#91 Tulane (8-5)

avg: 1430.19  •  sd: 68.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
209 Alabama-Birmingham Win 13-7 1457.61 Mar 11th Tally Classic XVII
88 Central Florida Win 10-8 1696.89 Mar 11th Tally Classic XVII
148 Minnesota-Duluth Win 13-4 1761.01 Mar 11th Tally Classic XVII
201 South Florida Win 13-5 1537.69 Mar 11th Tally Classic XVII
141 LSU Win 14-11 1497.2 Mar 12th Tally Classic XVII
62 Harvard Win 12-11 1693.9 Mar 12th Tally Classic XVII
49 Notre Dame Loss 10-13 1315.11 Mar 12th Tally Classic XVII
60 Middlebury Loss 8-13 1081.87 Mar 18th Centex 2023
79 Texas A&M Loss 11-12 1348.68 Mar 18th Centex 2023
112 Illinois Win 9-8 1440.98 Mar 18th Centex 2023
47 Colorado State Loss 9-12 1301.86 Mar 19th Centex 2023
112 Illinois Loss 10-15 862.38 Mar 19th Centex 2023
86 Dartmouth Win 14-12 1657.92 Mar 19th Centex 2023
**Blowout Eligible


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)