Modernize tag system, match engine, OBS exports, and output folder architecture

.gitignore

@@ -1,24 +1,12 @@
-# Python
+# Runtime / generated data
+../stats/
+stats/
+players/
+obs_exports/
+*.txt
+
+# Python cache
 __pycache__/
 *.pyc
-*.pyo
-*.pyd
-.env
-.venv
-venv/
-env/
 
-# VSCode / IDE
+*.db
-.vscode/
-.idea/
-
-# OS
-.DS_Store
-Thumbs.db
-
-# Our caches
-stats/data/cache/
-cache/
-
-# Ignore OBS output folder
-../stats/

config.json

@@ -1,49 +1,50 @@
 {
-  "season": 10,
+    "season": 10,
-  "teams": [
+    "teams": [
-    "TANK",
+        "TANK",
-    "MEX",
+        "MEX",
-    "TBDi",
+        "TBDi",
-    "EMRD",
+        "EMRD",
-    "DMND",
+        "DMND",
-    "Doc!",
+        "Doc!",
-    "HAXX",
+        "HAXX",
-    "SWUA",
+        "SWUA",
-    "ZTi",
+        "ZTi",
-    "SLNT",
+        "SLNT",
-    "HOBO",
+        "HOBO",
-    "just",
+        "just",
-    "VRUS",
+        "VRUS",
-    "FAM!",
+        "FAM!",
-    "SKY",
+        "SKY",
-    "LMB0",
+        "LMB0",
-    "F1R3",
+        "F1R3",
-    "RVNG",
+        "RVNG",
-    "DRGN",
+        "DRGN",
-    "STHX",
+        "STHX",
-    "EMU"
+        "EMU"
-  ],
+    ],
-  "tiers": {
+    "tiers": {
-    "TANK": "Dasher",
+        "TANK": "Dasher",
-    "MEX": "Sprinter",
+        "MEX": "Sprinter",
-    "TBDi": "Walker",
+        "TBDi": "Walker",
-    "EMRD": "Dasher",
+        "EMRD": "Dasher",
-    "DMND": "Sprinter",
+        "DMND": "Sprinter",
-    "Doc!": "Walker",
+        "Doc!": "Walker",
-    "HAXX": "Dasher",
+        "HAXX": "Dasher",
-    "SWUA": "Sprinter",
+        "SWUA": "Sprinter",
-    "ZTi": "Walker",
+        "ZTi": "Walker",
-    "SLNT": "Dasher",
+        "SLNT": "Dasher",
-    "HOBO": "Sprinter",
+        "HOBO": "Sprinter",
-    "just": "Walker",
+        "just": "Walker",
-    "VRUS": "Dasher",
+        "VRUS": "Dasher",
-    "FAM!": "Sprinter",
+        "FAM!": "Sprinter",
-    "SKY": "Walker",
+        "SKY": "Walker",
-    "LMB0": "Dasher",
+        "LMB0": "Dasher",
-    "F1R3": "Sprinter",
+        "F1R3": "Sprinter",
-    "RVNG": "Walker",
+        "RVNG": "Walker",
-    "DRGN": "Dasher",
+        "DRGN": "Dasher",
-    "STHX": "Sprinter",
+        "STHX": "Sprinter",
-    "EMU": "Walker"
+        "EMU": "Walker"
-  }
+    },
+    "output_root": "C:\\Projects\\Casting\\stats"
 }

dashleague_cast_tool.py

@@ -2,32 +2,62 @@ import os
 import json
 import math
 from collections import defaultdict
+
 import tkinter as tk
-from tkinter import ttk, messagebox
+from tkinter import ttk, messagebox, filedialog
+
 import requests
 
+# --- Internal modules ---
 from data.career_db import build_career_database
 from rankings import top_players_by_tag
 from team_identity import generate_team_identity, compare_team_identity
 from storylines import matchup_storyline
-from tags.objective_domination import compute_dom_objective
+
+from tags.objective_domination import (
+    compute_dom_objective_for_career_player,
+    compute_dom_objective_for_match_player,
+    compute_dom_objective_team_scores,
+)
+
 from predictions.prediction_engine import generate_predictions
 
 from match_engine import (
-    rank_match_players_sharpshooter,
     rank_match_players_slayer,
     rank_match_players_objpl,
     rank_match_players_objdom,
+    rank_match_players_sharpshooter,
     rank_match_players_clutch,
     split_two_columns,
     compute_slayer_prediction,
 )
 
+# ---------------------------------------------------------
+#  PATHS + CONFIG
+# ---------------------------------------------------------
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 CONFIG_PATH = os.path.join(BASE_DIR, "config.json")
-PLAYERS_DIR = os.path.join(BASE_DIR, "players")
+
-OBS_EXPORT_DIR = os.path.join(BASE_DIR, "obs_exports")
+# Load config (or default)
+if os.path.exists(CONFIG_PATH):
+    with open(CONFIG_PATH, "r", encoding="utf-8") as f:
+        config = json.load(f)
+else:
+    config = {}
+
+# Default output folder (outside repo)
+DEFAULT_OUTPUT_ROOT = os.path.abspath(os.path.join(BASE_DIR, "..", "stats"))
+
+# Allow override from config.json
+output_root = config.get("output_root", DEFAULT_OUTPUT_ROOT)
+
+# Build runtime directories
+STATS_DIR = output_root
+PLAYERS_DIR = os.path.join(STATS_DIR, "players")
+OBS_EXPORT_DIR = os.path.join(STATS_DIR, "obs_exports")
+
+os.makedirs(PLAYERS_DIR, exist_ok=True)
 os.makedirs(OBS_EXPORT_DIR, exist_ok=True)
 
 API_BASE = "https://dashleague.games/api/v1"
@@ -120,6 +150,13 @@ class DashLeagueGUI:
         self.rankings_button = ttk.Button(top_frame, text="Rankings", command=self.on_rankings)
         self.rankings_button.grid(row=0, column=5, padx=(0, 10))
         
+        self.output_folder_button = ttk.Button(
+            top_frame,
+            text="Select Output Folder",
+            command=self.choose_output_folder
+        )
+        self.output_folder_button.grid(row=0, column=6, padx=(0, 10))
+        
         self.map_var = tk.StringVar()
         self.map_var.set("Payload")  # default
 
@@ -213,11 +250,32 @@ class DashLeagueGUI:
             self.team_b_listbox.delete(0, tk.END)
             for p in team_players:
                 self.team_b_listbox.insert(tk.END, p.get("name", "Unknown"))
+                
+                
+    def normalize_player(p):
+        return {
+            "id": p.get("PlayerUUID"),
+            "name": p.get("PlayerGameName"),
+            "team": p.get("TeamUUID"),
+            "KD": p.get("KD", ""),
+            "kills": p.get("Kills", ""),
+            "deaths": p.get("Deaths", ""),
+            "headshots": p.get("Headshots", ""),
+            "accuracy": p.get("Accuracy", ""),
+            "PAY_PushTime": p.get("PAY_PushTime", ""),
+            "DOM_captures": p.get("DOM_Captures", ""),
+            "DOM_counters": p.get("DOM_Counters", ""),
+        }                
+                
+                
 
     def on_generate_slots(self):
         try:
             ensure_player_slots()
 
+            # -----------------------------
+            # 1. Collect selected players
+            # -----------------------------
             selected_a = [self.team_a_players[i] for i in self.team_a_listbox.curselection()]
             selected_b = [self.team_b_players[i] for i in self.team_b_listbox.curselection()]
 
@@ -226,34 +284,128 @@ class DashLeagueGUI:
             if not selected_b:
                 selected_b = self.team_b_players
 
-            all_players = sorted(selected_a, key=lambda x: x.get("name", "")) + \
+            # Sort and combine
-                          sorted(selected_b, key=lambda x: x.get("name", ""))
+            all_players = (
+                sorted(selected_a, key=lambda x: x.get("name", "")) +
+                sorted(selected_b, key=lambda x: x.get("name", ""))
+            )
 
