Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 1MB | csv zip | 4 years ago | 4 years ago | FiveThirtyEight - Soccer Spi |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
spi_global_rankings | 25kB | csv (25kB) , json (57kB) | ||
spi_matches | 1MB | csv (1MB) , json (4MB) | ||
soccer-spi_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 1MB | zip (1MB) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
name | 1 | string (default) | |
league | 2 | string (default) | |
rank | 3 | integer (default) | |
prev_rank | 4 | integer (default) | |
off | 5 | number (default) | |
def | 6 | number (default) | |
spi | 7 | number (default) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
league_id | 2 | integer (default) | |
league | 3 | string (default) | |
team1 | 4 | string (default) | |
team2 | 5 | string (default) | |
spi1 | 6 | number (default) | |
spi2 | 7 | number (default) | |
prob1 | 8 | number (default) | |
prob2 | 9 | number (default) | |
probtie | 10 | number (default) | |
proj_score1 | 11 | number (default) | |
proj_score2 | 12 | number (default) | |
score1 | 13 | integer (default) | |
score2 | 14 | integer (default) | |
xg1 | 15 | number (default) | |
xg2 | 16 | number (default) | |
nsxg1 | 17 | number (default) | |
nsxg2 | 18 | number (default) | |
adj_score1 | 19 | number (default) | |
adj_score2 | 20 | number (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/five-thirty-eight/soccer-spi
data info five-thirty-eight/soccer-spi
tree five-thirty-eight/soccer-spi
# Get a list of dataset's resources
curl -L -s https://datahub.io/five-thirty-eight/soccer-spi/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/five-thirty-eight/soccer-spi/r/0.csv
curl -L https://datahub.io/five-thirty-eight/soccer-spi/r/1.csv
curl -L https://datahub.io/five-thirty-eight/soccer-spi/r/2.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/five-thirty-eight/soccer-spi/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/five-thirty-eight/soccer-spi/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/five-thirty-eight/soccer-spi/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/five-thirty-eight/soccer-spi/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
files:
This file contains links to the data behind our Club Soccer Predictions and Global Club Soccer Rankings.
spi_matches.csv
contains match-by-match SPI ratings and forecasts back to 2016.
spi_global_rankings.csv
contains current SPI ratings and rankings for men’s club teams.
This dataset was scraped from FiveThirtyEight - soccer-spi