update READMEs
This commit is contained in:
Dhrumil Mehta
@@ -1,3 +1,7 @@
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# Congress Age
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This folder contains the data behind the story [Both Republicans And Democrats Have an Age Problem](https://fivethirtyeight.com/features/both-republicans-and-democrats-have-an-age-problem/)
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`congress-terms.csv` has an entry for every member of congress who served at any point during a particular congress between January 1947 and Februrary 2014.
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House membership data is from the [@unitedstates project](http://theunitedstates.io/), with Congress meeting numbers added using code from [GovTrack](https://www.govtrack.us/developers/api):
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@@ -5,4 +5,4 @@ files:
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---
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# Congress Generic Ballot Polls
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This contains the raw data behind "[Are Democrats Winning The Race For Congress?](https://projects.fivethirtyeight.com/congress-generic-ballot-polls/)"
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This readme contains links to the data behind [Are Democrats Winning The Race For Congress?](https://projects.fivethirtyeight.com/congress-generic-ballot-polls/). For the latest version of this updating data set, visit the links at the top of this README.
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# Congressional Resignations
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Data behind the story [We’ve Never Seen Congressional Resignations Like This Before](https://fivethirtyeight.com/features/more-people-are-resigning-from-congress-than-at-any-time-in-recent-history/).
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This folder contains data behind the story [We’ve Never Seen Congressional Resignations Like This Before](https://fivethirtyeight.com/features/more-people-are-resigning-from-congress-than-at-any-time-in-recent-history/).
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`congressional_resignations.csv` contains information about the 615 members of Congress who resigned or were removed from office from March 4, 1901 (the first day of the 57th Congress) through January 15, 2018, including the resigning member’s party and district, the date they resigned, the reason for their resignation and the source of the information about their resignation.
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### Cousin Marriage Data
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# Cousin Marriage
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The raw data behind the story [Dear Mona: How Many Americans Are Married To Their Cousins?]
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This folder contains data behind the story [Dear Mona: How Many Americans Are Married To Their Cousins?](https://fivethirtyeight.com/features/how-many-americans-are-married-to-their-cousins/).
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Header | Definition
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---|---------
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### Daily Show Guests
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# Daily Show Guests
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The raw data behind the story [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](http://fivethirtyeight.com/datalab/every-guest-jon-stewart-ever-had-on-the-daily-show/)
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This folder contains data behind the story [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](http://fivethirtyeight.com/datalab/every-guest-jon-stewart-ever-had-on-the-daily-show/).
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Header | Definition
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---|---------
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@@ -8,6 +8,6 @@ Header | Definition
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`GoogleKnowlege_Occupation` | Their occupation or office, according to Google's Knowledge Graph or, if they're not in there, how Stewart introduced them on the program.
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`Show` | Air date of episode. Not unique, as some shows had more than one guest
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`Group` | A larger group designation for the occupation. For instance, us senators, us presidents, and former presidents are all under "politicians"
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`Raw_Guest_List` | The person or list of people who appeared on the show, according to Wikipedia. The GoogleKnowlege_Occupation only refers to one of them in a given row.
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`Raw_Guest_List` | The person or list of people who appeared on the show, according to Wikipedia. The GoogleKnowlege_Occupation only refers to one of them in a given row.
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Source: Google Knowlege Graph, The Daily Show clip library, Wikipedia.
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### Democratic bench
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# Democratic bench
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This folder contains data behind the story [Some Democrats Who Could Step Up If Hillary Isn’t Ready For Hillary](https://fivethirtyeight.com/features/some-democrats-who-could-step-up-if-hillary-isnt-ready-for-hillary/).
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Header | Definition
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---|---------
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### Drug Use By Age
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# Drug Use By Age
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This directory contains the data behind the story [How Baby Boomers Get High](http://fivethirtyeight.com/datalab/how-baby-boomers-get-high/). It covers 13 drugs across 17 age groups.
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This directory contains data behind the story [How Baby Boomers Get High](http://fivethirtyeight.com/datalab/how-baby-boomers-get-high/). It covers 13 drugs across 17 age groups.
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Source: [National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data Archive](http://www.icpsr.umich.edu/icpsrweb/content/SAMHDA/index.html).
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Source: [National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data Archive](http://www.icpsr.umich.edu/icpsrweb/content/SAMHDA/index.html).