+            # Limit to 10
             all_players = all_players[:10]
+
+            # -----------------------------
+            # 2. Normalize keys BEFORE attaching tags
+            # -----------------------------
+            for p in all_players:
+                # Normalize stat keys to match DB/tag expectations
+                p["CP_Captures"] = p.get("CP_captures", 0)
+                p["DOM_Captures"] = p.get("DOM_captures", 0)
+                p["DOM_Counters"] = p.get("DOM_counters", 0)
+
+            # Save match players
             self.current_match_players = all_players
 
+            # Debug
+            if self.current_match_players:
+                print("DEBUG MATCH PLAYER:", self.current_match_players[0])
+            else:
+                print("DEBUG MATCH PLAYER: EMPTY LIST")
+
+            # -----------------------------
+            # 3. Load career DB
+            # -----------------------------
             career_path = os.path.join(BASE_DIR, "career_stats.json")
             career_db = None
             if os.path.exists(career_path):
                 with open(career_path, "r", encoding="utf-8") as f:
                     career_db = json.load(f)
-                    
+
-                        # Attach tag components from career DB to match players
+            # -----------------------------
+            # 4. Attach tag components
+            # -----------------------------
             if career_db is not None:
                 for p in all_players:
-                    pid = p.get("id")
+                    pid = (
+                        p.get("id")
+                        or p.get("PlayerUUID")
+                        or p.get("uuid")
+                    )
+
                     if pid and pid in career_db["players"]:
                         pdata = career_db["players"][pid]
 
-                        # Attach all tag components
                         p["clutch_components"] = pdata.get("clutch_components", {})
                         p["consistency_components"] = pdata.get("consistency_components", {})
                         p["objective_payload_components"] = pdata.get("objective_payload_components", {})
                         p["objective_domination_components"] = pdata.get("objective_domination_components", {})
                         p["sharpshooter_components"] = pdata.get("sharpshooter_components", {})
                         p["slayer_score_raw"] = pdata.get("slayer_score_raw", 0.0)
-                            
+                        
-                    
+                        
+            # -----------------------------
+            # 4b. Compute match-based ObjDOM
+            # -----------------------------
+            from tags.objective_domination import compute_dom_objective_team_scores
+
+            team_entries = []
+            for p in all_players:
+                pid = (
+                    p.get("id")
+                    or p.get("PlayerUUID")
+                    or p.get("uuid")
+                )
+                if not pid:
+                    continue
+
+                entry = career_db["players"].get(pid, {})
+                team_entries.append((pid, entry, p))
+
+            # Compute match ObjDOM scores
+            dom_scores = compute_dom_objective_team_scores(team_entries)
+
+            # Attach to each player
+            for pid, entry, mp in team_entries:
+                score = dom_scores.get(pid, 0.0)
+                mp["objective_domination_components"] = {
+                    "dom_score_raw": score,
+                    "dom_score_display": f"{score:.2f}",
+                }    
+
+
+            # -----------------------------
+            # 4c. Compute match-based ObjPL
+            # -----------------------------
+            from tags.objective_payload import compute_payload_objective_team_scores
+
+            pl_team_entries = []
+            for p in all_players:
+                pid = (
+                    p.get("id")
+                    or p.get("PlayerUUID")
+                    or p.get("uuid")
+                )
+                if not pid:
+                    continue
+
+                entry = career_db["players"].get(pid, {}) if career_db else {}
+                pl_team_entries.append((pid, entry, p))
+
+            if pl_team_entries:
+                pl_scores = compute_payload_objective_team_scores(pl_team_entries)
+
+                for pid, entry, mp in pl_team_entries:
+                    score = pl_scores.get(pid, 0.0)
+                    mp["objective_payload_components"] = {
+                        "payload_score_raw": score,
+                        "payload_score_display": f"{score:.2f}",
+                    }
+
+
+                
+
+            # -----------------------------
+            # 5. Write OBS slot files
+            # -----------------------------
             for idx, p in enumerate(all_players):
                 slot_dir = os.path.join(PLAYERS_DIR, f"p{idx}")
                 os.makedirs(slot_dir, exist_ok=True)
@@ -267,24 +419,21 @@ class DashLeagueGUI:
                 write_text(os.path.join(slot_dir, "Headshots.txt"), p.get("headshots", ""))
                 write_text(os.path.join(slot_dir, "Accuracy.txt"), p.get("accuracy", ""))
                 write_text(os.path.join(slot_dir, "PushTime.txt"), p.get("PAY_PushTime", ""))
-                write_text(os.path.join(slot_dir, "Captures.txt"), p.get("DOM_captures", ""))
+                write_text(os.path.join(slot_dir, "Captures.txt"), p.get("DOM_Captures", ""))
-                write_text(os.path.join(slot_dir, "Counters.txt"), p.get("DOM_counters", ""))
+                write_text(os.path.join(slot_dir, "Counters.txt"), p.get("DOM_Counters", ""))
 
-                # Tag exports now come from real tag engines via rankings/career_db
+                # Tag exports (use enriched match player dict)
-                if career_db is not None:
+                map_type = self.map_var.get()
-                    pid = p.get("id")
+                tags_text = self.generate_player_tags(p, map_type)
-                    if pid and pid in career_db["players"]:
+                write_text(os.path.join(slot_dir, "Tags.txt"), tags_text)
-                        player_entry = career_db["players"][pid]
-                        map_type = self.map_var.get()
-                        tags_text = self.generate_player_tags(player_entry, map_type)
-                        write_text(os.path.join(slot_dir, "Tags.txt"), tags_text)
 
             self.set_status("Player slots generated.")
             messagebox.showinfo("Success", "Player slots generated into p0–p9.")
+
         except Exception as e:
             messagebox.showerror("Error", f"Failed to generate slots:\n{e}")
             self.set_status("Generate failed.")
-            
+
         self.export_all_obs_files()
 
     def on_build_career_db(self):
@@ -344,7 +493,7 @@ class DashLeagueGUI:
             text = f"""PLAYER SPOTLIGHT — {name}
 
 Slayer Tier: {slayer_strength}
-Payload Objective Specialist: {obj_pl:.2f} (if not None)
+Payload Objective Specialist: {obj_pl if obj_pl is not None else "N/A"}
 
 Career Stats:
 KD: {career['KD']:.2f}
@@ -474,23 +623,24 @@ PushTime/Season: {derived.get('push_time_per_season', 0):.2f}
         if slayer_strength:
             tags_out.append(slayer_strength)
 
-        # Payload Objective Specialist
+        # Payload Objective Specialist (match-based)
         if map_type == "Payload":
-            obj_pl = player_entry.get("objective_payload_score")
+            pl_info = player_entry.get("objective_payload_components", {})
-            if obj_pl is not None:
+            pl_score = pl_info.get("payload_score_raw", 0.0)
-                tags_out.append(f"ObjPL {obj_pl:.2f}")
+            tags_out.append(f"ObjPL {pl_score:.2f}")
 
-        # Domination Specialist
+        # Domination Objective Specialist (match-based)
         if map_type == "Domination":
-            obj_dom = compute_dom_objective(player_entry)
+            dom_info = player_entry.get("objective_domination_components", {})
-            tags_out.append(f"ObjDOM {obj_dom:.2f}")
+            dom_score = dom_info.get("dom_score_raw", 0.0)
+            tags_out.append(f"ObjDOM {dom_score:.2f}")
 
         # Sharpshooter
         sharp = player_entry.get("sharpshooter_score")
         if sharp is not None:
             tags_out.append(f"Sharp {sharp:.2f}")
 
-        return ", ".join(tags_out)    
+        return ", ".join(tags_out)
         
         
 
@@ -523,7 +673,7 @@ PushTime/Season: {derived.get('push_time_per_season', 0):.2f}
 
         # --- Export to OBS ---
         try:
-            with open("obs_exports/storyline.txt", "w", encoding="utf-8") as f:
+            with open(os.path.join(OBS_EXPORT_DIR, "storyline.txt"), "w", encoding="utf-8") as f:
                 f.write(full_story)
         except Exception as e:
             messagebox.showerror("File Error", f"Could not write storyline export:\n{e}")
@@ -786,7 +936,7 @@ PushTime/Season: {derived.get('push_time_per_season', 0):.2f}
 
             full_story = storyline_text + "\n\nTEAM IDENTITY SUMMARY\n" + identity_text
 
-            write_text(os.path.join("obs_exports", "storyline.txt"), full_story)
+            write_text(os.path.join(OBS_EXPORT_DIR, "storyline.txt"), full_story)
         except Exception as e:
             print("Storyline export failed:", e)
 
