2024: Week 48 - Cross Sport League Table Ranks

Challenge by: Eden Thiede-Palmer

Eden created the challenge for this week. As a Data School Consultant who finishes training this week, it's a great example of how your data skills can help you dive deeper into your favourite topics - like sports! Over to Eden:

Who the best team/athlete in sports is always a debate amongst fans, and one of the challenges of the debate is that it's difficult to compare across sports.

I had the idea of creating one large league table across a few major sports, by creating a normalised score across different leagues.

Inputs

  1. 2023/24 FA Premier League Table 

  2. 2023/24 NFL League Table 

  3. 2023/24 Aviva Rugby Premiership League Table 

  4. 2023/24 NBA League Table 

Requirements

  • Input the data
  • Name the field used to rank each league table ‘Ranking Field’
    • Wins for NBA and NFL
    • Points for Rugby Aviva Premiership and Premier League
  • Name and / or calculate First and Second Tie Breaking Fields For each sport.
    • Premier League: Tie Breaker 1 = Wins, Tie Breaker 2 = Goals Scored
    • NFL: Tie Breaker 1 = Points Differential, Tie Breaker 2 = Points For
      • Points Differential = Points For - Points Against
    • NBA: Tie Breaker 1 = Games Behind, Tie Breaker 2 = Conference Wins
      • The Conference Record Field is structured Wins-Losses
    • Rugby: Tie Breaker 1 = Wins (W), Tie Breaker 2 = Points Differential (PD)
  • Make sure all the data types are accurate
  • Bring all the tables together into one dataset
  • Use the Table Names to create a field for the Sport
    • Removing the word Results
  • Calculate the Rank of each team within their own sport using the tie breaking fields to ensure unique ranks
  • Calculate the z-score for each team within their sport
    • x=Ranking Field
    • u=Mean of Ranking Field within sport
    • o=Standard Deviation of Ranking Field within sport
  • Calculate a Sport Specific Percentile Rank 
  • Create a Cross Sport Rank based on the z-scores and using the Sport Specific Percentile Rank to break ties
  • Remove unnecessary fields
  • Output the data
  • Create a second output that averages the Cross Sport Rank for each sport, to see which sport had the best season in 2023/24

Outputs

Output 1

  • 6 fields
    • Sport
    • Cross Sport Rank
    • Team
    • z-score
    • Ranking Field
    • Sport Specific Percentile Rank
  • 92 rows (93 including headers)

Output 2

  • 2 fields
    • Sport
    • Avg Cross Sport Rank
  • 4 rows
You can view the outputs here.

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

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