From c8f043155aaf7d3ca39734c727cb3e98027168e0 Mon Sep 17 00:00:00 2001 From: FireHorse Date: Thu, 9 Apr 2026 20:11:39 +1000 Subject: [PATCH] Modernize tag system, match engine, OBS exports, and output folder architecture --- .gitignore | 30 +- __pycache__/match_engine.cpython-313.pyc | Bin 8330 -> 8774 bytes __pycache__/rankings.cpython-313.pyc | Bin 4739 -> 6568 bytes __pycache__/storylines.cpython-313.pyc | Bin 6904 -> 7191 bytes __pycache__/team_identity.cpython-313.pyc | Bin 6185 -> 6472 bytes career_stats.json | 435672 +++++++++++---- config.json | 95 +- dashleague_cast_tool.py | 249 +- data/__pycache__/career_db.cpython-313.pyc | Bin 8517 -> 4821 bytes data/career_db.py | 295 +- data/player_history.py | 106 - match_engine.py | 127 +- .../prediction_engine.cpython-313.pyc | Bin 7691 -> 7841 bytes predictions/prediction_engine.py | 25 +- rankings.py | 76 +- storylines.py | 15 +- tags/__pycache__/consistency.cpython-313.pyc | Bin 1991 -> 1990 bytes .../objective_domination.cpython-313.pyc | Bin 1276 -> 4023 bytes .../objective_payload.cpython-313.pyc | Bin 3170 -> 4171 bytes tags/consistency.py | 112 +- tags/objective_domination.py | 153 +- tags/objective_payload.py | 195 +- team_identity.py | 15 +- 23 files changed, 326883 insertions(+), 110282 deletions(-) delete mode 100644 data/player_history.py diff --git a/.gitignore b/.gitignore index cbe5f61..532d13c 100644 --- a/.gitignore +++ b/.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 -.vscode/ -.idea/ - -# OS -.DS_Store -Thumbs.db - -# Our caches -stats/data/cache/ -cache/ - -# Ignore OBS output folder -../stats/ +*.db \ No newline at end of file diff --git a/__pycache__/match_engine.cpython-313.pyc b/__pycache__/match_engine.cpython-313.pyc index 42dbb2fc21bf5a6876abff8af60195025fd1421c..3b4e2f8d353c19bf327662fd735fe4269b682fe1 100644 GIT binary patch delta 3227 zcmeHJZERcB89v8f*S;V2z5cwmllZ2X6t`*ULYsb(tx1zo8bVoa&4QNAxQQX@h#j5l zRBfeUvmq@hLo_>^8nHi>_%SLr2^0w=gpl~dAEu2e+Q?i4LF%OaSr-~arA?FezSnVa zQpPli{hJj(KIc8}IXUNjpZDWJ-@60;bwv>gd@A4mVCo<71;0-H3)}A#j|)sN$QT(> z7cOkCzz^zd{)|L6&v7;;?WJ{TJ$eo%a$vb6Zvgz9EsLPq(bSDT4n4SdbWU48^ zL^HjBLDL5qGDX0!DFH@I88B)pfHB@p_w+bw9pk0xnb~q7H&L3-l}?>5jF+d*6mn0L zSZ+FB9-qw3Je5CJV72xpJ$`~lm!76$blW$M92|Rum2j6eb|i0=r;1OG^|MlbVjLzX zOQomyXO4Dv5bmliC7d#C)nHiDa!(e@8z<)q#WFj`;vm=g5!VQPmj5-@NuTB)%MbHO zUFR8hFHh-F{(g&0JzR6gQr@h`^aV)61g6;KZkW*^!&UzMcmO|0J${u@>2?|Dxu;mD=Ig~7#+ z%bDeHC-3u(wj@Afe1lssBm_@%!gqbYgWrhlq-8K{TJ5Fx;39k`GVmV>Z_{ION5hIu ztKhHtYOAxcT6ASyw|9g6hKC8^hxNN@p1-Z@!O_92bI)yMaL5@xuIy8efwAEpGlNBN zA2;-HBI~++qA%+k$CKcFSz$!rpZG$OE9*90Zn$CA3+jm;LEyhBnv%@{#eOWFCYW;LoF)#uGWv?CY~p~BCK>XFY($EEkw%Ji$uShP}(Ehmc{F^JS|`Waz}2 z_=xS*Z1%x{nrn7;YGQ+6u*TXE?m|cdfH!$WN+;3e44g&S4uA&naNfyJO1mqkaoB}` z95~7D#bGzXeE=DOHPH~IdlAsStOo(fdqrmZacmP*E*J9Cc63e^EOrp(I>I4P zXN{NO>FfXs9O>y9c0aE7BG_b>VbtI<{@Q#_iNFMcN>-ZxT8&p$Ly_u3*ObH@U6qv= z2ImJark@+SMI1`&NwfkAW$keF_^P1< zszMRbWI zx349LTl&e+GecLyov%(*hpxH~UFSEF?f;KL$e|liIQ2C|;lp;!u){za)`w6}#yDz< z6&{dwd}Wlp5xDp9U*e|!HcBvmei0>X0H)TR2}^AESvSwIxq?*^jmZ(T+g_w$$p6&A6KEU>m3dA8&j` z1;fod`E*;HueAnG{~e}ZYK07~xuJ}*a=oUx>Rpvx$!-_1jE9ABKbEDsX||N5O$3Do zi&xtu`Zxa5wwHofIWs!Lo{9MpzQ_AgeIC0?iu}i^-Mtw0^-78ztKF0_ENJzRN2)XA zCqpZ;GYEFc#)Bao)>5(dDh$`0l+tw%q2e_n!QY?Bv0Dx&b*za*_VcvS-_cLIY4GN0 c+68!uDl~S}h*Qta{jD^(eweB>Xjl1v0a#>%M*si- delta 2855 zcma)8Z){sv6~EW^dwzb;et!NJKPPeWlD2ft(zI1mk0whYZPKwdp(D3up^Y$Z;w&zS z9qw~RTgIA+52QnA3wINtVp7=nuu2sQio_RSgAX83hiE9;QUhZWNQf^eX;D8hjdPyk z)fwIBmHd16opbN?JLmk)IrrBC&-LrK0|Ax5_}87^6)u^Jx=Gc=-3MfgjFAytY|=

