Fix Missing Data: Win Percentage Components (ID: T01)

by ADMIN 54 views

Fix Missing Data: Win Percentage Components (ID: T01)

  • ID: T01
  • Name: Win Percentage (Overall, Home, Away, and Neutral)
  • Category: team_performance
  • Complexity: 1️⃣

The Win Percentage feature (T01) and its components (overall, home, away, and neutral site win percentages) appear to have approximately 50% missing values in the full_feature_set.parquet file. These are fundamental performance metrics and need to be available for all teams/seasons.

Specific Affected Columns

  • team_performance_T01_win_percentage
  • team_performance_T01_home_win_percentage
  • team_performance_T01_away_win_percentage
  • team_performance_T01_neutral_win_percentage

To address the missing data issue, we need to investigate the root cause of the problem. The following steps are required:

  • Review the complete implementation at src/features/team_performance/T01_win_percentage.py
  • Examine raw data sources in data/raw, especially the schedules and team_box categories
  • Analyze which specific teams/seasons have missing values (is there a pattern?)
  • Check if there are issues in the data processing pipeline that would cause win percentage data to be incomplete
  • Investigate whether the missing data corresponds to specific seasons, conferences, or game types
  • Verify if the feature calculation correctly handles teams with zero games played in specific venues
  • Check whether schedule data is properly linked to team identification data
  • Review if there are any inconsistencies in how wins/losses are recorded across different data sources

To fix the missing data issue, we need to implement the following changes:

  • Fix the implementation to properly calculate all win percentage components for all teams/seasons
  • Enhance the feature calculation to handle edge cases (teams with no games in certain venues)
  • Consider adding data validation steps to identify and report problematic data patterns
  • Ensure proper joining of schedule data with team identification data
  • Update unit tests at tests/features/team_performance/test_T01_win_percentage.py to cover the identified issues
  • Verify documentation at docs/features/team_performance/T01_win_percentage.md is accurate and complete
  • Add logging to help identify specific data quality issues during pipeline execution

Win Percentage is calculated as the number of wins divided by the total number of games played. This is done separately for all games, home games, away games, and neutral site games.

The formulas are:

  • Overall Win %: Wins / Games Played
  • Home Win %: Home Wins / Home Games Played
  • Away Win %: Away Wins / Away Games Played
  • Neutral Win %: Neutral Site Wins / Neutral Site Games Played

The issue may be related to:

  1. Inconsistent venue labeling (home/away/neutral) in the schedule data
  2. Missing game results for certain teams/seasons
  3. Problems with team identification across different data sources
  4. Edge cases where teams have no games in certain venues (should result in null, not missing values)
  5. Data processing issues when aggregating win/loss records

To investigate the missing data issue, we suggest the following approach:

  1. First determine whether the missing data follows specific patterns:

    • Are certain seasons more affected than others?
    • Are certain conferences more affected?
    • Is the pattern consistent across the four different win percentage metrics?

  2. Analyze the raw schedule data to verify if all games are properly captured:

    • Compare the number of games in schedules data with the number of games in team_box data
    • Verify that home/away/neutral designations are consistently applied

  3. Review the feature calculation process:

    • Ensure proper handling of division by zero (teams with no games in a venue should have null values, not missing data)
    • Check if any filtering steps might inadvertently remove valid games

  4. Consider adding validation code to verify data integrity before calculation

To ensure that the fix is successful, we need to meet the following acceptance criteria:

Feature Implementation

  • Features are calculated correctly for at least 95% of teams/seasons
  • Implementation includes appropriate handling for teams with no games in specific venues
  • Unit tests pass and achieve at least 90% code coverage
  • Tests specifically address the identified data issues
  • Documentation is complete and follows the template
  • FEATURES.md status is updated if needed
  • Code follows project coding standards
  • Performance is acceptable for dataset size

PR Preparation (Required Before Submission)

  • Step 1: Comprehensive Testing
    • All tests pass (run python -m pytest)
  • Step 2: Code Linting
    • Code passes linting with no warnings or errors (run ruff check .)
    • No linting exceptions or # noqa comments added
  • Step 3: End-to-End Validation
    • Pipeline runs successfully with existing data (run python run_pipeline.py)
    • Pipeline runs successfully with clean data (run python run_pipeline.py --clean-all)
    • Final verification passes (run pytest, ruff check ., and python run_pipeline.py)

The following features are related to the Win Percentage feature:

  • T02 Point Differential: Often used alongside win percentage to assess team strength
  • T09 Recent Form: Uses game results similar to win percentage but with recency weighting
  • T11 Strength of Schedule: May help explain win percentage in context of competition difficulty

The primary data sources should be the schedules files in both raw and processed data directories. Consider comparing implementation with similar sports analytics libraries to verify calculation approaches. NCAA's official game records could be used to validate data completeness for select teams/seasons.
Fix Missing Data: Win Percentage Components (ID: T01) - Q&A

A: The Win Percentage feature is a fundamental performance metric that calculates the number of wins divided by the total number of games played. It is essential for assessing team strength and is often used alongside other metrics such as point differential and recent form.

