#86 Dartmouth (4-9)

avg: 1436.97  •  sd: 56.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Texas** Loss 4-13 1614.75 Ignored Mar 18th Centex 2023
40 Colorado College Loss 9-12 1389.98 Mar 18th Centex 2023
39 Florida Loss 9-12 1396.05 Mar 18th Centex 2023
112 Illinois Win 12-8 1757.13 Mar 18th Centex 2023
112 Illinois Loss 11-12 1190.98 Mar 19th Centex 2023
91 Tulane Loss 12-14 1209.24 Mar 19th Centex 2023
51 Virginia Loss 5-9 1106.4 Mar 19th Centex 2023
16 British Columbia Loss 5-15 1392.55 Apr 1st Northwest Challenge Mens
232 Chico State** Win 14-5 1382.35 Ignored Apr 1st Northwest Challenge Mens
81 Whitman Win 13-12 1589.12 Apr 1st Northwest Challenge Mens
53 Utah Loss 9-10 1494.99 Apr 2nd Northwest Challenge Mens
44 Victoria Win 9-8 1821.72 Apr 2nd Northwest Challenge Mens
46 Western Washington Loss 11-14 1375.2 Apr 2nd Northwest Challenge Mens
**Blowout Eligible

FAQ

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 team’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
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. 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)