**avg:** 246.42 •
**sd:** 135.48 •
** top 16/20:** 0%

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

206 | Quahogs | Win 9-8 | 605.8 | Jul 8th | AntlerLock | |

163 | Sunken Circus | Loss 5-15 | 171.74 | Jul 8th | AntlerLock | |

111 | Lampshade** | Loss 6-15 | 405.42 | Ignored | Jul 8th | AntlerLock |

206 | Quahogs | Loss 3-7 | -119.2 | Jul 9th | AntlerLock |

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)