MTGA Rank Progression: Parsing Logs For Accurate Tracking

Are you an avid MTG Arena player eager to track your ranked progress? Currently, the MTGA-Companion app faces a challenge: displaying rank progression data. While the framework exists, the app isn't pulling rank information from MTGA Player.log files, leaving users with an empty rank progression chart. This article dives deep into the problem, outlining the steps to implement rank progression parsing from MTGA logs, ensuring you get the insightful data you deserve.

Understanding the Problem

The rank progression chart in MTGA-Companion should ideally show your journey through the ranked ladder. However, due to a missing link in the data pipeline, it currently displays the disheartening message: "No rank progression data available." The infrastructure – the rank_history table, RankProgression chart, and GetRankProgressionTimeline API – is all set. The snag? The log parser isn't extracting the crucial rank data from match events within the MTGA Player.log files.

Current State of Affairs

Let's break down what's working and what's not:

• ✅ Database schema includes the rank_history table.
• ✅ Matches table features rank_before and rank_after columns.
• ✅ The GUI boasts a functional Rank Progression chart component.
• ✅ The backend offers a GetRankProgressionTimeline API.
• ❌ The log parser, however, doesn't extract rank data from the logs.
• ❌ Consequently, the rank_history table remains empty (0 rows).
• ❌ All matches currently show rank_after: "Unranked".

Expected Behavior

The goal is to have the system intelligently extract rank information from MTGA logs whenever a match concludes. This involves several key actions:

1. Extract Rank Information: Dig into the match event JSON to find the relevant rank details.
2. Store Rank Snapshots: Populate the rank_history table with these extracted rank snapshots.
3. Update Match Records: Fill in the rank_before and rank_after columns in the matches table with the correct rank information.
4. Visualize the Data: Bring the Rank Progression chart to life with real, meaningful data.

Implementation Tasks: A Step-by-Step Guide

To achieve this, we need a systematic approach. Here's a breakdown of the implementation tasks:

1. Research MTGA Log Format: Deciphering the Data

Before we can extract the data, we need to understand where it lives. This involves:

• [ ] Identifying Relevant Log Events: Pinpointing which specific log events contain the rank information we need.
• [ ] Documenting the JSON Structure: Creating a clear map of the JSON structure for rank data within those events.
• [ ] Identifying Rank-Related Fields: Recognizing the key fields like rankClass, rankLevel, rankStep, and percentile that define a player's rank.
• [ ] Testing with Real Log Files: Putting our understanding to the test by examining actual log files from ranked matches.

This initial research is crucial for understanding how MTGA stores rank information in its logs. We need to know which events contain the data and how that data is structured. Understanding the JSON structure, specifically the rank-related fields like rankClass (Bronze, Silver, Gold, etc.), rankLevel (typically 1-4), rankStep, and percentile (for Mythic ranks), is vital for accurate parsing. Also, determining the season ordinal will facilitate tracking seasonal progression and maintain historical accuracy. Successfully completing these tasks will lay the foundation for the subsequent steps, ensuring our log parser can effectively extract and interpret the rank data. This groundwork will save time and effort in the long run by preventing misinterpretations and ensuring the reliability of the rank progression data displayed in the application. Consider using tools like JSON viewers or online formatters to help visualize the data structure within the logs. This deeper understanding will allow us to write more robust and efficient parsing logic, contributing to a smoother user experience overall.

2. Update Log Parser: The Data Extraction Engine

This is where the magic happens. We'll be modifying the log parser to extract the rank data. The primary file of interest is likely internal/mtga/logreader/arenastats.go (or potentially a new rank.go file).

• [ ] Create ParseRankData() Function: Develop a dedicated function to handle the extraction of rank snapshots from log entries.
• [ ] Parse Rank Information: Focus on parsing rank information from events like matchGameRoomStateChangedEvent or similar events that signal a change in match state.
• [ ] Extract Key Data Points: Specifically, extract the rank class (Bronze/Silver/Gold/etc.), level (1-4), step, and percentile (if applicable).
• [ ] Extract Season Ordinal: Capture the season ordinal to track progression across different seasons.
• [ ] Handle Pre- and Post-Match Data: Account for rank data that might be available both before and after a match.