A: The Win Percentage feature has approximately 50% missing values in the full_feature_set.parquet file. This means that the feature is not available for all teams/seasons, which can lead to inaccurate assessments of team strength.

A: The specific affected columns are:

  • team_performance_T01_win_percentage
  • team_performance_T01_home_win_percentage
  • team_performance_T01_away_win_percentage
  • team_performance_T01_neutral_win_percentage

A: To address the missing data issue, we need to investigate the root cause of the problem. The following steps are required:

  • Review the complete implementation at src/features/team_performance/T01_win_percentage.py
  • Examine raw data sources in data/raw, especially the schedules and team_box categories
  • Analyze which specific teams/seasons have missing values (is there a pattern?)
  • Check if there are issues in the data processing pipeline that would cause win percentage data to be incomplete
  • Investigate whether the missing data corresponds to specific seasons, conferences, or game types
  • Verify if the feature calculation correctly handles teams with zero games played in specific venues
  • Check whether schedule data is properly linked to team identification data
  • Review if there are any inconsistencies in how wins/losses are recorded across different data sources

A: To fix the missing data issue, we need to implement the following changes:

  • Fix the implementation to properly calculate all win percentage components for all teams/seasons
  • Enhance the feature calculation to handle edge cases (teams with no games in certain venues)
  • Consider adding data validation steps to identify and report problematic data patterns
  • Ensure proper joining of schedule data with team identification data
  • Update unit tests at tests/features/team_performance/test_T01_win_percentage.py to cover the identified issues
  • Verify documentation at docs/features/team_performance/T01_win_percentage.md is accurate and complete
  • Add logging to help identify specific data quality issues during pipeline execution

A: Win Percentage is calculated as the number of wins divided by the total number of games played. This is done separately for all games, home games, away games, and neutral site games.

The formulas are:

  • Overall Win %: Wins / Games Played
  • Home Win %: Home Wins / Home Games Played
  • Away Win %: Away Wins / Away Games Played
  • Neutral Win %: Neutral Site Wins / Neutral Site Games Played

The issue may be related to:

  1. Inconsistent venue labeling (home/away/neutral) in the schedule data
  2. Missing game results for certain teams/seasons
  3. Problems with team identification across different data sources
  4. Edge cases where teams have no games in certain venues (should result in null, not missing values)
  5. Data processing issues when aggregating win/loss records

A: To investigate the missing data issue, we suggest the following approach:

  1. First determine whether the missing data follows specific patterns:

    • Are certain seasons more affected than others?
    • Are certain conferences more affected?
    • Is the pattern consistent across the four different win percentage metrics?

  2. Analyze the raw schedule data to verify if all games are properly captured:

    • Compare the number of games in schedules data with the number of games in team_box data
    • Verify that home/away/neutral designations are consistently applied

  3. Review the feature calculation process:

    • Ensure proper handling of division by zero (teams with no games in a venue should have null values, not missing data)
    • Check if any filtering steps might inadvertently remove valid games

  4. Consider adding validation code to verify data integrity before calculation

A: To ensure that the fix is successful, we need to meet the following acceptance criteria:

Feature Implementation

  • Features are calculated correctly for at least 95% of teams/seasons
  • Implementation includes appropriate handling for teams with no games in specific venues
  • Unit tests pass and achieve at least 90% code coverage
  • Tests specifically address the identified data issues
  • Documentation is complete and follows the template
  • FEATURES.md status is updated if needed
  • Code follows project coding standards
  • Performance is acceptable for dataset size

PR Preparation (Required Before Submission)

  • Step 1: Comprehensive Testing
    • All tests pass (run python -m pytest)
  • Step 2: Code Linting
    • Code passes linting with no warnings or errors (run ruff check .)
    • No linting exceptions or # noqa comments added
  • Step 3: End-to-End Validation
    • Pipeline runs successfully with existing data (run python run_pipeline.py)
    • Pipeline runs successfully with clean data (run python run_pipeline.py --clean-all)
    • Final verification passes (run pytest, ruff check ., and python run_pipeline.py)

A: The following features are related to the Win Percentage feature:

  • T02 Point Differential: Often used alongside win percentage to assess team strength
  • T09 Recent Form: Uses game results similar to win percentage but with recency weighting
  • T11 Strength of Schedule: May help explain win percentage in context of competition difficulty

A: The primary data sources should be the schedules files in both raw and processed data directories. Consider comparing implementation with similar sports analytics libraries to verify calculation approaches. NCAA's official game records could be used to validate data completeness for select teams/seasons.