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2 + }, + { + "MatchID": "1a42898b-a29b-44f4-a5dc-19ad04f11048", + "CycleID": 52, + "Kills": 27, + "Deaths": 45, + "Damage": 2864, + "Score": 7264, + "Headshots": 9, + "Shots": 531, + "ShotsHit": 94, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 15, + "PAY_PushTime": 0, + "DOM_Captures": 6, + "DOM_Counters": 2, + "Rounds": 2, + "TeamUUID": "0e91fcd6-6e63-42ec-af5a-897fd685be63", + "TeamColorID": 2 + }, + { + "MatchID": "31c6a3a2-0baa-493e-b478-bdd70b6079e2", + "CycleID": 61, + "Kills": 108, + "Deaths": 134, + "Damage": 12107, + "Score": 29777, + "Headshots": 51, + "Shots": 2540, + "ShotsHit": 449, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 52, + "PAY_PushTime": 48, + "DOM_Captures": 32, + "DOM_Counters": 4, + "Rounds": 4, + "TeamUUID": "ffc06913-bc46-4aee-861f-3705611ff7d3", + "TeamColorID": 1 + }, + { + "MatchID": "4a84b71d-664a-4569-84b5-a0d3e08db586", + "CycleID": 49, + "Kills": 2, + "Deaths": 9, + "Damage": 314, + "Score": 714, + "Headshots": 1, + "Shots": 109, + "ShotsHit": 14, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 0, + "PAY_PushTime": 0, + "DOM_Captures": 2, + "DOM_Counters": 0, + "Rounds": 1, + "TeamUUID": "0e91fcd6-6e63-42ec-af5a-897fd685be63", + "TeamColorID": 2 + }, + { + "MatchID": "532c15da-dde8-4f37-927b-d983a7cc8f18", + "CycleID": 51, + "Kills": 115, + "Deaths": 138, + "Damage": 13180, + "Score": 24850, + "Headshots": 49, + "Shots": 2528, + "ShotsHit": 432, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 14, + "PAY_PushTime": 28, + "DOM_Captures": 36, + "DOM_Counters": 14, + "Rounds": 3, + "TeamUUID": "0e91fcd6-6e63-42ec-af5a-897fd685be63", + "TeamColorID": 1 + }, + { + "MatchID": "60283f21-5c54-473d-9c14-ddeb7354bd06", + "CycleID": 60, + "Kills": 18, + "Deaths": 31, + "Damage": 2164, + "Score": 8464, + "Headshots": 7, + "Shots": 821, + "ShotsHit": 82, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 0, + "PAY_PushTime": 0, + "DOM_Captures": 27, + "DOM_Counters": 9, + "Rounds": 1, + "TeamUUID": "ffc06913-bc46-4aee-861f-3705611ff7d3", + "TeamColorID": 1 + }, + { + "MatchID": "c64e7c3c-a7cb-4e79-9e28-995a58379b5c", + "CycleID": 62, + "Kills": 19, + "Deaths": 10, + "Damage": 1718, + "Score": 4018, + "Headshots": 1, + "Shots": 217, + "ShotsHit": 31, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 0, + "PAY_PushTime": 0, + "DOM_Captures": 11, + "DOM_Counters": 1, + "Rounds": 1, + "TeamUUID": "ffc06913-bc46-4aee-861f-3705611ff7d3", + "TeamColorID": 2 + }, + { + "MatchID": "d032aca0-f85e-4954-9583-0a497736fbb7", + "CycleID": 50, + "Kills": 44, + "Deaths": 92, + "Damage": 5673, + "Score": 