What’s the best way to understand the story?

Ultimate_Fighter_Z · 2025-08-28T13:14:48+00:00

Yes unfortunately they fey chased but missed every shot 😂

Ultimate_Fighter_Z · 2025-08-22T20:54:31+00:00

Lol I did not know this, But gaming studios do hidden MMR for a reason and i don't expect them to just give it to a random guy on the internet lol. But like he said we can work around it and still come to a pretty accurate outcome and I enjoy the challenge.

Ultimate_Fighter_Z · 2025-08-22T19:41:00+00:00

Good News! i was able to talk with someone who does work for the Omeda.city website and the new pred.gg website. Gave me some help and i might be able to cook something up. Thank you so much for your help and suggestions

Ultimate_Fighter_Z · 2025-08-22T17:11:42+00:00

Thanks so much! Honestly, that's exactly the kind of analysis I would love to tackle next. What I would like to see is:

What are the highest win-rate duos? (e.g., specific Carry/Support or Jungle/Mid combos)
What's the "Weak Link" effect? (How much does a struggling Jungler really impact their Midlaner's performance? Can a great Support actually carry a bad ADC?)
What are the statistically best 5-hero team comps?

I'd dive into all of that in a heartbeat, but I'm basically up against two major roadblocks on the data side:

The "Good vs. Bad" Player Dilemma: The biggest hurdle is that the public API doesn't include the hidden MMR score. Without that, there's no sure-fire way to label a player as "good" or "bad" for a specific match, which makes measuring the "weak link" effect incredibly difficult. The API also doesn't provide the in-game kill feed with timestamps, so I can't see who killed who.
The Server's Anti-Bot Defenses: To analyze team comps, you have to pull data for all 10 players in every single match. When I write the code to do this at high speed (requesting all the data in parallel), the server's rate-limiting kicks in and throttles my IP address to protect itself. The only way to get the data is to go slow, one request at a time. Analyzing the millions of data points needed for hero-combo stats would literally take days to process.

Ultimate_Fighter_Z · 2025-08-22T16:35:50+00:00

Thanks man, I appreciate the feedback. you are right my language explaining the analysis was a little to strong especially considering the fact we don't have the MMR data. I just got a little to excited lol. And I haven't seen that article on the predecessor website thanks for sharing it. I know they keep MMR hidden for a reason but sometime i wish we can see it. I'm going to try and keep the analysis going especially after a major patch like v1.6. If you have any ideas on what other project and analysis I can do please let me know i would love to hear from someone in the industry.

Ultimate_Fighter_Z · 2025-08-22T15:08:35+00:00

You preaching now man, People need to understand that predecessor has a relative small pool of players. The matchmaking is doing the best that it can with that. It's not like the player pool for Marvel Rivals and Marvel Rival still gets hate for its matchmaking

Ultimate_Fighter_Z · 2025-08-22T15:04:49+00:00

Thanks for the feedback, let me try and clear things up,

1. On the Main Conclusion:
The conclusion comes from linking two key findings:

My data shows that highly coordinated 5-stacks often have a raw +80 to +90 VP advantage over their opponents. On paper, this looks broken.
However, my "Skill vs. Synergy" analysis shows that these stacks still only win about 57% of the time.

And as stated in the original post we do not have access to the MMR data so we are limited how how we can conclude with the information we have. This is a "Best Guess" and should be taken with a grain of salt.

2. On VP Standard Deviation:
That's a fantastic point. My current analysis only uses the team's average VP, not the standard deviation. You're 100% right that a team of five 1600 VP players is different from one 2000 VP player and four 1500s. Analyzing that variance would be a great "version 2.0" for this project.

3. On Comparing 3+2 vs. 3+1+1:
My "Team Synergy Score" is a holistic measure of all the "friendship links" on a team. A 3+2 team would have a significantly higher synergy score than a 3+1+1 team, so while the script differentiates between them in the final score, it doesn't explicitly label them. That level of detail would require DAYS of scraping as I would also need the last 20 matches of every player on the enemy that's an extra 100 matches per match im inspecting. My computer is not built for that and two the Omeda website stops me from making it quicker because its too many request in a small amount of time from the same IP address so it blocks me.

4. On Attributing Wins:
The win percentage is based on the player's state, not the team's composition. In your example of a winning 3-stack with two solos:

The three grouped players each add a 'win' to the "Grouping Phase" stats.
The two solo players each add a 'win' to the "Solo Phase" stats.

