Add game iteration display in init_team_data function of app.py. Introduced a new line to output the current game number and display the team data for each game iteration, enhancing user feedback and clarity during the prediction process.

Refactor overall_team_data structure in app.py by removing the 'game' column and renaming it to 'playername' for improved clarity. This change streamlines the data representation in the init_team_data function, enhancing the organization of player statistics and maintaining focus on key performance metrics.

Refactor game prediction settings in app.py to support multiple games. Introduced dynamic tab creation for each game, allowing users to input win/loss settings and kill/death predictions individually. This enhances the user experience by providing a more organized and intuitive interface for managing predictions across multiple games.

Add prediction settings to app.py for match format selection. Introduced a new subheader and selectbox for users to choose between BO1, BO3, or BO5 formats, enhancing the user interface and improving the prediction process.

Refactor UI layout in app.py to enhance user experience. Updated the selection interface for teams, opponents, win/loss options, and prediction settings by organizing them into two-column layouts. This change improves the visual structure and accessibility of input fields, facilitating a more intuitive interaction for users when making predictions.

Enhance statistical projections in init_team_data function of app.py. Updated the calculation of Kill, Death, and Assist projections to incorporate kill and death predictions, improving the accuracy of player performance metrics during win and loss scenarios. This change enhances the depth of analysis available for team data, allowing for more informed decision-making based on projected statistics.

Refactor player data display in app.py by removing background gradient for improved clarity. This change simplifies the presentation of player statistics while maintaining precision formatting, enhancing the overall user experience in analyzing performance metrics.

Refactor player data display in app.py by simplifying the presentation of statistics. Removed the pivot table and directly displayed player metrics, enhancing clarity and improving the visual representation with background gradients. This change streamlines the data display process while maintaining the integrity of the information presented.

Enhance player data display in app.py by pivoting statistics for improved clarity. Introduced a pivot table to reorganize player metrics, ensuring percentiles are displayed in the correct order. Updated the background gradient direction for better visual representation, enhancing user experience in analyzing player performance.

Fix background gradient direction in player data display in app.py. Changed the gradient axis from 0 to 1 to enhance the visual representation of player statistics, improving clarity and user experience.

Refactor percentile keys in app.py for improved clarity. Changed percentile labels from 'P10', 'P25', 'P50', 'P75', 'P90' to '10%', '25%', '50%', '75%', '90%' to enhance readability and user understanding of statistical projections for player performance metrics.

Update app.py to convert unique player list to a list format for improved tab creation. This change enhances the functionality of player-specific tabs, ensuring compatibility with the Streamlit framework and improving user experience in navigating player statistics.

Enhance player data display in app.py by introducing individual player tabs. Each tab presents player-specific statistics, including percentiles for various metrics, improving the user experience and allowing for a more detailed analysis of player performance. This update builds on previous enhancements to statistical projections and opponent performance metrics.

Add simulation of statistical projections in app.py. Introduced a new function to simulate player statistics using a normal distribution, generating percentiles for kills, deaths, assists, and CS projections. This enhancement allows for a more comprehensive analysis of player performance by incorporating simulated data, improving the overall depth of statistical insights available in the application.

Enhance opponent performance data display in init_team_data function of app.py. Added a DataFrame to output opponent-specific metrics for kills, deaths, assists, and CS projections during both win and loss scenarios. Updated the return statement to include this new DataFrame, allowing for a more comprehensive analysis of opponent performance alongside team data. This change improves the visibility and usability of performance metrics for informed decision-making.

Refactor init_team_data function in app.py to improve data handling and output format. Removed unnecessary display of opponent performance metrics and changed the return format to set the playername as the index. This update enhances the clarity of the returned data structure while maintaining the integrity of team performance evaluations.

Refactor init_team_data function in app.py to streamline statistical calculations and enhance clarity. Removed redundant statistics calculations and reorganized the logic for player and team performance metrics, focusing on win/loss scenarios. This update improves the readability and efficiency of the code while maintaining the accuracy of performance evaluations based on opponent statistics.

Refactor opponent data retrieval in init_team_data function of app.py. Updated the query to exclude the opponent filter, allowing for a broader analysis of performance metrics over the specified date range. This change enhances the flexibility of data retrieval for team performance evaluations.

Add display of opponent performance boosts in init_team_data function of app.py. Enhanced the function by including output for opponent-specific metrics related to kills, deaths, assists, and CS projections during win scenarios. This update improves the visibility of opponent performance data, aiding in more informed team performance evaluations.

