[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-09-20-weekly-data-highlights-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},"2026-09-20-weekly-data-highlights","weekly",null,"2026-09-21 00:35:00","2026-09-21 00:35:16","\u002Fstatic\u002Fog\u002F2026-09-20-weekly-data-highlights.jpg",0,"Data Fluctuations and Weekly Champion Analysis","Canada 28 Data Fluctuations and Weekly Champion Analysis","Analyzing Canada 28 data trends from September 14 to 20, 2026, uncovering big\u002Fsmall and odd\u002Feven ratio patterns, and revealing top-performing algorithm rankings.","This week’s Canada 28 analysis highlights data fluctuations, big\u002Fsmall and odd\u002Feven ratio shifts, top-performing algorithms, and rare event records.","## Weekly Overview of Canada 28 Data\n\nDuring the period from September 14 to September 20, 2026, Canada 28 conducted a total of 2,783 draws, spanning draw numbers 3481597 to 3484409 over seven days. In terms of big\u002Fsmall ratio, big numbers accounted for 49.26%, while small numbers slightly led with 50.74%. The odd\u002Feven ratio was almost balanced, with odd numbers at 50.16% and even numbers at 49.84%.\n\n### Observing Data Trends\n\nDaily data revealed noticeable fluctuations in big\u002Fsmall and odd\u002Feven ratios. In the early part of the week (September 14 to 16), small numbers dominated, with September 15 showing the lowest big number ratio at just 46.52%. However, big numbers rebounded significantly on September 19, reaching 53.48%, and stabilized at 50.87% the following day. Regarding odd\u002Feven ratios, September 18 saw the lowest even number ratio of the week at 47.15%, while September 20 recorded the highest odd number ratio at 52.37%.\n\n### Hot Numbers and Span Distribution Analysis\n\nThe top five hot numbers this week were 13 (appearing 219 times, 7.87%), 12 (207 times, 7.44%), 16 (206 times, 7.4%), 15 (198 times, 7.11%), and 11 (190 times, 6.83%). These values were concentrated in the mid-range, indicating strong stability. Regarding span distribution, a span of 5 occurred most frequently, with 452 instances (16.24%), while a span of 0 was the least common, appearing only 27 times, highlighting the data's dispersion.\n\n## Weekly Champion Algorithm Performance\n\nBased on recent AI algorithm performance, the \"Mean Reversion\" algorithm led with an average weekly accuracy of 45.85%, earning the title of weekly champion. The \"Monte Carlo\" algorithm followed closely with 45.65%, and the \"Anti-Martingale\" algorithm ranked third with 45.54%. These three algorithms demonstrated consistently high accuracy in predicting big\u002Fsmall, odd\u002Feven, and numbers, significantly outperforming the average accuracy of other algorithms (41.47%).\n\n### Rare Event Tracking\n\nThis week’s data featured several rare events. On September 14, triple numbers 888, 666, and 999 appeared consecutively, marking the densest occurrence of such events in recent months. On September 19, triple 888 reappeared along with other extreme events, highlighting unique data trends.\n\n### Summary Observations and Records\n\nThis week, Canada 28 data exhibited clear cyclical fluctuations, with significant shifts in big\u002Fsmall and odd\u002Feven ratios midweek. The first three days were dominated by small numbers, while big numbers gained momentum later, leading to a balanced outcome. The stability rankings of AI algorithms reaffirmed the reliability of \"Mean Reversion\" and \"Monte Carlo,\" especially in predicting anomalies. This week’s rare events further emphasized the importance of these algorithms in identifying unusual patterns.",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]