#88 John Brown (9-1)

avg: 1226.55  •  sd: 60.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
148 Marquette Win 9-8 1005.94 Mar 2nd Midwest Throwdown 2019
35 Truman State Loss 5-11 1084.45 Mar 2nd Midwest Throwdown 2019
116 Air Force Win 10-3 1647.21 Mar 2nd Midwest Throwdown 2019
113 Oklahoma Win 13-12 1177.65 Mar 10th Dust Bowl 2019
- Oklahoma State Win 12-8 1033.11 Mar 10th Dust Bowl 2019
202 Colorado School of Mines** Win 15-1 1131.74 Ignored Mar 10th Dust Bowl 2019
162 Nebraska Win 13-8 1292.91 Mar 10th Dust Bowl 2019
- Kansas State Win 8-4 1436.56 Mar 31st Tulsa Toss Up 2019
174 Tulsa Win 10-7 1110.47 Mar 31st Tulsa Toss Up 2019
174 Tulsa Win 7-0 1320.8 Mar 31st Tulsa Toss Up 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)