Writing the ParseRankData() function is essential. This function should be designed to be robust and handle various scenarios, including different rank tiers and potential variations in the log format across MTGA updates. A key aspect of this stage is identifying the correct log events that reliably contain rank information. Events like matchGameRoomStateChangedEvent are likely candidates, but thorough investigation is needed to confirm their consistency. The extraction should be precise, capturing the rank class (e.g., Bronze, Silver, Gold), level (1-4), step (a finer-grained measure within each level), and percentile (which is particularly relevant for Mythic ranks). Furthermore, capturing the season ordinal is crucial for accurately tracking progression across different seasons and maintaining historical data integrity. Handling both pre-match and post-match rank data is also necessary to provide a comprehensive view of rank changes. Rigorous testing should be conducted with a variety of log files from different match types and rank tiers to ensure the ParseRankData() function works reliably and accurately. The error handling within the function should be robust to prevent unexpected failures and ensure that incomplete or malformed data doesn't corrupt the overall rank tracking process. By carefully addressing these points, we can create a reliable and efficient log parser that provides a solid foundation for the rank progression feature.

3. Update Storage Logic: Persisting the Data

Now that we're extracting the data, we need to store it. This involves modifying internal/storage/service.go and potentially app.go.

• [ ] Store Rank Snapshots: Implement the logic to store the extracted rank snapshots in the rank_history table.
• [ ] Populate Match Records: Update the rank_before and rank_after columns in the matches table with the corresponding rank information.
• [ ] Link Snapshots to Matches: Establish a clear link between rank snapshots and the matches they belong to, likely using timestamp correlation.
• [ ] Handle Deduplication: Implement logic to prevent duplicate rank snapshots from being stored.

The storage logic is a critical component that ensures the extracted rank data is persisted accurately and efficiently. This involves modifying the internal/storage/service.go file to include the necessary functions for writing rank snapshots to the rank_history table. It's also essential to update the matches table, specifically the rank_before and rank_after columns, with the relevant rank information for each match. Establishing a robust linking mechanism between rank snapshots and matches, likely through timestamp correlation, is crucial for maintaining data integrity and enabling accurate rank progression tracking. Furthermore, implementing deduplication logic is necessary to prevent redundant rank snapshots from being stored, optimizing storage space and ensuring data consistency. This could involve checking for existing snapshots with the same rank values and timestamps before inserting new records. The interaction with the database should be carefully optimized to minimize query latency and ensure scalability as the number of users and matches grows. Proper error handling and transaction management are also essential to ensure that data is written atomically and consistently, even in the face of unexpected errors. By implementing these storage logic updates thoroughly, we can create a reliable and efficient system for persisting rank data, forming a solid foundation for the rank progression feature.

4. Update Match Processing: Tying it All Together

This step focuses on integrating the rank data extraction into the existing match processing pipeline, specifically within internal/storage/service.go (in the extractMatchesFromEntries function).

• [ ] Extract Rank Data Alongside Match Data: Modify the match extraction process to also extract rank data from the log entries.
• [ ] Set rank_before and rank_after Fields: Populate the rank_before and rank_after fields when creating match records.
• [ ] Call Rank Storage Logic: Integrate the rank storage logic from step 3 into the match processing flow.

