Methodology

The Player DNA is an exploratory profile. It summarizes up to 35 indicators from a recent ranked-match window, grouped into 6 areas: Combat, Macro, Mechanics, Mental, Team and Tempo. It can suggest proximity to archetypes, but describes trends rather than personality, overall skill or the cause of a specific action.

Indicators are normalized to surface relative differences and suggest trends to revisit. A signature, block or archetype is not a verdict: it can change with the sample, role played and data available.

How a metric becomes a score

Every raw metric (CS/min, vision, damage…) is converted into a 0-100 score through linear interpolation against a rank-calibrated benchmark, then adjusted by a role multiplier. This is the machinery that powers the detailed match grade (next section) and the Player DNA z-scoring.

The score is then clamped between 0 and 100.

Benchmarks by rank

The bounds are calibrated on real data extracted on March 23, 2026 from my database (830k+ ranked games, EUW1). Manual refresh on major patches: a weekly scheduler is on the roadmap:

Role multipliers

Benchmarks are adjusted per role to compare a support and an ADC fairly:

Every match gets an overall grade (S, A, B, C, D) based on 6 weighted categories. The grade is computed relative to your rank, not in absolute terms: a B in Diamond is far better than an A in Bronze. Since July 2026, the scales are calibrated per role on real ranked games: a median performance for your role scores ~50, and an S is the top ~2% of performances: reachable in all five roles, support included.

The 6 categories

* The damage share is role-adjusted: the reference 8-35% range is multiplied by the role played (UTILITY × 0.45 → 3.6-15.75% range, BOTTOM × 1.15 → 9.2-40.25%, etc.). Before this fix, a tank support at 10% damage share was capped around 20/100 on this component (25% of Teamfight) no matter what: now a support at 12% scores the same as an ADC at 28%.

⚠️ These ranges are reachable targets per role, not an additive breakdown: the per-role medians don't sum to 100%. A team's damage share sums to 100% by construction (5 players split 100% of the damage), but each role has a different envelope for what it's expected to weigh individually.

Category weights per role

The 6 weights above are the laner weights (Top / Jungle / Mid / ADC). A support structurally has neither income (gold/CS) nor objective last-hits: judging them on that tanks their grade no matter what. The UTILITY role therefore redistributes those weights toward what a support actually controls.

How to read a grade (post-match)

Since 2026-05-20, every sub-grade (Laning / Combat / Survival / Objectives / Vision / Farm) in the match summary is clickable. Opening the detail shows you:

• Every sub-metric with your value vs the benchmark for your rank + role and a ▲ / ─ / ▼ status.
• The sub-metric's normalized 0-100 score (colored mini-bar) and its weight % in the category grade.
• A ● marker highlights benchmarks adjusted to your role when an appropriate calibration is available.
• An “X weighs the most in this grade” bubble pointing at the sub-metric responsible when the grade drags down: handy to know what to work on first.

Grade thresholds

Percentile by rank (interpolated)

The raw 0-100 score is converted into a percentile against players of the same rank via stepwise linear interpolation. That avoids discrete jumps: a 79 score is no longer abruptly at 78% and an 80 at 90%, the function lerps between anchors for a continuous progression.

Impact map (impact within the game)

The Impact map (impactScore, 0-100) measures your real weight in that specific game, not against your rank. It is role-aware:

For UTILITY, objective participation (a share of team damage: a support does 5-15% of it no matter what) is replaced by vision and presence, which actually measure their presence on the map:

MVP / LVP

The MVP badge goes to the player with the best globalScore (0-100) among the 10 players. The formula is role-aware:

For UTILITY, the formula drops CS/Gold (irrelevant for the role) and redistributes toward vision and impact:

The KDA contribution is capped at 8: without that cap, a Perfect KDA (e.g. 6/0/28 → KDA 34) saturated the score at 100 and killed MVP discrimination between Perfect-KDA players. impactScore itself is role-aware (see Impact map above, volume-weighted KP included).

Carry: every role in its own way

• Damage Carry: laners who carry through kills. Kill share ≥ 50% of the team, ≥ 5 kills, in a win.
• Engage Carry: supports who carry through engage/peel/vision. UTILITY + win + KP ≥ 65% + ≤ 3 deaths + ≥ 1.5 vision/min. Before 2026-05-09, the generic "Carry" badge structurally excluded supports.

“God of X” game badges

Every game also hands out “best of the game” badges on a given metric. Since 2026-05-30, the “God of X” badges require absolute floors on top of being the best: so a 1/3/1 in a stomp doesn't grab “Support God” just for being the only support on their team:

• Support God: best supportScore of the game, and ≥ 10 assists and ≥ 25 vision.
• Vision God: best vision score, and ≥ 25 total and ≥ 1.4/min (a long game with mediocre vision no longer cuts it).
• CS God: best CS/min among laners, ≥ 7 (supports excluded).

