**avg:** 529.18 •
**sd:** 102.07 •
** top 16/20:** 0%

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

125 | Ohio** | Loss 5-13 | 745.82 | Ignored | Feb 24th | 7th Annual Bens Barmitzvah 2018 |

219 | Ohio State-B | Loss 8-9 | 616.16 | Feb 24th | 7th Annual Bens Barmitzvah 2018 | |

121 | Dayton | Loss 6-13 | 763.47 | Feb 24th | 7th Annual Bens Barmitzvah 2018 | |

219 | Ohio State-B | Loss 4-10 | 141.16 | Feb 25th | 7th Annual Bens Barmitzvah 2018 | |

163 | Xavier | Loss 7-13 | 551.7 | Feb 25th | 7th Annual Bens Barmitzvah 2018 |

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)