@@ -819,6 +969,35 @@ PushTime/Season: {derived.get('push_time_per_season', 0):.2f}
         print(f"OBS files updated for {map_type}")   
 
 
+    from tkinter import filedialog, messagebox
+
+    def refresh_output_dirs(self):
+        global STATS_DIR, PLAYERS_DIR, OBS_EXPORT_DIR
+        STATS_DIR = self.output_root
+        PLAYERS_DIR = os.path.join(STATS_DIR, "players")
+        OBS_EXPORT_DIR = os.path.join(STATS_DIR, "obs_exports")
+        os.makedirs(PLAYERS_DIR, exist_ok=True)
+        os.makedirs(OBS_EXPORT_DIR, exist_ok=True)
+
+    def choose_output_folder(self):
+        folder = filedialog.askdirectory(title="Select output folder for OBS + players")
+        if not folder:
+            return
+
+        self.output_root = os.path.abspath(folder)
+        config["output_root"] = self.output_root
+
+        self.refresh_output_dirs()
+
+        with open(CONFIG_PATH, "w", encoding="utf-8") as f:
+            json.dump(config, f, indent=4)
+
+        messagebox.showinfo(
+            "Output Folder Set",
+            f"Output folder set to:\n{self.output_root}\n\n"
+            "Point OBS to this folder for players and exports."
+        )
+
 def main():
     root = tk.Tk()
     app = DashLeagueGUI(root)

data/career_db.py

@@ -1,221 +1,120 @@
 import os
 import json
 from collections import defaultdict
-import requests
+import data.db_access as db_access
-
-CACHE_DIR = os.path.join(os.path.dirname(__file__), "cache")
-SEASON_CACHE_DIR = os.path.join(CACHE_DIR, "seasons")
-
-os.makedirs(SEASON_CACHE_DIR, exist_ok=True)
 
 from tags.slayer import compute_slayer_for_career_player
 from tags.objective_payload import compute_payload_objective_for_career_player
 from tags.sharpshooter import compute_sharpshooter_for_career_player
 from tags.consistency import compute_consistency
-
+from tags.clutch import compute_clutch
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-API_BASE = "https://dashleague.games/api/v1"
-
-
-def fetch_json(url):
-    resp = requests.get(url, timeout=10)
-    resp.raise_for_status()
-    return resp.json()
-
-
-def detect_valid_seasons(max_season=50):
-    valid = []
-    for season in range(0, max_season + 1):
-        try:
-            url = f"{API_BASE}/stats?season={season}"
-            resp = requests.get(url, timeout=10)
-            if resp.status_code != 200:
-                continue
-            data = resp.json()
-            season_stats = data.get("data", [])
-            if isinstance(season_stats, list) and len(season_stats) > 0:
-                valid.append(season)
-        except Exception:
-            continue
-    return valid
-
-
-def fetch_season_stats(season):
-    url = f"{API_BASE}/stats?season={season}"
-    data = fetch_json(url)
-    return data.get("data", [])
 
 
 def build_career_database(output_path):
-    seasons = detect_valid_seasons()
+
-    if not seasons:
+    # ---------------------------------------------------------
-        print("No valid seasons found. Aborting career DB build.")
+    # 1. Load all players from the local SQLite DB
+    # ---------------------------------------------------------
+    player_rows = db_access.query("SELECT PlayerUUID, PlayerGameName FROM players;")
+
+    if not player_rows:
+        print("No players found in local DB.")
         return False
 
-    players = {}
+    final_db = {"players": {}, "league_averages": {}}
-    league_accumulator = defaultdict(float)
-    league_counts = defaultdict(int)
 
-    for season in seasons:
+    # ---------------------------------------------------------
-        season_stats = fetch_season_stats(season)
+    # 2. Build per-player career stats from local DB
-        if not isinstance(season_stats, list):
+    # ---------------------------------------------------------
+    for row in player_rows:
+        pid = row["PlayerUUID"]
+        name = row.get("PlayerGameName", "Unknown")
+
+        # Pull all matches from SQLite
+        matches = db_access.get_player_match_history(pid)
+
+        if not matches:
+            # Skip players with no match history
             continue
 
-        for entry in season_stats:
+        # Aggregate raw career totals
-            if not isinstance(entry, dict):
+        career_raw = defaultdict(float)
-                continue
-                
-            # Normalize inconsistent keys
-            if "loss" in entry and "losses" not in entry:
-                entry["losses"] = entry["loss"]
 
-            if "shots" not in entry:
+        for m in matches:
-                entry["shots"] = 0
+            career_raw["kills"] += m["Kills"]
+            career_raw["deaths"] += m["Deaths"]
+            career_raw["damage"] += m["Damage"]
+            career_raw["shots"] += m["Shots"]
+            career_raw["shots_hit"] += m["ShotsHit"]
+            career_raw["headshots"] += m["Headshots"]
+            career_raw["PAY_PushTime"] += m["PAY_PushTime"]
+            career_raw["DOM_captures"] += m["DOM_Captures"]
+            career_raw["DOM_counters"] += m["DOM_Counters"]
+            career_raw["maps"] += 1  # each match = 1 map for now
 
-            if "shots_hit" not in entry:
+        # Derived stats
-                entry["shots_hit"] = 0
+        maps = max(1, career_raw["maps"])
-
+        KD = career_raw["kills"] / career_raw["deaths"] if career_raw["deaths"] > 0 else career_raw["kills"]
-            if "headshots" not in entry:
+        accuracy = (career_raw["shots_hit"] / career_raw["shots"]) if career_raw["shots"] > 0 else 0.0
-                entry["headshots"] = 0
-
-            if "damage" not in entry:
-                entry["damage"] = 0
-
-            if "maps" not in entry or entry["maps"] in (None, "Coming Soon!"):
-                entry["maps"] = 0
-
-            if "accuracy" not in entry or isinstance(entry["accuracy"], str):
-                entry["accuracy"] = 0.0               
-                
-            pid = entry.get("id")
-            if not pid:
-                continue
-
-            if pid not in players:
-                players[pid] = {
-                    "name": entry.get("name", "Unknown"),
-                    "team_history": set(),
-                    "seasons_played": set(),
-                    "per_season": {},
-                    "career_raw": defaultdict(float)
-                }
-
-            p = players[pid]
-            team = entry.get("team") or "No Team"
-            p["team_history"].add(team)
-            p["seasons_played"].add(season)
-            p["per_season"][str(season)] = entry
-
-            for key in [
-                "kills",
-                "deaths",
-                "headshots",
-                "accuracy",
-                "PAY_PushTime",
-                "DOM_captures",
-                "DOM_counters",
-                "maps",
-                "wins",
-                "losses",
-                "damage",
-                "shots",
-                "shots_hit",
-            ]:
-                if key in entry:
-                    p["career_raw"][key] += entry.get(key, 0)
-
-            if "KD" in entry:
-                league_accumulator["KD"] += entry["KD"]
-                league_counts["KD"] += 1
-            if "accuracy" in entry:
-                league_accumulator["accuracy"] += entry["accuracy"]
-                league_counts["accuracy"] += 1
-            if "kills" in entry and "maps" in entry and entry["maps"] > 0:
-                league_accumulator["kills_per_map"] += entry["kills"] / entry["maps"]
-                league_counts["kills_per_map"] += 1
-            if "damage" in entry and "maps" in entry and entry["maps"] > 0:
-                league_accumulator["damage_per_map"] += entry["damage"] / entry["maps"]
-                league_counts["damage_per_map"] += 1
-            if "PAY_PushTime" in entry:
-                league_accumulator["push_time_per_season"] += entry["PAY_PushTime"]
-                league_counts["push_time_per_season"] += 1
-
-    league_averages = {}
-    for key in league_accumulator:
-        league_averages[key] = league_accumulator[key] / max(1, league_counts[key])
-
-    final_db = {"players": {}, "league_averages": league_averages}
-
-    for pid, pdata in players.items():
-        
-         # 🔥 Skip players with no valid stats
-        if "career_raw" not in pdata or not pdata["career_raw"]:
-            continue
-            
-        # print("DEBUG PLAYER:", pid, pdata["career_raw"])    
-
-        raw = pdata["career_raw"]
-
-        # 🔥 Ensure maps always exists
-        if "maps" not in raw:
-            raw["maps"] = 0
-
-        maps = raw["maps"] if raw["maps"] > 0 else 1
-        seasons_played = len(pdata["seasons_played"])
-
-        kills_per_map = raw["kills"] / maps
-        deaths_per_map = raw["deaths"] / maps
-        push_time_per_season = raw["PAY_PushTime"] / max(1, seasons_played)
-
-        KD = raw["kills"] / raw["deaths"] if raw["deaths"] > 0 else raw["kills"]
-        accuracy = raw["accuracy"] / max(1, seasons_played)
 
