**avg:** 6.18 •
**sd:** 178.91 •
** top 16/20:** 0%

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

114 | Jacksonville State** | Loss 2-13 | 557.08 | Ignored | Feb 29th | Mardi Gras XXXIII |

237 | Stephen F. Austin** | Loss 5-13 | 106.39 | Ignored | Feb 29th | Mardi Gras XXXIII |

60 | LSU** | Loss 1-13 | 824.45 | Ignored | Feb 29th | Mardi Gras XXXIII |

164 | Illinois State** | Loss 3-13 | 373.7 | Ignored | Feb 29th | Mardi Gras XXXIII |

230 | Sam Houston State | Loss 6-13 | 136.85 | Mar 1st | Mardi Gras XXXIII | |

341 | LSU-B | Loss 9-10 | -124.48 | Mar 1st | Mardi Gras XXXIII |

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)