#151 Arizona State (4-8)

avg: 1146.59  •  sd: 88.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 Washington** Loss 6-15 1390.14 Ignored Jan 28th Santa Barbara Invitational 2023
58 California-San Diego Loss 11-12 1456.41 Jan 28th Santa Barbara Invitational 2023
29 Utah State Loss 6-13 1238.27 Jan 28th Santa Barbara Invitational 2023
10 California-Santa Cruz** Loss 4-14 1489.74 Ignored Jan 28th Santa Barbara Invitational 2023
46 Western Washington Loss 6-15 1088.53 Jan 29th Santa Barbara Invitational 2023
163 Boston University Loss 3-4 976.13 Apr 1st Huck Finn1
94 Saint Louis Loss 3-7 824.8 Apr 1st Huck Finn1
274 DePaul Win 8-0 1212.69 Apr 1st Huck Finn1
207 Illinois State Win 12-4 1510.86 Apr 1st Huck Finn1
351 Central Michigan** Win 15-0 628.27 Ignored Apr 2nd Huck Finn1
116 John Brown Loss 7-13 748.44 Apr 2nd Huck Finn1
207 Illinois State Win 11-8 1276.47 Apr 2nd Huck Finn1
**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)