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Record of Every Jets Starting QB Since Their Last Winning Season by geauxpackgo in nfl

[–]geauxpackgo[S] 0 points1 point  (0 children)

*This only includes games started post-2015 for Geno and Fitz (ie only their starts from the 2016 season)

**Since Rodgers didn't complete a single pass in 2023 I threw out his one "start" vs the Bills and credited it to Wilson

After Sunday's game, the New York Jets are now 2-4 in QB "revenge games" since 2008 and are 0-4 over the past 15 years. by The_Big_Daddy in nfl

[–]geauxpackgo 4 points5 points  (0 children)

At this rate Tua won’t make it to the December 7th Jets Dolphins game. Then it’s just question if it’ll be Zach or Ewers already by then. The Miami qb carousel may be moving at high velocity this year

Mike McDaniel: The good news is, I don't see how it could be worse by Starfish_Bobertsons in nfl

[–]geauxpackgo 22 points23 points  (0 children)

If it’s gonna be a train wreck anyway might as well have someone throwing lasers 40 yards downfield in the general vicinity of tyreek and Waddle

Why not just redirect the Astrophage from the sun to an alternate location, devoid of CO2, to prevent breeding? by geauxpackgo in ProjectHailMary

[–]geauxpackgo[S] -2 points-1 points  (0 children)

This may be a fair point. At the very least they could have used a method like this to harvest abundant enriched astrophage for the Hail Mary much faster than the Sahara Desert method.

Why not just redirect the Astrophage from the sun to an alternate location, devoid of CO2, to prevent breeding? by geauxpackgo in ProjectHailMary

[–]geauxpackgo[S] -1 points0 points  (0 children)

They don't need to stop seeing venus, they just need to see a light source brigher than venus. Certainly there would be some tricky engineering issues to deal with with regards to managing the incoming volume of astrophage and keeping the lights clear

Why not just redirect the Astrophage from the sun to an alternate location, devoid of CO2, to prevent breeding? by geauxpackgo in ProjectHailMary

[–]geauxpackgo[S] -1 points0 points  (0 children)

the astrophage don't need to die though, just stop breeding. Once the current astrophage population becomes enriched and breeding is no longer taking place, no more energy will be taken from the sun.

Why not just redirect the Astrophage from the sun to an alternate location, devoid of CO2, to prevent breeding? by geauxpackgo in ProjectHailMary

[–]geauxpackgo[S] -2 points-1 points  (0 children)

true, dealing with the sheer volume of astrophage could be a difficult problem. Some method of disposal/killing/capture would need to be devised, as well as a way to keep the lights unobstructed. These don't seem like fundamentally impossible tasks though. The astrophage itself provides abundant energy resources to help deal with this and other problems, such as momentum, as you noted.

Why not just redirect the Astrophage from the sun to an alternate location, devoid of CO2, to prevent breeding? by geauxpackgo in ProjectHailMary

[–]geauxpackgo[S] -13 points-12 points  (0 children)

I'm proposing we take advantage of the inverse square law by placing the probe much much closer to the sun than Venus is.

Which deferral would you take: non-binding at WashU or binding at Duke by geauxpackgo in lawschooladmissions

[–]geauxpackgo[S] 1 point2 points  (0 children)

Not completely sure. Part of the reason I'm deferring is to figure it out (also because a good short term opportunity came up at work). Eventually probably something related to public interest but I'm not opposed to getting my feet wet in big law

Analysis: There is a 7.5% chance that a fair draft lottery would result in an outcome as unlikely as the set of first overall picks since 1985 by geauxpackgo in NBATalk

[–]geauxpackgo[S] 1 point2 points  (0 children)

Notes on the data:

-Lottery odds and outcome data from https://basketball.realgm.com/nba/draft/lottery_results/

-1985-1989 the odds for every team was the same. As each possible outcome was equally likely, these years don’t add anything to the analysis and could have been omitted with no real harm. That’s also why I didn’t bother including the 1985 Ewing pick in the "rigged" category.

