#85 Southern California (7-17)

avg: 956.99  •  sd: 67.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
118 San Diego State Win 10-0 1313.07 Feb 4th Stanford Open
41 Santa Clara Win 7-6 1531.66 Feb 4th Stanford Open
97 Claremont Win 6-5 1051.12 Feb 4th Stanford Open
33 Portland Loss 4-6 1136.35 Feb 4th Stanford Open
158 Stanford-B Win 11-1 991.49 Feb 5th Stanford Open
50 California-Santa Cruz Loss 3-9 706.35 Feb 5th Stanford Open
75 Nevada-Reno Win 7-6 1165.03 Feb 5th Stanford Open
50 California-Santa Cruz Loss 2-7 706.35 Feb 5th Stanford Open
12 California-Santa Barbara Loss 7-10 1543.12 Feb 18th President’s Day Invite
18 California-San Diego** Loss 4-11 1097.49 Ignored Feb 18th President’s Day Invite
49 Texas Loss 3-9 729.63 Feb 18th President’s Day Invite
25 Duke** Loss 4-10 968.74 Ignored Feb 18th President’s Day Invite
12 California-Santa Barbara** Loss 2-12 1332.78 Ignored Feb 19th President’s Day Invite
24 California-Davis** Loss 3-9 993.24 Ignored Feb 19th President’s Day Invite
19 Colorado State** Loss 1-10 1087 Ignored Feb 19th President’s Day Invite
54 Cal Poly-SLO Loss 6-8 951.7 Feb 19th President’s Day Invite
30 California Loss 7-10 1142.25 Feb 20th President’s Day Invite
99 Colorado College Loss 8-13 423.23 Mar 18th Womens Centex1
45 Florida Loss 3-13 761.44 Mar 18th Womens Centex1
73 Boston University Loss 1-13 456.8 Mar 18th Womens Centex1
15 Middlebury** Loss 5-13 1206.54 Ignored Mar 19th Womens Centex1
81 Trinity Loss 7-14 415.74 Mar 19th Womens Centex1
112 Rice Win 12-8 1242.57 Mar 19th Womens Centex1
99 Colorado College Win 10-3 1519.39 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)