ranks_sports {MSmix}

Sports Data (complete rankings with covariates)

Description

The Sports dataset was collected through an on-line questionnaire on sport preferences and habits administered within the 2016 Big Data Analystics in Sports (BDsports) project, developed by the Big and Open Data Innovation Laboratory (BODaI-Lab) of the University of Brescia. A sample of N=647 respondents provided their complete rankings of n=8 popular sports according to their personal preferences. The sports are: 1 = Soccer, 2 = Swimming, 3 = Volleyball, 4 = Cycling, 5 = Basket, 6 = Boxe and martial arts, 7 = Tennis and 8 = Jogging. The dataset also includes several covariates concerning respondents' socio-demographics characteristics and other sport-related information.

Usage

data(ranks_sports)

Format

A data frame gathering N=647 complete rankings of the sports in the first n=8 columns (rank 1 = most preferred item) and individual covariates in the remaining columns. The variables are detailed below:

Soccer

Rank assigned to Soccer.

Swimming

Rank assigned to Swimming.

Volleyball

Rank assigned to Volleyball.

Cycling

Rank assigned to Cycling.

Basket

Rank assigned to Basket.

Boxe_and_martial_arts

Rank assigned to Boxe and Martial Arts.

Tennis

Rank assigned to Tennis.

Jogging

Rank assigned to Jogging.

Gender

Gender.

Birth_month

Month of birth.

Birth_year

Year of birth.

Education

Education level.

Residence

Geographical area of residence.

Work

Type of work.

Smoking

Smoking status.

Sport_frequency

Number of times per week that the respondent plays sports.

Sport_hours

Number of hours per week that the respondent watches sports.

Sport_played

Sport played by the respondent.

Personality

Main aspect of respondent's personality.

Sport_motivation

Main reason why the respondent plays sport.

Sport_type

Favorite sport type.

Sport_relationships

Do you think that sport, especially in team games, allows you to make new friends?

Water

Quantity of water consumed per day.

Alcohol

Frequency of alcohol consumption.

Fastfood

Frequency of fast food consumption.

Food_supplements

Opinion about the use of food supplements in sports.

Massmedia

Prevalent mass media used to inquire about sport.

TV_space

Do you think that sport currently occupies the space it deserves on TV? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Magazine_space

Do you think that sport currently occupies the space it deserves on the magazines? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Radio_space

Do you think that sport currently occupies the space it deserves on the radio? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Internet_space

Do you think that sport currently occupies the space it deserves on internet? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Paid_channels

Do you think it is right that some sports are only accessible on paid channels? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Subscriptions

Any past or current subscription to a sport magazine/channel.

Psycol_well_being

Do you think that practicing sports affects psychological well-being? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Physical_well_being

Do you think that practicing sports affects physical well-being? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Sport_nutrition

Do you think nutrition affects sport? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Overall_health

Do you think that practicing sports affects health? Rating from 1=definitely not to 7=definitely yes with 4=indifferent.

Stress

Self-reported stress level on a scale between 0 and 100.

Economic_status

Level of satisfaction for one's own economic status: 0=not at all, 1=a little bit, 2=enough, 3=satisfied, 4=a lot. It is the only covariate with some NA's.

References

Simone, R., Cappelli, C. and Di Iorio, F., (2019). Modelling marginal ranking distributions: the uncertainty tree. Pattern Recognition Letters, 125, pages 278–288, DOI: 10.1016/j.patrec.2019.04.026.

Simone, R. and Iannario, M., (2018). Analysing sport data with clusters of opposite preferences. Statistical Modelling, 18(5-6), pages 505–524, DOI: 10.1177/1471082X18798455.

Examples

str(ranks_sports)
head(ranks_sports)