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Former NFL assistant & current Stanford Cardinal Defensive Coordinator Bobby April III talks Cover 3 Match variations & how they pattern match Mesh & Shallow/Dig in their 3 Match coverage. Access Coach April’s entire clinic “Cover 3 Variations” at:

Adam Gaylor

17,633 subscribers

85,031 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

3 条评论

Daniel Chamberlain 的头像

Daniel Chamberlain2 年前

@CoachAprilOLB Can we just talk about how perfect that dry erase board is?!?! Need one asap

Trevor Pruitt 的头像

Trevor Pruitt2 年前

@CoachAprilOLB Amazing stuff

Reed Hatley 的头像

Reed Hatley2 年前

@CoachAprilOLB Stealing the “if it’s gray you stay” line. Love it!

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a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,790 次观看 • 24 天前

Over the past 3 years, Labor has delivered 17 of the 55,000 social and affordable homes they promised in Canberra, and they celebrate as though they have succeeded. They’re clinically insane. If they were a home building business they would have been broke long ago. I have spent a lifetime in the residential building industry, from the time I was able to walk, to today. Clare O’Neil and her posse don’t have the faintest idea of the ‘how’, and no idea what they are doing - it is obvious. Dancing around on videos cut for social media selling failure as success is only part of the problem. The real problem is the government itself. No amount of window dressing will change the fact that big government socialists cause shortages. My clients range from Mum and Dad builders all the way to Australia’s largest. The most common complaint - the thing that gets in their way most - is the government. At all levels, no exceptions. To think that the government has virtually unlimited access to cash, can rush things through planning, can override all of their own bureaucracy, and they still can’t match the cadence of a major builder in Australia, is astounding. It is proof positive the private sector is superior. It is proof the talentless, feckless and incompetent elected goofballs working as hard as they can, cannot match the capability of a small suburban home builder. Rather than crowing and dancing, they should be crying in the corner. Broke and out of work. Like so many of the builders whose lives have been destroyed by legislation. Ill-fit, and utterly useless legislation. Maybe the CFMEU is in charge, and well, aside from heavying people and the good ol’ go slow, they can’t do much. Maybe it is time Albanese waived the white flag on housing supply and came to the professionals who know how to get things done. In the meantime, cut immigration -it just adds pressure on an already broken system. Aussies are actually phenomenal builders, we just have idiots in charge everywhere. A shocking government, a useless opposition and a crossbench more acquainted with nail polish than a nail gun. We can fix this, but not with these people. Housing is critical, everyone should have access to the dignity of a comfortable place to sleep, and a warm shower. I just want Australia back.

Over the past 3 years, Labor has delivered 17 of the 55,000 social and affordable homes they promised in Canberra, and they celebrate as though they have succeeded. They’re clinically insane. If they were a home building business they would have been broke long ago. I have spent a lifetime in the residential building industry, from the time I was able to walk, to today. Clare O’Neil and her posse don’t have the faintest idea of the ‘how’, and no idea what they are doing - it is obvious. Dancing around on videos cut for social media selling failure as success is only part of the problem. The real problem is the government itself. No amount of window dressing will change the fact that big government socialists cause shortages. My clients range from Mum and Dad builders all the way to Australia’s largest. The most common complaint - the thing that gets in their way most - is the government. At all levels, no exceptions. To think that the government has virtually unlimited access to cash, can rush things through planning, can override all of their own bureaucracy, and they still can’t match the cadence of a major builder in Australia, is astounding. It is proof positive the private sector is superior. It is proof the talentless, feckless and incompetent elected goofballs working as hard as they can, cannot match the capability of a small suburban home builder. Rather than crowing and dancing, they should be crying in the corner. Broke and out of work. Like so many of the builders whose lives have been destroyed by legislation. Ill-fit, and utterly useless legislation. Maybe the CFMEU is in charge, and well, aside from heavying people and the good ol’ go slow, they can’t do much. Maybe it is time Albanese waived the white flag on housing supply and came to the professionals who know how to get things done. In the meantime, cut immigration -it just adds pressure on an already broken system. Aussies are actually phenomenal builders, we just have idiots in charge everywhere. A shocking government, a useless opposition and a crossbench more acquainted with nail polish than a nail gun. We can fix this, but not with these people. Housing is critical, everyone should have access to the dignity of a comfortable place to sleep, and a warm shower. I just want Australia back.