Overall grades give an average, but a splitpushing Top and a carry ADC don't share the same responsibilities. The Role Signature is a composite indicator specific to each role, built from available metrics. It helps review a match and does not cover every responsibility of a role.

The 5 signatures

Deaths are tagged and scored from Riot timeline events (available positions, proximity, wards, objectives, HP, gold difference). The 0-100 score and categories are review heuristics: they do not prove intent, a team call or the exact cause of a death.

The 13 detected tags

Quality score

Base at 50. Each tag applies a bonus/penalty:

Contextual modifiers

The 14-day progression plan is generated server-side from your Player DNA, without a generative AI call. It relies on available signals, your role and measurable objectives: it remains a working reference, not a complete diagnosis.

How it's generated

Examples of measurable objectives

Each objective uses an extractable metric from match data, a target and hand-written action ideas. Here are examples:

Objective evaluation

When a compatible match and the necessary data are available, the system can evaluate each objective using explicit rules:

Validation appears after available data are refreshed. A single value does not replace the context of a match or sustained improvement.

Skill tree

The Progression tab also shows a micro-skill tree (recall timing, survival, objective focus, wave management, vision…). Scores use available timelines and stats over a recent window; they remain reading indicators, not an exhaustive measurement of every in-game decision.

Transparency rule: if a skill is not measurable on your recent games (missing timeline, missing data), it's shown as “unavailable”: never padded with a neutral grade that would fake it. The tree's overall grade only averages the skills that were actually evaluated.

With a sample of ~1,000 games per champion, raw winrate is too volatile. A champion at 60% winrate over 80 games isn't necessarily better than one at 52% over 3,000 games.

The small-sample problem

With little data, rarely played champions show extreme winrates (40% or 60%) through pure statistical variance. I combine two complementary estimators: Bayesian smoothing to estimate the true WR, and the Wilson score interval (95% lower bound) to penalize small-sample uncertainty.

1. Bayesian winrate formula

2. Wilson score interval (95% lower bound)

The Bayesian estimates a central point but says nothing about uncertainty. Two champions can share the same bayesianWR with radically different confidence intervals. The Wilson score gives the lower bound of the 95% CI: a champion with 50 games (σ ≈ 7%) mechanically ends up below a champion with 1000 games (σ ≈ 1.6%).

with z = 1.96 (95% CI), p̂ = observed winrate as a fraction, n = games.

3. Final score

Mix of 70% Bayesian (global stability) + 30% Wilson (penalizes uncertainty) + popularity bonus:

The bonus rewards popular champions (meta-validated by volume) with no positive cap. The penalty has been floored at −3 points since 2026-05-20: the Bayesian regression already handles small-sample reliability: beyond that, it would double-punish viable pocket picks (a champion at 200 games in a pool averaging 800 took −6, about a full tier on top of the regression).

The Bayesian/Wilson mix stabilizes tiers from patch to patch: “lucky outlier” champions climb less.

Tier assignment

Champions are sorted by descending score, then split by percentile:

The minimum threshold is adaptive: between 10 and 50 games to be included, based on the pool's average volume. On a freshly released patch (little data) the threshold drops toward 10 so the list doesn't empty out, then climbs back toward 50 as collection catches up: Bayesian/Wilson pulls small samples toward the 50% prior anyway.

The theoretical rank is an experimental estimate based on available performance indicators. It does not say which rank you should be at and may be unavailable when the sample or calibration cannot support a sufficiently reliable reading.

Algorithm

Weighting (sum = 1.0)

Confidence score

The estimate is hidden when history or signal is insufficient. This confidence score describes the strength of the available signal; it does not guarantee that the estimate reflects a real rank.

This tool reviews your recent session (consecutive matches less than 90 minutes apart) using seven heuristic rules. It does not detect or diagnose tilt or mental state.

Score calculation

31 badges across 6 active categories, each with 4 progressive rarity levels. Thresholds are fixed and absolute (not rank-relative), but some badges are adjusted by main role to avoid comparing different responsibilities.

Each badge has 4 tiers: Bronze → Silver → Gold → Legendary. Progress toward the next tier is shown as a percentage.

The system highlights unusual performances or situations from the available statistics. These are heuristic signals: they cannot establish a player's intention, AFK status or smurf status.

These indicators put individual contributions in context against the team result. They help compare metrics, but cannot assign a win, loss or carry to one person with certainty.