DOI: 10.5281/zenodo.34939
title: What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions In Online Sports Viewing

description: <strong>1. Chat message data</strong>

- kbo2011_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2011<br>
- kbo2012_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2012

Each MySQL table consists of the following six columns.<br>
1. comment_id: Integer variable to identify each comment<br>
2. gid: Integer variable to identify each game<br>
3. uid: Integer variable to identify each user<br>
4. category_id: A team name that a user selected when a chat message is posted<br>
5. content: Chat message text<br>
6. post_time_relative: Elapsed seconds since the game starts

 

<strong>2. KBO game information</strong>

- game_basic.sql: KBO game information in 2011/2012<br>
<br>
(This data was collected from here, http://sports.news.naver.com/schedule/index.nhn?uCategory=&category=kbo&year=2010&month=03)

This MySQL table consists of the following 12 columns.<br>
1. gid: Integer variable to identify each game<br>
2. game: Game ID used in Naver Sports<br>
3. year: year of the game play<br>
4. month: month of the game play<br>
5. date: date of the game play<br>
6. time: time of the game play<br>
7. day: day of the game play<br>
8. team_away: name of the away team<br>
9 team_home: name of the home team<br>
10. score_away: score by the away team<br>
11. score_home: score by the home team<br>
12. location: location of the game play

 

<strong>3. KBO game play-by-play data </strong>

- game_playbyplay.sql: playbyplay data in 2011/2012

(This data was collected from Naver Sports; EX, http://sports.news.naver.com/gameCenter/gameRecord.nhn?category=kbo&gameId=20100327HHSK0)

This MySQL table consists of the following 21 columns.

1. play_no:  Integer variable to identify each play<br>
2. gid: Integer variable to identify each game<br>
3. inning: inning of the play<br>
4. pitcher: current pitcher name<br>
5. batter: current batter name<br>
6. batter_no: current batter order<br>
7. pitch: the pitch result<br>
8. strike: current number of strikes<br>
9. ball: current number of balls<br>
10. out_cnt: current number of outs<br>
11. score_home: current score of the home team<br>
12. score_away: current score of the away team<br>
13. 1st_base: player name on the 1st base <br>
14. 2nd_base: player name on the 2nd base<br>
15. 3rd_base: player name on the 3rd base<br>
16. runner_state: overall state of the three bases (ex: 1 --&gt; A player is on 1st base, but 2nd and 3rd bases are empty, 13 --&gt; Two players are on 1st and 3rd bases respectively, but 2nd base is empty)<br>
17. runner_cnt: the number of players on bases<br>
18. result_r: r by this play<br>
19. result_rbi: rbi by this play<br>
20. result_score_home: total home score after this play<br>
21. result_score_away: total away score after this play

 

<strong>4. Winning rate data </strong>

- game_winrate.sql: winning rates of the home/away teams for an inning

(This data is calculated based on the playbyplay data. For the details, please refer to our paper--currently under review)

This MySQL table consists of the following 21 columns.

1. play_no: Integer variable to identify each play<br>
2. gid: Integer variable to identify each game<br>
3. inning: inning of the play<br>
4. away_win: probability that the away team will win this game<br>
5. draw: probability that the game will finish a tie<br>
6. home_win: probability that the home team will win this game

 

If you have any question about the dataset, please contact Minsam Ko (msko@kaist.ac.kr).