Refactor statistical calculations in init_team_data function of app.py. Replaced direct calculations for kill, death, assist, and CS projections with apply method for improved readability and performance. This change enhances the clarity of the code and maintains the accuracy of team performance evaluations based on opponent statistics.

Enhance opponent performance metrics in init_team_data function of app.py. Added calculations for kills, deaths, assists, and CS projections based on opponent statistics for both win and loss scenarios. This update improves the accuracy of team performance evaluations by incorporating opponent-specific boosts, enhancing the overall depth of statistical analysis.

Refactor init_team_data function in app.py to enhance clarity and streamline table handling. Replaced count_var with a loop to iterate through data tables, improving readability and organization of statistical calculations for team performance metrics. This change contributes to better structure and understanding of kills, deaths, assists, and total CS analysis.

Refactor init_team_data function in app.py to improve variable handling and clarity. Introduced a count_var to streamline the distinction between raw_display and raw_opponent data tables, enhancing the readability of statistical calculations for kills, deaths, assists, and total CS. This change contributes to better organization and understanding of team performance metrics.

Enhance team data analysis in app.py by adding opponent selection and refining statistical calculations. Introduced a select box for choosing opponents, updated the init_team_data function to include opponent data in performance metrics, and streamlined calculations for kills, deaths, assists, and total CS. This improves the depth and usability of team performance evaluations.

Refactor player statistics handling in init_team_data function of app.py. Updated column names and adjusted data selection for win/loss metrics to improve clarity and consistency in performance analysis. This change enhances the overall data structure for better usability in team performance evaluations.

Refactor kill and death prediction inputs in app.py. Introduced a selection box for users to choose between predicting kills/deaths or using averages, enhancing user experience. Updated input validation to ensure minimum values are set correctly, improving data integrity in team performance projections.

Refactor date handling in init_conn function of app.py. Updated date range logic to use the current date for filtering, setting min_date to one year ago and max_date to one day ago. This change enhances the accuracy and relevance of game log retrieval for analysis.

Add advanced statistical calculations to init_team_data in app.py. Implemented new metrics for league, opponent, player, and team performance, including averages and boost calculations for kills, deaths, assists, and total CS. This enhancement improves the depth of analysis available for team performance evaluation.

Update date input defaults in app.py to enhance user experience. Changed the start date to 30 days prior to the maximum date and ensured proper date formatting for both start and end date inputs. This improves the clarity and usability of date selection for users.

Enhance date filtering in init_team_data function of app.py. Updated date range queries to convert date objects to formatted datetime strings, ensuring accurate filtering of game logs. This change improves data consistency and reliability in analysis.

Update cache duration in init_team_data function in app.py from 100 to 60 seconds to optimize data retrieval efficiency. This change aims to balance performance and resource usage during data analysis.

Refactor date input handling in app.py to improve date selection logic. Updated date filtering to ensure proper date formatting and consistency, allowing for seamless selection of date ranges including a "Last Year" option. This enhances user experience and data analysis capabilities.

Fix date parsing in init_conn function of app.py to include time component. Updated date format from "%Y-%m-%d" to "%Y-%m-%d %H:%M:%S" for accurate datetime object creation, enhancing data consistency in game logs retrieval.

Refactor date handling in init_conn function of app.py. Updated date retrieval to parse dates into datetime objects for improved consistency and accuracy in data analysis. This change enhances the reliability of date-related operations in the application.

Update cache duration in init_team_data function in app.py from 60 to 100 seconds to improve data retrieval efficiency. This change aims to enhance performance during data analysis by allowing a longer cache lifespan.

Refactor date input handling in app.py for improved date selection. Updated date filtering logic to ensure proper date formatting and consistency when selecting date ranges, enhancing user experience in the analysis tool.

Enhance date filtering options in team analysis. Added functionality to select date ranges for data analysis, including a "Last Year" option and a custom date range input. Updated init_team_data function to filter game logs based on selected dates, improving data relevance for analysis.

Update prediction inputs and enhance team data projection in app.py. Changed default values for predicted team kills and deaths. Added projected assists to team data output based on win/loss conditions, improving the analysis capabilities.

Refactor init_team_data function in app.py to streamline team data output. Added filtering to include only relevant columns and implemented dropna() and reset_index() for cleaner dataframe presentation.

Refactor team data display in app.py to include total CS metrics and improve formatting. Added display formats for percentage values and enhanced dataframe styling for better visualization in the analysis output.

Enhance team analysis functionality with sidebar options for team selection and prediction settings. Updated init_team_data function to incorporate win/loss predictions and display projected kills and deaths based on user input.

Initial commit loading app.py, app.yaml, and requirements.txt