**avg:** -906.72 •
**sd:** 436.62 •
** top 16/20:** 0%

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

24 | Carleton College-Eclipse** | Loss 0-13 | 1132.83 | Ignored | Mar 18th | College Southerns XXI |

- | Georgia Southern | Loss 6-7 | -792.96 | Mar 18th | College Southerns XXI | |

132 | Emory** | Loss 0-13 | 162.73 | Ignored | Mar 18th | College Southerns XXI |

110 | Charleston** | Loss 1-15 | 318.95 | Ignored | Mar 19th | College Southerns XXI |

220 | Emory-B | Loss 3-5 | -1112.84 | Mar 19th | College Southerns XXI | |

216 | Florida-B | Loss 5-11 | -863.51 | Mar 19th | College Southerns XXI |

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)