Performance dashboard
Aggregated trends across completed matches, roles, and champions make longer-term patterns easier to see.
Windows desktop application · version 4.5a
Review the match.
Train the pattern.
RONAVEMI turns evidence from a player’s own completed League of Legends matches into understandable reviews, several preparation options, and focused five-game training priorities.
Private pre-release. No public download. No Riot approval or endorsement is claimed.
CURRENT STATUS
Private prototype
4.5aVersion
WindowsPlatform
EUWInitial region
FreeMonetization
- Completed-match analysis
- Player-controlled decisions
- Local-first data handling
- Private Riot review build on request
What RONAVEMI does
Coaching after the game, preparation before the next one.
RONAVEMI is designed for self-analysis across a player’s own match history. It connects performance evidence to a manageable training focus without automating play or dictating a single decision.
Match review
Death Coach and Fight Cluster views connect post-game situations to evidence and learning priorities.
Training blocks
Findings become reviewable five-game priorities instead of an endless list of generic tips.
Preparation options
Champion-pool and draft-preparation views present several static or aggregate options with context and risks.
Real v4.5a interface
A reviewer can inspect the complete product flow here.
Every image below was captured from the accepted RONAVEMI v4.5a Tk desktop interface at commit ce725389085f3252cb68207bafb46801929ca890. The isolated reviewer profile and all 45 matches are synthetic. No real Riot ID, PUUID, API credential, local path, or player data is shown.
Review evidence, not a public release.
The production candidate remains private. There is no public executable or production key on this website. Full-resolution images open in a new tab.
Overview and detail profiles
From account-level orientation to an opened role and champion profile.
- Reviewer sees
- An opened Jungle profile with role KPIs, played champions, matchups and completed games.
- What it does
- Aggregates the selected player's completed-match evidence for one role.
- Player value
- Makes role-specific patterns and sample strength easier to compare.
- Integrity
- Post-game evidence only; no live session or hidden opponent data.
- Reviewer sees
- An opened Taliyah Jungle profile with personal KPIs, identity, strengths, risk and current focus.
- What it does
- Combines completed-match evidence with clearly separated champion knowledge.
- Player value
- Turns champion history into a readable, role-specific review surface.
- Integrity
- Sample limits remain visible; no alternative MMR and no automated action.
Preparation for the next game
Player-controlled options and a reviewable plan, without champion-select automation.
- Reviewer sees
- A user-selected Viego matchup plan with a rule, goal, personal focus and 30-second preparation summary.
- What it does
- Turns existing personal evidence and champion knowledge into a pre-game learning plan.
- Player value
- Gives the player a small, reviewable focus for the next completed match.
- Integrity
- Preparation only: no live read, hidden data, automation or mandatory action.
- Reviewer sees
- A manual Taliyah Jungle query, one optional ban, alternatives and the available evidence.
- What it does
- Ranks review options from the synthetic profile's completed-match evidence.
- Player value
- Helps the player make an informed choice while keeping confidence visible.
- Integrity
- User-triggered and optional; it neither reads live champion select nor performs a ban.
Completed data and traceable analysis
Riot match data becomes a local history, then inspectable evidence for analysis.
- Reviewer sees
- A filterable history of 45 synthetic completed matches and data-completeness status.
- What it does
- Provides the local evidence layer used by match, role, champion and coach views.
- Player value
- Lets the player find a match and trace later conclusions back to source data.
- Integrity
- Completed-match history only; no live-game tracking.
- Reviewer sees
- The completed games behind Taliyah Jungle conclusions, with match and development evidence.
- What it does
- Exposes sortable source rows and comparable windows behind champion-level analysis.
- Player value
- Makes coaching statements auditable instead of presenting an unexplained score.
- Integrity
- Uses completed matches and preserves missing values; no hidden live data.
Development, intelligence and coaching
Long-term champion evidence, cross-account performance signals and concrete learning steps remain separate.
- Reviewer sees
- Champion-specific 10-game development windows and an explicit insufficient-trend state.
- What it does
- Compares equally sized completed-match windows only when the sample is sufficient.
- Player value
- Supports long-term improvement without turning a single match into a trend.
- Integrity
- Missing evidence stays missing; no causal claim or forced conclusion is created.
- Reviewer sees
- Cross-account signals, priorities, sample basis and explicit non-causal wording.
- What it does
- Consolidates recurring patterns across completed matches into evidence-ranked signals.
- Player value
- Helps the player select a learning priority before opening concrete Coach steps.
- Integrity
- Statistical association is labelled as association, not proof of cause or a command.
Trust, provenance and controls
The product exposes data quality, source boundaries and its independent legal status.
User flow
A deliberate, player-controlled workflow.
The current build is a private pre-release prototype. A controlled test profile and redacted demonstration package can be supplied to Riot through an agreed secure channel.
- 01
Create a local profile
The player enters their own Riot ID, platform, and region.
- 02
Load permitted data
The player deliberately retrieves account, rank, completed-match, and timeline data through documented Riot API flows.
- 03
Review evidence
Dashboard, match review, and coaching views explain supported patterns; unclear evidence remains labelled as unclear.
- 04
Choose a training focus
RONAVEMI suggests reviewable priorities and several preparation options. The player makes the final decision.
- 05
Control local data
The player can inspect source and freshness information, export profile data, reset derived coaching data, and delete local match or profile data.
Game integrity
Clear boundaries by design.
RONAVEMI supports
- Post-game learning from completed matches
- Longitudinal self-analysis
- Static and aggregate preparation context
- Multiple evidence-backed options
RONAVEMI does not
- Automate gameplay or champion select
- Reveal hidden players or unknown session data
- Calculate an alternative MMR or ELO
- Dictate one mandatory player decision
Data and sources
Local-first, transparent, and gated.
Version 4.5a has no RONAVEMI cloud account, cloud synchronization, telemetry, advertising, or tracking. Profiles, Riot identifiers, completed-match data, timelines, training history, and derived analytics are stored under the user’s Windows account.
Riot data
The private prototype uses Riot Account-v1, Match-v5, League-v4, a Summoner-v4 compatibility fallback, and Riot Data Dragon. Active requests use HTTPS.
API-key handling
A development key entered for private testing is held in process memory for that session and is not intentionally persisted. A production secret will not be embedded in the desktop client.
OP.GG gate
The private development build can consume labelled, aggregated OP.GG metadata. Written permission for public use has been requested from OP.GG and is pending. The integration will be replaced or removed before public distribution if that permission is not obtained.
Contact
Reviewer or privacy question?
RONAVEMI is operated by Joel Standhaft, an individual developer in Germany. Missing postal operator details remain subject to legal review and have not been invented.