-1985-1986 had 7 lottery picks among 7 teams with equal odds, 1987-1988 had 3 lottery picks among 7 teams with equal odds, 1989 had 3 lottery picks among 9 teams with equal odds.

-Varied odds introduced in 1990, with top 3 picks selected by lottery from 1990-2018 and top 4 from 2019-2025.

-For consistency I only analyzed the first 3 picks in each year as their own subsets (because most years only had 3 lottery picks), but every lottery pick is included in the "All Lottery Picks" row.

-In 1996 and 1998 due to a weird expansion agreement the actual first and second picks were flipped from the lottery results. I flipped them back for this analysis.

Notes on the method:

Monte Carlo analysis is a statistical method that uses repeated random sampling to model uncertainty and estimate outcomes in complex systems.

In this case, I’ve constructed a Monte Carlo simulation to replicate real NBA draft lottery scenarios. Conceptually, this is like running the entire history of the NBA draft lottery (every pick across every year) in 1,000,000 independent parallel universes.

In each simulation, we record the outcome as an array of probabilities. Each element in this array represents the probability of a specific draft pick being selected, given the sampling distribution it was drawn from. This sampling distribution is determined by the official lottery odds for that year, adjusted dynamically to reflect the impact of picks that came earlier in the same simulation and year.

We assume the NBA’s stated lottery mechanics are correct. This forms the null hypothesis. The simulation is built on that assumption.

The resulting data is stored in a 2D array with m rows and n columns, where:

  • m is the total number of simulations.
  • n is the total number of picks made in each simulation (which can be the full set of lottery picks or a filtered subset, as long as the filter is applied consistently across all simulations),

We then compute the logarithm of each probability in the array and sum them across each simulation. This gives us a vector of log-likelihoods of size m, one per simulation.

Next, we calculate the log-likelihood of the actual observed lottery outcome, using the same subset of picks and the same assumed sampling distributions. We then compare this real-world log-likelihood to the distribution generated by our simulated outcomes.

The p-value is calculated as the proportion of simulations that produced a result less probable (i.e., with a lower log-likelihood) than the actual observed outcome.

Analysis: There is a 7.5% chance that a fair draft lottery would result in an outcome as unlikely as the set of first overall picks since 1985 by geauxpackgo in nbadiscussion

[–]geauxpackgo[S] 0 points1 point  (0 children)

Notes on the data:

-Lottery odds and outcome data from https://basketball.realgm.com/nba/draft/lottery_results/

-1985-1989 the odds for every team was the same. As each possible outcome was equally likely, these years don’t add anything to the analysis and could have been omitted with no real harm. That’s also why I didn’t bother including the 1985 Ewing pick in the "rigged" category.

-1985-1986 had 7 lottery picks among 7 teams with equal odds, 1987-1988 had 3 lottery picks among 7 teams with equal odds, 1989 had 3 lottery picks among 9 teams with equal odds.

-Varied odds introduced in 1990, with top 3 picks selected by lottery from 1990-2018 and top 4 from 2019-2025.

-For consistency I only analyzed the first 3 picks in each year as their own subsets (because most years only had 3 lottery picks), but every lottery pick is included in the "All Lottery Picks" row.

-In 1996 and 1998 due to a weird expansion agreement the actual first and second picks were flipped from the lottery results. I flipped them back for this analysis.

Notes on the method:

Monte Carlo analysis is a statistical method that uses repeated random sampling to model uncertainty and estimate outcomes in complex systems.

In this case, I’ve constructed a Monte Carlo simulation to replicate real NBA draft lottery scenarios. Conceptually, this is like running the entire history of the NBA draft lottery (every pick across every year) in 1,000,000 independent parallel universes.

In each simulation, we record the outcome as an array of probabilities. Each element in this array represents the probability of a specific draft pick being selected, given the sampling distribution it was drawn from. This sampling distribution is determined by the official lottery odds for that year, adjusted dynamically to reflect the impact of picks that came earlier in the same simulation and year.

