**avg:** 311.2 •
**sd:** 100.8 •
** top 16/20:** 0%

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

86 | Arizona State-B-B | Loss 3-7 | 697.07 | Feb 22nd | Pomona Sweethearts 2020 | |

189 | Cal Poly-Pomona | Loss 3-10 | 279.26 | Feb 22nd | Pomona Sweethearts 2020 | |

279 | Cal State-Fullerton | Loss 5-8 | 6.49 | Feb 22nd | Pomona Sweethearts 2020 | |

159 | California-Irvine** | Loss 1-13 | 391.54 | Ignored | Feb 22nd | Pomona Sweethearts 2020 |

163 | UCLA-B** | Loss 3-11 | 380.45 | Ignored | Feb 22nd | Pomona Sweethearts 2020 |

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)