**avg:** -278.91 •
**sd:** 161.54 •
** top 16/20:** 0%

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

188 | Georgia Tech-B** | Loss 1-15 | -143.87 | Ignored | Jan 20th | Starkville Qualifiers |

209 | Alabama-B | Loss 9-13 | -133.4 | Jan 20th | Starkville Qualifiers | |

164 | Mississippi State-C** | Loss 1-13 | 22.64 | Ignored | Jan 20th | Starkville Qualifiers |

78 | Georgia State** | Loss 2-15 | 596.15 | Ignored | Jan 21st | Starkville Qualifiers |

188 | Georgia Tech-B** | Loss 1-15 | -143.87 | Ignored | Jan 21st | Starkville Qualifiers |

221 | LSU-B | Loss 4-11 | -437.48 | Jan 21st | Starkville Qualifiers |

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)