The objective of this project is to transform a semi-structured text file containing chess tournament results into a clean and structured CSV file. Each row in the final dataset will represent a single player and include the player’s name, state, total number of points, pre-tournament rating, and the average pre-tournament rating of their opponents.
The first step in this project is to closely examine the structure of the raw text file in order to identify consistent patterns that define individual player records. Although the data is not provided in a standard tabular format, the layout follows a predictable structure that allows the information to be parsed systematically.
Each player’s information will be treated as a single record. From each record, the player’s name, state, total number of points, and pre-tournament rating will be extracted using string-based parsing methods.
Total points will be calculated based on standard chess scoring: a win counts as 1 point, a draw as 0.5 points, and a loss as 0 points. These values will be summed across all rounds to determine each player’s overall score.
Next, opponent pre-tournament ratings will be identified from the round-by-round results associated with each player. These opponent ratings will be collected for each player and used to calculate the average opponent pre-rating by summing the opponent ratings and dividing by the total number of games played.
Once all required fields have been extracted and calculated, the data will be assembled into a structured data frame where each row represents a single player. The final dataset will then be exported as a CSV file that can be easily imported into a SQL database or used for further analysis. All work will be completed within an R Markdown file to ensure reproducibility.
One anticipated challenge is that the input file relies on visual formatting rather than explicit delimiters, which requires careful parsing to accurately extract individual values. Another challenge is isolating opponent pre-tournament ratings from the round results, which contain both match outcomes and opponent identifiers.
Additionally, some players may have a different number of games due to byes or withdrawals, meaning the number of opponent ratings used in the average calculation may vary. Ensuring that all extracted values remain correctly aligned for each player is essential to producing accurate results.