#125 Colorado School of Mines (12-8)

avg: 1278.32  •  sd: 59.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
50 Stanford Loss 10-11 1507.74 Feb 9th Stanford Open 2019
254 Cal Poly-Pomona Win 11-9 1089.39 Feb 9th Stanford Open 2019
111 Washington University Loss 7-11 846.57 Feb 9th Stanford Open 2019
406 Colorado School of Mines - B** Win 13-0 787.01 Ignored Feb 23rd Denver Round Robin 2019
238 Denver Win 13-3 1497.8 Feb 23rd Denver Round Robin 2019
273 Colorado State-B Win 13-4 1375.73 Feb 23rd Denver Round Robin 2019
170 Colorado-Denver Win 13-5 1683.91 Feb 23rd Denver Round Robin 2019
123 New Mexico Win 13-11 1508.25 Mar 16th Air Force Invite 2019
75 Air Force Loss 10-13 1149.4 Mar 16th Air Force Invite 2019
382 Air Force-B** Win 13-0 906.13 Ignored Mar 16th Air Force Invite 2019
307 Colorado Mesa** Win 13-3 1240.69 Ignored Mar 16th Air Force Invite 2019
244 Colorado-B Win 11-3 1477.2 Mar 17th Air Force Invite 2019
170 Colorado-Denver Loss 10-11 958.91 Mar 17th Air Force Invite 2019
341 Colorado-Colorado Springs** Win 13-2 1127.82 Ignored Mar 17th Air Force Invite 2019
74 Arizona Loss 6-10 982.93 Mar 23rd Trouble in Vegas 2019
34 UCLA Loss 4-13 1128.73 Mar 23rd Trouble in Vegas 2019
76 Utah Win 10-9 1598.73 Mar 23rd Trouble in Vegas 2019
254 Cal Poly-Pomona Win 15-6 1440.19 Mar 24th Trouble in Vegas 2019
65 Florida Loss 10-13 1207.6 Mar 24th Trouble in Vegas 2019
133 Utah State Loss 11-13 1016.43 Mar 24th Trouble in Vegas 2019
**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)