[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-10-04-week-ai-performance-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},"2026-10-04-week-ai-performance","weekly",null,"2026-10-05 00:35:00","2026-10-05 00:35:22","\u002Fstatic\u002Fog\u002F2026-10-04-week-ai-performance.jpg",0,"Extreme Events and AI Performance in This Week's Canada 28 Data","Canada 28 Weekly Data: Extreme Events and AI Algorithm Performance","A review of this week's Canada 28 data, highlighting frequent extreme events and standout AI algorithm performance, with key trends and deviations analyzed.","This week's Canada 28 data review highlights frequent extreme events, including multiple triples and sum-zero outcomes, with AI algorithms like 'Local Entropy' and 'Kelly Formula' achieving top hit rates.","## Frequent Appearance of Extreme Events\n\nThis week's Canada 28 data revealed a notable frequency of extreme events. Between October 1 and October 4, there were 13 occurrences of such events, including several rare triple numbers. For instance, the triple \"888\" reappeared on the morning of October 4. Additionally, sum-zero outcomes were observed on October 1 and October 2, a rare phenomenon in historical data that sparked significant discussion among players.\n\nStatistics show that October 1 experienced a surge in triple numbers, featuring combinations like \"000,\" \"777,\" and \"888.\" Whether this unusual fluctuation in the data will persist remains to be seen.\n\n## Analysis of Big\u002FSmall and Odd\u002FEven Ratios\n\nWeekly statistics indicate that the overall big-to-small ratio was 50.24% to 49.76%, while the odd-to-even ratio stood at 49.58% to 50.42%, reflecting near balance. However, daily fluctuations were evident. On September 30, the proportion of big numbers peaked at 50.87% for the week, whereas on October 2, it dropped to 47.76%. This variability underscores the high randomness of the overall trends, making prediction challenging.\n\n### Daily Distribution Comparison\n\n| Date       | Total Rounds | Big Number Ratio | Odd Number Ratio |\n|------------|--------------|------------------|------------------|\n| 2026-09-28 | 403          | 50.37%           | 48.88%           |\n| 2026-09-29 | 309          | 50.16%           | 52.10%           |\n| 2026-09-30 | 403          | 50.87%           | 50.62%           |\n| 2026-10-01 | 402          | 48.76%           | 48.26%           |\n| 2026-10-02 | 402          | 47.76%           | 47.76%           |\n| 2026-10-03 | 402          | 53.73%           | 52.74%           |\n| 2026-10-04 | 402          | 50.00%           | 47.26%           |\n\n## Top Performance by AI Algorithms\n\nAmong this week's 30 AI algorithms, \"Local Entropy\" and \"Kelly Formula\" tied for first place with an overall hit rate of 45.42%, delivering outstanding performance, particularly in number predictions where they achieved a high score of 56.2%. Meanwhile, the \"Deep Learning\" algorithm ranked third with a 45.21% overall accuracy, closely competing with the top two.\n\nOn the other hand, algorithms such as \"Double Step Transition,\" \"Chasing Failures,\" and \"Decay Omission\" underperformed, with overall accuracy rates below 36%, indicating room for improvement.\n\n## Reflections on the Data\n\nSome might interpret the frequent occurrence of extreme events as indicative of underlying patterns. But is this truly the case? This week's analysis suggests that these events are largely manifestations of randomness rather than predictive signals. Players are advised to remain rational and avoid overinterpreting short-term phenomena.\n\nA question for you: How should one adjust their mindset and strategy in the face of data fluctuations and random events?",10,[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.",[42,48,53,58,64,70,76,82,88,94,99,105,111,117,123,129,135,141,146,152,158,164,170,176,182,188,194,200,206,212],{"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":93},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":95,"nameZhCn":96,"nameZhTw":97,"nameEnUs":98,"sortOrder":16},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",{"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]