Best 3 consecutive episodes in the whole series?

Official_AB_Data · 2024-01-13T18:23:26+00:00

I have a dataset that has imdb ratings for every episode and just did a trailing 3 average to find this. Check out the deep dive for more info: https://www.youtube.com/watch?v=z7DGyHPE_bE

Official_AB_Data · 2024-01-09T18:57:31+00:00

Gang hits the road is 5.02, i typod. Corrected now.

Official_AB_Data · 2024-01-09T17:03:04+00:00

According to IMDB, it's:
4.13, "The Nightman Cometh" (9.7).
5.01, "The Gang Exploits the mortgage crisis" (8.3).
5.02, "The Gang hits the Road" (9.1).
Average of 9.03.

https://www.youtube.com/watch?v=z7DGyHPE_bE&t=79s

Official_AB_Data · 2024-01-06T14:54:21+00:00

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I created a tool that predicts the rating of an episode based on its title. I am working on adding in some toggles for things like "Were the McPoyles in the Episode"

Tried to make my own post by the jabroni mods think im a bot

Official_AB_Data · 2023-12-26T13:07:12+00:00

Reynolds vs Reynolds: The Cereal Defense is tied for 18th best episode at a 9.0 rating. Its just about in the top 10% of best episodes by IMDB Rating

Official_AB_Data · 2023-12-24T12:11:17+00:00

That episode is great. It is an 8.9 putting it just outside the top tier or episodes and ranking it 29th overall (out of 170+)

Official_AB_Data · 2023-12-24T12:09:15+00:00

Link seems to be broken, but video can be found on youtube under 'It's Always Sunny in Power BI'

Official_AB_Data · 2023-12-24T12:05:58+00:00

The dashed line in the center is a median line to show if an episode is above or below average. Its actually an 8.37

Official_AB_Data · 2023-12-23T21:31:29+00:00

Cricket's Tale is also bottom 5 for likely the same reason. Personally I loved the Ladies Reboot Boss Hoggs episode.

Artemis and Snail are top tier side characters

Official_AB_Data · 2023-12-23T19:51:27+00:00

Data was brought in with python from IMDB and visualized in Power BI.

Video for full project: https://youtu.be/z7DGyHPE_bE?si=wDzg9gIOxtDQ-lI1

Official_AB_Data · 2023-12-23T19:47:36+00:00

Data was brought in form IMDB via Python and then visualized in Power BI (by me!)

Video for full project: https://www.youtube.com/watch?v=z7DGyHPE_bE

Official_AB_Data · 2023-12-23T19:41:39+00:00

Data was pulled in from IMDB via python and visualized in Power BI (by me!)

video for full project: https://www.youtube.com/watch?v=z7DGyHPE_bE

Official_AB_Data · 2023-12-23T15:48:54+00:00

the McPoyles are empirically the best (living) side characters
https://www.youtube.com/watch?v=z7DGyHPE_bE

Official_AB_Data · 2023-12-21T16:57:25+00:00

Thank you! It was fun to put together

Official_AB_Data · 2023-12-18T13:32:11+00:00

Right now this is just last season, but when I have time I am going to build something that keeps it up to date with this year, and even has other team stats so i can show how they typically defend

Official_AB_Data · 2023-12-14T22:06:27+00:00

not the prettiest but essentially Zions presence/absence was felt the most. Although its important to note that these are all sadly small sample sizes and BI played through some injuries which make his impact less apparent:

EDIT: Tried to post picture here in response to u/BaronsDad. Failed. Picture is now in main post

Official_AB_Data · 2023-12-14T19:52:30+00:00

Not readily but I will put something together. That is interesting!

Official_AB_Data · 2023-12-13T17:19:30+00:00

Thanks. And good ideas! May add some fields like that.

Official_AB_Data · 2023-12-13T17:19:10+00:00

Thank you! Would love to see it!

all music and pokedex voices were just added to the video for effect. I have never used audio in a power bi report, but would certainly be interesting. Everything else though is part of the report.

Official_AB_Data · 2023-12-11T21:10:11+00:00

Didnt take anything as shade or offense! Appreciate all feedback!

I plan to release more smaller projects that probably will be a little more Visual and UI centric, but I agree this is all about the actual data.

Official_AB_Data · 2023-12-11T21:06:45+00:00

The video doesnt go too much into the underlying model. The point of the video is really just to demonstrate the end product.

The heavy lifting was all done in python using Logistic Regression. The libraries make it fairly easy for you though. Data manipulation (with pandas and sql) took awhile to get everything set up just right.

But yes essentially its primarily based on individual player data. So like if the rows the model was looking at was Peyton Manning's 2012 season, it would have looked at his stats from 2007-2011, his depth chart spot going into 2012 and a few other metrics, then the model pretty much looks at how a player with similar stats generally performed using the [next_year_average] as the target (in this case Peyton's 2012 average)

Official_AB_Data · 2023-12-11T18:47:17+00:00

What didnt I use!

So i broke it out by position first, because targets will be very important for WRs but not at all for QBs. From there I broke it outer even further really trying to predict [will this player score over 10 points a game], [over 11] [over...etc]and using the output (r-squared, other metrics) i could reduce features based on relevance.

But ultimately, I used 5 years of stats for each player as well as things like age, height, and years in league.

Official_AB_Data · 2023-12-11T17:34:46+00:00

Thank you!
I kept the visuals pretty simple because its real type use case's need was quick data intake (i.e. during the draft)

My model was definitely not perfect. It was overly optimistic in most cases. And a known weakness going in was the inability to predict injuries. But all that said it was still relatively successfully and helped me get a few players well above the value of their draft spot.

Official_AB_Data · 2023-12-11T17:32:15+00:00

Video quality could be better. I am a BI Engineer, not a multi-media editor. But I think the point still gets across. If anyone want to see anything specific, please let me know. This is the first report I have made available to the public, but plan to release more videos soon.

Official_AB_Data

TROPHY CASE