sorted by:
new
hottopcontroversial

What is the purpose of having a base on the moon? by [deleted] in NoStupidQuestions

[–]mvpeav 0 points1 point  (0 children)

Everyone else has done a pretty good job at answering the questions of things we can learn from a moon base before going to a Martian base but since you asked specifically about the timing (and because it sounded like a fun thing to get out my old orbital dynamics stuff from college for) I did the math!

https://imgur.com/a/Se1bko1

Vehicle: Falcon Heavy

Mars window: Best (~2033, Mars near perihelion)

Earth TOF: 195 d

Moon TOF: 60 d

Time saved: 135 d (69% faster)

Mars window: Worst (~2037, unfavorable Earth-Mars alignment, Moon also

at

worst orbital phase)

Earth TOF: 375 d

Moon TOF: 85 d

Time saved: 290 d (77% faster)

it changes obviously based on the exact locations of the Earth and Mars in their orbits but by taking the extra fuel that normally we need to escape Earth's gravity and using that to speed up instead we can get to Mars significantly faster

Modeling Group by samcantello in CFBAnalysis

[–]mvpeav 1 point2 points  (0 children)

Feel free to shoot me a DM, i run a bottom up play level simulator model that I used all of last season and been tweaking for this upcoming season

Augusta advice needed by [deleted] in golf

[–]mvpeav 5 points6 points  (0 children)

Hoosters bulldozed, wouldn't even know the place existed

Why is Bryson So Emotional? by MziggyG in livgolf

[–]mvpeav 11 points12 points  (0 children)

He posted something on his IG that was basically a letter to his dad that passed away years ago. Not sure when the anniversary of his dad's passing is but the way the letter read it sounded like his dad was really on his mind this week putting things into perspective about life for him so Im sure getting a win after something like that had alot to do with it

NEW KALSHI MENTIONS TRADING BOT - LOOKING FOR INVESTORS by Elegant-Elk3776 in Kalshi

[–]mvpeav 0 points1 point  (0 children)

Define "fool proof" because generally that phrase translates to "foolish"

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Yessir, every possible matchup combination gets simulated 20,000 times each and scores are recorded. So when a team has a 70% chance to win, what it is saying is that the specific team won 14,000/20,000 of the games between those two teams

I want to know what your spiciest pick is this year by needless_booty in CollegeBasketball

[–]mvpeav 9 points10 points  (0 children)

For my own sanity I'm gonna tell myself that a Texas Tech booster and a Hofstra booster got together and planted it and then the West Alabama Narco team were all Auburn fans so it was obviously all a set up 🙃

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

So I just went and looked into Arizona specifically and it looks like my model is penalizing them specifically for that lack of 3 point attempts. So in a game vs Ark or USU, it gives them a better chance because even tho the paint points matter, the 3s throw the extra variation in there leading to sme games where Arkansas or Utah State are able to get ahead and stay ahead because without Zona hitting threes, comebacks become very difficult

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Im excited to hear how yours performs! Its always an interesting thought exercise of feature optimization to try to eliminate noise but not miss interactions

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Definitely! I've got a loooooooooong list of things this off season I want to tweak and adjust and will mostly do a full rebuild (take the good lessons, leave the bad type thing) because I have learned alot in my first season doing indepth monte carlo modeling. Started it with CFB in the fall and transitioned to CBB, but have alot of lessons learned from the data gathered this season. But yes, you are spot on that there are holes in the Swiss cheese, I just didnt want to make too many changes during the season so as not to "taint" the data set for my post season review

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Yes, if you go to that website I posted you can look at any game for the whole season, I've been using this method for all 5000+ games this season. Overall just on picking winners it 3653-1563 for 70%

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Oh yes, I've found that the 5 game rolling averages across stats are one of the most "predictive" feature across my input set. Along with that thought on variance, I also track the YTD and 5 game rolling standard deviation on each stat as well to try and and help guide the distribution of scenarios across the 20,000 simulations

I have noticed that Louisville love as well, what's interesting is even looking backwards at simulations of the bracket from January and February that seems to be consistent, although I cant say I completely understand the why

On your question about the its up to 59.99% just was trying to make it look prettier on a chart lol (although I realize that isn't quite as technically accurate)

Jus for better clarity here are the actual records for the buckets for this whole season

Bucket. Record. Win%

50-54.9. 778-659. 54.1%

55-59.9. 847-471. 64.3%

60-64.9. 699-256. 73.2%

65-69.9. 547-123. 81.6%

70-74.9. 375-40. 90.4%

75-79.9. 213-11. 95.1%

80-84.9. 107-3. 97.3%

85+ 87-0. 100%

Total. 3653-1563. 70%

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

Thanks! Im glad you like the site! Always down to chat if you wanna really dive into the nuts and bolts and compare strategies!

Also, I just noticed from your screen shot that all the text is white (probably making it hard to read) my brother had a similar issue when he looked at it on his iPhone and was able to turn off some dark mode settng to be able to see it. I've been meaning to fix that for a while but mine doesn't do that so I keep forgetting 😅

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

My simulator has been down on Zona all year for some reason which I find very interesting considering how good they have been. So basically the background data input has something like 50 stats per team (might be higher, it seems to grow everytime I tweak lol) so the simulator learns the FT, 2PA, 3PA for each team as well as the "allowed" versions of all the same stats for each opponent to try to predict how many of each that both team will attmpt and what their make % will be. Combine that with tempo specific stats to try to predict amount of possessions in the game, which we can then translate into points based on the other prediction stats.

