**avg:** 184.86 •
**sd:** 68.31 •
** top 16/20:** 0%

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

169 | Chico State** | Loss 2-13 | 484.3 | Ignored | Feb 2nd | Presidents Day Qualifiers Men |

444 | California-Irvine-B** | Win 13-2 | 12.99 | Feb 2nd | Presidents Day Qualifiers Men | |

414 | UCLA-B | Win 11-9 | 380.7 | Feb 2nd | Presidents Day Qualifiers Men | |

244 | Colorado-B** | Loss 1-13 | 277.2 | Ignored | Feb 2nd | Presidents Day Qualifiers Men |

261 | Cal Poly-SLO-B** | Loss 3-13 | 221.14 | Ignored | Feb 3rd | Presidents Day Qualifiers Men |

344 | California-Irvine | Loss 9-12 | 160.91 | Feb 3rd | Presidents Day Qualifiers Men |

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)