6633, + "Headshots": 16, + "Shots": 994, + "ShotsHit": 181, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 0, + "PAY_PushTime": 7, + "DOM_Captures": 4, + "DOM_Counters": 1, + "Rounds": 2, + "TeamUUID": "0e91fcd6-6e63-42ec-af5a-897fd685be63", + "TeamColorID": 1 + }, + { + "MatchID": "dfacf101-a405-4474-87c1-1b00caa1f8d1", + "CycleID": 50, + "Kills": 94, + "Deaths": 114, + "Damage": 9587, + "Score": 17607, + "Headshots": 33, + "Shots": 2363, + "ShotsHit": 380, + "CTF_Captures": 0, + "CTF_Returns": 0, + "CP_Captures": 32, + "PAY_PushTime": 32, + "DOM_Captures": 6, + "DOM_Counters": 1, + "Rounds": 4, + "TeamUUID": "0e91fcd6-6e63-42ec-af5a-897fd685be63", + "TeamColorID": 1 + } + ], + "slayer_score_raw": 2370.5046121593296, + "slayer_score_display": "2370.50", + "slayer_strength": "Elite Slayer", + "sharpshooter": { + "accuracy": 0.16431240317142076, + "hs_rate": 0.10759844703272324, + "dps": 4.81718764239497, + "score_raw": 1.0722018685606516, + "score_display": "1.07" + }, + "objective_payload_components": { + "payload_matches": 5, + "total_push_time": 191.0, + "avg_push_time": 38.2, + "presence_norm": 0.5555555555555556, + "push_norm": 0.12733333333333335, + "psi_norm": 0.21976592977893367, + "payload_score_raw": 0.31710874151134233, + "payload_score_display": "0.32" + }, + "consistency_components": { + "match_count": 9, + "kd_std": 0.4390988339581061, + "dmg_std": 4440.3667210359945, + "acc_std": 0.02561059474259941, + "consistency_norm": 0.0002251323616518653, + "consistency_raw": 0.0002251323616518653 + }, + "clutch_components": { + "clutch_norm": 0.42728113796507516 } } }, "league_averages": { - "KD": 0.9569344042838016, - "accuracy": 13.08515841142347, - "kills_per_map": 35.881002477300186, - "damage_per_map": 4037.545932232703, - "push_time_per_season": 292.7576974564926 + "KD": 0.9200868743316173, + "accuracy": 0.13098406713697494, + "kills_per_map": 68.9582241244039, + "damage": 142861.1649245064 } } \ No newline at end of file diff --git a/config.json b/config.json index d7377f1..7ae2d9a 100644 --- a/config.json +++ b/config.json @@ -1,49 +1,50 @@ { - "season": 10, - "teams": [ - "TANK", - "MEX", - "TBDi", - "EMRD", - "DMND", - "Doc!", - "HAXX", - "SWUA", - "ZTi", - "SLNT", - "HOBO", - "just", - "VRUS", - "FAM!", - "SKY", - "LMB0", - "F1R3", - "RVNG", - "DRGN", - "STHX", - "EMU" - ], - "tiers": { - "TANK": "Dasher", - "MEX": "Sprinter", - "TBDi": "Walker", - "EMRD": "Dasher", - "DMND": "Sprinter", - "Doc!": "Walker", - "HAXX": "Dasher", - "SWUA": "Sprinter", - "ZTi": "Walker", - "SLNT": "Dasher", - "HOBO": "Sprinter", - "just": "Walker", - "VRUS": "Dasher", - "FAM!": "Sprinter", - "SKY": "Walker", - "LMB0": "Dasher", - "F1R3": "Sprinter", - "RVNG": "Walker", - "DRGN": "Dasher", - "STHX": "Sprinter", - "EMU": "Walker" - } + "season": 10, + "teams": [ + "TANK", + "MEX", + "TBDi", + "EMRD", + "DMND", + "Doc!", + "HAXX", + "SWUA", + "ZTi", + "SLNT", + "HOBO", + "just", + "VRUS", + "FAM!", + "SKY", + "LMB0", + "F1R3", + "RVNG", + "DRGN", + "STHX", + "EMU" + ], + "tiers": { + "TANK": "Dasher", + "MEX": "Sprinter", + "TBDi": "Walker", + "EMRD": "Dasher", + "DMND": "Sprinter", + "Doc!": "Walker", + "HAXX": "Dasher", + "SWUA": "Sprinter", + "ZTi": "Walker", + "SLNT": "Dasher", + "HOBO": "Sprinter", + "just": "Walker", + "VRUS": "Dasher", + "FAM!": "Sprinter", + "SKY": "Walker", + "LMB0": "Dasher", + "F1R3": "Sprinter", + "RVNG": "Walker", + "DRGN": "Dasher", + "STHX": "Sprinter", + "EMU": "Walker" + }, + "output_root": "C:\\Projects\\Casting\\stats" } \ No newline at end of file diff --git a/dashleague_cast_tool.py b/dashleague_cast_tool.py index 615168e..05a47a6 100644 --- a/dashleague_cast_tool.py +++ b/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", "")) + \ - sorted(selected_b, key=lambda x: x.get("name", "")) + # Sort and combine + 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, "Counters.txt"), p.get("DOM_counters", "")) + 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", "")) - # Tag exports now come from real tag engines via rankings/career_db - if career_db is not None: - pid = p.get("id") - if pid and pid in career_db["players"]: - 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) + # Tag exports (use enriched match player dict) + map_type = self.map_var.get() + tags_text = self.generate_player_tags(p, 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") - if obj_pl is not None: - tags_out.append(f"ObjPL {obj_pl:.2f}") + pl_info = player_entry.get("objective_payload_components", {}) + pl_score = pl_info.get("payload_score_raw", 0.0) + 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) - tags_out.append(f"ObjDOM {obj_dom:.2f}") + dom_info = player_entry.get("objective_domination_components", {}) + 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) diff --git a/data/__pycache__/career_db.cpython-313.pyc b/data/__pycache__/career_db.cpython-313.pyc index 8672f92abde6cca952f3b45768ee290175f6a902..9cc0a8d5e1d00aa712417b6708fbacb5edee6664 100644 GIT binary patch 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compute_sharpshooter_for_career_player from tags.consistency import compute_consistency - - -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", []) +from tags.clutch import compute_clutch 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 = {} - league_accumulator = defaultdict(float) - league_counts = defaultdict(int) + final_db = {"players": {}, "league_averages": {}} - for season in seasons: - season_stats = fetch_season_stats(season) - if not isinstance(season_stats, list): + # --------------------------------------------------------- + # 2. Build per-player career stats from local DB + # --------------------------------------------------------- + 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: - if not isinstance(entry, dict): - continue - - # Normalize inconsistent keys - if "loss" in entry and "losses" not in entry: - entry["losses"] = entry["loss"] + # Aggregate raw career totals + career_raw = defaultdict(float) - if "shots" not in entry: - entry["shots"] = 0 + for m in matches: + 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: - entry["shots_hit"] = 0 - - if "headshots" not in entry: - 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 stats + maps = max(1, career_raw["maps"]) + KD = career_raw["kills"] / career_raw["deaths"] if career_raw["deaths"] > 0 else career_raw["kills"] + accuracy = (career_raw["shots_hit"] / career_raw["shots"]) if career_raw["shots"] > 0 else 0.0 derived = { - "kills_per_map": kills_per_map, - "deaths_per_map": deaths_per_map, - "push_time_per_season": push_time_per_season, + "kills_per_map": career_raw["kills"] / maps, + "deaths_per_map": career_raw["deaths"] / maps, + "push_time_per_season": career_raw["PAY_PushTime"], # no seasons now } final_db["players"][pid] = { - "name": pdata["name"], - "team_history": list(pdata["team_history"]), - "seasons_played": list(pdata["seasons_played"]), - "per_season": pdata["per_season"], + "name": name, "career": { - "kills": raw["kills"], - "deaths": raw["deaths"], + "kills": career_raw["kills"], + "deaths": career_raw["deaths"], "KD": KD, "accuracy": accuracy, - "push_time": raw["PAY_PushTime"], - "captures": raw["DOM_captures"], - "counters": raw["DOM_counters"], - "maps": raw["maps"], - "wins": raw["wins"], - "losses": raw["losses"], - "damage": raw["damage"], - "shots": raw.get("shots", 0), - "shots_hit": raw.get("shots_hit", 0), - "headshots": raw.get("headshots", 0), - "damage_dealt": raw.get("damage", 0), + "push_time": career_raw["PAY_PushTime"], + "captures": career_raw["DOM_captures"], + "counters": career_raw["DOM_counters"], + "maps": career_raw["maps"], + "damage": career_raw["damage"], + "shots": career_raw["shots"], + "shots_hit": career_raw["shots_hit"], + "headshots": career_raw["headshots"], }, "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 - if "career" not in pdata: - print("SKIPPING PLAYER WITH NO CAREER:", pid) - continue + league_acc["KD"] += c["KD"] + league_count["KD"] += 1 + + 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"] = sharp + pdata["sharpshooter"] = compute_sharpshooter_for_career_player(pdata["career"]) # Payload Objective Specialist - obj_components = compute_payload_objective_for_career_player(pdata, league_averages) - pdata["objective_payload_components"] = obj_components - - - # --- Consistency Tag --- - from tags.consistency import compute_consistency - - matches = pdata.get("matches", []) + pdata["objective_payload_components"] = compute_payload_objective_for_career_player( + pdata, league_averages + ) + # 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) diff --git a/data/player_history.py b/data/player_history.py deleted file mode 100644 index 463586d..0000000 --- a/data/player_history.py +++ /dev/null @@ -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 \ No newline at end of file diff --git a/match_engine.py b/match_engine.py index 3a08312..a07adea 100644 --- a/match_engine.py +++ b/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. - players = list of player_entry dicts for the current match. + Rank players by Domination Objective Specialist score (match-based). + 