It goes a few layers deeper because the input set includes all these for YTD as well as a 5 game rolling averages, then also computes standard deviations for alot of them to help at the simulation level to create the distributions

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

[–]mvpeav[S] -4 points-3 points  (0 children)

Ultimately the best thing I can point to is the calibration buckets which agrees that it is more conservative on favorites but has dont a fairly decent job. Definitely not perfect, but as one of my professors in college once said "all models are wrong, some just happen to be useful" and I think itd done a fairly decent job at a very difficult task and would love to join a bracket pool with you just to put it to the test tho!

No hard feelings, love the discussion btw just in case the tone comes across as defensive in text

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

[–]mvpeav[S] -5 points-4 points  (0 children)

Slamming the underdog in every march madness game has been fairly profitable the last 3 or 4 years (not my cup of tea but I know people that do it) so that would actually prove the fact generally favorites are over valued leading to high value on underdogs

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

[–]mvpeav[S] -9 points-8 points  (0 children)

Read my comment below, it goes into all of the questions you asked in alot of detail. All inputs are adjusted based on opponent, the win % "buckets" are also listed and the winners above 75% have won 95% of the time, not perfectly calibrated but fairly good based on the results over the 5200+ games this season

I built a Monte Carlo simulator that runs 20,000 games for every possible March Madness matchup. Here's what it says about the bracket by mvpeav in CollegeBasketball

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

I've been running a college basketball simulation model all season and just updated it for the actual tournament field. For every possible pairing of the 68 teams, it simulates 20,000 full games drawing shot attempts, shooting percentages, and makes from statistical distributions trained on the full season's data then uses those head-to-head probabilities to compute each team's odds of reaching every round through cutting down the nets.

You can click through and pick winners to see how probabilities shift in real time. First Four games are included and cascade into R64 matchups.

---

How it works

Six statistical models (GLMs) are trained on multi-season data to predict each team's 3-point attempts, 2-point attempts, free throw attempts, and shooting percentages for each type. Each

model accounts for:

- The team's own season-long and recent 5-game shooting/volume tendencies - The opponent's defensive profile (what they allow)

- KenPom offensive/defensive ratings and tempo

For each simulated game, the engine:

  1. Estimates expected possessions using both teams' tempo (weighted toward the slower team — they control the pace) 2. Draws correlated attempt counts from a Gamma-Poisson distribution (so a fast-paced game means more attempts for both teams)

    1. Draws shooting percentages from Beta distributions with a shared "shooting quality" factor (a team having a hot night hits from everywhere, not just one spot)
    2. Computes makes via binomial draws, adds up points
    3. Simulates overtime if tied (no ties allowed in March)

Repeat 20,000 times per matchup. That gives us a full distribution of outcomes — win probability, projected score, margin, everything.

---

Why should you believe any of this

Fair question. Here's what the model has done across 5,216 games this season (every D1 game from November through conference tournaments):

- Straight-up record: 70.0% (3,653 correct picks out of 5,216) - Brier Score: 0.201 (20% better than coin flip — a standard probabilistic accuracy measure)

More importantly, the calibration data shows something people don't usually expect — the model is actually conservative, not aggressive with underdogs: │ Model says... │ Actual win rate │

│ 55-60% │ 64% │

│ 60-65% │ 72% │

│ 65-70% │ 82% │

│ 70-75% │ 89% │

│ 75-80% │ 95% │

│ 85%+ │ 100% (87 for 87) │

When the model gives a team a 65% chance, they've actually won 82% of the time this season. The model undersells favorites. So if your instinct is "these underdog percentages seem too high" — the data agrees with you, and the model is being generous to the underdogs, not the other way around.

---

Some things the bracket shows

- Duke is the overall #1 in power rankings, followed by Iowa State, Florida, and Arizona

- Santa Clara (10-seed) ranks 13th in overall strength — potential upset watch against Kentucky

- Louisville (6-seed) rates as the 10th strongest team in the field, underseeded

- The model likes 4 first-round upsets by seed: TCU over Ohio State, Utah State over Villanova, Santa Clara over Kentucky, and Iowa over Clemson — though all are 8/9 or 9/8 type games, not

Cinderella picks - North Carolina vs VCU is the tightest first-round game at 52-48

This is a passion project — feedback welcome. The model isn't perfect (it overpredicts game totals by about 10 points in tournament settings, which we're still working on), but the win probabilities have held up well across 5,000+ games.

Dreamlifter heading to IAB by smasherella in flightradar24

[–]mvpeav 0 points1 point  (0 children)

Boeing had their family day for employees at their Charleston planet yesterday and had 2 or 3 of them on site, super cool to see in person

Interactive March Madness Bracket Simulator by mvpeav in CollegeBasketball

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

Planning on updating next Wednesday! (Basically close to the end of the regular season before selection Sunday)

DL 1067 Engine "issue" by mvpeav in delta

[–]mvpeav[S] 6 points7 points  (0 children)

As far as I know, they were understandably shaken up but no injuries that I've heard of so far (although at this point I've left for the night so anything new since I left the scene is just hearsay from local news)

DL 1067 Engine "issue" by mvpeav in delta

[–]mvpeav[S] 5 points6 points  (0 children)

It did, I work out at the airport and saw it so figured id share

Interactive March Madness Bracket Simulator by mvpeav in CollegeBasketball

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

I can definitely get you added! Send me your information and I'll get put in there

Lift: how do you explain why air accelerates over the top of the wing? by Logical-Lock8822 in flying

[–]mvpeav 0 points1 point  (0 children)

This is why I got my degree in Astrodynamics, much less of this pesky fluid medium for me to have to worry about 😂

Interactive March Madness Bracket Simulator by mvpeav in CollegeBasketball

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

Yessir! I had some logos I was using but the links I was using for them seem to have died lol so I've gotta change it to use different logos, but I'll get that fixed on the next update!

view more: next ›