**avg:** 169.35 •
**sd:** 296.22 •
** top 16/20:** 0%

# | Opponent | Result | Game Rating | Status | Date | Event |
---|---|---|---|---|---|---|

49 | Oregon** | Loss 4-15 | 665.51 | Ignored | Jan 25th | Pacific Confrontational Invite 2020 |

86 | Oregon State | Loss 6-10 | 169.35 | Jan 25th | Pacific Confrontational Invite 2020 | |

32 | Whitman College** | Loss 4-15 | 906.61 | Ignored | Jan 25th | Pacific Confrontational Invite 2020 |

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a teamâ€™s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation

- Calculate uncertainy for USAU ranking averge
- Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
- Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
- Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
- Subtract one from each fraction for "autobids"
- Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded

There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)