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Header | Definition
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---|---------
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3
early-senate-polls/README.md
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3
early-senate-polls/README.md
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# Early Senate Polls
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This folder contains data behind the story [Early Senate Polls Have Plenty to Tell Us About November](https://fivethirtyeight.com/features/early-senate-polls-have-plenty-to-tell-us-about-november/).
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### FIFA teams under Blatter
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# FIFA teams under Blatter
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The raw data behind the story [Blatter’s Reign At FIFA Hasn’t Helped Soccer’s Poor](http://fivethirtyeight.com/features/blatters-reign-at-fifa-hasnt-helped-soccers-poor/)
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This folder contains data behind the story [Blatter’s Reign At FIFA Hasn’t Helped Soccer’s Poor](http://fivethirtyeight.com/features/blatters-reign-at-fifa-hasnt-helped-soccers-poor/).
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Header | Definition
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---|---------
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### Endorsements through June 30
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# Endorsements through June 30
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The raw data behind the story [Pols And Polls Say The Same Thing: Jeb Bush Is A Weak Front-Runner](http://fivethirtyeight.com/features/pols-and-polls-say-the-same-thing-jeb-bush-is-a-weak-front-runner/)
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This folder contains data behind the story [Pols And Polls Say The Same Thing: Jeb Bush Is A Weak Front-Runner](http://fivethirtyeight.com/features/pols-and-polls-say-the-same-thing-jeb-bush-is-a-weak-front-runner/).
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This data includes something we call "endorsement points," an attempt to quantify the importance of endorsements by weighting each one according to the position held by the endorser: 10 points for each governor, 5 points for each senator and 1 point for each representative
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This data includes something we call "endorsement points," an attempt to quantify the importance of endorsements by weighting each one according to the position held by the endorser: 10 points for each governor, 5 points for each senator and 1 point for each representative.
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Header | Definition
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---|---------
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# Fandango
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This directory contains the data behind the story [Be Suspicious Of Online Movie Ratings, Especially Fandango’s](http://fivethirtyeight.com/features/fandango-movies-ratings/).
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`fandango_score_comparison.csv` contains every film that has a Rotten Tomatoes rating, a RT User rating, a Metacritic score, a Metacritic User score, and IMDb score, and at least 30 fan reviews on Fandango. The data from Fandango was pulled on Aug. 24, 2015.
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Column | Definition
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--- | -----------
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FILM | The film in question
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RottenTomatoes | The Rotten Tomatoes Tomatometer score for the film
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RottenTomatoes_User | The Rotten Tomatoes user score for the film
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RottenTomatoes | The Rotten Tomatoes Tomatometer score for the film
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RottenTomatoes_User | The Rotten Tomatoes user score for the film
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Metacritic | The Metacritic critic score for the film
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Metacritic_User | The Metacritic user score for the film
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IMDB | The IMDb user score for the film
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Fandango_Stars | The number of stars the film had on its Fandango movie page
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Fandango_Ratingvalue | The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
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Fandango_Ratingvalue | The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
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RT_norm | The Rotten Tomatoes Tomatometer score for the film , normalized to a 0 to 5 point system
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RT_user_norm | The Rotten Tomatoes user score for the film , normalized to a 0 to 5 point system
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Metacritic_norm | The Metacritic critic score for the film, normalized to a 0 to 5 point system
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--- | ---------
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FILM | The movie
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STARS | Number of stars presented on Fandango.com
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RATING | The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
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VOTES | number of people who had reviewed the film at the time we pulled it.
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RATING | The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
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VOTES | number of people who had reviewed the film at the time we pulled it.
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### FIFA
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# FIFA
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This directory contains the data behind the story [How To Break FIFA](http://fivethirtyeight.com/features/how-to-break-fifa/).
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### Flying Etiquette Survey
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# Flying Etiquette Survey
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Results of a SurveyMonkey survey commissioned by FiveThirtyEight for the story [41 Percent of Fliers Say It’s Rude To Recline Your Airplane Seat](http://fivethirtyeight.com/datalab/airplane-etiquette-recline-seat)
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This folder contains data behind the story [41 Percent of Fliers Say It’s Rude To Recline Your Airplane Seat](http://fivethirtyeight.com/datalab/airplane-etiquette-recline-seat).
