Game Analysis

New Hero Launch Data Insight

A full-dimension quantitative analysis of a new hero's win rate, pick rate, ban rate, combat performance, and ecosystem impact after launch, outputting balance-adjustment and operations-strategy recommendations.

IndustriesMOBACompetitive Games

Agent · Skills Live

skill · loadedindustry+100

Run this Skill on my data

Segmented breakdown

by cohort

Analysis readyevidence attached

New Hero Launch Data Insight is a game-operations capability in ThinkingAI's industry Skill library. After a new hero launches, it helps stat designers and version operations quantitatively assess hero strength and ecosystem impact across the full chain of acquisition, gacha, progression, combat, clearing, ecosystem, and payment, outputting a professional-grade analysis report ready for version review and balance adjustment. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.

Live walkthrough

AgentSave as AgentExport session

new-hero-launch-insight

New hero Canglan is on day 7 since launch. The design team is pushing to nerf it, and monetization says don't touch it. Run a full-funnel health check for me; for the version review I need one actionable call, not fragments of data that each argue their own side.

Standardize the definitions: set control groups by matched rank and power band, and strip out day-one launch noise

412,000 valid match samples

Acquisition and payment path: the gacha_pull event and daily revenue stats

First-week revenue $1.71M, 70% concentrated on day one

Progression scan: distribution of level / star stall points

37% stall at level 60, 41% stall at 5 stars

Combat performance: win rate by rank, pick rate, and ban rate

Meta impact: team-composition similarity in high-tier matches and crowd-out of same-role heroes

Composition similarity 52%, approaching a single-pole meta

Output a six-dimension rating and intervention recommendation

The six-dimension check is done, and the conclusion can go straight into the review: Canglan doesn't need an emergency nerf. The huge split between a 56.3% win rate at the top (T1) and 45.1% at the low ranks is rooted in a progression bottleneck, not in skill numbers.

1. Six-dimension check summary

Dimension	Key data	Benchmark / definition	Rating
Acquisition	First-week acquisition rate 41%	Same-price new-hero average 38%	Normal
Payment	First-week revenue $1.71M, day one $1.2M (70%), dropping to $280K on day two	Peer-hero day-one share average 45%	Front-loaded
Progression	Max-level share 23%; 37% stall at level 60, 41% stall at 5 stars	Comparable heroes at 35% max-level by day 7	Severe bottleneck
Power	Top win rate 56.3% (7.2pp above average); only 45.1% at low ranks	Balance range ±3pp	T1 / split
Clears	High-difficulty dungeon clear efficiency +18% with Canglan	Matched-power control group	Confirms power
Meta	Ban rate 28%; high-tier composition similarity 52%	Ban-rate warning line 30%	Borderline

← Scroll to see more

2. How the six dimensions form one chain

The causal chain: progression materials are handed out too tightly → mainstream players stall at level 60 / 5 stars (37% / 41% stall), and the 23% max-level share is far below the 35% seen for comparable heroes → a half-built Canglan only manages a 45.1% win rate at low ranks, so players never feel the T1 power → they pull it but don't build it, and payment stalls on day one (day-two revenue falls from $1.2M to $280K) → meanwhile fully-built top players hit 56.3%, pushing the ban rate to 28% and high-tier composition similarity to 52%. The 'too strong' the design team sees and the 'won't sell' monetization sees are two sides of the same bottleneck.

Conclusion

The core call: this isn't an 'overpowered hero,' it's that the power experience is split in two by a progression bottleneck. Cutting numbers directly would hurt the 77% of mainstream players who aren't max-level, and still wouldn't rescue the payment curve. The recommendation is to first increase event drops of level-60 breakthrough materials and 5-star shards this week (without touching monetization pricing), then recheck low-rank win rate and ban rate next week before deciding whether to change any numbers. Historical benchmarks for the same kind of intervention: max-level share 23% → 34%, low-rank win rate 45.1% → 48.3%, ban rate falling back to 22%, with no nerf needed in most cases.

The six-dimension health report is archived to the version-review dashboard, and it will alert automatically if the ban rate crosses the 30% warning line.

On your data

That was a simulated run

Leave your work email and we will run a live walkthrough on your real business data.

The problem

Data assessment after a new hero launches is the most critical and error-prone step of version iteration. More than 70% of new heroes are found to have a strength imbalance or ecosystem imbalance within a week, but the problem is usually not failing to spot the anomaly, it is that each department looks at only one dimension: design looks at win rate, operations looks at gacha revenue, and no one produces a full-chain conclusion. This fragmented view means each department makes a partially correct judgment but cannot assemble a complete conclusion.

What it does

Full-chain six-dimension analysis: acquisition rate, gacha behavior, progression, combat performance (win rate/pick rate/ban rate), clearing efficiency, and payment contribution

Rigorous statistical calibration: unified definitions, sample-size validation, outlier removal, and control-group variable control, with quantifiable, traceable conclusions

Controlled-variable comparison: compare within the same time period, level band, and power range to avoid misjudgment from invalid cross-dimension comparisons

When to use it

First-week full-dimension data monitoring after a new hero launches

Win-rate, pick-rate, and ban-rate combined diagnosis when hero strength is abnormal

Comparative assessment of multiple heroes launched in the same period

Assessing a hero's ecosystem impact: whether it squeezes out other heroes' pick space

Data support for version review and balance-adjustment decisions

In the field

Case

A MOBA · new hero Shadow Assassin launch assessment

Day-one gacha revenue was $1.2M, but the Skill found 70% of revenue concentrated on day one, dropping to 280K the next day; on combat, a 56% win rate, 28% pick rate, and 42% ban rate severely squeezed out the old hero of the same role; and the high-tier win rate was 62% versus 48% at low tiers. It recommended urgently lowering the skill coefficient, increasing energy cost, and extending the ban cooldown, with the balance plan live within 48 hours.

FAQ

What tracking events does the analysis need?

It needs gacha events, battle-settlement events, hero-progression events, and login events. The Skill automatically matches fields from AE metadata and clearly flags any missing ones.

How long after launch is best for the analysis?

We recommend a quick assessment on day 3, a full assessment on day 7, and deep ecosystem-impact analysis on day 14, with an urgent-anomaly alert possible on day 1.

What if it conflicts with the design team's internal test conclusion?

The Skill is based on real player data, and if it conflicts with the internal test, it flags the dimensions and quantified magnitude by which the live environment differs from the test environment.