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69,344 次观看 • 1 年前

I'm a little shocked. last 9 consecutive World Cup matches called correctly by this thing i built a couple weeks ago.. so the way it works is simple... to be clear, i'm not using AI or any of that LLM stuff because honestly a chatbot has never watched a football match in its life... and you can tell when it tries to pretend it has. instead i'm using regular numbers, the stuff sports analysts have been using for decades. i trained my modal with this data: > FIFA's official world ranking, because that gives you the baseline of how good each team actually is on paper > an Elo rating, the same system chess uses, which updates faster than FIFA so it catches teams that are getting hot or falling off > the last 5 matches each team played with goals scored and conceded, because momentum in football is real even when the squad on paper hasn't changed > expected goals (xG) from those same 5 matches, which measures how many goals a team SHOULD have scored based on the quality of chances they created, because raw goals can be misleading when one team got lucky with a long shot and the other missed three open chances > i also keep feeding it with player injuries and suspensions for the matchday squad, because if a team is missing their top scorer or first-choice keeper the whole calculation has to shift and the model weights that drop in > the all-time head-to-head history, because sometimes one team just has another team's number for whatever reason and the raw rankings don't catch that pattern on their own > a simulation that runs each potential match thousands of times in the background so the model can also tell you who's most likely to win the entire World Cup, not just any single match ▫️Here's what a prediction actually looks like look at the screenshot. it's Argentina vs Austria. the model picks Argentina to win 2-1, gives them a 71% chance against Austria's 7%, with a 22% chance of a draw... { argentina won by 2-0 } what i like is it tells you exactly why, not just gives you a number. - Argentina is ranked #2 in the world while Austria is #24. - Argentina has won their last 5 matches scoring 15 goals and conceding only 1. - Austria has 4 wins and a draw in their last 5 with 11 goals scored and 3 conceded. - And the two teams have only played twice ever, with the last match being a 1-1 draw all the way back in 1990. so you're not taking the prediction on faith, you can see exactly what's driving it. i love what i have built... feel free to drop any 2 World Cup teams in the comments and i'll run the model for you tonight..

I'm a little shocked. last 9 consecutive World Cup matches called correctly by this thing i built a couple weeks ago.. so the way it works is simple... to be clear, i'm not using AI or any of that LLM stuff because honestly a chatbot has never watched a football match in its life... and you can tell when it tries to pretend it has. instead i'm using regular numbers, the stuff sports analysts have been using for decades. i trained my modal with this data: > FIFA's official world ranking, because that gives you the baseline of how good each team actually is on paper > an Elo rating, the same system chess uses, which updates faster than FIFA so it catches teams that are getting hot or falling off > the last 5 matches each team played with goals scored and conceded, because momentum in football is real even when the squad on paper hasn't changed > expected goals (xG) from those same 5 matches, which measures how many goals a team SHOULD have scored based on the quality of chances they created, because raw goals can be misleading when one team got lucky with a long shot and the other missed three open chances > i also keep feeding it with player injuries and suspensions for the matchday squad, because if a team is missing their top scorer or first-choice keeper the whole calculation has to shift and the model weights that drop in > the all-time head-to-head history, because sometimes one team just has another team's number for whatever reason and the raw rankings don't catch that pattern on their own > a simulation that runs each potential match thousands of times in the background so the model can also tell you who's most likely to win the entire World Cup, not just any single match ▫️Here's what a prediction actually looks like look at the screenshot. it's Argentina vs Austria. the model picks Argentina to win 2-1, gives them a 71% chance against Austria's 7%, with a 22% chance of a draw... { argentina won by 2-0 } what i like is it tells you exactly why, not just gives you a number. - Argentina is ranked #2 in the world while Austria is #24. - Argentina has won their last 5 matches scoring 15 goals and conceding only 1. - Austria has 4 wins and a draw in their last 5 with 11 goals scored and 3 conceded. - And the two teams have only played twice ever, with the last match being a 1-1 draw all the way back in 1990. so you're not taking the prediction on faith, you can see exactly what's driving it. i love what i have built... feel free to drop any 2 World Cup teams in the comments and i'll run the model for you tonight..

Axel Bitblaze 🪓

34,535 次观看 • 1 个月前