Mega Crit Games’ villainous deck builder Slay the Spire has amassed over 500,000 players since entering Early Access in November, according to SteamSpy, and has received almost unanimous praise from critics and gamers – not bad for a game with just two full-time developers.

It’s even more impressive when you consider the unusual mix of genres. Balancing a deck of cards alone is a big endeavor, and tiny changes can throw entire styles of play out of shape. Stir in a villain’s random events and sprinkle a few items on top that can turn a barrel upside down and you have a potential recipe for mayhem.

How did Seattle’s Mega Crit manage to keep the game so tight? And how do you approach the mammoth task of balancing such a complicated game?

The key, developers Anthony Giovannetti and Casey Yano tell Gamasutra, is player feedback. In particular, it’s about collecting data on every single run and using it to make informed, specific changes to certain maps and enemies.

Even at an early prototype stage when the game was being tested by Netrunner players, the team created a metrics server to track every decision a player made, Giovannetti says.

“There is no way to get everything right intuitively.”

“We have so many cards and so many interactions that even though we have a pretty strong card game background, we can’t intuitively get everything right,” he adds. “I said at one point,” Look, we’re not. In order to be reasonably able to even so many cards, we don’t have a team of people doing this. ‘…[so] We took a data-driven approach. I’m just a big fan of data-driven decisions. “

“I once said, ‘Look, we’re not going to be able to balance that many cards, we don’t have a team of people to do that’ …[so] We took a data-driven approach. “

In the beginning, the couple kept adding cards, first in piles to create a deck archetype, and then individual cards to “shape” those styles of play. Game testers enjoyed adding new cards all the time, and some piled thousands of hours in-game, all of which were fed into the team’s metrics server.

The data told Yano and Giovannetti how many times players selected a particular card when it was offered to them during their dungeon crawl, what they selected, how often that card appeared in sweepstakes, and how much damage players had on average with that card – Taken by a certain enemy.

According to Giovannetti, there was no “mathematical” approach to responding to this data. The team looked for patterns and tried to intuitively decide how to make a card more fun, less powerful, or more attractive to players. There was a lot of trial and error involved.

“We knew there was going to be an impact. If I change a card and it’s part of a particular strategy, it will have a different impact on the cards in that strategy,” he says.

“So we made the change and saw what people thought of what we thought and we kept tweaking the buttons until we decided on a place that was good. We weren’t afraid to throw things away and start over with whole archetypes. My outlook was to be more aggressive about making changes while testing – you’d better make the changes early and see how they work. “

As the player base has been booming over the past few months, the amount of data is also increasing. “We get more data in one hour than during the entire prototyping process [phase]. Our samples are now so large that they are really accurate, ”explains Giovannetti.

Yano tells me that the key to using this data is to make sure everything is filterable and categorized. It is also important to consider a certain question when looking at the data that cannot be answered simply by playing the game.

“When we first created our metrics, we had three charts. Now we have at least 90.”

“There are a lot of filters [and] Check box, it’s important that it can be filtered for a specific thing, “says Yano.” When we first created our metrics, we had three charts. Now we have at least 90. “

And these graphics don’t just sit idle; They directly influence the decisions of the developers. Both men have a deep understanding of how the game works, of course, but sometimes they make mistakes and the data shows them where they are wrong.

How often is a card selected and how often is it a winner?

The two most important metrics, Giovannetti says, are how often a player picks a card when they have a choice (too low and there is “basically no card in our game at this point”) and how often a card is in a competition appears (too high and you know the card is overwhelmed).

The changes the team made to Dual Wield, which allows players to duplicate cards, are a good example. It was strong at prototyping, but many of its best interactions didn’t make it to Early Access. The pair could see that the players weren’t picking it up very often, so they switched their duplicating power from the top card of your deck to any card in your hand. From the resulting data, it emerged that the buff was too strong and players could drive Dual Wield to victory.

“It was totally broken,” says Giovannetti. “You could copy skills and become infinite [being able to kill an enemy in one turn by duplicating particular cards] really easy. Getting infinite is number one that we try to make really rare. It makes actually playing the game trivial. We then adjusted it so that only skill cards can be copied. So it was a change that we thought was benign, that we quickly found out how degenerate it was, and that we then easily falsified. “

The team approaches the opponents of the game in the same way. For example, the data showed that players with many Power cards in their decks had issues with one of the game’s final bosses, The Awakened One, which gains strength when the player uses a Power card. To prevent this from happening, the team reduced the rate at which the boss was gaining strength and compensated for this with a general burst of damage.

By looking at the numbers later, the couple found that overall more players beat the boss, says Yano. “This is the first boss that players fight when they get to the end, so we didn’t want him to go weak.” By tweaking the damage, retesting, and re-tweaking, developers have reached a level they’re happy with – all thanks to the data.

“Without that, we’d just get people with anecdotal evidence that this boss is much stronger or weaker now, and based entirely on play style or other circumstances,” Yano adds.

That’s not to say that anecdotal, subjective feedback is by no means useless. The team has a public Discord server where players can share their thoughts using tags like “bugs” or “feedback”. A bot collects this information and forwards it to the team.

Data alone is not enough

“The numbers are really useful, but they don’t tell us how things feel. That’s why we think it’s still very important, ”says Giovannetti. “Including non-data is more difficult, but useful. We don’t have a good hard and fast rule [about when to act on it], but a single well-founded post is far more useful than many players saying ‘nerf this’. “

“I would do something similar with any other game I make in the future, and I would recommend other indies to use it whenever they can.”

Overall, Giovannetti says the fact that the game is a single player roguelike makes balancing its many cards a lot easier than most card games. Players don’t compete against each other, so the decks don’t have to be the same and the team can make changes for entertainment value rather than in the name of sheer balance.

Also, rogue events add an element of randomness that makes the game more repeatable and forces players to switch styles. And the fact that it’s still in Early Access helps too. “If we make a mistake, we release weekly patches,” he adds. “Because things are in flux, players can expect the balance to change.”

However, the team can only react this quickly because of its data-driven approach. “I think it was really confirmed,” concludes Giovannetti. “Any other game I make in the future I would do similarly, and I would recommend other indies to use it whenever they can.”

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Dicey Dungeons，它一直在做alpha dance on itch。IO从去年夏天开始，挑战玩家–作为六大移动骰子之一征服一个不断变化的、程序生成的地下城，与敌人战斗，寻找并最终杀死Lady Luck。

6个可用类中的每一个–战士、小偷、机器人、傻瓜、发明家和女巫; have their own set of skills, but battles (against vampire vacuums and polite snowmen) are generally built around dice throwing and deck building. Dice results are placed in slots that trigger different offensive and defensive skills.

The Dicey Dungeon Steam page shows some of the different approaches different classes are taking in defeating Lady Luck. The thief can steal random enemy equipment in each round, the robot wins dice via a Push-Your-Luck-Blackjack game and the inventors destroy their own equipment after each successful fight in order to obtain parts for powerful new devices.

It is certainly a fascinating endeavor, and it looks and sounds great too, thanks to the art of Marlowe Dobbe and the music of Chipzel. We’ll know if Cavanagh conjured up another winner when Dicey Dungeons rolls out onto Steam on August 13th.

The post Tremendous Hexagon dev's daft deck-builder Dicey Dungeons will get August launch on PC • Eurogamer.web first appeared on DECKSAND FENCES DAILY.