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`flying-etiquette.csv` contains the results of a SurveyMonkey survey commissioned by FiveThirtyEight for the story.
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16
food-world-cup/README.md
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food-world-cup/README.md
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# Food World Cup
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This folder contains data behind the stories:
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* [The FiveThirtyEight International Food Association’s 2014 World Cup](https://fivethirtyeight.com/features/the-fivethirtyeight-international-food-associations-2014-world-cup/)
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* [What is Americans’ Favorite Global Cuisine?](https://fivethirtyeight.com/features/what-is-americans-favorite-global-cuisine/)
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Anwser key for the responses to the "Please rate how much you like the traditional cuisine of X:" questions.
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Value | Description
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------|--------------
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5 | I love this country's traditional cuisine. I think it's one of the best in the world.
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4 | I like this country's traditional cuisine. I think it's considerably above average.
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3 | I'm OK with this county's traditional cuisine. I think it's about average.
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2 | I dislike this country's traditional cuisine. I think it's considerably below average.
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1 | I hate this country's traditional cuisine. I think it's one of the worst in the world.
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N/A | I'm unfamiliar with this country's traditional cuisine.
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Anwser key for the responses to the "Please rate how much you like the traditional cuisine of X:" questions.
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5: I love this country's traditional cuisine. I think it's one of the best in the world.
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4: I like this country's traditional cuisine. I think it's considerably above average.
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3: I'm OK with this county's traditional cuisine. I think it's about average.
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2: I dislike this country's traditional cuisine. I think it's considerably below average.
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1: I hate this country's traditional cuisine. I think it's one of the worst in the world.
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N/A: I'm unfamiliar with this country's traditional cuisine.
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### Historical FiveThirtyEight Senate Forecasts
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# Historical FiveThirtyEight Senate Forecasts
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The data behind the story [How The FiveThirtyEight Senate Forecast Model Works](http://fivethirtyeight.com/features/how-the-fivethirtyeight-senate-forecast-model-works/)
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This folder contains the data behind the story [How The FiveThirtyEight Senate Forecast Model Works](http://fivethirtyeight.com/features/how-the-fivethirtyeight-senate-forecast-model-works/).
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Header | Definition
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### Goose
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# Goose
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The raw data behind the stories:
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The data behind the stories:
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* [The Save Ruined Relief Pitching. The Goose Egg Can Fix It](https://fivethirtyeight.com/features/goose-egg-new-save-stat-relief-pitchers/)
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* [Kenley Jansen Is The Model Of A Modern Reliever](https://fivethirtyeight.com/features/kenley-jansen-is-the-model-of-a-modern-reliever/)
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### Hate-crimes data
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# Hate Crimes
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The raw data behind the story [Higher Rates Of Hate Crimes Are Tied To Income Inequality](https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/)
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This folder contains data behind the story [Higher Rates Of Hate Crimes Are Tied To Income Inequality](https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/).
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Header | Definition
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### Every mention of the 2016 primary candidates in hip-hop songs
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# Hip Hop Candidate Lyrics
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The raw data behind the story [ Hip-Hop Is Turning On Donald Trump](http://projects.fivethirtyeight.com/clinton-trump-hip-hop-lyrics/)
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This folder contains data behind the story [ Hip-Hop Is Turning On Donald Trump](http://projects.fivethirtyeight.com/clinton-trump-hip-hop-lyrics/).
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`genius_hip_hop_lyrics.csv` contains every mention of the 2016 primary candidates in hip-hop songs.
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Header | Definition
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`song` | Song name
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`artist` | Artist name
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`sentiment` | Positive, negative or neutral
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`theme` | Theme of lyric
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`theme` | Theme of lyric
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`album_release_date` | Date of album release
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`line` | Lyrics
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`url` | Genius link
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Source: [Genius](http://genius.com/)
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3
historical-ncaa-forecasts/README.md
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3
historical-ncaa-forecasts/README.md
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# NCAA Bracket
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This folder contains data behind the story [The NCAA Bracket: Checking Our Work](https://fivethirtyeight.com/datalab/the-ncaa-bracket-checking-our-work).
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# An Inconvenient Sequel
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Raw data behind the story [Al Gore’s New Movie Exposes The Big Flaw In Online Movie Ratings](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/)
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This folder contains data behind the story [Al Gore’s New Movie Exposes The Big Flaw In Online Movie Ratings](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/).
