Narrative and Chance in Orion Trail

Orion Trail is a parodic space game in which you’re going on exploratory missions, trying to conserve your resources and keep your crew alive, and sending out the occasional away team: in other words, a number of structural concepts from Oregon Trail, redone with Star Trek tropes. It’s casually entertaining.

Though there is text associated with the various encounters in space, it feels more like it’s there to provide flavor and variety than like it’s contributing to any significant character arc. You have a few named crew members, but there’s also no major relationship development with them, so far as I saw. (If you want Social Life Simulator in Space, see Redshirt instead.) So in sum, Orion Trail is amusing, but not the kind of work that I would usually cover here – except that it has a probability mechanic that I really liked and found narratively expressive, and I wanted to talk about that here.

Game designers have long complained about the fact that players don’t understand probability very well. If you tell them that they have a 9/10 chance of winning an upcoming encounter, they tend to read that as you will win, and they get frustrated if they don’t. Giving them a fake die roll, so that they can actually see and have a tactile sense of something coming out wrong, may help with this.

Orion Trail goes a step further: it presents the probable outcomes of an interaction with an interface functionally similar to a roulette wheel. You can immediately see how many chances there are for critical or regular failures and successes (this varies depending on how hard the encounter is to start with), and also visualize how the stat bonuses are changing things.

Here’s the Probability Drive before it’s applied my stat bonus. It’s not entirely encouraging – one critical hit and two regular successes, but eight regular failures and one critical miss.

Screen Shot 2016-01-05 at 8.03.04 PM

Then, based on my high stat in Bravado, it converts five of those orange failure squares to green success squares. Then it runs its roulette-like behavior, rotating through the possible squares until it lands on its final answer:

Screen Shot 2016-01-05 at 8.03.17 PM

Whew, we’re in luck (this time).

Now, when you go on a mission planet-side (as opposed to an in-space encounter), the setup changes. You are allowed to send only one of your four crew members with their stat bonuses, but you also have a certain number of red-shirt away team members. Those get added to your probability drive like so:

Screen Shot 2016-01-05 at 9.51.31 PM

Any time the dial lands on one of your crew members, you survive… but that crew member dies, and the next test on your away mission will be that much more likely to fail. Sort of like this…

Screen Shot 2016-01-05 at 9.55.04 PM

So there are a couple of nice aspects here. One, it’s making the probabilities feel more intuitive to the player, which is helpful; two, it’s using changes to the probability drive to create a sense of heightening danger as the mission proceeds. And since several of these away missions let you abort and go home if you feel the scenario going badly, that dramatizes how the player is pushing their luck. Jon Ingold sometimes talks about the value of getting the player to commit multiple times over to a risky action, so that if something bad happens, they really feel they own it. Orion Trail‘s use of the probability drive on away missions gets at that effect, requiring the player to commit again and again to increasingly risky actions.

Even so, I never felt all that much investment in my main crew, let alone the anonymous redshirts who die in such numbers. The fact that I played it during the same few days as Black Closet throws the lack of characterization into higher relief than it probably deserves. But Orion Trail is trying for a much lighter story with a lot of comedic distance from its events.

I can totally see more applications for this way of doing probability in a narrative games, though.

Disclosure: I received a free copy of this game as an IGF judge.

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