[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-10-03-rare-cold-7-breaks-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},"2026-10-03-rare-cold-7-breaks","daily",null,"2026-10-04 00:30:00","2026-10-04 00:32:42","\u002Fstatic\u002Fog\u002F2026-10-03-rare-cold-7-breaks.jpg",0,"Cold Number Sum 7 Finally Breaks 230-Draw Drought","Canada 28 Cold Number Sum 7 Breaks 230-Draw Drought","Canada 28's cold number sum 7 finally appeared last night, ending a 230-draw absence. Span distribution concentrated on 5 and 6, with the 'volatility' AI algorithm performing best.","Canada 28's cold number sum 7 finally appeared last night, ending a 230-draw absence. The 'volatility' AI algorithm led with a hit rate of 45.89%.","## Cold Number Sum 7 Breaks 230-Draw Record\n\nOn October 3, 2026, Canada 28 witnessed a historic moment as the cold number sum 7 finally appeared in draw number 3489636, ending a staggering 230-draw drought. This event stood out as the highlight of yesterday's results.\n\nMany speculate that the appearance of a cold number might lead to a subsequent surge in similar occurrences. However, this isn't necessarily the case. Out of the 402 draws conducted on October 3, cold number sums appeared more frequently but still remained relatively rare. While sum 7 made its long-awaited return, other cold sums like 0 and 1, which have also been absent for over 200 draws, continued to elude players, defying some earlier predictions.\n\n## Span Distribution Focused on 5 and 6\n\nThe span distribution throughout the day showed a clear concentration. Spans of 5 and 6 appeared 65 and 61 times respectively, representing a significant portion of all spans. In contrast, span 0 only showed up twice, and span 9 appeared just 14 times. Does this clustering indicate an emerging pattern, or is it merely a result of random fluctuation? Readers are encouraged to analyze further.\n\n## AI Algorithm Performance: Volatility Takes the Lead\n\nAmong the 30 AI algorithms evaluated yesterday, the 'volatility' algorithm stood out with an impressive overall hit rate of 45.89%, significantly above the average of 41.59%. On the other hand, the least effective algorithms—'double-step transition,' 'miss tracking,' and 'decay miss'—lagged behind with hit rates of 35.45%, 37.06%, and 37.06% respectively. For users keen on leveraging AI predictions, these results offer valuable insights.\n\nWill the breakthrough of sum 7 lead to a wave of cold number appearances? And how will the span concentration evolve in the coming days? These questions may require more data to answer definitively.",6,[18,27,35],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-10-12-extreme-sum-zero","extreme","E2","2026-10-12 15:23:26","\u002Fstatic\u002Fog\u002F2026-10-12-extreme-sum-zero.jpg","Rare Event: Canada 28 Sum Drops to 0","Canada 28 draw #3493115 recorded a rare sum of 0, with only a 0.1% chance of happening. The last instance was just a day prior.",2,{"slug":28,"type":20,"extremeSubtype":29,"publishAt":30,"coverImage":31,"viewCount":10,"title":32,"summary":33,"readMinutes":34},"2026-10-12-extreme-triple-zero","E1","2026-10-12 15:23:15","\u002Fstatic\u002Fog\u002F2026-10-12-extreme-triple-zero.jpg","Canada 28 Triple Number 000 Appears, Probability Only 1%","In draw #3493115, Canada 28 produced the rare triple number 000, an extreme event with a probability as low as 1%.",3,{"slug":36,"type":20,"extremeSubtype":29,"publishAt":37,"coverImage":38,"viewCount":10,"title":39,"summary":40,"readMinutes":34},"2026-10-12-extreme-triple-three","2026-10-12 06:38:20","\u002Fstatic\u002Fog\u002F2026-10-12-extreme-triple-three.jpg","Canada 28 Triple Number 333 Appears with Just 1% Probability","In draw #3492965 of Canada 28, the rare triple number 333 emerged, surprising everyone with its 1% probability.",[42,48,53,58,64,70,75,81,87,93,99,105,111,117,123,129,135,141,146,152,158,164,170,176,182,188,194,200,206,212],{"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":16},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",{"code":76,"nameZhCn":77,"nameZhTw":78,"nameEnUs":79,"sortOrder":80},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":82,"nameZhCn":83,"nameZhTw":84,"nameEnUs":85,"sortOrder":86},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",8,{"code":88,"nameZhCn":89,"nameZhTw":90,"nameEnUs":91,"sortOrder":92},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"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]