[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-08-monthly-ai-analysis-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-monthly-ai-analysis","monthly",null,"2026-09-01 00:40:00","2026-09-01 00:40:41","\u002Fstatic\u002Fog\u002F2026-08-monthly-ai-analysis.jpg",0,"Canada 28 August Algorithm Performance and Extreme Data Highlights","Canada 28 August Data Summary: Algorithm Performance and Key Events","Review of Canada 28's August performance, highlighting standout algorithms and frequent extreme data occurrences. Detailed analysis of trends and value distributions.","In August 2026, Canada 28 saw impressive algorithm performance, with the Kelly formula, local entropy, and deep learning algorithms leading the pack. Rare triple numbers and extreme sums added fresh intrigue to the data.","## August Data Overview\n\nIn August 2026, Canada 28 conducted 12,345 draws, spanning from August 1st's draw #3463908 to August 31st's draw #3476369. The distribution of big and small numbers was nearly even, with big numbers accounting for 49.78% and small numbers 50.22%. For odd and even numbers, evens had a slight edge at 50.93%. This balanced data distribution provided a stable testing ground for algorithm performance.\n\n### AI Algorithm Monthly Leaders\n\nAccording to the statistics, the Kelly formula algorithm ranked first with an average accuracy of 45.59%, followed closely by the local entropy algorithm at 45.58%, and the deep learning algorithm at 45.4%. These three algorithms demonstrated exceptional stability, particularly in predicting combinations of small and even numbers.\n\nThe Kelly formula algorithm excelled in detecting fluctuations in large datasets, maintaining high accuracy across diverse data scenarios. The local entropy algorithm showed strength in handling wide-ranging spans, proving its capability in predicting irregular data. Meanwhile, the deep learning algorithm often achieved remarkable results when hot numbers appeared, thanks to its ability to capture complex patterns.\n\n### Extreme Events Monthly Recap\n\nAugust was marked by frequent extreme events, including multiple occurrences of triple numbers and extreme sums. For instance, on August 1st, the triple number \"6+6+6\" appeared three times, alongside the extreme sum of 27. On August 25th, the sum of 27 reappeared, drawing widespread attention. Notably, there were instances of 10 consecutive draws of all big or all small numbers, showcasing the extremity of the data.\n\nSome specific events include:\n- **August 1st:** Triple number \"6+6+6\" appeared three times, a historic moment for Canada 28.\n- **August 2nd:** 10 consecutive all-small draws, a rare occurrence with a probability of just 0.1%.\n- **August 30th:** Another streak of 10 consecutive all-small draws sparked discussions.\n\n### Weekly Data Trend Changes\n\nThe five weeks of August displayed evolving trends. In the first week, big numbers had the highest proportion at 51.43%, but this gradually declined to 49.08% in subsequent weeks. Odd and even proportions also fluctuated significantly, with the fifth week showing near parity, but a noticeable drop in even numbers by the sixth week.\n\n### Hot Values and Span Analysis\n\nThis month, the hot sums were concentrated around 14, 13, and 12, with frequencies of 8.26%, 7.52%, and 7.28%, respectively, making them the top three popular sums. Span distribution was relatively even, with spans of 5 and 6 being the most frequent, occurring 1,839 and 1,824 times, respectively.\n\n## Summary and Future Outlook\n\nThe data fluctuations in August provided rich scenarios for testing prediction algorithms. While the Kelly formula algorithm maintained its consistent performance, competition among other algorithms is intensifying. Whether new algorithms will disrupt the current stability remains to be seen, and we will continue monitoring developments in Canada 28.",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]