Wilson Computer Ratings team analysis
Skidmore (10-11)

Skidmore's opponents in order of rating: 
     Team            Rating        Score  Effect  W- L
   ---------------   ------       ------- ------ -----
   Union (New York)   476.3 LOSS   73-81         17- 4 Division III
   Union (New York)   476.3 LOSS   62-80         17- 4 Division III
   W. Connecticut     475.8 LOSS   68-69         16- 5 Division III
   Cncrdia-Mhd        468.5 LOSS   60-83         16- 6 Division III
   Vassar             454.7 LOSS   46-67         15- 7 Division III
   Vassar             454.7 LOSS   75-82         15- 7 Division III
   Rensselaer         453.9 LOSS   56-76         16- 5 Division III
   Hobart             407.4 LOSS   70-86         11-10 Division III
   St. Lawrence       407.1 LOSS   66-75         11-10 Division III
   Ithaca             389.9 LOSS   70-96          8-13 Division III
   SUNY-Plattsburgh   379.8 LOSS   73-80         11-10 Division III
>> Skidmore           400.9 <<                   10-11 Division III
   Rensselaer         453.9 WIN    67-60    ++   16- 5 Division III
   Middlebury         419.0 WIN    91-87    ++    8-14 Division III
   Hobart             407.4 WIN   101-94    ++   11-10 Division III
   Coast Guard        396.9 WIN    83-71    +     7-14 Division III
   Clarkson           373.1 WIN    86-82    +    11-10 Division III
   Rochester Tech     348.5 WIN    74-68    +     5-18 Division III
   Norwich            342.5 WIN    67-61    +     8-13 Division III
   SUNY-Purchase      334.7 WIN    58-50    +     9-13 Division III
   Bard               259.0 WIN    82-33    -     1-20 Division III
   Bard               259.0 WIN    74-52    -     1-20 Division III
Games against teams within about 100 rating points are often the best indicators of a team's actual strength.

"Effect" ranges from "---", a game that caused a large decrease in the team rating, to "+++", a game that produced a large increase in rating.

Note that wins over very weak teams may actually hurt a team's rating (the opposite is true for losses to very good teams).

shopify analytics ecommerce