*tl;dr: skip to #5*
Just posting this here for reference:
https://www.reddit.com/r/smashbros/comments/577588/updated_using_results_since_116_i_made_a_100/
Notice how Pac-Man is above Corrin, Robin, Lucario, Villager, Ryu, Pit, Yoshi, and Ike among others, and almost at the level of Ness, Captain Falcon, and Donkey Kong? He's still showing up, people!
I'm always skeptical about what comes off of Reddit, so I went through and looked at the numbers. I think my skepticism is justified here.
1. The "point system" is really odd, quote the creator, "Yeah, this is the best I could come up with, It's better than the last one but It's still really bad."
2. People tend to ignore size of the tournament; here a 300-person tournament is worth just as much as a 700-person or 1000+ tournament, which in my opinion is silly. 4th/700 is simply not the same as 4th/300, a place at a tournament with more people should be weighed more.
3. The tournaments chosen skew the data. Here's the tournament list, by area:
Texas – 2
Georgia – 1
New Jersey –2
Mexico –2
Illinois—2
Florida—2
Tennessee—1
California—3
Virginia—1
Japan—2
Massachusetts—1
Netherlands—1
Michigan—1
And here it is again by common US boundaries:
(US)
West --3
Mid-West --3
South--7
North-East--2
(Japan) 2
(Mexico) 2
(Europe) 1
This means some people's performance is weighed more heavily solely because they attend more tournaments, not necessarily performance. Overall, players from south US are drastically more determinant of the tier list than Japan and Europe.
4. A couple players travel really well (Dabuz plays in the US, Mexico, and Japan!), so their performance is weighed more heavily. Example: a player may take 1st in one tournament, but if they only play in that tournament, their character performance is worth less than someone who, say, takes 1st in three tournaments.
5. Finally, the factors above combine to skew some characters heavily. Usually because a player plays in too many tournaments so their vote is worth much more, or a sum of average-ish performances pushes the character higher. The biggest offenders here are:
(High outliers)

(Low outliers)
Basically, the way this list was constructed is skewed in such a way that it benefits/hurts these characters by a significant amount. The data is certainly useful, but some characters have a wide margin of error.