Adding constraints to remove opposing teams against my goalie and forcing 3+ teams used

func/dk_nhl_go/NHL_seed_frames.go

@@ -656,7 +656,7 @@ func generateBaseArrays(cPlayers, wPlayers, dPlayers, gPlayers, flexPlayers []Pl
 					oppMatches++
 				}
 
-				if oppMatches > 2 {
+				if oppMatches >= 1 {
 					continue
 				}
 			}

func/fd_nhl_go/NHL_seed_frames.go

@@ -661,7 +661,7 @@ func generateBaseArrays(cPlayers, wPlayers, dPlayers, gPlayers, flex1Players, fl
 					oppMatches++
 				}
 
-				if oppMatches > 2 {
+				if oppMatches >= 1 {
 					continue
 				}
 			}

src/sports/nhl_functions.py

@@ -234,6 +234,20 @@ def init_team_results(model_source: DataFrame, position_reqs: dict, salary_cap:
                 ) <= salary_cap
             )
 
+        # No opposing skaters against selected G
+        for i, row in filtered_df.iterrows():
+            if 'G' in row['eligible_positions']:
+                goalie_opp = row['Opp']
+                goalie_var = x.get((i, 'G'))
+                if goalie_var:
+                    for j, srow in filtered_df.iterrows():
+                        if srow['Team'] == goalie_opp and 'G' not in srow['eligible_positions']:
+                            for pos in srow['eligible_positions']:
+                                if (j, pos) in x:
+                                    solver.Add(goalie_var + x[(j, pos)] <= 1)
+                            if ('FLEX' in position_reqs or 'FLEX1' in position_reqs or 'FLEX2' in position_reqs) and j in util_x:
+                                solver.Add(goalie_var + util_x[j] <= 1)
+
         # Max skaters per team (exclude P/SP positions, include UTIL)
         teams = filtered_df['Team'].unique()
         for team in teams:
@@ -254,6 +268,25 @@ def init_team_results(model_source: DataFrame, position_reqs: dict, salary_cap:
             
             solver.Add(team_constraint <= max_skaters)
 
+        # At least 3 different teams constraint
+        team_used = {}
+        for team in teams:
+            team_used[team] = solver.BoolVar(f'team_used_{team}')
+            team_players_sum = solver.Sum(
+                x[(i, pos)]
+                for i in filtered_df.index
+                for pos in filtered_df.loc[i, 'eligible_positions']
+                if filtered_df.loc[i, 'Team'] == team
+            )
+            if 'FLEX' in position_reqs or 'FLEX1' in position_reqs or 'FLEX2' in position_reqs:
+                team_util = [util_x[i] for i in util_x.keys() if filtered_df.loc[i, 'Team'] == team]
+                if team_util:
+                    team_players_sum += solver.Sum(team_util)
+            M = sum(position_reqs.values())
+            solver.Add(team_players_sum <= M * team_used[team])
+            solver.Add(team_used[team] <= team_players_sum)
+        solver.Add(solver.Sum([team_used[team] for team in teams]) >= 3)
+
         # Total players constraint (include UTIL)
         total_players = solver.Sum(x[(i, pos)] for i in filtered_df.index for pos in filtered_df.loc[i, 'eligible_positions'])
         if 'FLEX' in position_reqs or 'FLEX1' in position_reqs or 'FLEX2' in position_reqs:
@@ -460,7 +493,6 @@ def init_team_results(model_source: DataFrame, position_reqs: dict, salary_cap:
             final_df = final_df.sort_values(by='Total_Median', ascending=False)
             
             # Add to all results
-            print(final_df.head(10))
             all_team_results.append(final_df)
 
     return all_team_results