[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-08-09-week-data-patterns-en-US":3,"reports-algorithms":41},{"slug":4,"type":5,"extremeSubtype":6,"publishAt":7,"updateTime":8,"coverImage":9,"viewCount":10,"title":11,"metaTitle":12,"metaDesc":13,"summary":14,"content":15,"readMinutes":16,"related":17},"2026-08-09-week-data-patterns","weekly",null,"2026-08-10 00:35:00","2026-08-10 00:35:19","\u002Fstatic\u002Fog\u002F2026-08-09-week-data-patterns.jpg",0,"Weekly Canada 28 Data Analysis and AI Performance","Canada 28 Weekly Data Analysis and AI Algorithm Performance","Analyzing Canada 28 data from August 3 to August 9, 2026, covering big\u002Fsmall ratio shifts, extreme events, and AI algorithm trends.","This week's Canada 28 data reveals a near balance in big\u002Fsmall ratio (50.37% vs 49.63%) and a slight edge for even numbers (51.94%). The 'Anti-Martingale' algorithm performed best, while several rare events drew attention.","## Weekly Data Overview\n\nYesterday's Canada 28 data shows that during the week of August 3 to August 9, 2026, a total of 2,813 predictions were made, spanning draw numbers from 3,464,713 to 3,467,525 over seven days.\n\nLooking at the big\u002Fsmall ratio, big numbers accounted for **50.37%** (1,417\u002F2,813), while small numbers made up **49.63%** (1,396\u002F2,813), indicating a slight tendency toward balance. Daily data comparisons show big numbers were dominant earlier in the week, such as on August 5, where big numbers reached **55.83%**, but later shifted toward equilibrium, with big numbers dropping to **48.51%** on August 9.\n\nThe odd\u002Feven ratio showed a slight advantage for even numbers, with odd numbers at **48.06%** (1,352\u002F2,813) and even numbers at **51.94%** (1,461\u002F2,813). This trend was also evident in daily data, such as August 4, where odd numbers accounted for **45.27%**, while even numbers were more active.\n\n### Hot Numbers and Span Analysis\n\nIn terms of hot numbers, the top five most frequent sums were 14, 12, 13, 16, and 15, appearing **208** times (**7.39%**), **205** times (**7.29%**), **201** times (**7.15%**), **198** times (**7.04%**), and **195** times (**6.93%**), respectively.\n\nSpan data (the difference between the highest and lowest numbers) revealed that a span of 6 was most common, accounting for **15.1%**, while a span of 0 was the rarest, occurring only **29** times.\n\n## AI Algorithm Weekly Leaderboard\n\nAmong 30 algorithms, the 'Anti-Martingale' ranked first with an average accuracy of **44.89%**, followed by 'Local Entropy' (**44.77%**) and 'Kelly Criterion' (**44.75%**).\n\nNotably, the 'Anti-Martingale' algorithm processed **10,404** samples this week, demonstrating stable and outstanding overall performance with a prediction accuracy of **54.17%**, securing the top spot across three metrics.\n\n## Extreme Events Highlights\n\nSeveral rare events occurred this week, including multiple instances of triple numbers:\n\n- On August 3, triple numbers 7+7+7 and 0+0+0 appeared, triggering notable discussions.\n- On August 8, triple numbers 4+4+4 and 8+8+8 emerged in consecutive draws.\n- On August 5, the sum of 0 appeared repeatedly, sparking considerable interest.\n\nThough these events are statistically rare, they will serve as critical references for future data model adjustments.\n\n## Weekly Trends and Next Week's Outlook\n\nOver the past week, data distribution leaned toward balance, with no significant deviations in big\u002Fsmall or odd\u002Feven ratios. AI algorithm performance continued to be a highlight, with the 'Anti-Martingale' showing impressive stability.\n\nNext week, we will continue monitoring shifts in big\u002Fsmall ratios and the probability of extreme events, hoping for further insights from the evolving data.",9,[18,27,35],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-10-11-extreme-sum-zero-v2","extreme","E2","2026-10-11 11:56:58","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-sum-zero-v2.jpg","Canada 28 Draw #3492654 Hits a Total Sum of 0","Canada 28 draw #3492654 recorded an extremely rare total sum of 0, with a probability of just 0.1%. The last similar result occurred in draw #3492637.",2,{"slug":28,"type":20,"extremeSubtype":29,"publishAt":30,"coverImage":31,"viewCount":10,"title":32,"summary":33,"readMinutes":34},"2026-10-11-extreme-triple-zero","E1","2026-10-11 11:56:49","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-triple-zero.jpg","Canada 