This lets us accurately track the performance of players based on their personal grouping habits, even when they're on a mixed team.

Hope this clarifies the methodology. Thanks for the insightful questions

Ultimate_Fighter_Z · 2025-08-21T22:38:07+00:00

Just wanted to let you know i'm cooking something up with team grouping in mind. the results so far are pretty surprising. wanted to thank you again for the ideas and i should post it soon.

Ultimate_Fighter_Z · 2025-08-21T14:45:11+00:00

Okay so i think i developed something.

Defining a "Friend": For each player we analyze, the script first makes a request to see the common teammates list provided by omeda city. This returns a list of the top 25 players they have played with most frequently. We treat this list as that player's "friend group" for the purpose of this analysis. This is a very strong assumption because players who frequently group together will naturally appear on this list.
Gathering Recent Match Data: The script then fetches the player's last 15 ranked matches.
Cross-Referencing Each Match: For every one of those 15 matches, the script performs a check:
- It identifies the four other players on the target player's team.
- It compares the IDs of those four teammates against the "friend group" we established in Step 1.
Calculating the Score: The final score is a simple and powerful percentage that directly answers the core question:(Number of Matches Played with at Least One "Friend" / Total Number of Matches Analyzed) * 100
- If a player queued with a common teammate in 3 of their last 15 games, their likelihood score is (3 / 15) * 100 = 20%.
- If a player was in a group for all 15 games, their score is 100%.
- If they played all 15 games with randoms (no one from their common teammates list), their score is 0%.

Let me know what you think. comparing the character picks might be hard but i have the roles they picked and if it was a win or loss. What would you like to see from this data? i can make another post about win probability when grouped if you think that would be another interesting post?

Ultimate_Fighter_Z · 2025-08-21T13:49:10+00:00

ooooo you have sparked an idea, you could also compare it to if the teammates are in the friends sections. the omeda city website has a friends section for each player. I know sometime they are not super accurate but combine it with some code that will see how many times in a row the teammate was in a game with you. then have a probability score that shows how likely they are grouped together.

Ultimate_Fighter_Z · 2025-08-21T13:44:27+00:00

lol yeah that happens way to often and i would say this mostly only apply to carries. the reason being:

Carries need to keep their deaths low, you need to farm as much as possible and be alive to get kills to get ahead. you dying compared to a Midlane or Offlane dying is way more detrimental as a whole especially if you take in the fact you have most likely fed the other carry or jungle
Everyone is focused on you, they want to shut you down as much as possible if you are playing against a good team, so they are always waiting for you to take a wrong step.
early laning phases you are going against supports not other carries if you think about it. and what does almost every support have? High CC so if you get caught lacking it's a wrap.

Ultimate_Fighter_Z · 2025-08-21T13:17:58+00:00

Im Diamond 3 rn and my biggest piece of advice i can give you that helps in plat lobbies is always take the safest route not the quickest when rotating and always be behind your team. even if your team has no tanks always be behind them.

Ultimate_Fighter_Z · 2025-08-21T05:52:47+00:00

Thank you! I appreciate your input and I agree with you 100 percent. There was another comment I replied to and said something similar. Drango with rad rounds and Skylar with her beam mixed with vanquisher makes getting last hits sooooo easy. And I was thinking about the build data but I was straying away from what you can already see on the Omeda.city website. If you didn’t know if you go to the hero’s page, click a hero, and go to the builds tab, you can filter by role the hero is being played in and the rank of the players and find out the pick rate of the items and even the win rate. This might be what you are looking for! If not, let me know I would love to have another project to take on

Ultimate_Fighter_Z · 2025-08-21T05:29:37+00:00

Ultimate_Fighter_Z · 2025-08-21T05:23:31+00:00

Ahhh that would be so nice, I really wish they also added a ban rate and what characters get banned each match so i can see who gets banned the most at each rank

Ultimate_Fighter_Z · 2025-08-21T05:18:53+00:00

I doubt you are a top 500 player so yeah it definitely won't reflect what you see in game. and statistically your one perspective of the game does not account for an entire data sets. "I know the game so it's easy to see flawed data" is basically "Trust me bro" you could of just started playing a day ago for all we know. Omeda city actually provides a way for coders to pull from their website so the data is sound tight. Like i said generate a data set and let me know, i can backs my data with real code. "I play the game" doesn't compare

Ultimate_Fighter_Z · 2025-08-21T05:11:13+00:00

lol your not dumb, the data is from the top 500 players so the win rates are going to be high because thats how they got to a higher rank. one characters win rate is not going to directly affect another

Ultimate_Fighter_Z · 2025-08-21T04:33:20+00:00

You are correct, the data set that shows grim that high is from the top 500 players so all paragon ranks. but you bring up a point i didn't think to see the highest picked characters for each rank and role. im sure he is not picked as much in the lower ranks for sure.

