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Guild Ball Player Spreadsheet

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Hey everyone,

On my way to work today I had a passing thought while mulling Sirens 10 hit boxes: "What is the average hit boxes across all models in GB?". Thus I realized that once all the data for each player was put into a spreadsheet then these types of questions could be answered easily. (I really do like statistics)

The raw data can be accessed via: This Google Sheet thus allowing you to download it as an excel file or CSV/TSV file to work with. 



The variables recorded are:

Name: The name of the model, and veteran models are denoted by "Veteran Brisket" and the non veteran version would just say 'Brisket"

Team: What team they play on. Compound was tricky and I chose to list him twice, once under Alchemist and once under Engineers

Role: This denotes if the model is either a Captain, Mascot or Player. This makes it easier to sort them out via excels sort function or another data sifting software. That will allow us to ask questions such as: "What is the average number of hit boxes across all models in GB?" or "What is the average number of hit boxes across all models in GB, excluding Mascots?"

Mov_Jog: Is their jog distance (inches).

Mov_Sprint: Is their Sprint/Charge distance (inches).

Tac: the value for a models Tac.

Kick_Skill: The first kick value on the card which indicates the number of dice rolled during a kick attempt.

Kick_Distance: The distance a model can kick (inches).

Defense: The model's base defense value on the card.

Armor: The model's base armor value on the card.

Inf_Gen: The amount of influence the model generates at the beginning of the turn.

Inf_Max: The maximum amount of influence the model can be allocated at the beginning of the turn.

Health: The starting number of health boxes the model starts with at the beginning of the game.


I readily acknowledge that these data statistics do not represent any buffs/de-buffs that are present in the game nor do they represent the dynamics of a consistently changing game state, although it can serve as a way to develop benchmarks and averages either between teams or across the game as a whole. 

So feel free to use this as a fun tool to play around with (let me know if you find anything interesting). If you have any specific data mining questions you may have but might be unsure of how to calculate and I can try to find the answer. Also, if you notice any errors or have reccomended fixes let me know and I can change it straight away!


-The procrastinating graduate student


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You are both very welcome! 

Also today at work I ran some r code on the data to have the data give a summary of each data statistic: GB Data Analysis Output

Anything you see after a "#" is a annotated comment to help with the bolded titles to help let you follow the flow of the document.

For the first step all I did was make separate sets of data sheets each either having just players, mascots, captains or both players and captains along with the All_players which means every model in the game.

For the second part I just had R calculate a "summary" of the data for each statistic individually for each group defined above. The summary consist of: the minimum value, 1st quartile, Median, Mean, 3rd quartile, and the maximum value all within the statistic for the group.

I know it looks a bit daunting and hard to digest at first (R spits out the results like this) but after a little it will start to make sense. If you have any questions feel free to ask and I will be glad to help explain it!

This data does include the two new exciting releases we received today.




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On 5/16/2017 at 5:47 AM, Cadilon said:

Is this spreadsheet still available? The link is dead. 


I will make one and share it if work is slow today.

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