You find here R functions for summarizing spirit scores for Ultimate tournaments. It is based on the method the WFDF uses.

Quickstart

Very quickly, here is how you summarize all the data:

> spiritData <- loadSpiritData("example/example-tournament.csv")

> leaderBoard(spiritData)
      team spiritScore selfScore
1 Floaters        10.5      10.0
2  Discers        10.0      10.5
3 Cleaters         9.0       9.5

> comparisonMatrix(spiritData)
         Cleaters Discers Floaters
Cleaters      9.5    11.0       10
Discers       8.0    10.5       11
Floaters     10.0     9.0       10

Here, all entries are average scores.

Documentation

Basic Use

The script expects the raw data to be assembled in a CSV file. It has to have columns Team1, Team2, Evaluator, and Evaluated; the first two identify the game (so you'd have four rows with the same values here for each game), the other two who scored whom. All other columns are interpreted as (equally weighted) scoring categories.

For example:

Team1,Team2,Evaluator,Evaluated,Rules,Contact,Fair,Attitude,Communication
Discers,Floaters,Discers,Floaters,2,3,2,2,2
Discers,Floaters,Discers,Discers,2,2,2,1,2
Discers,Floaters,Floaters,Discers,1,2,2,2,2
Discers,Floaters,Floaters,Floaters,2,2,2,2,2
Cleaters,Floaters,Cleaters,Floaters,2,2,1,2,3
Cleaters,Floaters,Cleaters,Cleaters,3,2,2,2,1
Cleaters,Floaters,Floaters,Cleaters,2,2,2,2,2
Cleaters,Floaters,Floaters,Floaters,2,2,2,2,2
Cleaters,Discers,Cleaters,Discers,3,2,1,2,3
Cleaters,Discers,Cleaters,Cleaters,3,1,2,2,1
Cleaters,Discers,Discers,Cleaters,2,1,1,2,2
Cleaters,Discers,Discers,Discers,2,3,3,2,2

Now, to get started you need to load the data into your R session (or script):

> source("evaluate-spirit.R")
> spiritData <- loadSpiritData("example/example-tournament.csv")
> spiritData
      Team1    Team2 Evaluator Evaluated Rules Contact Fair Attitude Communication
1   Discers Floaters   Discers  Floaters     2       3    2        2             2
2   Discers Floaters   Discers   Discers     2       2    2        1             2
3   Discers Floaters  Floaters   Discers     1       2    2        2             2
4   Discers Floaters  Floaters  Floaters     2       2    2        2             2
5  Cleaters Floaters  Cleaters  Floaters     2       2    1        2             3
6  Cleaters Floaters  Cleaters  Cleaters     3       2    2        2             1
7  Cleaters Floaters  Floaters  Cleaters     2       2    2        2             2
8  Cleaters Floaters  Floaters  Floaters     2       2    2        2             2
9  Cleaters  Discers  Cleaters   Discers     3       2    1        2             3
10 Cleaters  Discers  Cleaters  Cleaters     3       1    2        2             1
11 Cleaters  Discers   Discers  Cleaters     2       1    1        2             2
12 Cleaters  Discers   Discers   Discers     2       3    3        2             2

Here we see spirit scores for three games. Each game produces four rows since both teams assess themselves and the other team, respectively.

You can access the teams and score categories as follows:

> teams(spiritData)
[1] "Cleaters" "Discers" "Floaters"
> scoreNames(spiritData)
[1] "Rules" "Contact" "Fair" "Attitude" "Communication"

The central function is score(...). You can use it to filter and summarize multiple assessments. Here are some usage examples:

> score(spiritData, from = "Cleaters")
      From    To Score Rules Contact  Fair Attitude Communication
1 Cleaters    NA  10.5   2.5       2     1        2             3

> score(spiritData, from = "Cleaters", average = FALSE)
      From       To Score Rules Contact  Fair Attitude Communication
1 Cleaters Floaters    10     2       2     1        2             3
2 Cleaters  Discers    11     3       2     1        2             3

> score(spiritData, to = "Cleaters")
   From       To Score Rules Contact  Fair Attitude Communication
1    NA Cleaters     9     2     1.5   1.5        2             2

> score(spiritData, from = "Floaters", to = "Cleaters")
      From       To Score Rules Contact  Fair Attitude Communication
1 Floaters Cleaters    10     2       2     2        2             2

There is a helper function selfScore(...) that calls score(...) with parameters from and to set to the same team; self-explanatory.

Creating presentable documents

You can use knitr to create HTML and PDF documents presenting the scores. Find example code here and website here. There are few limits to what you can do -- if you know your way around R.

Advantages over spreadsheets

Most spirit evaluation I've seen were made by hand (which is slow an error-prone) or using spreadsheets. Here are some advantages of a scripted solution:

• Robust and testable -- the same function is used for all data; you can all too easily mess up one of the many individual formulae in a spreadsheet.
• It scales better to large numbers of teams and games.
• More flexible -- the number of teams and games does not matter; it's easy to customize the scoring categories.
• Data analysis -- the power of R is at your beck and call. Does the game outcome correlate with spirit? Are there teams that consistently self-score higher then other teams score them? Are there trends over time?