         derived = {
-            "kills_per_map": kills_per_map,
+            "kills_per_map": career_raw["kills"] / maps,
-            "deaths_per_map": deaths_per_map,
+            "deaths_per_map": career_raw["deaths"] / maps,
-            "push_time_per_season": push_time_per_season,
+            "push_time_per_season": career_raw["PAY_PushTime"],  # no seasons now
         }
 
         final_db["players"][pid] = {
-            "name": pdata["name"],
+            "name": name,
-            "team_history": list(pdata["team_history"]),
-            "seasons_played": list(pdata["seasons_played"]),
-            "per_season": pdata["per_season"],
             "career": {
-                "kills": raw["kills"],
+                "kills": career_raw["kills"],
-                "deaths": raw["deaths"],
+                "deaths": career_raw["deaths"],
                 "KD": KD,
                 "accuracy": accuracy,
-                "push_time": raw["PAY_PushTime"],
+                "push_time": career_raw["PAY_PushTime"],
-                "captures": raw["DOM_captures"],
+                "captures": career_raw["DOM_captures"],
-                "counters": raw["DOM_counters"],
+                "counters": career_raw["DOM_counters"],
-                "maps": raw["maps"],
+                "maps": career_raw["maps"],
-                "wins": raw["wins"],
+                "damage": career_raw["damage"],
-                "losses": raw["losses"],
+                "shots": career_raw["shots"],
-                "damage": raw["damage"],
+                "shots_hit": career_raw["shots_hit"],
-                "shots": raw.get("shots", 0),
+                "headshots": career_raw["headshots"],
-                "shots_hit": raw.get("shots_hit", 0),
-                "headshots": raw.get("headshots", 0),
-                "damage_dealt": raw.get("damage", 0),
             },
             "derived": derived,
+            "matches": matches,
         }
-        
-        from data.player_history import get_player_match_history
 
-        final_db["players"][pid]["matches"] = get_player_match_history(pid)
+    # ---------------------------------------------------------
+    # 3. Compute league averages (local-only)
+    # ---------------------------------------------------------
+    league_acc = defaultdict(float)
+    league_count = defaultdict(int)
 
-
-                   
-
-     # Compute all tag scores per player
     for pid, pdata in final_db["players"].items():
+        c = pdata["career"]
 
-        # 🔥 Skip players missing a career block
+        league_acc["KD"] += c["KD"]
-        if "career" not in pdata:
+        league_count["KD"] += 1
-            print("SKIPPING PLAYER WITH NO CAREER:", pid)
+
-            continue
+        league_acc["accuracy"] += c["accuracy"]
+        league_count["accuracy"] += 1
+
+        league_acc["kills_per_map"] += pdata["derived"]["kills_per_map"]
+        league_count["kills_per_map"] += 1
+
+        league_acc["damage"] += c["damage"]
+        league_count["damage"] += 1
+
+    league_averages = {
+        key: league_acc[key] / max(1, league_count[key])
+        for key in league_acc
+    }
+
+    final_db["league_averages"] = league_averages
+
+    # ---------------------------------------------------------
+    # 4. Compute all tags
+    # ---------------------------------------------------------
+    for pid, pdata in final_db["players"].items():
 
         # Slayer
         slayer_info = compute_slayer_for_career_player(pdata, league_averages)
@@ -224,34 +123,30 @@ def build_career_database(output_path):
         pdata["slayer_strength"] = slayer_info["strength"]
 
         # Sharpshooter
-        sharp = compute_sharpshooter_for_career_player(pdata["career"])
+        pdata["sharpshooter"] = compute_sharpshooter_for_career_player(pdata["career"])
-        pdata["sharpshooter"] = sharp
 
         # Payload Objective Specialist
-        obj_components = compute_payload_objective_for_career_player(pdata, league_averages)
+        pdata["objective_payload_components"] = compute_payload_objective_for_career_player(
-        pdata["objective_payload_components"] = obj_components
+            pdata, league_averages
-        
+        )
-        
-         # --- Consistency Tag ---
-        from tags.consistency import compute_consistency
-
-        matches = pdata.get("matches", [])
 
+        # Consistency
+        matches = pdata["matches"]
         consistency_raw, components = compute_consistency(matches)
-
         pdata["consistency_components"] = {
             **components,
             "consistency_raw": consistency_raw,
-            "consistency_norm": components.get("consistency_norm", consistency_raw)
+            "consistency_norm": components.get("consistency_norm", consistency_raw),
         }
-        
-        # --- Clutch Tag ---
-        from tags.clutch import compute_clutch
 
+        # Clutch
         pdata["clutch_components"] = {
             "clutch_norm": compute_clutch(pdata, league_averages)
         }
 
+    # ---------------------------------------------------------
+    # 5. Save DB
+    # ---------------------------------------------------------
     with open(output_path, "w", encoding="utf-8") as f:
         json.dump(final_db, f, indent=2)
 

data/player_history.py

@@ -1,106 +0,0 @@
-import os
-import json
-import requests
-
-from data.season_cache import fetch_season_match_list, BASE as API_BASE
-from data.match_cache import fetch_match_details
-
-CACHE_DIR = os.path.join(os.path.dirname(__file__), "cache")
-PLAYER_CACHE_DIR = os.path.join(CACHE_DIR, "players")
-SEASON_CACHE_DIR = os.path.join(CACHE_DIR, "seasons")
-
-os.makedirs(PLAYER_CACHE_DIR, exist_ok=True)
-os.makedirs(SEASON_CACHE_DIR, exist_ok=True)
-
-
-def _detect_cached_seasons():
-    out = []
-    for filename in os.listdir(SEASON_CACHE_DIR):
-        if filename.endswith("_matches.json"):
-            sid = filename.replace("_matches.json", "")
-            if sid.isdigit():
-                out.append(sid)
-    return out
-
-
-def _fetch_all_seasons_from_api():
-    try:
-        resp = requests.get(f"{API_BASE}/seasons", timeout=10)
-        resp.raise_for_status()
-        return resp.json()
-    except Exception as e:
-        print("ERROR fetching seasons:", e)
-        return []
-
-
-def get_player_match_history(player_id):
-    """Build full match history for a player using cached data."""
-
-    cache_path = os.path.join(PLAYER_CACHE_DIR, f"{player_id}_matches.json")
-
-    # --- Use cached player history if available ---
-    if os.path.exists(cache_path):
-        try:
-            with open(cache_path, "r", encoding="utf-8") as f:
-                return json.load(f)
-        except Exception as e:
-            print("ERROR reading cached player history:", e)
-
-    history = []
-
-    # --- Load cached seasons ---
-    seasons = _detect_cached_seasons()
-
-    # --- If no cached seasons exist, fetch them ---
-    if not seasons:
-        print("No cached seasons found. Fetching from API...")
-        season_list = _fetch_all_seasons_from_api()
-
-        for s in season_list:
-            sid = s.get("id")
-            if sid:
-                print(f"Caching season {sid} match list...")
-                fetch_season_match_list(sid)
-
-        seasons = _detect_cached_seasons()
-
-    # --- Process each season ---
-    for season_id in seasons:
-        print(f"Processing season {season_id}...")
-        match_list = fetch_season_match_list(season_id)
-
-        for match_entry in match_list:
-            match_id = match_entry.get("id")
-            if not match_id:
-                continue
-
-            match_data = fetch_match_details(match_id)
-            if not match_data:
-                continue
-
-            mode = match_data.get("mode", "").lower()
-
-            for p in match_data.get("players", []):
-                if p.get("id") == player_id:
-
-                    entry = {
-                        "kills": p.get("kills", 0),
-                        "deaths": p.get("deaths", 0),
-                        "accuracy": p.get("accuracy", 0.0),
-                        "damage": p.get("damage", 0),
-                        "push_time": p.get("PAY_PushTime", 0),
-                        "captures": p.get("DOM_captures", 0),
-                        "counters": p.get("DOM_counters", 0),
-                        "mode": mode
-                    }
-
-                    history.append(entry)
-
-    # --- Cache the built history ---
-    try:
-        with open(cache_path, "w", encoding="utf-8") as f:
-            json.dump(history, f, indent=2)
-    except Exception as e:
-        print("ERROR writing player history:", e)
-
-    return history

