EDA Project: WNBA 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 WNBA shot data. This dataset contains all shot attempts in the 2022 WNBA season (through June 10th) accessed using the wehoop package. The code chunk at the end shows how this dataset was constructed in R.

Each row of the dataset corresponds to a single shot attempt and has the following columns:

• sequence_number: number indicating order the shot attempt took place in the game
• period_display_value: Name display of period
• period_number: Period number
• home_score: Home team score following shot attempt
• coordinate_x: Horizontal location in feet of shot attempt where the hoop would be located at 25 feet
• coordinate_y: Vertical location in feet of shot attempt with respect to the target hoop (the hoop should be a little in front of 0 but ESPN’s coordinate system is not exact)
• scoring_play: Indicator if shot was made or not
• clock_display_value: Time displayed on clock moment of shot
• team_id: Unique identifier for team the player is on
• type_id: Identifier for the shot type
• type_text: Text description of the shot type
• away_score: Away team score following the shot attempt
• text: Detailed text description of shot attempt
• score_value: Value of the shot attempt
• participants_0_athlete_id: Unique identifier for the first person listed in the text description of the shot attempt (typically the person attempting the shot unless it is blocked)
• participants_1_athlete_id: Unique identifier for the potential second person listed in the text description of the shot attempt (typically the person who assisted the shot attempt if scoring_play == TRUE)
• game_id: Unique identifier for a game
• away_team_id: Unique identifier for away team
• away_team_name: Away team name
• away_team_mascot: Away team mascot
• away_team_abbrev: Abbreviation for away team
• away_team_name_alt: Alternate name for away team
• home_team_id: Unique identifier for home team
• home_team_name: Home team name
• home_team_mascot: Home team mascot
• home_team_abbrev: Abbreviation for home team
• home_team_name_alt: Alternate name for home team
• clock_minutes: Minutes remaining in the period displayed on the clock
• clock_seconds: Seconds remaining in the period displayed on the clock
• half: Game half
• lag_half: Previous play’s game half (can ignore)
• lead_half: Following play’s game half (can ignore)
• game_play_number: number indicating order the shot attempt took place in the game (similar to sequence number it appears…)

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

Code to build dataset

library(wehoop)
wnba_pbp_data <- load_wnba_pbp(2022)

# Get the shots and clean this data a bit:
wnba_shots_data <- wnba_pbp_data %>%
  filter(shooting_play)
# Remove unnecessary columns:
wnba_shots_data <- wnba_shots_data %>%
  dplyr::select(-shooting_play, -id, -participants_2_athlete_id,
                -type_abbreviation, -season, -season_type, 
                -home_team_spread, -game_spread, -home_favorite)
# Save this file:
write_csv(wnba_shots_data, 
          "data/sports/eda_projects/wnba_shots_2022.csv")