28 Triple Number 000 Reappears, Rare 1% Probability","Canada 28's draw #3492654 featured the rare triple number 000, with a theoretical probability of just 1%. This marks the second occurrence within an hour.",3,{"slug":36,"type":20,"extremeSubtype":21,"publishAt":37,"coverImage":38,"viewCount":10,"title":39,"summary":40,"readMinutes":26},"2026-10-11-extreme-sum-zero","2026-10-11 10:46:56","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-sum-zero.jpg","Canada 28 Hits Rare Sum 0 Again","On October 11, JND28 draw #3492634 recorded an extremely rare sum of 0, with a theoretical probability of only 0.1%, marking the second occurrence in just two days.",[42,48,53,58,64,70,76,82,88,93,99,105,111,117,123,129,135,141,146,152,158,164,170,176,182,188,194,200,206,212],{"code":43,"nameZhCn":44,"nameZhTw":45,"nameEnUs":46,"sortOrder":47},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",1,{"code":49,"nameZhCn":50,"nameZhTw":51,"nameEnUs":52,"sortOrder":26},"deep_neural_network","深度神经网络","深度神經網路","Deep Neural Network",{"code":54,"nameZhCn":55,"nameZhTw":56,"nameEnUs":57,"sortOrder":34},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",{"code":59,"nameZhCn":60,"nameZhTw":61,"nameEnUs":62,"sortOrder":63},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":65,"nameZhCn":66,"nameZhTw":67,"nameEnUs":68,"sortOrder":69},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":71,"nameZhCn":72,"nameZhTw":73,"nameEnUs":74,"sortOrder":75},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",6,{"code":77,"nameZhCn":78,"nameZhTw":79,"nameEnUs":80,"sortOrder":81},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":83,"nameZhCn":84,"nameZhTw":85,"nameEnUs":86,"sortOrder":87},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",8,{"code":89,"nameZhCn":90,"nameZhTw":91,"nameEnUs":92,"sortOrder":16},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",{"code":94,"nameZhCn":95,"nameZhTw":96,"nameEnUs":97,"sortOrder":98},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":100,"nameZhCn":101,"nameZhTw":102,"nameEnUs":103,"sortOrder":104},"volatility","波动率","波動率","Volatility",11,{"code":106,"nameZhCn":107,"nameZhTw":108,"nameEnUs":109,"sortOrder":110},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":112,"nameZhCn":113,"nameZhTw":114,"nameEnUs":115,"sortOrder":116},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":118,"nameZhCn":119,"nameZhTw":120,"nameEnUs":121,"sortOrder":122},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":124,"nameZhCn":125,"nameZhTw":126,"nameEnUs":127,"sortOrder":128},"momentum","动量加速度","動量加速度","Momentum",15,{"code":130,"nameZhCn":131,"nameZhTw":132,"nameEnUs":133,"sortOrder":134},"quantile","分位数","分位數","Quantile",16,{"code":136,"nameZhCn":137,"nameZhTw":138,"nameEnUs":139,"sortOrder":140},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":142,"nameZhCn":143,"nameZhTw":143,"nameEnUs":144,"sortOrder":145},"local_entropy","局部熵","Local Entropy",18,{"code":147,"nameZhCn":148,"nameZhTw":149,"nameEnUs":150,"sortOrder":151},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":153,"nameZhCn":154,"nameZhTw":155,"nameEnUs":156,"sortOrder":157},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":159,"nameZhCn":160,"nameZhTw":161,"nameEnUs":162,"sortOrder":163},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":165,"nameZhCn":166,"nameZhTw":167,"nameEnUs":168,"sortOrder":169},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":171,"nameZhCn":172,"nameZhTw":173,"nameEnUs":174,"sortOrder":175},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":177,"nameZhCn":178,"nameZhTw":179,"nameEnUs":180,"sortOrder":181},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":183,"nameZhCn":184,"nameZhTw":185,"nameEnUs":186,"sortOrder":187},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":189,"nameZhCn":190,"nameZhTw":191,"nameEnUs":192,"sortOrder":193},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":195,"nameZhCn":196,"nameZhTw":197,"nameEnUs":198,"sortOrder":199},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":201,"nameZhCn":202,"nameZhTw":203,"nameEnUs":204,"sortOrder":205},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":207,"nameZhCn":208,"nameZhTw":209,"nameEnUs":210,"sortOrder":211},"number_combination","数字组合","數字組合","Number Combination",29,{"code":213,"nameZhCn":214,"nameZhTw":215,"nameEnUs":216,"sortOrder":217},"recent_chain","近链推演","近鏈推演","Recent Chain",30]