**avg:** -392.76 •
**sd:** 238.6 •
** top 16/20:** 0%

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

316 | Rensselaer Polytech | Loss 4-9 | -259.26 | Mar 26th | Late Blooming Bids | |

- | New Haven | Loss 4-13 | -595.71 | Mar 26th | Late Blooming Bids | |

322 | Vassar** | Loss 1-12 | -296.33 | Mar 26th | Late Blooming Bids | |

157 | Yale** | Loss 0-14 | 525.16 | Ignored | Mar 26th | Late Blooming Bids |

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)