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Data contains [IMDb ratings](http://www.imdb.com/title/tt6322922/ratings) for the film "An Inconvenient Sequel: Truth to Power" collected daily from July 17 to August 29, 2017.
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`ratings.csv` contains [IMDb ratings](http://www.imdb.com/title/tt6322922/ratings) for the film "An Inconvenient Sequel: Truth to Power" collected daily from July 17 to August 29, 2017.
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3
infrastructure-jobs/README.md
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3
infrastructure-jobs/README.md
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# Infrastructure Jobs
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This folder contains data behind the story [Using Infrastructure Jobs as a Measuring Stick For State-Level Spending](https://fivethirtyeight.com/features/using-infrastructure-jobs-as-a-measuring-stick-for-state-level-spending/).
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# Librarians
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The data behind the story [Where Are America’s Librarians?](https://fivethirtyeight.com/features/where-are-americas-librarians/)
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This folder contains data behind the story [Where Are America’s Librarians?](https://fivethirtyeight.com/features/where-are-americas-librarians/).
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### Love Actually
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# Love Actually
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This directory contains the data behind the story: [The Definitive Analysis Of ‘Love Actually,’ The Greatest Christmas Movie Of Our Time](https://fivethirtyeight.com/features/the-definitive-analysis-of-love-actually-the-greatest-christmas-movie-of-our-time/)
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This directory contains the data behind the story [The Definitive Analysis Of ‘Love Actually,’ The Greatest Christmas Movie Of Our Time](https://fivethirtyeight.com/features/the-definitive-analysis-of-love-actually-the-greatest-christmas-movie-of-our-time/).
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There are two data files:
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* `love_actually_appearances.csv` - A table of the central actors in "Love Actually" and which scenes they appear in
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* `love_actually_adjacencies.csv` - The adjacency matrix of which actors appear in the same scene together
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`love_actually_appearances.csv` contains a table of the central actors in "Love Actually" and which scenes they appear in.
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`love_actually_adjacencies.csv` contains the adjacency matrix of which actors appear in the same scene together.
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You'll notice there are a lot of “Love Actually” actors who we didn’t track in the data. That’s because they rarely cross storylines. When they do, it’s in the company of the actor who we *did* include, the linchpin of that storyline.
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@@ -1,4 +1,4 @@
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### Mad Men
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# Mad Men
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This directory contains the data behind the story [‘Mad Men’ Is Ending. What’s Next For The Cast?](http://fivethirtyeight.com/datalab/mad-men-is-ending-whats-next-for-the-cast/).
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@@ -1,12 +1,8 @@
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### Male flight attendants
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# Male Flight Attendants
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This repo contains the data from the article on the gender divide in various U.S. occupations
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This folder contains the data behind the story [Dear Mona, How Many Flight Attendants Are Men?](http://fivethirtyeight.com/datalab/dear-mona-how-many-flight-attendants-are-men/).
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[Dear Mona, How Many Flight Attendants Are Men?](http://fivethirtyeight.com/datalab/dear-mona-how-many-flight-attendants-are-men/)
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`male-flight-attendants.tsv`:
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The tab-separated text file contains the percentage of U.S. employees that are male in 320 different job categories.
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`male-flight-attendants.tsv` contains the percentage of U.S. employees that are male in 320 different job categories.
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Source: [IPUMS](https://usa.ipums.org/usa/), 2012
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@@ -1,4 +1,5 @@
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March Madness Predictions 2015
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==============================
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# March Madness Predictions
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Data files for [FiveThirtyEight's 2015 March Madness Predictions](http://fivethirtyeight.com/interactives/march-madness-predictions-2015/), updated each time we calculate new odds.
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This folder contains data behind the [2015 March Madness Predictions](http://fivethirtyeight.com/interactives/march-madness-predictions-2015/).
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Data was updated each time we calculate new odds.
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@@ -1 +1,3 @@
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http://fivethirtyeight.com/interactives/march-madness-predictions/
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# March Madness Predictions
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This folder contains data behind the [2014 NCAA Tournament Predictions](http://fivethirtyeight.com/interactives/march-madness-predictions/).
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@@ -1,6 +1,10 @@
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These files contain data used in <a href="http://fivethirtyeight.com/features/marriage-isnt-dead-yet/">FiveThirtyEight's story</a> on marriage trends.File names are self-explanatory. Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via <a href="https://usa.ipums.org/usa/cite.shtml">IPUMS USA</a>.
