Have you ever wondered what the best Local area to hunt is? The worst? Of course you haven’t! The answer is obvious: L2 is the best and L4 is the worst. Well, I was bored and curious so I decided to prove this using my favorite method, numbers!
Warning: Math Ahead!
Numerical Ranking Method
I see 3 factors here: Desirability, Availability, and, um, Needability… OK, I can’t force a rhyming scheme in here, but what I mean is
- How desirable of a hybrid does a spawn make?
- What are the actual chances of a spawn?
- How much DNA is needed for fusing?
It may surprise you to find out that #3 doesn’t have much of an impact. With average fuses, it takes about 25,300 Blue DNA to get a level 30 Indoraptor G2. It takes about 27,000 Erlikosaurus DNA to get a level 30 Erlidominus. That’s only a 6.5% difference which is small enough that I’m comfortable ignoring it for the sake of simplifying the analysis. I may come back to that in the future, though.
I already have the data for #2 from the APK. I take separate the spawn chances for each local creature by Dawn, Day, Dusk, and Night. I can use these percentages directly to weigh the availability portion of our formula.
The trickier one is #3. I could use the Tier List to give weights to each creature. The issue there is it sets the desirability of the best Tyrant equal to that of the worst Tyrant. To get around that, we could use sim performance to rank desirability. The issue there is that the sim has gaps in the results for factors like swap in and team play. Since the Tier List is a peer reviewed process that utilizes sim results among other factors, I decided that is the more robust method. Now the only decision is how to weigh each Tier. I decided to show the results using the following 2 methods:
|Tier||Linear Weight||Skewed Weight|
So our final formula is simply a summation of [spawn_chance] x [tier_weight]
Well those are both surprising sets of results! Overall, other than Parks, L4 seems to be the best? Well, it has 2 High Apex Commons, a Tyrant Rare, and 2 Tyrant Epics. Maybe you don’t care about Commons, though. That’s why I left the Rarity and time of day breakdowns visible. That way you can narrow down to the info that’s relevant to you personally. Still, when the data doesn’t match preconceived notions, there’s always a chance that the analysis method was flawed. If you have any feedback or suggestions for improving this analysis, be sure to let me know on our Discord!