EDA Project: WTA data

Overview

This project will begin on Monday June 13th, and conclude with a 10-15 minute presentation on Friday, June 24th (either during the morning session from 10:30 to 12 PM or in the afternoon from 1:30 to 3 PM). The goal of this project is to practice understanding the structure of a dataset, and to practice generating and evaluating hypotheses using fundamental EDA and data visualization techniques.

Deliverables

Your team is expected to produce R Markdown slides (an example template will be provided shortly) to accompany your 10-15 minute presentation with the following information:

• Explanation of the data structure of the dataset,

• Three hypotheses you are interested in exploring,

• Three data visualizations exploring the hypotheses, at least two of which must be multivariate. Each visualization must be in a different format from the other two, and you must have at least one categorical and one continuous visualization.

• One clustering example,

• Conclusions reached for the hypotheses based on your EDA and data visualizations.

Timeline

There will be two submission deadlines:

Friday, June 17th @ 5:00 PM EST - Each student will push their individual code for the project thus far to their GitHub accounts for review. We will then provide feedback on the code submitted.

Thursday, June 23rd @ 11:59 PM EST - Slides and full code must be completed and ready for presentation. Send your slides to Prof Yurko’s email (ryurko@andrew.cmu.edu). All code, visualizations, and presentations must be made in R. Take advantage of examples from lecture and the presentation template, but also feel free to explore material online that may be relevant!

Data

Your team is assigned the WTA match data. This dataset contains all WTA matches between 2018 and 2022 (through June 10th), courtesy of Jeff Sackmann’s famous tennis repository. The code chunk at the end shows how this dataset was processed in R.

Each row of the dataset corresponds to a single WTA match and has the following columns:

• tourney_id: a unique identifier for each tournament, such as 2020-888. The exact formats are borrowed from several different sources, so while the first four characters are always the year, the rest of the ID doesn’t follow a predictable structure
• tourney_name: name of the tournament
• surface: type of court surface
• draw_size: number of players in the draw, often rounded up to the nearest power of 2. (For instance, a tournament with 28 players may be shown as 32.)
• tourney_level: see link below
• tourney_date: eight digits, YYYYMMDD, usually the Monday of the tournament week.
• match_num: a match-specific identifier. Often starting from 1, sometimes counting down from 300, and sometimes arbitrary.
• winner_id: the player identifier for the winner of the match
• winner_seed: seed of winning player
• winner_entry: ‘WC’ = wild card, ‘Q’ = qualifier, ‘LL’ = lucky loser, ‘PR’ = protected ranking, ‘ITF’ = ITF entry, and there are a few others that are occasionally used
• winner_name: Name of the winning player
• winner_hand: R = right, L = left, U = unknown. For ambidextrous players, this is their serving hand.
• winner_ht: height in centimeters, where available
• winner_ioc: three-character country code
• winner_age: age, in years, as of the tourney_date
• loser_id: (see the above for winner but now for the losing player)
• loser_seed:
• loser_entry:
• loser_name:
• loser_hand:
• loser_ht:
• loser_ioc:
• loser_age:
• score: final match score
• best_of: ‘3’ or ‘5’, indicating the the number of sets for this match
• round: tournament round
• minutes: match length in minutes
• w_ace: winner’s number of aces
• w_df: winner’s number of doubles faults
• w_svpt: winner’s number of serve points
• w_1stIn: winner’s number of first serves made
• w_1stWon: winner’s number of first-serve points won
• w_2ndWon: winner’s number of second-serve points won
• w_SvGms: winner’s number of serve games
• w_bpSaved: winner’s number of break points saved
• w_bpFaced: winner’s number of break points faced
• l_ace: (see the above for winner but now for the losing player)
• l_df:
• l_svpt:
• l_1stIn:
• l_1stWon:
• l_2ndWon:
• l_SvGms:
• l_bpSaved:
• l_bpFaced:
• winner_rank: winner’s WTA rank, as of the tourney_date, or the most recent ranking date before the tourney_date
• winner_rank_points: number of ranking points, where available
• loser_rank:
• loser_rank_points:

Note that a full glossary of the features available for match data can be found here.

Code to build dataset

wta_2018_2022_matches <-
  map_dfr(c(2018:2022),
          function(year) {
            read_csv(paste0("https://raw.githubusercontent.com/JeffSackmann/tennis_wta/master/wta_matches_",
                            year, ".csv")) %>%
              mutate(winner_seed = as.character(winner_seed),
                     loser_seed = as.character(loser_seed))
          })

# Save this file:
write_csv(wta_2018_2022_matches, 
          "data/sports/eda_projects/wta_matches_2018_2022.csv")