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# Marriage
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Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have <i>ever</i> been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is <i>currently</i> divorced, conditional on having ever been married.
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This folder contains data behind the story [Marriage Isn’t Dead — Yet](http://fivethirtyeight.com/features/marriage-isnt-dead-yet/).
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Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via [IPUMS USA](https://usa.ipums.org/usa/cite.shtml).
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Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have *ever* been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is *currently* divorced, conditional on having ever been married.
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|
||||
Variable names are as follows. Number in variable names are age ranges, so `all_2534` is the marriage rate for everyone ages 25 to 34.
|
||||
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
# Mayweather Vs McGregor
|
||||
# Mayweather vs McGregor
|
||||
|
||||
Raw data behind the story [The Mayweather-McGregor Fight As Told Through Emojis
|
||||
](https://fivethirtyeight.com/?post_type=fte_features&p=161615)
|
||||
This folder contains data behind the story [The Mayweather-McGregor Fight As Told Through Emojis](https://fivethirtyeight.com/?post_type=fte_features&p=161615).
|
||||
|
||||
This data contains 12,118 tweets that contain one or more emojis and match one or more of the following hashtags: #MayMac, #MayweatherMcGregor, #MayweatherVMcGregor, #MayweatherVsMcGregor, #McGregor and #Mayweather. Data was collected on August 27, 2017 between 12:05 a.m. and 1:15 a.m. EDT using the Twitter streaming API.
|
||||
`tweets.csv` contains 12,118 tweets that contain one or more emojis and match one or more of the following hashtags: #MayMac, #MayweatherMcGregor, #MayweatherVMcGregor, #MayweatherVsMcGregor, #McGregor and #Mayweather. Data was collected on August 27, 2017 between 12:05 a.m. and 1:15 a.m. EDT using the Twitter streaming API.
|
||||
@@ -1,6 +1,10 @@
|
||||
# MLB All-Star Teams
|
||||
|
||||
This folder contains data behind the story [The Best MLB All-Star Teams Ever](http://fivethirtyeight.com/features/the-best-mlb-all-star-teams-ever/).
|
||||
|
||||
Estimates of most talented MLB All-Star teams, 1933-2015
|
||||
|
||||
Team talent estimates:
|
||||
`allstar_team_talent.csv` contains team talent estimates with the following headers:
|
||||
|
||||
Header | Definition
|
||||
---|---------
|
||||
@@ -21,7 +25,7 @@ Header | Definition
|
||||
`no_1_player` | Best player according to combo of actual PA/IP and talent
|
||||
`no_2_player` | 2nd-best player according to combo of actual PA/IP and talent
|
||||
|
||||
Player talent estimates:
|
||||
`allstar_player_talent.csv` contains team player estimates with the following headers:
|
||||
|
||||
Header | Definition
|
||||
---|---------
|
||||
@@ -41,4 +45,3 @@ Header | Definition
|
||||
`PITper9innASG` | Expected pitching runs added above average (from talent) based on IP in ASG, scaled to a 9-inning game
|
||||
`TOTper9innASG` | Expected runs added above average (from talent) based on PA/IP in ASG, scaled to a 9-inning game
|
||||
|
||||
http://fivethirtyeight.com/features/the-best-mlb-all-star-teams-ever/
|
||||
|
||||
@@ -4,6 +4,6 @@ files:
|
||||
---
|
||||
# MLB Elo
|
||||
|
||||
This contains the raw data behind [The Complete History Of MLB](https://projects.fivethirtyeight.com/complete-history-of-mlb/) and our [MLB Predictions](https://projects.fivethirtyeight.com/2017-mlb-predictions/).
|
||||
This readme contains links to the data behind [The Complete History Of MLB](https://projects.fivethirtyeight.com/complete-history-of-mlb/) and our [MLB Predictions](https://projects.fivethirtyeight.com/2017-mlb-predictions/). For the latest version of this updating data set, visit the links at the top of this README.
|
||||
|
||||
* `mlb_elo.csv` - Game-by-game Elo ratings and forecasts back to 1871.
|
||||
`mlb_elo.csv` contains game-by-game Elo ratings and forecasts back to 1871.
