**avg:** 21.72 •
**sd:** 181.12 •
** top 16/20:** 0%

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

88 | Kentucky** | Loss 1-6 | 480.73 | Ignored | Feb 11th | 2023 TOTS The Only Tenn I See |

179 | LSU | Loss 5-7 | 19.46 | Feb 11th | 2023 TOTS The Only Tenn I See | |

56 | Tennessee** | Loss 0-11 | 740.42 | Ignored | Feb 11th | 2023 TOTS The Only Tenn I See |

77 | Tennessee-Chattanooga** | Loss 1-11 | 579.39 | Ignored | Feb 11th | 2023 TOTS The Only Tenn I See |

195 | Georgia Tech-B | Win 7-6 | 281.73 | Feb 12th | 2023 TOTS The Only Tenn I See | |

179 | LSU | Loss 3-8 | -252.4 | Feb 12th | 2023 TOTS The Only Tenn I See |

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)