**avg:** 242.5 •
**sd:** 159.75 •
** top 16/20:** 0%

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

182 | Dayton** | Loss 0-13 | 590.4 | Ignored | Feb 3rd | Huckin in the Hills X |

319 | Edinboro | Loss 8-13 | 158 | Feb 3rd | Huckin in the Hills X | |

194 | Ohio | Loss 7-13 | 585.09 | Feb 3rd | Huckin in the Hills X | |

126 | Towson** | Loss 4-13 | 789.09 | Ignored | Feb 3rd | Huckin in the Hills X |

319 | Edinboro | Loss 6-12 | 74.85 | Feb 4th | Huckin in the Hills X | |

68 | Franciscan** | Loss 1-15 | 1060.48 | Ignored | Feb 4th | Huckin in the Hills X |

292 | Kent State | Loss 4-15 | 147.63 | Feb 4th | Huckin in the Hills X |

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)