match_engine.py

@@ -5,7 +5,10 @@ import json
 import math
 
 from tags.objective_payload import compute_payload_objective_team_scores
-from tags.objective_domination import compute_dom_objective
+from tags.objective_domination import (
+    compute_dom_objective_for_match_player,
+    compute_dom_objective_team_scores,
+)
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 CAREER_DB_PATH = os.path.join(BASE_DIR, "career_stats.json")
@@ -44,7 +47,7 @@ def rank_match_players_slayer(match_players):
         ranked.append({
             "id": pid,
             "name": entry["name"],
-            "team": entry["team_history"][-1] if entry["team_history"] else p.get("team", "Unknown"),
+            "team": p.get("team", "Unknown"),
             "score_raw": score,
             "score_display": entry.get("slayer_score_display", f"{score:.2f}"),
         })
@@ -53,53 +56,82 @@ def rank_match_players_slayer(match_players):
     for i, r in enumerate(ranked, 1):
         r["rank"] = i
     return ranked
-    
+
-def rank_match_players_objdom(players):
+
+def rank_match_players_objdom(match_players):
     """
-    Rank players by Domination Objective Specialist score.
+    Rank players by Domination Objective Specialist score (match-based).
-    players = list of player_entry dicts for the current match.
+    Uses DOM_Captures and DOM_Counters from match player stats.
     """
 
-    ranked = []
-    for p in players:
-        score = compute_dom_objective(p)
-        ranked.append({
-            "name": p["name"],
-            "team": p["team"],
-            "score_raw": score,
-            "score_display": f"{score:.2f}",
-        })
-
-    # Sort high → low
-    ranked.sort(key=lambda x: x["score_raw"], reverse=True)
-
-    # Assign global rank
-    for i, entry in enumerate(ranked, start=1):
-        entry["rank"] = i
-
-    return ranked    
-
-
-def rank_match_players_objpl(match_players):
-    """
-    match_players: list of dicts from stats API (current season),
-    each with at least: id, name, team
-    Returns: list of ranked players with ObjPL score
-    """
     db = load_career_db()
     if db is None:
         return []
 
-    # Build team entries for ObjPL engine
     team_entries = []
     for p in match_players:
-        pid = p.get("id")
+        pid = (
+            p.get("id")
+            or p.get("PlayerUUID")
+            or p.get("uuid")
+        )
+        if not pid:
+            continue
+
         entry = _get_player_entry(db, pid)
         if not entry:
             continue
-        if "objective_payload_components" not in entry:
+
+        team_entries.append((pid, entry, p))
+
+    if not team_entries:
+        return []
+
+    obj_scores = compute_dom_objective_team_scores(team_entries)
+
+    ranked = []
+    for pid, entry, mp in team_entries:
+        score = obj_scores.get(pid)
+        if score is None:
             continue
-        team_entries.append((pid, entry))
+
+        ranked.append({
+            "id": pid,
+            "name": entry["name"],
+            "team": mp.get("team", "Unknown"),
+            "score_raw": score,
+            "score_display": f"{score:.2f}",
+        })
+
+    ranked.sort(key=lambda x: x["score_raw"], reverse=True)
+
+    for i, r in enumerate(ranked, 1):
+        r["rank"] = i
+
+    return ranked
+
+
+def rank_match_players_objpl(match_players):
+    db = load_career_db()
+    if db is None:
+        return []
+
+    team_entries = []
+    for p in match_players:
+        pid = (
+            p.get("id")
+            or p.get("PlayerUUID")
+            or p.get("uuid")
+        )
+        if not pid:
+            continue
+
+        entry = _get_player_entry(db, pid)
+        if not entry:
+            continue
+
+        # ObjPL is match-based; we still attach career entry for name/team history
+        team_entries.append((pid, entry, p))
 
     if not team_entries:
         return []
@@ -107,21 +139,24 @@ def rank_match_players_objpl(match_players):
     obj_scores = compute_payload_objective_team_scores(team_entries)
 
     ranked = []
-    for pid, entry in team_entries:
+    for pid, entry, mp in team_entries:
-        score = obj_scores.get(pid, 0.0)
+        score = obj_scores.get(pid)
-        if score <= 0:
+        if score is None:
             continue
+
         ranked.append({
             "id": pid,
             "name": entry["name"],
-            "team": entry["team_history"][-1] if entry["team_history"] else "Unknown",
+            "team": mp.get("team", "Unknown"),
             "score_raw": score,
             "score_display": f"{score:.2f}",
         })
 
     ranked.sort(key=lambda x: x["score_raw"], reverse=True)
+
     for i, r in enumerate(ranked, 1):
         r["rank"] = i
+
     return ranked
 
 
@@ -137,7 +172,6 @@ def split_two_columns(ranked_players, team_a_name, team_b_name):
         elif p["team"] == team_b_name:
             right.append(p)
         else:
-            # If team name mismatch, leave them out of columns
             pass
     return left, right
 
@@ -168,7 +202,8 @@ def compute_slayer_prediction(team_a_players, team_b_players):
         "teamA_win": round(pA * 100),
         "teamB_win": round(pB * 100),
     }
-    
+
+
 def rank_match_players_sharpshooter(match_players):
     db = load_career_db()
     if db is None:
@@ -189,7 +224,7 @@ def rank_match_players_sharpshooter(match_players):
         ranked.append({
             "id": pid,
             "name": entry["name"],
-            "team": entry["team_history"][-1] if entry["team_history"] else p.get("team", "Unknown"),
+            "team": p.get("team", "Unknown"),
             "score_raw": score,
             "score_display": sharp.get("score_display", f"{score:.2f}"),
         })
@@ -199,7 +234,8 @@ def rank_match_players_sharpshooter(match_players):
         r["rank"] = i
 
     return ranked
-    
+
+
 def rank_match_players_clutch(players):
     ranked = []
     for p in players:
@@ -216,5 +252,4 @@ def rank_match_players_clutch(players):
     for i, r in enumerate(ranked, start=1):
         r["rank"] = i
 
-    return ranked
+    return ranked
-    

predictions/prediction_engine.py

@@ -1,8 +1,8 @@
 import math
-from tags.objective_domination import compute_dom_objective
+from tags.objective_domination import compute_dom_objective_for_career_player
 
 def logistic_win_chance(teamA_avg, teamB_avg, k=1.1):
-    """Generic logistic win chance for non-Slayer tags."""
+    """Generic logistic win chance for all tags."""
     if max(teamA_avg, teamB_avg) == 0:
         return 50, 50
 
@@ -14,19 +14,24 @@ def logistic_win_chance(teamA_avg, teamB_avg, k=1.1):
 
 
 def compute_tag_averages(teamA_players, teamB_players):
-    """Compute all tag averages for both teams."""
+    """Compute all tag averages for both teams using career identity scores."""
 
     def avg_slayer(players):
         return sum(p.get("slayer_score_raw", 0.0) for p in players) / max(1, len(players))
 
     def avg_objpl(players):
         return sum(
-            p.get("objective_payload_components", {}).get("push_time_norm", 0.0)
+            p.get("objective_payload_components", {}).get("payload_score_raw", 0.0)
             for p in players
         ) / max(1, len(players))
 
     def avg_objdom(players):
-        return sum(compute_dom_objective(p) for p in players) / max(1, len(players))
+        return sum(
+            compute_dom_objective_for_career_player(
+                p.get("career_entry", {})
+            ).get("dom_score_raw", 0.0)
+            for p in players
+        ) / max(1, len(players))
 
     def avg_sharp(players):
         return sum(
@@ -57,7 +62,7 @@ def compute_tag_averages(teamA_players, teamB_players):
 
 
 def generate_predictions(teamA_players, teamB_players):
-    """Full Option D prediction engine."""
+    """Full Option D prediction engine (modernized)."""
 
