#133 Bates (13-11)

avg: 1325.44  •  sd: 69.31  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
215 Northeastern-B Win 8-5 1469.38 Mar 8th Grand Northeast Kickoff 2025
223 Colby Loss 5-6 857.72 Mar 8th Grand Northeast Kickoff 2025
385 New Hampshire** Win 9-0 802.59 Ignored Mar 8th Grand Northeast Kickoff 2025
399 Middlebury-B** Win 13-0 686 Ignored Mar 8th Grand Northeast Kickoff 2025
312 Amherst Win 9-7 908.27 Mar 9th Grand Northeast Kickoff 2025
262 Brown-B Win 8-5 1282.6 Mar 9th Grand Northeast Kickoff 2025
215 Northeastern-B Win 15-10 1469.38 Mar 9th Grand Northeast Kickoff 2025
214 MIT Loss 10-11 895.68 Mar 22nd PBR State Open
95 Bowdoin Loss 9-10 1355.17 Mar 22nd PBR State Open
348 Bentley** Win 15-5 1058.17 Ignored Mar 22nd PBR State Open
162 Brandeis Loss 8-10 965.55 Mar 23rd PBR State Open
105 Boston University Win 9-7 1728.99 Mar 23rd PBR State Open
281 Worcester Polytechnic Win 13-4 1363.99 Mar 23rd PBR State Open
75 Wesleyan Loss 8-13 1103.2 Mar 29th Easterns 2025
33 Elon Loss 5-13 1242.66 Mar 29th Easterns 2025
78 Richmond Loss 8-13 1086.3 Mar 29th Easterns 2025
102 North Carolina-Asheville Win 13-12 1584.86 Mar 29th Easterns 2025
76 Williams Loss 12-13 1467.52 Mar 30th Easterns 2025
67 Franciscan Win 13-10 1966.39 Mar 30th Easterns 2025
36 Middlebury Loss 9-15 1303.49 Mar 30th Easterns 2025
36 Middlebury Loss 7-15 1218.97 Apr 12th North New England D III Mens Conferences 2025
223 Colby Win 14-8 1518.76 Apr 12th North New England D III Mens Conferences 2025
399 Middlebury-B** Win 15-1 686 Ignored Apr 12th North New England D III Mens Conferences 2025
70 Dartmouth Loss 8-9 1493.5 Apr 13th North New England D III Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)