Understanding the beer pong calculator — concept and principle
This calculator works out how many cups are in a triangular rack, how many throws you can expect to need to clear it at your hit rate, and the chance of clearing it within a given number of throws. It is a fun way to see probability in action.
How it is calculated
Cups c = r × (r + 1) ÷ 2 for r rows
Expected throws = c ÷ p, where p is the hit rate
Chance to clear within N throws = Σ (k = c…N) C(N, k) × p^k × (1 − p)^(N − k)
Expected cups hit in N throws = N × p
The principle
A triangle of r rows holds a triangular number of cups. If every throw hits with the same probability and each hit removes one cup, the number of throws to clear the rack follows a negative binomial distribution with mean c ÷ p. Clearing within N throws means getting at least c hits in N independent tries, which is a binomial probability.
Worked example
Default inputs: 4 rows, 25% hit rate, 30 throws.
- Cups = 4 × 5 ÷ 2 = 10.
- Expected throws = 10 ÷ 0.25 = 40; expected turns (2 throws each) = 20.
- Expected hits in 30 throws = 30 × 0.25 = 7.5.
- Chance of at least 10 hits in 30 throws = 19.7%.
| Your hit rate per throw (%) | Expected throws to clear all cups |
|---|---|
| 6.25 | 160 |
| 12.5 | 80 |
| 18.8 | 53.3 |
| 25 | 40 |
| 31.2 | 32 |
| 37.5 | 26.7 |
| 50 | 20 |
How to read the result
Because 30 throws is fewer than the 40 expected, clearing the rack is unlikely, about one chance in five. Small changes in hit rate make a big difference. House rules such as re-racks, bounce shots and balls back are ignored.
Common mistakes
- Assuming a constant hit rate when players tire or improve.
- Forgetting that the other team's defence changes outcomes.
- Confusing expected throws with guaranteed throws.
Frequently asked questions
How many cups in a standard rack?
Ten, in four rows.
Why is the expected number of throws c ÷ p?
Each cup takes on average 1 ÷ p throws to hit.
Does the order of hits matter?
Not for this calculation; only the number of hits counts.
Sources: Ross SM, A First Course in Probability, Pearson (binomial and negative binomial distributions)