     team_a = teamA_players[0].get("team", "Team A")
     team_b = teamB_players[0].get("team", "Team B")
@@ -67,14 +72,8 @@ def generate_predictions(teamA_players, teamB_players):
 
     # --- Per-tag win chances ---
     per_tag = {}
-
     for tag, (a_avg, b_avg) in avgs.items():
-        if tag == "Slayer":
+        per_tag[tag] = logistic_win_chance(a_avg, b_avg)
-            # Slayer uses its own prediction function
-            # Convert to simple logistic for consistency
-            per_tag[tag] = logistic_win_chance(a_avg, b_avg)
-        else:
-            per_tag[tag] = logistic_win_chance(a_avg, b_avg)
 
     # --- Weighted overall prediction ---
     weights = {

rankings.py

@@ -41,24 +41,20 @@ def top_players_by_tag(tag_name):
         return results
 
     if tag_name == "Payload Objective Specialist":
-        # For rankings, we approximate team context by using league‑wide push components
-        # and treat all players as if they were on one "virtual team".
-        team_entries = [(pid, p) for pid, p in players.items()]
-        obj_scores = compute_payload_objective_team_scores(team_entries)
-
         for pid, p in players.items():
-            score = obj_scores.get(pid, 0.0)
+            comp = p.get("objective_payload_components", {})
+            score = comp.get("payload_score_raw", 0.0)
             if score <= 0:
                 continue
+
             results.append({
                 "id": pid,
                 "name": p["name"],
                 "team": p["team_history"][-1] if p["team_history"] else "Unknown",
                 "score_raw": score,
-                "score_display": f"{score:.2f}",
+                "score_display": comp.get("payload_score_display", f"{score:.2f}"),
             })
 
-        results = [r for r in results if r["score_raw"] > 0]
         results.sort(key=lambda x: x["score_raw"], reverse=True)
         return results
 
@@ -105,4 +101,68 @@ def rank_match_players_clutch(players):
     for i, r in enumerate(ranked, start=1):
         r["rank"] = i
 
+    return ranked
+    
+    
+def rank_match_players_objpl(players):
+    """
+    Rank players by match-based Payload Objective Specialist score.
+    Uses match stats only (PAY_PushTime, damage, deaths).
+    """
+
+    db = load_career_db()
+    if db is None:
+        return []
+
+    team_entries = []
+    for p in players:
+        pid = (
+            p.get("id")
+            or p.get("PlayerUUID")
+            or p.get("uuid")
+        )
+        if not pid:
+            continue
+
+        career_entry = db["players"].get(pid, {})
+
+        # Normalize match keys
+        push = p.get("PAY_PushTime", 0) or 0
+        dmg = p.get("damage", p.get("Damage", 0)) or 0
+        deaths = p.get("deaths", p.get("Deaths", 0)) or 0
+
+        # Only include players with actual payload participation
+        if push <= 0:
+            continue
+
+        # Attach normalized values back to match player
+        p["PAY_PushTime"] = push
+        p["damage"] = dmg
+        p["deaths"] = deaths
+
+        team_entries.append((pid, career_entry, p))
+
+    if not team_entries:
+        return []
+
+    # Compute match-based ObjPL
+    obj_scores = compute_payload_objective_team_scores(team_entries)
+
+    ranked = []
+    for pid, entry, mp in team_entries:
+        score = obj_scores.get(pid, 0.0)
+
+        ranked.append({
+            "id": pid,
+            "name": entry.get("name", mp.get("name", "Unknown")),
+            "team": mp.get("team", "Unknown"),
+            "score_raw": score,
+            "score_display": f"{score:.2f}",
+        })
+
+    ranked.sort(key=lambda x: x["score_raw"], reverse=True)
+
+    for i, r in enumerate(ranked, 1):
+        r["rank"] = i
+
     return ranked

storylines.py

@@ -1,5 +1,9 @@
 from rankings import load_career_db
-from tags.objective_domination import compute_dom_objective
+from tags.objective_domination import (
+    compute_dom_objective_for_career_player,
+    compute_dom_objective_for_match_player,
+    compute_dom_objective_team_scores
+)
 
 
 def matchup_storyline(teamA_players, teamB_players):
@@ -17,7 +21,7 @@ def matchup_storyline(teamA_players, teamB_players):
 
     def avg_obj_payload(players):
         return sum(
-            p.get("objective_payload_components", {}).get("push_time_norm", 0.0)
+            p.get("objective_payload_components", {}).get("payload_score_raw", 0.0)
             for p in players
         ) / max(1, len(players))
 
@@ -28,7 +32,12 @@ def matchup_storyline(teamA_players, teamB_players):
         ) / max(1, len(players))
 
     def avg_obj_dom(players):
-        return sum(compute_dom_objective(p) for p in players) / max(1, len(players))
+        return sum(
+            compute_dom_objective_for_career_player(
+                p.get("career_entry", {})
+            ).get("dom_score_raw", 0.0)
+            for p in players
+        ) / max(1, len(players))
         
     def avg_consistency(players):
         return sum(

tags/consistency.py

@@ -2,89 +2,61 @@ import math
 
 def compute_consistency(matches):
     """
-    matches = list of dicts, each containing:
+    Compute consistency based on per-match performance stability.
-        - kills
+    Uses:
-        - deaths
+      - KD per match
-        - accuracy
+      - Damage per match
-        - damage
+      - Accuracy per match
-        - push_time (payload)
-        - captures
-        - counters
-        - mode
     """
 
-    # --- Not enough matches to compute variance ---
+    if not matches:
-    if len(matches) < 2:
         return 0.0, {
-            "per_match_perf": [],
+            "match_count": 0,
-            "floor": 0.0,
+            "kd_std": 0.0,
-            "variance": 0.0,
+            "dmg_std": 0.0,
-            "variance_adj": 0.0,
+            "acc_std": 0.0,
-            "variance_score": 0.0,
+            "consistency_norm": 0.0,
-            "avg_perf": 0.0,
-            "consistency_norm": 0.0
         }
 
-    per_match_perf_list = []
+    kds = []
+    dmgs = []
+    accs = []
 
     for m in matches:
+        kills = m["Kills"]
+        deaths = m["Deaths"]
+        damage = m["Damage"]
+        shots = m["Shots"]
+        shots_hit = m["ShotsHit"]
 
-        # --- Slayer percentile (per match) ---
+        kd = kills / max(1, deaths)
-        kd = m["kills"] / max(1, m["deaths"])
+        acc = shots_hit / shots if shots > 0 else 0.0
-        slayer_pct = percentile_of_kd(kd)
 
-        # --- Objective percentile (per match) ---
+        kds.append(kd)
-        if m["mode"] == "payload":
+        dmgs.append(damage)
-            obj_raw = m["push_time"]
+        accs.append(acc)
-        elif m["mode"] == "domination":
-            obj_raw = m["captures"] + m["counters"]
-        else:
-            obj_raw = 0
 
-        objective_pct = percentile_of_objective(obj_raw)
+    def std(values):
+        if len(values) <= 1:
+            return 0.0
+        mean = sum(values) / len(values)
+        var = sum((v - mean) ** 2 for v in values) / len(values)
+        return math.sqrt(var)
 
-        # --- Accuracy percentile (per match) ---
+    kd_std = std(kds)
-        accuracy_pct = percentile_of_accuracy(m["accuracy"])
+    dmg_std = std(dmgs)
+    acc_std = std(accs)
 
-        # --- Composite per-match performance ---
+    # Lower variance = more consistent
-        perf = (
+    # Normalize into a 0–1 score
-            slayer_pct * 0.40 +
+    raw = 1.0 / (1.0 + kd_std + dmg_std + acc_std)
-            objective_pct * 0.40 +
-            accuracy_pct * 0.20
-        )
 
