**avg:** 219.65 •
**sd:** 175.7 •
** top 16/20:** 0%

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

90 | Northern Arizona** | Loss 4-13 | 777.6 | Ignored | Feb 10th | Stanford Open 2018 |

131 | Chico State** | Loss 3-12 | 588.64 | Ignored | Feb 10th | Stanford Open 2018 |

65 | California-Santa Barbara** | Loss 1-13 | 862.37 | Ignored | Feb 10th | Stanford Open 2018 |

276 | San Jose State | Loss 1-3 | 98.83 | Feb 11th | Stanford Open 2018 | |

329 | California-Irvine | Win 13-12 | 589.87 | Feb 11th | Stanford Open 2018 | |

320 | Caltech | Loss 4-13 | -95.15 | Feb 11th | Stanford Open 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)