Updating match processing involves modifying the extractMatchesFromEntries function within internal/storage/service.go to seamlessly incorporate rank data extraction. The process entails extracting rank information alongside existing match data. Then, populate the rank_before and rank_after fields when creating match records. It is essential to accurately reflect the player's rank before and after each match. To complete this process, call the rank storage logic, implemented in the previous steps, during match processing. This is where the rank snapshots are stored in the rank_history table. Thorough testing is required to guarantee that rank data is extracted and stored correctly for all match types and that the overall match processing flow remains efficient. The error handling should be extended to encompass potential issues during rank data extraction or storage, preventing match processing failures. Furthermore, ensure that the changes are backward-compatible and do not disrupt the existing match processing functionality. Properly integrating rank data extraction into the match processing pipeline is pivotal for providing a complete and accurate picture of a player's progression in MTG Arena.

5. Testing: Ensuring Accuracy and Reliability

Testing is paramount to ensure the entire system works as expected.

• [ ] Test with Ranked Matches: Use log files containing ranked matches to test the system.
• [ ] Verify rank_history Table: Confirm that the rank_history table is being populated with the correct data.
• [ ] Verify Match Records: Ensure that match records have the correct rank_before and rank_after values.
• [ ] Test Rank Progression Chart: Verify that the Rank Progression chart displays the data accurately.
• [ ] Test Multiple Seasons: Test the system with log data spanning multiple seasons.
• [ ] Test Rank Transitions: Specifically test scenarios where players transition between rank tiers (e.g., Bronze to Silver).

Rigorous testing is paramount to ensure the accuracy, reliability, and robustness of the rank progression implementation. This includes using a diverse set of log files containing ranked matches from different seasons and rank tiers. It is also important to verify that the rank_history table is populated correctly with accurate rank snapshots, and that the rank_before and rank_after fields in the matches table reflect the player's rank before and after each match. The Rank Progression chart should also accurately display the rank data, providing a clear and informative visualization of the player's progress. Special attention should be paid to testing rank transitions, such as moving from Bronze to Silver or Gold to Platinum, to ensure that these transitions are captured and displayed correctly. Performance testing is also important to ensure that the rank progression implementation does not introduce any significant performance bottlenecks. Edge cases and error scenarios should be tested thoroughly to identify and address any potential issues. By conducting comprehensive testing, we can ensure that the rank progression feature provides accurate, reliable, and valuable insights into players' ranked performance in MTG Arena.

Acceptance Criteria: Defining Success

To ensure we've met our goals, we need clear acceptance criteria:

• [ ] Ranked Match Data: Ranked (Ladder) matches extract and store rank information successfully.
• [ ] Rank History Population: The rank_history table contains rank snapshots as expected.
• [ ] Match Record Accuracy: Matches have rank_before and rank_after fields populated correctly.
• [ ] Chart Functionality: The Rank Progression chart displays real progression data accurately.
• [ ] Historical Data Support: The system works with historical log data (can parse old matches).
• [ ] Real-time Polling: The system works with real-time poller (new matches get rank data).
• [ ] Comprehensive Rank Support: Handles all rank classes: Bronze, Silver, Gold, Platinum, Diamond, Mythic.
• [ ] Test Coverage: Comprehensive test coverage to ensure reliability.

Related Files: Navigating the Codebase

Here's a reminder of the key files involved:

• internal/mtga/logreader/arenastats.go - Match parsing logic
• internal/storage/service.go:extractMatchesFromEntries() - Match extraction function
• internal/storage/rank_history.go - Rank storage logic (if this file exists)
• app.go:processNewEntries() - Real-time processing logic
• Database: rank_history table, matches.rank_before/rank_after columns

Priority Justification: Why This Matters

This implementation is a high priority because:

• It's a core feature of the application, providing rank tracking for competitive players.
• The existing UI component is currently useless, leading to a poor user experience.
• The underlying infrastructure is already in place, minimizing the effort required.
• The implementation is relatively straightforward once the log format is understood.

Conclusion

By implementing these steps, we can bridge the gap between MTGA logs and the Rank Progression chart, providing valuable insights to players tracking their ranked progress. This feature will significantly enhance the user experience and provide a tangible benefit to competitive MTG Arena players. Don't forget to check out the official MTG Arena website for more information on the game and its features.