-        per_match_perf_list.append(perf)
-
-    # --- Floor ---
-    floor_score = percentile(min(per_match_perf_list))
-
-    # --- Variance (normalized by match count) ---
-    raw_variance = variance(per_match_perf_list)
-    adj_variance = raw_variance * (1 + (1 / len(matches)))
-    variance_score = 1 / (1 + adj_variance)
-
-    # --- Average performance ---
-    avg_perf = sum(per_match_perf_list) / len(per_match_perf_list)
-
-    # --- Final Consistency ---
-    consistency_raw = (
-        floor_score * 0.40 +
-        variance_score * 0.40 +
-        avg_perf * 0.20
-    )
-
-    # --- Normalized consistency (already 0–1) ---
-    consistency_norm = round(consistency_raw, 4)
-
-    # --- Component block returned to DB builder ---
     components = {
-        "per_match_perf": per_match_perf_list,
+        "match_count": len(matches),
-        "floor": floor_score,
+        "kd_std": kd_std,
-        "variance": raw_variance,
+        "dmg_std": dmg_std,
-        "variance_adj": adj_variance,
+        "acc_std": acc_std,
-        "variance_score": variance_score,
+        "consistency_norm": raw,
-        "avg_perf": avg_perf,
-        "consistency_norm": consistency_norm
     }
 
-    return consistency_norm, components
+    return raw, components

tags/objective_domination.py

@@ -1,29 +1,140 @@
 import math
 
-def compute_dom_objective(player_entry):
+# ---------------------------------------------------------
+# 1. CAREER DOMINATION OBJECTIVE SPECIALIST (ObjDOM)
+# ---------------------------------------------------------
+
+def compute_dom_objective_for_career_player(pdata, league_averages=None):
     """
-    Domination Objective Specialist (ObjDOM)
+    Compute a career Domination Objective score using ONLY Domination stats.
 
-    Uses only website stats:
+    A "Domination map" is any match where DOM_Captures > 0 or DOM_Counters > 0.
-      - DOM_captures
+    Payload and Control Point stats are ignored.
-      - DOM_counters
-
-    Log-scaled normalization:
-        CapRate      = log(1 + captures) / log(1 + 20)
-        CounterRate  = log(1 + counters) / log(1 + 20)
-
-    Formula (0–1 scale):
-        ObjDOM = 0.40 * CapRate + 0.60 * CounterRate
-
-    Counters are weighted more heavily because they prevent enemy scoring.
     """
 
-    caps = player_entry.get("DOM_captures", 0) or 0
+    matches = pdata.get("matches", [])
-    counters = player_entry.get("DOM_counters", 0) or 0
+    if not matches:
+        return {
+            "dom_matches": 0,
+            "total_captures": 0,
+            "total_counters": 0,
+            "avg_captures": 0.0,
+            "avg_counters": 0.0,
+            "dom_score_raw": 0.0,
+            "dom_score_display": "0.00",
+        }
 
-    # Log-scaled normalization (smooths extremes, expands mid-range)
+    dom_matches = 0
-    cap_rate = math.log1p(caps) / math.log1p(20)
+    total_caps = 0
-    counter_rate = math.log1p(counters) / math.log1p(20)
+    total_counters = 0
 
-    score = (0.40 * cap_rate) + (0.60 * counter_rate)
+    for m in matches:
-    return round(score, 3)
+        caps = m.get("DOM_Captures", 0) or 0
+        counters = m.get("DOM_Counters", 0) or 0
+
+        # Only count Domination maps
+        if caps > 0 or counters > 0:
+            dom_matches += 1
+            total_caps += caps
+            total_counters += counters
+
+    if dom_matches == 0:
+        return {
+            "dom_matches": 0,
+            "total_captures": 0,
+            "total_counters": 0,
+            "avg_captures": 0.0,
+            "avg_counters": 0.0,
+            "dom_score_raw": 0.0,
+            "dom_score_display": "0.00",
+        }
+
+    avg_caps = total_caps / dom_matches
+    avg_counters = total_counters / dom_matches
+
+    # Log-scaled normalization (smooth extremes, expand mid-range)
+    # Soft caps: ~20 caps / 20 counters across career
+    cap_rate = math.log1p(avg_caps) / math.log1p(20.0)
+    counter_rate = math.log1p(avg_counters) / math.log1p(20.0)
+
+    # Counters weighted more heavily (deny enemy scoring)
+    raw = (0.40 * cap_rate) + (0.60 * counter_rate)
+
+    return {
+        "dom_matches": dom_matches,
+        "total_captures": total_caps,
+        "total_counters": total_counters,
+        "avg_captures": avg_caps,
+        "avg_counters": avg_counters,
+        "dom_score_raw": raw,
+        "dom_score_display": f"{raw:.2f}",
+    }
+
+
+# ---------------------------------------------------------
+# 2. MATCH-BASED DOMINATION OBJECTIVE SPECIALIST
+# ---------------------------------------------------------
+
+def compute_dom_objective_for_match_player(p):
+    """
+    Extract match-level Domination stats for a single player.
+    Used only for match-based ObjDOM.
+    """
+
+    caps = p.get("DOM_Captures", 0) or 0
+    counters = p.get("DOM_Counters", 0) or 0
+
+    return {
+        "captures": caps,
+        "counters": counters,
+    }
+
+
+# ---------------------------------------------------------
+# 3. TEAM SCORE CALCULATION (MATCH-BASED ObjDOM)
+# ---------------------------------------------------------
+
+def compute_dom_objective_team_scores(team_entries):
+    """
+    team_entries = list of (pid, entry, match_player)
+    entry = career DB entry (ignored for match ObjDOM)
+    match_player = match player dict with DOM stats
+    """
+
+    # Total team captures / counters
+    team_caps = sum(mp.get("DOM_Captures", 0) or 0 for _, _, mp in team_entries)
+    team_counters = sum(mp.get("DOM_Counters", 0) or 0 for _, _, mp in team_entries)
+
+    # Avoid division by zero
+    if team_caps <= 0:
+        team_caps = 1
+    if team_counters <= 0:
+        team_counters = 1
+
+    scores = {}
+
+    for pid, entry, mp in team_entries:
+        caps = mp.get("DOM_Captures", 0) or 0
+        counters = mp.get("DOM_Counters", 0) or 0
+
+        # 1. Presence on objective (share of team captures)
+        cap_presence = caps / team_caps
+
+        # 2. Counter presence (share of team counters)
+        counter_presence = counters / team_counters
+
+        # 3. Log scaling to smooth extremes
+        cap_rate = math.log1p(caps) / math.log1p(10.0)      # per-map soft cap ~10 caps
+        counter_rate = math.log1p(counters) / math.log1p(10.0)
+
+        # Blend presence + impact
+        # Captures: both presence + rate
+        # Counters: weighted more heavily (deny enemy scoring)
+        cap_component = 0.5 * cap_presence + 0.5 * cap_rate
+        counter_component = 0.5 * counter_presence + 0.5 * counter_rate
+
+        score = (0.40 * cap_component) + (0.60 * counter_component)
+
+        scores[pid] = round(score, 4)
+
+    return scores

tags/objective_payload.py

@@ -1,79 +1,158 @@
 import math
 
+# ---------------------------------------------------------
+# 1. CAREER PAYLOAD OBJECTIVE SPECIALIST (MODERNIZED)
+# ---------------------------------------------------------
 
-def compute_payload_objective_for_career_player(p, league_averages):
+def compute_payload_objective_for_career_player(pdata, league_averages=None):
-    career = p["career"]
+    """
-    derived = p["derived"]
+    Modernized career Payload Objective Specialist score.
+    Mirrors the structure of Domination's career tag.
 
