Wilson Computer Ratings team analysis
St. Anselm (12-10)

St. Anselm's opponents in order of rating: 
     Team            Rating        Score  Effect  W- L
   ---------------   ------       ------- ------ -----
   Vermont            660.1 LOSS   49-84         22- 6 Division I
   Bentley            567.3 LOSS   67-76         18- 3 Division II
   Bentley            567.3 LOSS   54-64         18- 3 Division II
   S. Connecticut     538.2 LOSS   55-63         18- 5 Division II
   S. Connecticut     538.2 LOSS   47-67         18- 5 Division II
   Assumption         530.7 LOSS   60-61         17- 6 Division II
   Mass.-Lowell       513.6 LOSS   50-54          7-18 Division I
   American Intl.     505.0 LOSS   52-67         15- 8 Division II
   Southern NewHamp   490.1 LOSS   56-75         12-10 Division II
   Adelphi            476.6 LOSS   51-53         12-11 Division II
>> St. Anselm         489.7 <<                   12-10 Division II
   Felician College   502.9 WIN    64-56    ++   19- 6 Division II
   Southern NewHamp   490.1 WIN    77-55    ++   12-10 Division II
   St. Michael's      479.4 WIN    84-79    +    12-11 Division II
   Adelphi            476.6 WIN    52-42    +    12-11 Division II
   Molloy             445.7 WIN    63-51    +    10-15 Division II
   Post               439.3 WIN    78-46    +     9-12 Division II
   Pace               421.1 WIN    68-58    +     6-19 Division II
   Pace               421.1 WIN    65-55    +     6-19 Division II
   Queens (NY)        409.8 WIN    71-68    +     7-17 Division II
   Franklin Pierce    383.9 WIN    63-49          2-22 Division II
   Franklin Pierce    383.9 WIN    74-49          2-22 Division II
   Goldey-Beacom      376.3 WIN    61-43    -     5-17 Division II
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