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: - score = obj_scores.get(pid, 0.0) - if score <= 0: + for pid, entry, mp in team_entries: + score = obj_scores.get(pid) + 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 - \ No newline at end of file + return ranked \ No newline at end of file diff --git a/predictions/__pycache__/prediction_engine.cpython-313.pyc b/predictions/__pycache__/prediction_engine.cpython-313.pyc index b36f033275f1e56e064bca09680c14e9a842d9db..51f686e4e5538a62e91f82ed44164c0d8ee28475 100644 GIT binary patch delta 1898 zcma)6OKclO7@qNZ*Uz=r@iTGKtX$FWM zZNvek6r_X!RY6dS#DQ``>VX5Lhn^5Z2$VyM6(M@6%BhJXq+SsJtQ`S`7XHSJKeD5cHVnr042xv~fksV(Y)piA!TQkM{)O1NtjTQ5$;_*p6Q_7yuQ{zRF%A|>| zlhjl$J)@JwnOj()Ni2ucxtv@|PZ(Xq0%s|fJCDML@N@|$QK^RGnzI0jCGv$|-=&NJj4?4f)ZN+Yl<&Cb(VPI99A>cdY`D5i(O2S%vP4NU;zOjwDeA{|t=Ej={70L3Rht zp}UCpJJ;+e)U<%tZAjd4Q}SKl7x{(4rQ^$zQsFG3=eEapF0;^msb|^Ky3Sj~wlx9t z?-^zacJF;q3cy0ynLaVW8VI~Bw{0{P^a;J@i0@%ZnL{hyx>e@HDs!UZfK>(Yl5^dO zL}>xP*Rybv_Aa~SRTnSCz*|R*{JplnJKN&_uaI=o6~Qg^OK*&R>WW4?S$ZB&y|QbU zin{Ye5DE2+n*l*&1YwN!nd#CB4kwGO}=||pryd8q- zeW(qOw8v$}QVZy?bi88=v)RR9Cxbo)k281%z_0_zHG+QrMQVs00hYvMO8|3dO~8B- zuL)K@w&oT9B--eEGa3M4hFzr%5@Ep_veHJs_I;>@5Sj*Yfnzu|m}K|Eq>;fyZQHO{ z<640?6sb19%BuoAHq{P~qf00VNk?rH(!4vc>ZGziLA`#7{#3VyZ&;763yu*?QsS=F`x?$}z*CT-z?4xG_ zEn=N1^_%d>KLLUpi-OIZC5Meqb31Hq_DDB*zqmHD2{}NL;#vsjr z6$VB)7rBN<>GjAq7_F5^0*_OrUImw5uV2|`ju_S!WjD!5FgWE9Y$VeaFjuo!6bme!rJJ_>h}N;^rkP` z;G6~F0jAYO6VazyZ#HlKcy?*_;^`ml(RuFN(RqC(y!Erha(MT=ZGk)QUgeP7ao2g} zYVY!_Jf}3*FAsRQ6}GnMP%F@MMC?xxu8`#5Ax#NVBp7I;P|A(|+3arp6(vxxt*ut2M3E zF?U3hqcq6B6YA#dS}mm#cZ;XYdYCaQEDLHr%}V%Xu>$m;h)L??cTInCx8A?QGL~0Q z4CGSj#6Tv?(yRhz^fE6pRRX9;Ez3+og$%IV#%+c=KCSO++&ABK%XDzTba2_}d$6tr z<7?q&%8--QAk+e=6aeOPYG4K0Gsn<#C;aQX00KlN7fGB*zN_?mDR{+pOR8Ux>aX=Z zPz#$auXyNYhf6y`0a#&@+%B^(+M^%#E!m?>Wzn0$7f;_*=Ucx#z0?_7EQ>7|Vyqa; z1!J)0f5onbM@;2Z=I5=|eB9*qHsK*260;9Xt;Q;}HzPRb?wYFT<5@cSsx`n5St{u^ zK4ytNl40J`WyEXRU?`-8(JDWlq#a0tJa3g~0~{stNRm6&-NyPYBv06mH`n1fjL?D* zLFhu*2cQ}N9=G zsk_TKkVW)wwI%SRGEp~36b%o$P&bX=t--OIRcUAEfmrrE- zAxKZ*A-3T|!k-*Ul@ zf*x&b0O1${UM`fetL4{eKhJu$Lrr?u(?XN{2af_ 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 \ No newline at end of file diff --git a/storylines.py b/storylines.py index f03833e..a8565bc 100644 --- a/storylines.py +++ b/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( diff --git a/tags/__pycache__/consistency.cpython-313.pyc b/tags/__pycache__/consistency.cpython-313.pyc index 170c8c5e954835f631cd98dc9c494dd71bd69e13..7d4079b8c55ae38304d9c826cf5bc590807a202b 100644 GIT binary patch literal 1990 zcmZ`)QE$^$6h5{S$F&nDPN0Rf8y-a}(bO^mEv%rz)~Z9RU=zj?QY$Lsm^5u-ch_!5 zw`q#Dm#`v{3OZHV9y;v>c$&05OxnwS0v%|}ygnfDF!imh)5P9(uG1uil^fr4zkBbw z$LF5&9cL>R69L;d@^6Ko!T?X`q|w-Iu$4rx3>s+67!c6dG!Q1wWVj8gaKU7@_2>}6 z&SBrWiL1+?!