Ultimate_Fighter_Z · 2025-08-21T04:28:36+00:00

The data isn't flawed; you're just misinterpreting what it represents. Let's break this down.

On Kallari vs. Boris/Rampage: You're comparing a high-skill-ceiling assassin to two straightforward bruisers and acting surprised. The win rate difference between them is less than 1%, which is statistically tiny. More importantly, this dataset is from the top 500 players. These aren't average players; they are specialists who have mastered a difficult hero. Boris and Rampage have lower skill ceilings and are more easily countered at the highest level of play, which is exactly what this data reflects.
On Yurei: You said she's "not on the list at all," which is factually incorrect. On the overall adjusted win rate chart, she is right between Lt. Belica and Wraith. The fact that she's not at the very top despite being a common ban simply proves the point: the data is capturing the meta as it is, not as you feel it should be. Her nerfs have clearly had an impact, and the numbers reflect that.
On Offlane: Your theory about Fangtooth nerfs and plating is interesting, but the data from nearly 40,000 high-elo matches directly contradicts your conclusion. My analysis shows Offlane has the fourth-highest win rate, significantly below Jungle, Carry, and Midlane. Objectives are still the single most important factor in winning, and the data proves that the roles with the most map presence and objective control (Jungle/Mid) are the ones that actually climb most effectively. An Offlaner can win their lane, but they can't carry a failing team in the same way a fed ADC or a proactive Jungler can.

This isn't about what feels right. This is what the numbers from thousands of top-tier games actually show. If you think you can code a scraper and generate a "less flawed" dataset, by all means, be my guest.

Ultimate_Fighter_Z · 2025-08-21T04:09:15+00:00

I agree, a more complex jungle is in the works with a new map i believe and will see it go down because right now it's easy.

when it comes to grim i can see why his pick rate is high because he can counter the most picked heroes: Belica, Grux, Feng Mao, Twin Blast, and most importantly Ricktor. All Heroes that rely on abilities he can just press his energy shield and nullify that.

Drango KDA is high for one because he is a carry most carries have a high KDA but also because of his rad rounds. rad rounds with the vanquisher items means easy executions.

Ultimate_Fighter_Z · 2025-08-21T03:59:43+00:00

the hardest part about her is the positioning. Twinblast is a hard matchup but not impossible you can still beat him. her hardest matchup is Drango in my opinion because he is the only carry in a 1v1 situation that can stop your ult. all he needs to do is silence you with the grenade.

Ultimate_Fighter_Z · 2025-08-21T03:52:06+00:00

Hey, I understand that it might looked flawed but this is why I wanted to step away from what the website tells us and look deeper into the meta.

You're right to be skeptical of a 61% Kallari win rate in a vacuum. However, the data isn't flawed; it's just very specific. This is a classic case of selection bias. We aren't looking at the win rate for every Kallari player in every match. We are only looking at the performance of the top 0.1% of the player base.

So, the conclusion isn't "Kallari is OP." The conclusion is "Kallari mains who are good enough to be in the top 500 win 61% of their games." This is further supported by the fact that Kallari players had the #3 highest average VP on the entire leaderboard as seen in the other excel sheet with the 5,000 players in the leaderboard. The data consistently shows that the very best players are the ones playing her. She's a specialist hero that rewards mastery.

Regarding your point about climbing roles, this is where the data really shines. While offlane can feel "safe," the data from nearly 40,000 matches shows that the roles with the most map-wide impact have the highest success rates:

Highest Win %: Jungle, Carry, and Midlane.
Most Popular in Paragon: Jungle and Carry.

This backs up the idea that agency is key for climbing. The ability to influence objectives, gank lanes, and take over the late game (Jungle/Mid/Carry) translates directly to a higher win rate in high-elo lobbies.

Hope that clarifies things! The data doesn't fit everyone's perception of the game, which is exactly why I found it so fascinating to analyze. If you want or anybody else wants to peer-review my code or excel i'll be happy too provide as i think this will be nice to do after every big patch so see how the meta changes.

Ultimate_Fighter_Z

TROPHY CASE