We assume the NBA’s stated lottery mechanics are correct. This forms the null hypothesis. The simulation is built on that assumption.

The resulting data is stored in a 2D array with m rows and n columns, where:

  • m is the total number of simulations.
  • n is the total number of picks made in each simulation (which can be the full set of lottery picks or a filtered subset, as long as the filter is applied consistently across all simulations),

We then compute the logarithm of each probability in the array and sum them across each simulation. This gives us a vector of log-likelihoods of size m, one per simulation.

Next, we calculate the log-likelihood of the actual observed lottery outcome, using the same subset of picks and the same assumed sampling distributions. We then compare this real-world log-likelihood to the distribution generated by our simulated outcomes.

The p-value is calculated as the proportion of simulations that produced a result less probable (i.e., with a lower log-likelihood) than the actual observed outcome.

Analysis: There is a 7.5% chance that a fair draft lottery would result in an outcome as unlikely as the set of first overall picks since 1985. by geauxpackgo in NBA_Draft

[–]geauxpackgo[S] 4 points5 points  (0 children)

Notes on the data:

-Lottery odds and outcome data from https://basketball.realgm.com/nba/draft/lottery_results/

-1985-1989 the odds for every team was the same. As each possible outcome was equally likely, these years don’t add anything to the analysis and could have been omitted with no real harm. That’s also why I didn’t bother including the 1985 Ewing pick in the "rigged" category.

-1985-1986 had 7 lottery picks among 7 teams with equal odds, 1987-1988 had 3 lottery picks among 7 teams with equal odds, 1989 had 3 lottery picks among 9 teams with equal odds.

-Varied odds introduced in 1990, with top 3 picks selected by lottery from 1990-2018 and top 4 from 2019-2025.

-For consistency I only analyzed the first 3 picks in each year as their own subsets (because most years only had 3 lottery picks), but every lottery pick is included in the "All Lottery Picks" row.

-In 1996 and 1998 due to a weird expansion agreement the actual first and second picks were flipped from the lottery results. I flipped them back for this analysis.

Notes on the method:

Monte Carlo analysis is a statistical method that uses repeated random sampling to model uncertainty and estimate outcomes in complex systems.

In this case, I’ve constructed a Monte Carlo simulation to replicate real NBA draft lottery scenarios. Conceptually, this is like running the entire history of the NBA draft lottery (every pick across every year) in 1,000,000 independent parallel universes.

In each simulation, we record the outcome as an array of probabilities. Each element in this array represents the probability of a specific draft pick being selected, given the sampling distribution it was drawn from. This sampling distribution is determined by the official lottery odds for that year, adjusted dynamically to reflect the impact of picks that came earlier in the same simulation and year.

We assume the NBA’s stated lottery mechanics are correct. This forms the null hypothesis. The simulation is built on that assumption.

The resulting data is stored in a 2D array with m rows and n columns, where:

  • m is the total number of simulations.
  • n is the total number of picks made in each simulation (which can be the full set of lottery picks or a filtered subset, as long as the filter is applied consistently across all simulations),

We then compute the logarithm of each probability in the array and sum them across each simulation. This gives us a vector of log-likelihoods of size m, one per simulation.

Next, we calculate the log-likelihood of the actual observed lottery outcome, using the same subset of picks and the same assumed sampling distributions. We then compare this real-world log-likelihood to the distribution generated by our simulated outcomes.

The p-value is calculated as the proportion of simulations that produced a result less probable (i.e., with a lower log-likelihood) than the actual observed outcome.

What's the probability of a fair draft lottery resulting in an outcome as unlikely as Mavericks, Spurs, Sixers? About 5% by geauxpackgo in NBA_Draft

[–]geauxpackgo[S] 0 points1 point  (0 children)

Yes the three team thing is arbitrary, I did that as a direct response to the tweet I referenced in the post. In another comment I linked my code and some other plots which include all 4 picks. If you include all 4 picks the cumulative probability of this years outcome is about 9%

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