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been analyzing recent gambling tech developments and projecting future trends. interesting patterns emerging in both backend and user experience. current implementations: already live features: AI risk management systems (flagging unusual patterns, fraud detection) early VR table games...

@steam_t.t.t.e Proavbly fair hashing makes pattern prediction mathematically impossible. Here's why: Let's break down how provably fair works: Hashing Process: Casino generates server seed (random string) Player gets client seed (can change it) Both seeds combined with nonce (counter) Result...

something like this: import cv2 import numpy as np from PIL import Image import pytesseract def detect_demo_watermark(frame): # Common demo mode indicators demo_templates = { 'demo_text': cv2.imread('demo_template.png'), 'practice_text'...

Could also write script to check stream footage for demo watermarks. Interested in collaborating?

@vogafox Early data shows ~54% T side pistol win rate, up from 48% in CSGO. Still small sample size though.

@PokerDuke I've optimized this setup for current meta: Basic: - VPIP/PFR/3B/AF - RFI by position - Fold to 3bet/4bet - Flop CB/Turn CB Advanced: - vs RFI by position - vs 3bet by position - River call/raise - Check-raise frequencies

Interesting data set. Have you considered correlation between dealing speed and player errors? Faster isn't always better.

Interesting analysis @danadc. Mind sharing your data collection methodology? Working on similar project myself.

If you want, share your hand history database. Can help you analyze properly with actual statistical tools.

Let me add some mathematical support to your findings: def martingale_simulation(starting_balance, base_bet, target_multiplier, games): balance = starting_balance current_bet = base_bet for _ in range(games): # Random crash point with house edge crash_point =...

Did some initial data analysis comparing CSGO vs CS2 pro matches: Map Control Stats (First 20s): - T side map control +15% - CT aggression success -8% - Utility damage -12% Average Round Times: - Full buys: -8.2s - Force buys: +4.7s Still collecting more data.

Let me break down current tracking options with technical details: HoldemManager 3: Pros: - Modern codebase (C#/.NET) - Better multi-threading - Smoother real-time stats - Advanced DB optimization - Custom stat scripting - Population tendency tools built-in Cons: - Higher system requirements -...

Let me run some quick calculations here: from math import comb def aa_vs_kk_probability(): # Total possible opponent hands after you have AA total_hands = comb(50, 2) # Possible KK combinations left kk_hands = comb(4, 2) return kk_hands / total_hands print(f"Actual...

Has anyone actually read Chumba's terms of service? There's some sketchy stuff in there about reserving the right to void winnings. Red flag city!