**avg:** -303.87 •
**sd:** 284.2 •
** top 16/20:** 0%

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

315 | Carnegie Mellon-B | Loss 0-13 | -369.59 | Feb 29th | Huckin in the Hills VII | |

38 | Miami (Ohio)** | Loss 0-13 | 1017.37 | Ignored | Feb 29th | Huckin in the Hills VII |

295 | West Virginia** | Loss 1-13 | -238.14 | Feb 29th | Huckin in the Hills VII | |

210 | The Ohio State University-B** | Loss 0-13 | 207.62 | Ignored | Feb 29th | Huckin in the Hills VII |

297 | Dayton-B** | Loss 3-14 | -246.25 | Ignored | Mar 1st | Huckin in the Hills VII |

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)