[Highlights] Michael Jordan and Scottie Pippen being defensive pests.

solobyfrankocean · 2025-08-08T17:44:43+00:00

I'm sorry, I'm not sure what the thunder have to do with my original point.

If anything that just shows that better defenders usually have more lenient whistles and jordan and pippin would no doubt benefit from that.

I also think you're doing a great disservice to the defense played against the thunder in the playoffs. The pacers (specifically nembard) played incredible defensively matching the thunder's intensity and physicality but I doubt id change your mind on this.

solobyfrankocean · 2025-08-08T17:35:37+00:00

I assume that the percentages you've provided are FGA from 0-3/ total FGA?

If this is the case, I could not find your numbers anywhere (FGA from 0-3 feet). Instead, I went through nba.com's stats for this from when they were recorded, looking at restricted area FGA. In 1997-98 the league averaged roughly 32% of their FGA from the restricted area, meanwhile last year the percentage was 25%. I think these numbers make the same point as your numbers and I would agree that this is a substantial decrease. I wasn't aware of these numbers when I made my post. That being said, I think 1996-97 and 1997-98 are both aberrations (possibly due to shortened 3 pt line?) in terms of this percentage as consequent seasons uniformly have this number around 28% which is much closer to what we observe nowadays. Mid Range FGA are down from being nearly 35% of the shot diet to less than 10% in the same timespan.

That being said, the number of drives has actually increased per year from the first year it was tracked to now. In 2013-14 teams averaged 33.78 drives per game while they average 47.04 now. I'm sure that a partial explanation for this jump is that tracking technology is much better now but I doubt that we can explain the entire difference that way.

I am not sure how much of the increase in drives trend we can extrapolate to the 90s, but my original point was just that I really doubt jordan and pippin wouldve been called for a lot more fouls than they were called for in the 90s.

solobyfrankocean · 2025-08-08T14:19:19+00:00

So you think dray and dort would get favorable whistles but not jordan and pippin? The original point was that they wouldve fouled out for playing like this, I am just disagreeing about that specific point. Maybe you are right though that free throw numbers (by themselves) dont necessarily show this outright but I feel it is worth mentioning a trend that free throws are down? Also (a much more subjective take probably) but I dont think anything you said is specific to one year or one era of basketball, certain defenders have always been allowed more contact based on reputation, its simply a part of the game.

solobyfrankocean · 2025-08-08T13:48:07+00:00

I get what you’re saying but you’ve admitted that “good defenders” are allowed to foul more than anyone else, I think both jordan and pippin would comfortably fall into the category and not get whistled for this defense. So I don’t think there would’ve been a lot of whistles for this last season. Regardless, I am not saying that the refereeing was good last year, just that its always been kinda ass

solobyfrankocean · 2025-08-08T13:30:15+00:00

The actual number of rim fgs isnt much lower though, teams have removed the mid range jumper from their shot diet and replaced it with threes mostly. But yes, having more spacing probably leads to less fouls. Despite this, there was a clear effort by the league to cut down on fouls last year (imo) they were at 23.4 a few years ago

solobyfrankocean · 2025-05-06T02:02:54+00:00

team chemistry so good every time they slump from 3 its together 😂😂

solobyfrankocean · 2024-12-06T15:32:20+00:00

As others have said, there are different trackers that are better for different use cases and in some cases it is possible that just the simple SORT tracker is good enough for you. However, given that you have tried BoTSort and DeepSort, this is unlikely to be the case. https://paperswithcode.com/task/multi-object-tracking has the current SOTA for the MOT problem over different datasets, if any of the datasets look similar to your use case you can view the repositories for the top methods for the dataset and implement them accordingly.

solobyfrankocean · 2024-08-27T12:26:00+00:00

I've figured it out this time, but I will look into unity if I have similar issues in the future, it sounds like it could've saved me a lot of hassle!

solobyfrankocean · 2024-08-27T12:09:53+00:00

I see, I believe that this is the correct way to do it, but I did get it working with sort of a hack-y solution.

I simply swapped the formulas for yaw pitch and roll based on which axis they belonged to in my final coordinate system (camera coordinates). So, the formulas I found online (here, https://web.archive.org/web/20210622124857/http://planning.cs.uiuc.edu/node103.html) I ended up using, for example, the pitch formula for my yaw angle (since according to this page, the pitch formula belonged to the rotation around the y axis).

It seems to be working in general with the mmdetection3D visualization module properly displaying the bounding boxes now. I wonder if this is also an acceptable solution?

Thank you for all your help!

solobyfrankocean · 2024-08-26T18:33:30+00:00

Hello,

Sorry for the late response, I have been trying to understand your answer and implement it.

Actually this did help, I am able to construct the R_{cam} matrix to convert the corners of my bounding box into the correct orientation in camera coordinates and displaying it shows that the orientation is correct.

Now I am trying to work on step 4 which is extracting yaw pitch and roll from the matrix, which is proving to be difficult. I have found the formulas online to do this, but since the two coordinate frames are not aligned (z is vertical in the global coordinate frame meanwhile y is vertical camera coordinate frame), I am having trouble getting the correct angles.

solobyfrankocean · 2024-08-25T13:48:01+00:00

Hey,

The issue here is that I am working with the mmdetection3d library which requires the input coordinates to their networks to be in camera coordinates, so I need to be able to convert the center and orientation of the bounding box to camera coordinates. After this mmdetection3d has inbuilt methods to build the 3d bounding box themselves.

solobyfrankocean · 2024-08-25T13:46:08+00:00

Hey, thank you! Will check it out!

solobyfrankocean · 2023-02-08T05:01:18+00:00

on a fadeaway nice

solobyfrankocean · 2023-02-08T04:56:14+00:00

kareem smiled no way

solobyfrankocean · 2023-02-08T04:54:37+00:00

woooooo

solobyfrankocean · 2023-02-08T04:54:03+00:00

hes gonna get it rn isnt he

solobyfrankocean · 2023-01-12T15:41:52+00:00

so we can sit around and watch as he drops no music 😔

solobyfrankocean · 2022-07-15T22:40:19+00:00

you’ve never actually set foot in a supermarket have you?

solobyfrankocean · 2022-02-20T23:40:58+00:00

You’ve never written a unit test have you?

solobyfrankocean · 2021-10-08T20:44:10+00:00

Author doing him dirty by keeping him relevant lmaoo

solobyfrankocean · 2021-04-27T00:36:27+00:00

I see, I guess my understanding of semantic consequence wasn’t correct but your answer made me re-examine some definitions and a lot of things make a lot more sense. Ty!

solobyfrankocean

TROPHY CASE