#128 Colorado School of Mines (12-9)

avg: 1203.86  •  sd: 66.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
373 Creighton** Win 11-0 891.68 Ignored Feb 24th Dust Bowl 2018
387 North Texas-B** Win 11-0 783.67 Ignored Feb 24th Dust Bowl 2018
336 Texas-Dallas-B** Win 11-1 1024.3 Ignored Feb 24th Dust Bowl 2018
199 Stephen F Austin Win 11-2 1534.47 Feb 24th Dust Bowl 2018
70 Arkansas Loss 8-14 903.5 Feb 25th Dust Bowl 2018
162 Saint Louis Loss 13-15 865.57 Feb 25th Dust Bowl 2018
156 Colorado-Denver Loss 13-15 892.73 Feb 25th Dust Bowl 2018
35 Air Force Loss 10-12 1401.45 Mar 3rd Air Force Invite 2018
85 Colorado College Loss 5-11 799.36 Mar 3rd Air Force Invite 2018
355 Colorado Mesa University** Win 13-3 954.28 Ignored Mar 3rd Air Force Invite 2018
159 Colorado-B Win 9-4 1694.56 Mar 3rd Air Force Invite 2018
341 Air Force Academy-B Win 10-5 980.43 Mar 4th Air Force Invite 2018
85 Colorado College Loss 5-6 1274.36 Mar 4th Air Force Invite 2018
355 Colorado Mesa University Win 10-6 850.44 Mar 4th Air Force Invite 2018
146 Nevada-Reno Loss 7-8 1024.3 Mar 24th Trouble in Vegas 2018
186 Cal Poly-Pomona Win 12-8 1424.26 Mar 24th Trouble in Vegas 2018
214 California-Santa Cruz Win 11-7 1372.47 Mar 24th Trouble in Vegas 2018
67 Utah Loss 9-10 1332.97 Mar 24th Trouble in Vegas 2018
211 Utah State Win 9-5 1436.9 Mar 24th Trouble in Vegas 2018
130 North Texas Win 9-5 1721.19 Mar 25th Trouble in Vegas 2018
176 Colorado State-B Loss 8-9 901.62 Mar 25th Trouble in Vegas 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)