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
### Most Common Name
|
||||
# Most Common Name
|
||||
|
||||
This directory contains the code and data behind the story:
|
||||
|
||||
[Dear Mona, What’s The Most Common Name In America?](http://fivethirtyeight.com/features/whats-the-most-common-name-in-america/)
|
||||
This directory contains the code and data behind the story [Dear Mona, What’s The Most Common Name In America?](http://fivethirtyeight.com/features/whats-the-most-common-name-in-america/).
|
||||
|
||||
The main script file is `most-common-name.R`
|
||||
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
### 2016 murder data
|
||||
# 2016 Murder Data
|
||||
|
||||
The raw data behind the story [A Handful Of Cities Are Driving 2016's Rise In Murder](http://fivethirtyeight.com/features/a-handful-of-cities-are-driving-2016s-rise-in-murders/)
|
||||
|
||||
There are two files:
|
||||
This folder contains data behind the story [A Handful Of Cities Are Driving 2016's Rise In Murder](http://fivethirtyeight.com/features/a-handful-of-cities-are-driving-2016s-rise-in-murders/).
|
||||
|
||||
`murder_2016_prelim.csv` contains preliminary 2016 murder counts for 79 large U.S. cities. 2015 figures are counts through the same data a year ago. Sources are listed in the file.
|
||||
|
||||
|
||||
@@ -4,6 +4,6 @@ files:
|
||||
---
|
||||
# NBA Elo
|
||||
|
||||
This contains the raw data behind [The Complete History Of The NBA](https://projects.fivethirtyeight.com/complete-history-of-the-nba/) and our [NBA Predictions](https://projects.fivethirtyeight.com/2018-nba-predictions/).
|
||||
This contains the raw data behind [The Complete History Of The NBA](https://projects.fivethirtyeight.com/complete-history-of-the-nba/) and our [NBA Predictions](https://projects.fivethirtyeight.com/2018-nba-predictions/). For the latest version of this updating data set, visit the links at the top of this README.
|
||||
|
||||
* `nba_elo.csv` - Game-by-game Elo ratings and forecasts back to 1946.
|
||||
* `nba_elo.csv` contains game-by-game Elo ratings and forecasts back to 1946.
|
||||
|
||||
@@ -1,4 +1,8 @@
|
||||
Historical results of NBA draft projection model, 2001-2015.
|
||||
# NBA Draft 2015
|
||||
|
||||
This folder contains data behind the story [Projecting The Top 50 Players In The 2015 NBA Draft Class](http://fivethirtyeight.com/features/projecting-the-top-50-players-in-the-2015-nba-draft-class/).
|
||||
|
||||
`historical_projections.csv` contains historical results of the NBA draft projection model, 2001-2015.
|
||||
|
||||
Header | Definition
|
||||
---|---------
|
||||
@@ -11,5 +15,3 @@ Header | Definition
|
||||
`Starter` | Probability of becoming a starting-caliber player (10 per draft, SPM >= +0.5)
|
||||
`Role Player` | Probability of becoming a role player (25 per draft, SPM >= -1.4)
|
||||
`Bust` | Probability of becoming a bust (everyone else, SPM < -1.4)
|
||||
|
||||
http://fivethirtyeight.com/features/projecting-the-top-50-players-in-the-2015-nba-draft-class/
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
### Historical NBA Elo
|
||||
# Historical NBA Elo
|
||||
|
||||
This directory contains the data behind the [Complete History Of The NBA](http://fivethirtyeight.com/interactives/the-complete-history-of-every-nba-team-by-elo) interactive. Data updated periodically. Game information is from [Basketball-Reference.com](http://www.basketball-reference.com/).
|
||||
|
||||
|
||||
3
nba-tattoos/README.md
Normal file
3
nba-tattoos/README.md
Normal file
@@ -0,0 +1,3 @@
|
||||
# NBA Tattoos
|
||||
|
||||
This folder contains data behind the story [What Ethan Swan Learned From Tracking Every Tattoo in the NBA](https://fivethirtyeight.com/features/what-ethan-swan-learned-from-tracking-every-tattoo-in-the-nba/)
|
||||
@@ -1,10 +1,6 @@
|
||||
### NBA Win Probabilities
|
||||
# NBA Win Probabilities
|
||||
|
||||
This directory contains the data behind the story:
|
||||
This directory contains the data behind the story [Every NBA Team’s Chance Of Winning In Every Minute Across Every Game](https://fivethirtyeight.com/features/every-nba-teams-chance-of-winning-in-every-minute-across-every-game/).
