**avg:** 515.39 •
**sd:** 150.69 •
** top 16/20:** 0%

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

173 | Purdue-B | Loss 6-9 | 514.95 | Mar 7th | DiscThrow Inferno March 2020 | |

38 | Miami (Ohio)** | Loss 3-15 | 1017.37 | Ignored | Mar 7th | DiscThrow Inferno March 2020 |

120 | Wright State | Loss 8-12 | 690.32 | Mar 7th | DiscThrow Inferno March 2020 | |

173 | Purdue-B | Loss 5-12 | 333.51 | Mar 8th | DiscThrow Inferno March 2020 | |

38 | Miami (Ohio)** | Loss 3-15 | 1017.37 | Ignored | Mar 8th | DiscThrow Inferno March 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)