Many people know anecdotally that PIR tends to buff itself more than the other units. Recently I raised this matter and we've done some experiments. Bubbles tested PIR with one other unit on board and the buffs seemed random. Berto did another test with nine units on board (pictured). PIR stayed on the board for 34 turns and it buffed itself seven times. Now, does it mean anything?
Using statistics, it is possible to compute the probability of this happening if PIR buffed units truly randomly. For example, with nine units on board, we'd expect PIR to buff itself with 1/9 probability each turn. So what's the probability of PIR buffing itself seven times in 34 turns if it indeed buffed units randomly?
The probability of that happening is less than 3% (calulations below for the interested). In science, people usually consider things significant if that kind of probability (p-value) is less than 5%. It means that Berto found something.
I played two ranked games with PIR and have written down the number of units on board each time (2-8) and whether PIR buffed itself. In 12 turns, it buffed itself 9 times. The probability of that happening if it buffed randomly is 0.0009. It's not 1%, it's about one-tenth of 1%. It's like the probability of finding a $100 bill on the floor in a shopping mall. It just doesn't happen for most people.
Now, if you don't believe me, play PIR (making sure that there are multiple units on board at least sometimes), write down the buffs, and see what you get.
Here's the science for the interested. One can use the Poisson binomial distribution (https://en.wikipedia.org/wiki/Poisson_binomial_distribution) to model the situation. Here's R code which you can run online at https://rdrr.io/snippets:
library(poisbinom)
units_on_board = c(2,4,6,7,8,2,3,3,4,4,3,3)
times_pir_buffed_itself = 9
p_value = 1 - poisbinom::ppoisbinom(times_pir_buffed_itself - 1, 1/units_on_board)
print( p_value )
Just modify units_on_board and times_pir_buffed_itself
In probability theory and statistics, the Poisson binomial distribution is the discrete probability distribution of a sum of independent Bernoulli trials that are not necessarily identically distributed. The concept is named after Siméon Denis Poisson.
In other words, it is the probability distribution of the
number of successes in a collectio...