**avg:** -573.31 •
**sd:** 503.48 •
** top 16/20:** 0%

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

42 | Wave** | Loss 1-7 | 437.44 | Ignored | Sep 9th | 2023 Womens Capital Sectional Championship |

37 | Agency** | Loss 1-13 | 549.08 | Ignored | Sep 9th | 2023 Womens Capital Sectional Championship |

94 | Dissent | Loss 4-9 | -573.31 | Sep 9th | 2023 Womens Capital Sectional Championship |

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)