**avg:** 17.59 •
**sd:** 182.65 •
** top 16/20:** 0%

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

137 | California-B** | Loss 1-13 | 347.01 | Ignored | Mar 9th | Irvine Open |

88 | California-Irvine** | Loss 0-13 | 709.28 | Ignored | Mar 9th | Irvine Open |

186 | UCLA-B | Loss 2-4 | 89.33 | Mar 9th | Irvine Open | |

184 | California-Davis-B | Loss 3-7 | -3.72 | Mar 10th | Irvine Open | |

85 | California-San Diego-B** | Loss 1-9 | 739.27 | Ignored | Mar 10th | Irvine Open |

184 | California-Davis-B | Loss 2-10 | -3.72 | Apr 14th | Southwest Dev Womens Conferences 2024 | |

85 | California-San Diego-B** | Loss 0-15 | 739.27 | Ignored | Apr 14th | Southwest Dev Womens Conferences 2024 |

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)