot game expert by any means, and I feel like I’m missing critical pieces here. If anyone has insights or best practices on how to go about testing the RTP more effectively or how randomness plays into this, I’d really appreciate your thoughts!
Sounds like you’re really diving into the world of slot game development! Hitting that 99% RTP target can definitely be tricky, especially with those wild results you’re getting. Let’s break down a few things that might help you out.
First, regarding your RTP calculations, it’s super important to make sure you’re accounting for all wins correctly. Since you have 20 win lines, you need to ensure that your win calculations are comprehensive—that means checking every possible combination! If you’ve not set up your scatter wins and they might have big impacts, be sure to include them in future tests.
About your testing volume—yeah, running over 300,000 spins is a smart move. The random function you’re using is important too; while the default random function works for many applications, you might want to consider switching to something more robust like the Mersenne Twister for better randomness in your tests. The quality of randomness can influence your RTP skew, but it’s also tied to the number of spins you run.
Don’t forget about the significance of volatility! Your game’s design can affect the RTP, so try varying your paytable and see if it makes a difference. Testing with different setups might give you a clearer picture of what’s happening with your numbers.
Finally, don’t be afraid to reach out to other developers or even check online communities focused on game development for more advice. There’s a lot to learn, and getting insight from others can help bridge gaps you might be unsure about. Good luck, and keep at it!
Your initial RTP figures—hovering around 1500% to 1300%—strongly suggest an underlying issue beyond just randomness causing variance. The most immediate suspects are likely the paytable or payout logic implemented in code, especially since you’re working with multiple win lines from YAML reel data. Double-check that you’re correctly interpreting your YAML configuration, that paylines are accurately evaluated, that multiple simultaneous line wins are correctly added, and particularly ensure no unintended multiplier factors or incorrect symbol indexing are inflating your payouts. To reliably validate an RTP of 99%, it’s essential to conduct rigorous statistical verification by simulating several million spins if possible, carefully logging and auditing intermediate calculations, and verifying against known mathematical benchmarks.
Regarding randomness, using the default random number function provided by Go’s runtime (math/rand package with proper seeding) typically suffices for a simulation of this nature. Shifting to a more robust random number generator such as the Mersenne Twister or crypto-grade randomness is unlikely to significantly impact your RTP measurement accuracy at large spin counts—it mostly stabilizes variance rather than drastically shifting RTP long-term. The primary consideration is the sheer number of simulated spins; higher iteration counts (hundreds of thousands up to millions) smooth out volatility and facilitate a reliable representation of the expected RTP. Ensuring methodological rigor in your payout calculations, rather than switching random-number sources, remains the critical step to achieving and accurately measuring your target RTP.