[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-05-31-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},"2026-05-31-week-ai-performance","weekly",null,"2026-06-01 00:35:00","2026-06-01 00:35:16","\u002Fstatic\u002Fog\u002F2026-05-31-week-ai-performance.jpg",0,"AI Stability in Top Three, Extreme Events Frequent","Canada 28: AI Performance and Extreme Event Review","Reviewing Canada 28's weekly AI algorithm performance, showcasing high stability in the top three while frequent extreme events like rare triples and span extremes draw attention. Analyze the probability and significance of these deviations.","This week, Canada 28's AI algorithms showed stable performance, with the top three standing out. Rare extreme events like triple numbers and span extremes occurred frequently, highlighting notable data deviations.","## Weekly Overview\n\nYesterday's Canada 28 data shows that over the past seven days, 2,760 draws were conducted, spanning May 25, 2026, to May 31, 2026. Overall, the big\u002Fsmall ratio was 50.94% big numbers to 49.06% small numbers, while the odd\u002Feven ratio was 49.53% odd numbers to 50.47% even numbers. These figures closely align with the theoretical probability of 50%, reflecting a reasonable performance under the law of large numbers.\n\n### 7-Day Trend Analysis\n\nDaily data revealed some fluctuations in big\u002Fsmall and odd\u002Feven ratios. On May 29, the proportion of big numbers hit a weekly low at 45.52%, marking the only significant deviation from the average. Conversely, on May 31, the big number ratio rebounded to 52.99%, indicating a short-term reversal trend. Such variations can be attributed to the effects of small sample sizes.\n\n### AI Algorithm Performance\n\nAmong the 30 AI algorithms analyzed this week, the top three performers were \"Monte Carlo,\" \"Mean Reversion,\" and \"Anti-Martingale.\" \"Monte Carlo\" led with an overall accuracy rate of 45.4%, based on a sample size of 7,932 predictions. \"Mean Reversion\" matched this accuracy rate, showcasing equally strong performance. \"Anti-Martingale\" followed closely with a 45.3% accuracy rate, securing its place in the top three.\n\nNotably, these algorithms demonstrated balanced performance across multiple dimensions. For instance, the \"Monte Carlo\" algorithm achieved accuracy rates of 50.98% in odd-number predictions and 24.56% in combination predictions. This balance indicates the algorithms' adaptability to fluctuating data patterns.\n\n### Extreme Event Highlights\n\nThis week saw frequent occurrences of extreme events, with rare triple numbers and span extremes drawing attention. On May 31, the triple number \"9+9+9\" reappeared, an event with an approximate theoretical probability of 1%, making it exceptionally rare. Additionally, span extremes of 9 were observed on multiple dates, such as May 27 through May 29, where several draws reached a span of 9. These occurrences significantly exceeded theoretical expectations, likely reflecting the randomness and short-term deviations in the data.\n\n### Summary and Outlook\n\nThe algorithm performance and extreme events this week showcased the diversity and volatility of the data. The top three algorithms demonstrated stability, while the extreme events provided valuable insights into anomalies. Next week, we look forward to further validating the patterns and probability theories underlying these observations in Canada 28 draws.",9,[18,27,35],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-06-03-extreme-triple-5","extreme","E1","2026-06-03 02:01:48","\u002Fstatic\u002Fog\u002F2026-06-03-extreme-triple-5.jpg","Rare Triple Number Emerges: 5+5+5 Stuns in Canada 28","Today's Canada 28 draw 3440224 featured the rare triple number 5+5+5, with an extreme probability of only 1%, marking the second consecutive day of such phenomena.",2,{"slug":28,"type":29,"extremeSubtype":6,"publishAt":30,"coverImage":31,"viewCount":10,"title":32,"summary":33,"readMinutes":34},"2026-06-02-extreme-events","daily","2026-06-03 00:30:00","\u002Fstatic\u002Fog\u002F2026-06-02-extreme-events.jpg","Rare Triple Numbers Appear Frequently, AI Algorithm Shines","Yesterday's Canada 28 draws featured three rare triple numbers, while AI algorithms achieved an impressive 41.6% hit rate, showcasing remarkable performance.",8,{"slug":36,"type":20,"extremeSubtype":21,"publishAt":37,"coverImage":38,"viewCount":10,"title":39,"summary":40,"readMinutes":26},"2026-06-02-extreme-triple-7","2026-06-02 16:43:49","\u002Fstatic\u002Fog\u002F2026-06-02-extreme-triple-7.jpg","Rare Triple Number Appears: 7+7+7 Stuns in Draw","Draw #3440074 of Canada 28 featured the rare triple number 7+7+7, with a theoretical probability of just 1%, making it an exceptional occurrence.",[42,48,53,59,65,71,77,83,88,93,99,105,111,117,123,129,135,141,146,152,158,164,170,176,182,188,194,200,206,212],{"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":58},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",3,{"code":60,"nameZhCn":61,"nameZhTw":62,"nameEnUs":63,"sortOrder":64},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":66,"nameZhCn":67,"nameZhTw":68,"nameEnUs":69,"sortOrder":70},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":72,"nameZhCn":73,"nameZhTw":74,"nameEnUs":75,"sortOrder":76},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",6,{"code":78,"nameZhCn":79,"nameZhTw":80,"nameEnUs":81,"sortOrder":82},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":84,"nameZhCn":85,"nameZhTw":86,"nameEnUs":87,"sortOrder":34},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",{"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 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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]