#57 Whitman (18-4)

avg: 1506.56  •  sd: 61.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
208 Occidental Win 11-5 1520.5 Feb 10th Stanford Open 2018
225 California-B Win 11-7 1320.49 Feb 10th Stanford Open 2018
79 California-Davis Loss 9-10 1289.63 Feb 10th Stanford Open 2018
343 Texas-B** Win 12-4 994.96 Ignored Feb 10th Stanford Open 2018
158 Lewis & Clark Win 13-5 1702.13 Feb 11th Stanford Open 2018
100 Arizona Loss 8-12 894.32 Feb 11th Stanford Open 2018
90 Northern Arizona Win 11-8 1743.21 Feb 11th Stanford Open 2018
85 Colorado College Loss 8-11 1033.76 Feb 11th Stanford Open 2018
146 Nevada-Reno Win 11-3 1749.3 Mar 3rd Big Sky Brawl 2018
191 Montana State Win 11-6 1517.14 Mar 3rd Big Sky Brawl 2018
353 Montana-B** Win 11-3 958 Ignored Mar 3rd Big Sky Brawl 2018
222 Brigham Young-B** Win 11-4 1463.47 Ignored Mar 3rd Big Sky Brawl 2018
206 Washington State Win 11-6 1470.27 Mar 3rd Big Sky Brawl 2018
247 Boise State** Win 13-4 1378.11 Ignored Mar 4th Big Sky Brawl 2018
146 Nevada-Reno Win 13-8 1645.46 Mar 4th Big Sky Brawl 2018
127 Montana Win 10-7 1596.72 Mar 4th Big Sky Brawl 2018
304 Olivet Nazarene** Win 12-4 1168.42 Ignored Mar 17th D III Midwestern Invite 2018
192 Cedarville Win 13-3 1567.72 Mar 17th D III Midwestern Invite 2018
110 Michigan Tech Win 13-9 1709.9 Mar 17th D III Midwestern Invite 2018
35 Air Force Loss 11-15 1258.41 Mar 18th D III Midwestern Invite 2018
110 Michigan Tech Win 15-5 1891.33 Mar 18th D III Midwestern Invite 2018
232 St. Thomas** Win 15-6 1419.34 Ignored Mar 18th D III Midwestern Invite 2018
**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)