[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-07-26-week-ai-trends-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},"2026-07-26-week-ai-trends","weekly",null,"2026-07-27 00:35:00","2026-07-27 00:35:17","\u002Fstatic\u002Fog\u002F2026-07-26-week-ai-trends.jpg",0,"Analysis of Canada 28 AI Algorithm Performance This Week","Canada 28 Weekly Report: AI Algorithm Trends and Rare Triple Numbers Analysis","A look at Canada 28's weekly data: AI algorithm accuracy analysis, anti_martingale leads with 45.29% hit rate, frequent triple numbers spark discussion, and insights into big\u002Fsmall and odd\u002Feven ratios.","This week's Canada 28 data stood out with the anti_martingale algorithm achieving a 45.29% hit rate, frequent triple numbers drawing attention, and stable big\u002Fsmall ratios.","## Weekly Data Overview\n\nFrom July 20 to 26, 2026, Canada 28 held a total of 2,815 draws. The frequency of big and small numbers was nearly even, with big numbers accounting for 50.05% and small numbers 49.95%. In terms of odd\u002Feven ratios, odd numbers made up 50.41% while even numbers were at 49.59%, closely aligning with theoretical expectations. This stable distribution suggests no significant deviations in trends, though some interesting fluctuations are worth noting.\n\n### Review of Data Trends\n\nAt the start of the week, on July 20 and 21, the data leaned towards big numbers, reaching 49.5% and 50.87%, respectively. However, on Wednesday, July 22, the proportion of big numbers peaked at 52.62%, marking the week's high. The dominance of big numbers then gradually diminished, with small numbers slightly rebounding on the 24th and 25th to 50.75% and 50.62%, respectively. By the weekend of the 26th, the ratio returned to a near-even split.\n\nThe odd\u002Feven ratio also experienced fluctuations. On July 23, the proportion of odd numbers dropped to just 45.27%, significantly deviating from the theoretical 50% and marking the week's lowest point. By the latter half of the week, the odd ratio rebounded to around 50%, indicating notable variability in this metric.\n\n## AI Algorithm Performance: Star of the Week\n\nThis week, the performance of 30 AI algorithms was analyzed. Among them, the anti_martingale algorithm stood out, achieving a leading overall hit rate of 45.29%, a 2% improvement compared to the previous week. Following closely were the local_entropy and kelly algorithms, both tied at 44.87%.\n\nIt's worth highlighting that the sample size for this week reached 11,220, lending greater statistical significance to these results. The anti_martingale algorithm's number prediction accuracy was particularly impressive at 53.83%, significantly outperforming other algorithms.\n\n## Tracking Extreme Events\n\nBetween July 23 and 26, several rare extreme events were observed. For instance, in draw number 3460446, a triple number \"4\" appeared, and on July 23 and 24, multiple instances of triple numbers were recorded. Notably, July 24 saw repeated occurrences of a sum value of 0, an exceptionally rare event compared to historical data.\n\n## Popular Sums and Span Analysis\n\nThe most frequent sums this week were 13 (210 times, 7.46%) and 15 (207 times, 7.35%). Regarding span, the span of 5 was the most active, accounting for 15.31%. This distribution aligns with historical data trends and may be a focal point for players seeking strategic insights.\n\n## Summary\n\nOverall, this week's Canada 28 data exhibited stability while also showcasing short-term fluctuations. The anti_martingale algorithm's performance deserves attention, and the occurrence of rare extreme events continues to inspire new strategies for players. Data enthusiasts might find valuable insights by delving deeper into these phenomena to uncover potential patterns.",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]