#178 Tulsa (6-6)

avg: 484.26  •  sd: 72.08  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
167 Air Force Win 10-7 940.37 Feb 22nd Dust Bowl 2020
120 Denver Loss 7-10 539.17 Feb 22nd Dust Bowl 2020
211 Colorado School of Mines Win 9-7 438.26 Feb 22nd Dust Bowl 2020
200 Oklahoma Win 8-6 566.85 Feb 22nd Dust Bowl 2020
229 Missouri** Win 15-2 467.53 Ignored Feb 23rd Dust Bowl 2020
135 John Brown Loss 8-15 260.21 Feb 23rd Dust Bowl 2020
57 Kansas** Loss 3-13 770.45 Ignored Mar 7th Midwest Throwdown 2020
80 St Olaf** Loss 2-13 619.07 Ignored Mar 7th Midwest Throwdown 2020
200 Oklahoma Win 8-5 719.96 Mar 7th Midwest Throwdown 2020
244 Northwestern-B** Win 8-2 182.67 Ignored Mar 7th Midwest Throwdown 2020
153 Illinois Loss 6-8 392.38 Mar 8th Midwest Throwdown 2020
162 Wisconsin-Eau Claire Loss 4-8 12.39 Mar 8th Midwest Throwdown 2020
**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)