(qCLEr;*X7>&hV|8afcVeY<1Qmy(PmlrzpDSkdk4Wmg;)O)k3l0E%b@HYq^7>s`jd5BUDEXw1b5a 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matches to compute variance --- - if len(matches) < 2: + if not matches: return 0.0, { - "per_match_perf": [], - "floor": 0.0, - "variance": 0.0, - "variance_adj": 0.0, - "variance_score": 0.0, - "avg_perf": 0.0, - "consistency_norm": 0.0 + "match_count": 0, + "kd_std": 0.0, + "dmg_std": 0.0, + "acc_std": 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 = m["kills"] / max(1, m["deaths"]) - slayer_pct = percentile_of_kd(kd) + kd = kills / max(1, deaths) + acc = shots_hit / shots if shots > 0 else 0.0 - # --- Objective percentile (per match) --- - if m["mode"] == "payload": - obj_raw = m["push_time"] - elif m["mode"] == "domination": - obj_raw = m["captures"] + m["counters"] - else: - obj_raw = 0 + kds.append(kd) + dmgs.append(damage) + accs.append(acc) - 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) --- - accuracy_pct = percentile_of_accuracy(m["accuracy"]) + kd_std = std(kds) + dmg_std = std(dmgs) + acc_std = std(accs) - # --- Composite per-match performance --- - perf = ( - slayer_pct * 0.40 + - objective_pct * 0.40 + - accuracy_pct * 0.20 - ) + # Lower variance = more consistent + # Normalize into a 0–1 score + raw = 1.0 / (1.0 + kd_std + dmg_std + acc_std) - 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, - "floor": floor_score, - "variance": raw_variance, - "variance_adj": adj_variance, - "variance_score": variance_score, - "avg_perf": avg_perf, - "consistency_norm": consistency_norm + "match_count": len(matches), + "kd_std": kd_std, + "dmg_std": dmg_std, + "acc_std": acc_std, + "consistency_norm": raw, } - return consistency_norm, components \ No newline at end of file + return raw, components \ No newline at end of file diff --git a/tags/objective_domination.py b/tags/objective_domination.py index fba438b..0379922 100644 --- a/tags/objective_domination.py +++ b/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: - - DOM_captures - - 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. + A "Domination map" is any match where DOM_Captures > 0 or DOM_Counters > 0. + Payload and Control Point stats are ignored. """ - caps = player_entry.get("DOM_captures", 0) or 0 - counters = player_entry.get("DOM_counters", 0) or 0 + matches = pdata.get("matches", []) + 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) - cap_rate = math.log1p(caps) / math.log1p(20) - counter_rate = math.log1p(counters) / math.log1p(20) + dom_matches = 0 + total_caps = 0 + total_counters = 0 - score = (0.40 * cap_rate) + (0.60 * counter_rate) - return round(score, 3) \ No newline at end of file + for m in matches: + 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 \ No newline at end of file diff --git a/tags/objective_payload.py b/tags/objective_payload.py index a8a7cd8..0ee6d13 100644 --- a/tags/objective_payload.py +++ b/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): - career = p["career"] - derived = p["derived"] +def compute_payload_objective_for_career_player(pdata, league_averages=None): + """ + Modernized career Payload Objective Specialist score. + Mirrors the structure of Domination's career tag. - push_time_total = career.get("push_time", 0) or 0 - damage = career.get("damage", 0) or 0 - deaths = career.get("deaths", 0) or 0 - push_time_per_season = derived.get("push_time_per_season", 0) or 0 + Components: + - PresenceNorm: fraction of matches that were Payload + - PushNorm: average push time normalized to 300s soft cap + - PSINorm: damage-per-death survivability normalized - # Push Survivability Index (PSI) - # PSI = Damage / (Deaths + 1) * 1 / sqrt(PushTime + 1) - psi = 0.0 - if push_time_total > 0: - psi = (damage / (deaths + 1)) * (1.0 / math.sqrt(push_time_total + 1)) + Final score: + 0.40 * PushNorm + + 0.40 * PresenceNorm + + 0.20 * PSINorm + """ - league_push_time_per_season = league_averages.get("push_time_per_season", 1.0) - push_time_norm = push_time_per_season / league_push_time_per_season if league_push_time_per_season > 0 else 0.0 + matches = pdata.get("matches", []) + 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, - "push_time_per_season": push_time_per_season, - "push_time_norm": push_time_norm, - "psi": psi, + "payload_matches": payload_matches, + "total_push_time": total_push, + "avg_push_time": avg_push, + "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): - """ - team_player_entries: list of (player_id, player_entry_from_career_db) +# --------------------------------------------------------- +# 2. MATCH-BASED PAYLOAD OBJECTIVE SPECIALIST (MODERNIZED) +# --------------------------------------------------------- - Returns dict {pid: obj_pl_score} +def compute_payload_objective_for_match_player(p): """ - # PresencePL: share of team push time - total_push_time = sum(p["objective_payload_components"]["push_time_total"] - for _, p in team_player_entries) - if total_push_time <= 0: - total_push_time = 1.0 + Extract match-level Payload stats for a single player. + Normalized naming for consistency. + """ + + 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"] - psi = comp["psi"] + for pid, entry, mp in team_entries: + 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 - # We'll compute team PSI mean and std first - scores[pid] = { - "presence_pl": presence_pl, - "push_time_norm": push_time_norm, - "psi": psi, - } + # --- Presence --- + presence = push / team_push - # PSI normalization within team - psi_values = [v["psi"] for v in scores.values()] - 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 + # --- PushNorm --- + push_norm = min(push / 300.0, 1.0) - for pid, v in scores.items(): - if std_psi > 0: - psi_norm = (v["psi"] - mean_psi) / std_psi - else: - psi_norm = 0.0 + # --- PSINorm --- + psi_raw = dmg / (deaths + 1) + psi_norm = psi_raw / (psi_raw + 300.0) - # Final ObjPL score (website‑only version) - # 0.40 Presence + 0.40 PushTimeNorm + 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 + # --- Final Score --- + score = (0.40 * presence) + (0.40 * push_norm) + (0.20 * psi_norm) - return {pid: v["obj_pl"] for pid, v in scores.items()} \ No newline at end of file + scores[pid] = round(score, 4) + + return scores \ No newline at end of file diff --git a/team_identity.py b/team_identity.py index bdbbc09..a9b01c0 100644 --- a/team_identity.py +++ b/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 = {