-    push_time_total = career.get("push_time", 0) or 0
+    Components:
-    damage = career.get("damage", 0) or 0
+    - PresenceNorm: fraction of matches that were Payload
-    deaths = career.get("deaths", 0) or 0
+    - PushNorm: average push time normalized to 300s soft cap
-    push_time_per_season = derived.get("push_time_per_season", 0) or 0
+    - PSINorm: damage-per-death survivability normalized
 
-    # Push Survivability Index (PSI)
+    Final score:
-    # PSI = Damage / (Deaths + 1) * 1 / sqrt(PushTime + 1)
+        0.40 * PushNorm
-    psi = 0.0
+      + 0.40 * PresenceNorm
-    if push_time_total > 0:
+      + 0.20 * PSINorm
-        psi = (damage / (deaths + 1)) * (1.0 / math.sqrt(push_time_total + 1))
+    """
 
-    league_push_time_per_season = league_averages.get("push_time_per_season", 1.0)
+    matches = pdata.get("matches", [])
-    push_time_norm = push_time_per_season / league_push_time_per_season if league_push_time_per_season > 0 else 0.0
+    if not matches:
+        return {
+            "payload_matches": 0,
+            "total_push_time": 0.0,
+            "avg_push_time": 0.0,
+            "presence_norm": 0.0,
+            "push_norm": 0.0,
+            "psi_norm": 0.0,
+            "payload_score_raw": 0.0,
+            "payload_score_display": "0.00",
+        }
+
+    total_matches = len(matches)
+    payload_matches = 0
+    total_push = 0.0
+    total_damage = 0.0
+    total_deaths = 0
+
+    for m in matches:
+        push = m.get("PAY_PushTime", 0) or 0
+        dmg = m.get("Damage", 0) or 0
+        deaths = m.get("Deaths", 0) or 0
+
+        if push > 0:
+            payload_matches += 1
+            total_push += push
+            total_damage += dmg
+            total_deaths += deaths
+
+    if payload_matches == 0:
+        return {
+            "payload_matches": 0,
+            "total_push_time": 0.0,
+            "avg_push_time": 0.0,
+            "presence_norm": 0.0,
+            "push_norm": 0.0,
+            "psi_norm": 0.0,
+            "payload_score_raw": 0.0,
+            "payload_score_display": "0.00",
+        }
+
+    # --- PresenceNorm ---
+    presence_norm = payload_matches / total_matches
+
+    # --- PushNorm ---
+    avg_push = total_push / payload_matches
+    push_norm = min(avg_push / 300.0, 1.0)
+
+    # --- PSINorm ---
+    psi_raw = total_damage / (total_deaths + 1)
+    psi_norm = psi_raw / (psi_raw + 300.0)
+
+    # --- Final Score ---
+    raw = (0.40 * push_norm) + (0.40 * presence_norm) + (0.20 * psi_norm)
 
     return {
-        "push_time_total": push_time_total,
+        "payload_matches": payload_matches,
-        "push_time_per_season": push_time_per_season,
+        "total_push_time": total_push,
-        "push_time_norm": push_time_norm,
+        "avg_push_time": avg_push,
-        "psi": psi,
+        "presence_norm": presence_norm,
+        "push_norm": push_norm,
+        "psi_norm": psi_norm,
+        "payload_score_raw": raw,
+        "payload_score_display": f"{raw:.2f}",
     }
 
 
-def compute_payload_objective_team_scores(team_player_entries):
+# ---------------------------------------------------------
-    """
+# 2. MATCH-BASED PAYLOAD OBJECTIVE SPECIALIST (MODERNIZED)
-    team_player_entries: list of (player_id, player_entry_from_career_db)
+# ---------------------------------------------------------
 
-    Returns dict {pid: obj_pl_score}
+def compute_payload_objective_for_match_player(p):
     """
-    # PresencePL: share of team push time
+    Extract match-level Payload stats for a single player.
-    total_push_time = sum(p["objective_payload_components"]["push_time_total"]
+    Normalized naming for consistency.
-                          for _, p in team_player_entries)
+    """
-    if total_push_time <= 0:
+
-        total_push_time = 1.0
+    push = p.get("PAY_PushTime", 0) or 0
+    dmg = p.get("damage", p.get("Damage", 0)) or 0
+    deaths = p.get("deaths", p.get("Deaths", 0)) or 0
+
+    return {
+        "push_time": push,
+        "damage": dmg,
+        "deaths": deaths,
+    }
+
+
+# ---------------------------------------------------------
+# 3. TEAM SCORE CALCULATION (MATCH-BASED, MODERNIZED)
+# ---------------------------------------------------------
+
+def compute_payload_objective_team_scores(team_entries):
+    """
+    team_entries = list of (pid, career_entry, match_player)
+
+    Components:
+    - Presence: push_time / team_total_push
+    - PushNorm: push_time / 300s soft cap
+    - PSINorm: damage-per-death normalized
+
+    Final score:
+        0.40 * Presence
+      + 0.40 * PushNorm
+      + 0.20 * PSINorm
+    """
+
+    # Total team push time
+    team_push = sum(mp.get("PAY_PushTime", 0) or 0 for _, _, mp in team_entries)
+    if team_push <= 0:
+        team_push = 1  # avoid div-by-zero
 
     scores = {}
-    for pid, pdata in team_player_entries:
-        comp = pdata["objective_payload_components"]
-        presence_pl = comp["push_time_total"] / total_push_time
 
-        push_time_norm = comp["push_time_norm"]
+    for pid, entry, mp in team_entries:
-        psi = comp["psi"]
+        push = mp.get("PAY_PushTime", 0) or 0
+        dmg = mp.get("damage", mp.get("Damage", 0)) or 0
+        deaths = mp.get("deaths", mp.get("Deaths", 0)) or 0
 
-        # Normalize PSI within team to avoid extreme outliers dominating
+        # --- Presence ---
-        # We'll compute team PSI mean and std first
+        presence = push / team_push
-        scores[pid] = {
-            "presence_pl": presence_pl,
-            "push_time_norm": push_time_norm,
-            "psi": psi,
-        }
 
-    # PSI normalization within team
+        # --- PushNorm ---
-    psi_values = [v["psi"] for v in scores.values()]
+        push_norm = min(push / 300.0, 1.0)
-    if psi_values:
-        mean_psi = sum(psi_values) / len(psi_values)
-        var_psi = sum((x - mean_psi) ** 2 for x in psi_values) / max(1, len(psi_values))
-        std_psi = math.sqrt(var_psi)
-    else:
-        mean_psi = 0.0
-        std_psi = 1.0
 
-    for pid, v in scores.items():
+        # --- PSINorm ---
-        if std_psi > 0:
+        psi_raw = dmg / (deaths + 1)
-            psi_norm = (v["psi"] - mean_psi) / std_psi
+        psi_norm = psi_raw / (psi_raw + 300.0)
-        else:
-            psi_norm = 0.0
 
-        # Final ObjPL score (website‑only version)
+        # --- Final Score ---
-        # 0.40 Presence + 0.40 PushTimeNorm + 0.20 PSI_norm
+        score = (0.40 * presence) + (0.40 * push_norm) + (0.20 * psi_norm)
-        obj_pl = (0.40 * v["presence_pl"]) + (0.40 * v["push_time_norm"]) + (0.20 * psi_norm)
-        v["obj_pl"] = obj_pl
 
-    return {pid: v["obj_pl"] for pid, v in scores.items()}
+        scores[pid] = round(score, 4)
+
+    return scores

team_identity.py

@@ -1,5 +1,9 @@
 from rankings import load_career_db
-from tags.objective_domination import compute_dom_objective
+from tags.objective_domination import (
+    compute_dom_objective_for_career_player,
+    compute_dom_objective_for_match_player,
+    compute_dom_objective_team_scores
+)
 
 
 def generate_team_identity(player_list):
@@ -17,7 +21,7 @@ def generate_team_identity(player_list):
 
     def avg_payload(players):
         return sum(
-            p.get("objective_payload_components", {}).get("push_time_norm", 0.0)
+            p.get("objective_payload_components", {}).get("payload_score_raw", 0.0)
             for p in players
         ) / max(1, len(players))
 
@@ -40,7 +44,12 @@ def generate_team_identity(player_list):
         ) / max(1, len(players))
 
     def avg_objdom(players):
-        return sum(compute_dom_objective(p) for p in players) / max(1, len(players))
+        return sum(
+            compute_dom_objective_for_career_player(
+                p.get("career_entry", {})
+            ).get("dom_score_raw", 0.0)
+            for p in players
+        ) / max(1, len(players))
 
     # --- Compute identity scores ---
     identity = {