#122 Northwestern (10-14)

avg: 1344.78  •  sd: 40.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 British Columbia** Loss 3-13 1430.23 Ignored Mar 8th Stanford Invite 2025 Mens
46 Stanford Loss 8-13 1245.39 Mar 8th Stanford Invite 2025 Mens
131 Santa Clara Loss 9-10 1197.07 Mar 8th Stanford Invite 2025 Mens
145 Wisconsin-Milwaukee Win 13-12 1394.92 Mar 9th Stanford Invite 2025 Mens
54 UCLA Loss 9-13 1264.93 Mar 9th Stanford Invite 2025 Mens
118 Mississippi State Win 10-9 1482.25 Mar 29th Huck Finn 2025
53 Purdue Loss 8-15 1128.08 Mar 29th Huck Finn 2025
72 St Olaf Loss 9-14 1116.01 Mar 29th Huck Finn 2025
204 Saint Louis Win 11-8 1407.99 Mar 29th Huck Finn 2025
87 Missouri S&T Loss 8-15 926.27 Mar 30th Huck Finn 2025
151 Macalester Win 13-11 1490.16 Mar 30th Huck Finn 2025
49 Chicago Loss 8-11 1363.72 Apr 12th Illinois D I Mens Conferences 2025
258 DePaul Win 9-6 1259.34 Apr 12th Illinois D I Mens Conferences 2025
236 Illinois State Win 15-9 1423.53 Apr 12th Illinois D I Mens Conferences 2025
297 Loyola-Chicago Win 11-5 1301.18 Apr 12th Illinois D I Mens Conferences 2025
59 Illinois Loss 8-13 1154.03 Apr 13th Illinois D I Mens Conferences 2025
236 Illinois State Win 12-9 1253.42 Apr 13th Illinois D I Mens Conferences 2025
95 Southern Illinois-Edwardsville Loss 9-10 1345.96 Apr 13th Illinois D I Mens Conferences 2025
56 Indiana Loss 10-14 1260.64 Apr 26th Great Lakes D I Mens Regionals 2025
78 Notre Dame Loss 13-14 1429.57 Apr 26th Great Lakes D I Mens Regionals 2025
95 Southern Illinois-Edwardsville Win 14-10 1869.66 Apr 26th Great Lakes D I Mens Regionals 2025
26 Michigan Loss 8-15 1338.67 Apr 27th Great Lakes D I Mens Regionals 2025
78 Notre Dame Win 15-12 1855.06 Apr 27th Great Lakes D I Mens Regionals 2025
53 Purdue Loss 9-15 1177.4 Apr 27th Great Lakes D I Mens Regionals 2025
**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)