#87 Southern California (7-17)

avg: 1086.09  •  sd: 66.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 San Diego State Win 10-0 1444.36 Feb 4th Stanford Open
39 Santa Clara Win 7-6 1664.36 Feb 4th Stanford Open
90 Claremont Win 6-5 1189.06 Feb 4th Stanford Open
34 Portland Loss 4-6 1266.83 Feb 4th Stanford Open
158 Stanford-B Win 11-1 1121.55 Feb 5th Stanford Open
50 California-Santa Cruz Loss 3-9 837.65 Feb 5th Stanford Open
79 Nevada-Reno Win 7-6 1300.72 Feb 5th Stanford Open
50 California-Santa Cruz Loss 2-7 837.65 Feb 5th Stanford Open
12 California-Santa Barbara Loss 7-10 1668.76 Feb 18th President’s Day Invite
17 California-San Diego** Loss 4-11 1224.58 Ignored Feb 18th President’s Day Invite
48 Texas Loss 3-9 859.95 Feb 18th President’s Day Invite
28 Duke Loss 4-10 1082.04 Feb 18th President’s Day Invite
12 California-Santa Barbara** Loss 2-12 1458.42 Ignored Feb 19th President’s Day Invite
25 California-Davis** Loss 3-9 1119.95 Ignored Feb 19th President’s Day Invite
18 Colorado State** Loss 1-10 1211.5 Ignored Feb 19th President’s Day Invite
53 Cal Poly-SLO Loss 6-8 1079.53 Feb 19th President’s Day Invite
31 California Loss 7-10 1268.29 Feb 20th President’s Day Invite
91 Colorado College Loss 8-13 558.9 Mar 18th Womens Centex1
47 Florida Loss 3-13 867.9 Mar 18th Womens Centex1
75 Boston University Loss 1-13 621.57 Mar 18th Womens Centex1
16 Middlebury** Loss 5-13 1235.84 Ignored Mar 19th Womens Centex1
83 Trinity Loss 7-14 534.3 Mar 19th Womens Centex1
112 Rice Win 12-8 1348.77 Mar 19th Womens Centex1
91 Colorado College Win 10-3 1655.06 Mar 19th Womens Centex1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)