#50 Whitman (1-10)

avg: 1508.9  •  sd: 94.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 Oregon Win 7-6 2142.73 Mar 2nd Stanford Invite 2019
5 Carleton College-Syzygy Loss 5-7 1937.36 Mar 2nd Stanford Invite 2019
19 UCLA Loss 5-13 1365.86 Mar 2nd Stanford Invite 2019
39 California-Davis Loss 9-11 1344.07 Mar 3rd Stanford Invite 2019
38 Florida Loss 5-9 1082.05 Mar 3rd Stanford Invite 2019
32 Brigham Young Loss 7-13 1152.95 Mar 30th NW Challenge Tier 1 Womens
7 Western Washington Loss 11-15 1818.5 Mar 30th NW Challenge Tier 1 Womens
13 Stanford Loss 10-15 1602.03 Mar 30th NW Challenge Tier 1 Womens
24 Washington Loss 8-14 1336.55 Mar 30th NW Challenge Tier 1 Womens
8 Dartmouth** Loss 6-15 1558.85 Ignored Mar 31st NW Challenge Tier 1 Womens
11 Pittsburgh Loss 6-15 1483.27 Mar 31st NW Challenge Tier 1 Womens
**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)