|
||||
|
||||
[Every NBA Team’s Chance Of Winning In Every Minute Across Every Game](https://fivethirtyeight.com/features/every-nba-teams-chance-of-winning-in-every-minute-across-every-game/)
|
||||
|
||||
There is one data file:
|
||||
|
||||
* `nba.tsv` - The 2014-15 NBA season win probabilities for each team over the course of a game, as of February 18, 2015
|
||||
`nba.tsv` contains the 2014-15 NBA season win probabilities for each team over the course of a game, as of February 18, 2015.
|
||||
|
||||
|
||||
@@ -1,32 +1,31 @@
|
||||
# The Next Bechdel Test
|
||||
Data for [The Next Bechdel Test](https://projects.fivethirtyeight.com/next-bechdel/) story.
|
||||
# The Next Bechdel Test
|
||||
|
||||
## Data included
|
||||
This folder contains data behind the story [The Next Bechdel Test](https://projects.fivethirtyeight.com/next-bechdel/).
|
||||
|
||||
1. `nextBechal_allTests.csv` powers the graphics on the page, and shows the high-level breakdown of which movies passed and failed
|
||||
- Each row is one of the 50 top-grossing movies from 2016
|
||||
- Each column is one of the tests. A `0` means the movie failed that test, a `1` means it passed.
|
||||
`nextBechal_allTests.csv` and shows the high-level breakdown of which movies passed and failed
|
||||
- Each row is one of the 50 top-grossing movies from 2016.
|
||||
- Each column is one of the tests. A `0` means the movie failed that test, a `1` means it passed.
|
||||
|
||||
2. `nextBechal_castGender.csv` Estimated gender for the entire cast for every movie, including whether a role was supporting or main. Data obtained from [The Numbers](http://the-numbers.com)
|
||||
|
||||
Variable | Definition
|
||||
---|---------
|
||||
`MOVIE` | Title of the film
|
||||
`ACTOR` | Full name of the actor
|
||||
`CHARACTER` | All characters played by the actor in that movie
|
||||
`TYPE` | Leading, Supporting, Cameo or Lead Ensemble Member
|
||||
`BILLING` | Billing number
|
||||
`GENDER` | Estimated gender of the actor
|
||||
`nextBechal_castGender.csv` contains the estimated gender for the entire cast for every movie, including whether a role was supporting or main. Data was obtained from [The Numbers](http://the-numbers.com)
|
||||
|
||||
Variable | Definition
|
||||
---|---------
|
||||
`MOVIE` | Title of the film
|
||||
`ACTOR` | Full name of the actor
|
||||
`CHARACTER` | All characters played by the actor in that movie
|
||||
`TYPE` | Leading, Supporting, Cameo or Lead Ensemble Member
|
||||
`BILLING` | Billing number
|
||||
`GENDER` | Estimated gender of the actor
|
||||
|
||||
|
||||
3. `nextBechal_crewGender.csv` crew for every movie, by probablity that a give first name is male.
|
||||
|
||||
Variable | Definition
|
||||
---|---------
|
||||
`MOVIE` | Title of the film
|
||||
`DEPARTMENT` | Full name of the actor
|
||||
`FULL_NAME` | Actor's first and last name
|
||||
`FIRST_NAME` | Just first name of actor
|
||||
`IMDB` | Actor's IMDB page
|
||||
`GENDER_PROB` | Percent chance that a given name is male
|
||||
`GENDER_GUESS` | Based on the probablity, guess if the name is male or female
|
||||
`nextBechal_crewGender.csv` contains data for the crew for every movie, by probablity that a give first name is male.
|
||||
|
||||
Variable | Definition
|
||||
---|---------
|
||||
`MOVIE` | Title of the film
|
||||
`DEPARTMENT` | Full name of the actor
|
||||
`FULL_NAME` | Actor's first and last name
|
||||
`FIRST_NAME` | Just first name of actor
|
||||
`IMDB` | Actor's IMDB page
|
||||
`GENDER_PROB` | Percent chance that a given name is male
|
||||
`GENDER_GUESS` | Based on the probablity, guess if the name is male or female
|
||||
Reference in New Issue
Block a user