**avg:** -417.33 •
**sd:** 169.65 •
** top 16/20:** 0%

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

73 | Truman State** | Loss 1-9 | 676.3 | Ignored | Mar 7th | Midwest Throwdown 2020 |

178 | Tulsa** | Loss 2-8 | -115.74 | Ignored | Mar 7th | Midwest Throwdown 2020 |

201 | Luther** | Loss 3-8 | -346.16 | Ignored | Mar 7th | Midwest Throwdown 2020 |

191 | Minnesota-Duluth** | Loss 2-10 | -219.22 | Mar 7th | Midwest Throwdown 2020 | |

229 | Missouri | Loss 5-7 | -460.61 | Mar 8th | Midwest Throwdown 2020 | |

223 | Wisconsin | Loss 1-8 | -595.57 | Mar 8th | Midwest Throwdown 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)