Marvel Rivals Competitive Meta Analysis: Season 2 Week 1

Welcome to the first of my Marvel Rivals Meta Analysis series! In this series, I’ll examine and analyze week-over-week shifts in the Rivals Competitive meta across the entire roster, all ranks, and all platforms. I’ll discuss trends and identify opportunities for heroes that may be positioned to outperform relative to the rest of the roster. I’m thrilled to have an entire week of data from the launch of Season Two: Hellfire Gala to support my analysis. This isn’t a hero tier list, it is an analysis of hero performance and data shifts over time.

Before we jump in, let’s talk about how this works.

How are heroes scored and weighted?

My analysis uses a simple weighted scoring system to evaluate hero performance. Data is centralized and snapshotted from multiple third-party tools. Win rate, pick rate, and ban rate are locked in for each hero at the start of the week, then again after seven days have passed. I then examine the shifts in performance and pass everything through a weighted scoring system.

Hero Rankings Weighted Scoring = (3x win rate) + (1x pick rate) - (1x ban rate)

The idea is simple. Win rate should be the most significant factor in ranking heroes. Pick rate is evaluated and factored in. How often players are picking certain heroes can be indicative.

Why is ban rate penalized in the weighted scoring model? Aren’t heroes that are being banned more frequently more likely to be OP?

Ban rate can be a decent proxy for power level—or at least annoyance to play against. Unfortunately, it also works against you when selecting which characters to play and specialize in competitive. There is an opportunity cost to picking, practicing, and playing a character that is likely to get banned. In general, the more you play on a hero, the more you can expect to improve and for those improvements to be represented in your overall win rate with a hero. If you are unable to play your main over a third of competitive games you play, you are potentially hamstringing your performance and practice time with improving and mastering those heroes.

Another thing to note is that ban rate effectively reduces the pick rate of heroes. While a banned hero would not always have been picked for play in a game, the higher the ban rate, the more likely it is that a player who wants to cannot play that character in a match. Therefore, I’ve factored in a penalty for ban rate in my weighted scoring model. The lower the ban rate, the lower the penalty. Ban rate penalty currently represents 20% of a character’s weighted score.

Movers & Shakers

This weighted scoring system looks at the change or delta (Δ) between the current week's data and the previous week's snapshot. This gives us a good trend of how heroes are progressing and allows us to identify the biggest winners and losers of the week. In this category, heroes who are winning more, being picked more, and being banned less will receive more of a lift. Heroes who are performing worse week over week will be ranked lower in this list.

The scoring system weights must drastically change for this category. Hero win rates remain relatively stable week to week without a balance patch, but pick rates vary drastically as the meta evolves. Using the same weighted system for rankings would result in scores being almost entirely driven by changes to pick rate alone, which paints a severely skewed and flawed glimpse of actual performance.

Movers & Shakers Weighted Scoring = (20x Δwin rate) + (2x Δpick rate) - (3x Δban rate)

Once Mover & Shaker values are created, I run a z-score analysis to determine how many standard deviations (σ) each data point is from the mean of the set to look for statistical outliers and put everything on a comparable scale for analysis. This helps with machine learning and data smoothing/reliability over time.

The limitations of weighted scoring systems

With the amount of data to gather, compile, calculate, and analyze, it would require substantial effort to segment everything into different Elo brackets for ranked play. To overcome this limitation, I’ll provide callouts in my analysis for certain heroes who are performing well in various competitive rankings. For example, Hela is arguably the best DPS character in the game in Celestial+ lobbies, but is much less frightening in the hands of a Silver player. Less than 8% of the player base is competing in Celestial+ lobbies anyway, so an overall view of the competitive landscape and all its rankings is by default normalized across the entire distribution of the player base.

Heroes with outsized ban rates and reasonable win rates will be punished more heavily in the rankings for the week. Groot is a great example of an outlier that does not play well into a weighted score system that penalizes ban rate. With my system, Groot was ranked at the bottom of the hero rankings list due to a 39.42% ban rate, 9.66% pick rate, and 49.94% win rate. Groot is being banned in almost four out of every ten games, and is only being played in one in ten competitive games. All of that is working against Groot’s rankings, especially his position as the most-banned hero in the game currently. Black Widow has a sub-40 % win rate and is sitting above Groot in the rankings of this system. I don’t know anyone who would argue Black Widow is a better character than Groot in Rivals.

Despite all of this, there is no question that Groot is a great Vanguard. Many top players consider him one of the best heroes in the game in any meta, and his ability to dish out incredible disruption and damage is practically unmatched across the remaining Vanguard lineup. In cases like Groot, commentary and subjective analysis outside the weighted scoring system, or modifications or manual interventions to the system, become necessary. For now, heroes like Groot will be added to a watch list as we tune the model with machine learning and make improvements over time.

Improving the model over time

With community and pro feedback, I’ll improve the weighted scoring system over time. It’s likely this is not the optimal form, but it does give a good baseline to compare data over time and early shifts in the Marvel Rivals Season Two Meta. Any potential changes to the weighted scoring system will be called out in future articles in the series.

Note on Team-ups: Due to the nature of some third-party data capture tools, some Team-Ups that involve more than two characters are split into individual pairs while others are captured by the full set. This creates some issues with data accuracy, which I’ll work to smooth out in future articles in the series through my own tooling and possible improvements in third-party tools.

Access the data!

You can access my data for the week in this Google Sheet right here. Save a copy and you'll be able to edit and better explore the data that fuels this week's analysis. All data for season two was gathered with third-party community tools. Official Netease Dev data will be used whenever it becomes available.