Final Project Part 1

final_project_part1
Hannah Rosenbaum
Author

Hannah Rosenbaum

Published

April 24, 2023

Background

Fifty years after the passage of the Title IX Amendment, collegial sports equity has shown relatively minimal change. Allocations of sports budgets often highlight pay discrepancies in participants being “spent [on] $4,285 per men’s participant versus $2,588 per women’s participant.” (Feinberg, D., & Hunzinger, E) With these vast differences in individual spending by gender, we see this phenomenon only heightened in the NCAA with women’s basketball. Women’s basketball not only fares having lower budgets from the NCAA but also, per an ESPN report, “is underpaying the NCAA for the tournament rights for 29 championships causing the association to lose out on substantial and crucial revenue... denoting that the current budget of $81 million to $112 million multiples more than what the network currently gives.” (Zimmbalist) Thus, there is not only a discrepancy in budget allocations among the participants by gender but also amongst large broadcast networks.

Significant systemic issues occur within the gendered branding of ‘March Madness.’ This can be seen with differentiated treatment of male versus female brackets due to the lack of general awareness of when the women’s bracket games even occur. Largely the inequity of the ‘March Madness’ tournament derives from a differentiation from the NCAA in “distribution agreements, corporate sponsorships, distribution of revenue, organizational structure and culture all to prioritize Division I men’s basketball over everything else... to perpetuate gender inequities.” (Blinder) Likewise, this institutional creation of a high investment in TV rights for men’s basketball and minimal airtime for the women’s bracket has led to smaller budgeting and fewer avenues to earn revenue. This has led women’s teams to be “starved of a starring role in the national discourse.” (Blinder) Thus, it creates a circular effect in women’s basketball, deriding fewer resources even within facilities at the NCAA tournament in 2021 and in general awareness of TV times.

I am primarily interested in discussing sports equity in women’s basketball due to my own personal experience at UF of wanting to watch NCAA basketball for women but having no general knowledge of when women play. I believe that the discussion of equity in sports for women is essential because of the common dismissal of watching women’s sports as a pastime.

Research Questions

1. Is there a relationship between a higher percentage of female students in post-secondary education and a portion of female athletes at those institutions?

2. In NCAA sports, is there a relationship between expenditure on university sports programs and the percentage of females in university sports programs?

3. In NCAA sports, is there a relationship between revenue from university sports programs and the percentage of females in university sports programs?

Hypothesis Testing

1. Ho: There is no relationship between a higher percentage of female students in post-secondary education and the percentage of female athletes.

Ha: There is a relationship between a higher percentage of female students in post-secondary education and percentage of female athletes.

2. Ho: There is no relationship between expenditure on university sports programs and the percentage of females in university sports programs.

Ha: There is a relationship between expenditure on university sports programs and the percentage of females in university sports programs.

3. Ho: There is no relationship between revenue from university sports programs and the percentage of females in sports programs.

Ha: There is a relationship between revenue from university sports programs and the percentage of females in sports programs.

Descriptive Statistics

The Equity in Athletics Disclosures Act requires the full financial disclosure of total expenditures, revenue, staffing, and recruiting efforts by men’s and women’s athletic programs (Mock, J.T.). Data provided by the Equity in Sports project is from all postsecondary programs that receive government funding from Title IV funding and is an online database of funding expenses from 2015-2019.

There are 132,327 rows and a total of 28 columns.

Read in Sports Equity data-set

Code 
sports <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-29/sports.csv')
`curl` package not installed, falling back to using `url()`
Rows: 132327 Columns: 28
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): institution_name, city_txt, state_cd, zip_text, classification_nam...
dbl (20): year, unitid, classification_code, ef_male_count, ef_female_count,...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Code 
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Create data-frames: Critical dimensions, Attendance specific, Basketball specific

Code 
data <- as.data.frame(sports[, c("year", "institution_name", "sports", "ef_male_count", "ef_female_count", "sum_partic_men", "sum_partic_women", 'rev_men', 'rev_women', 'exp_men', 'exp_women')])
attendance_data <- sports[,c("institution_name", "sports", "ef_male_count", "ef_female_count", "sum_partic_men", "sum_partic_women")] %>% group_by(institution_name, sports) %>% summarise(across(everything(), mean), .groups='drop')
basketball <- filter(sports, sports=='Basketball')

Scatter plots comparing Expenditures against Revenue by Gender

Code 
plot(data$exp_men, data$rev_men)

Code 
plot(data$exp_women, data$rev_women)

Descriptive Statistics

Code 
glimpse(data)
Rows: 132,327
Columns: 11
$ year             <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
$ institution_name <chr> "Alabama A & M University", "Alabama A & M University…
$ sports           <chr> "Baseball", "Basketball", "All Track Combined", "Foot…
$ ef_male_count    <dbl> 1923, 1923, 1923, 1923, 1923, 1923, 1923, 1923, 1923,…
$ ef_female_count  <dbl> 2300, 2300, 2300, 2300, 2300, 2300, 2300, 2300, 2300,…
$ sum_partic_men   <dbl> 31, 19, 61, 99, 9, 0, 0, 7, 0, 0, 32, 13, 0, 10, 2, 3…
$ sum_partic_women <dbl> 0, 16, 46, 0, 0, 21, 25, 10, 16, 9, 0, 20, 68, 7, 10,…
$ rev_men          <dbl> 345592, 1211095, 183333, 2808949, 78270, NA, NA, 7827…
$ rev_women        <dbl> NA, 748833, 315574, NA, NA, 410717, 298164, 131145, 3…
$ exp_men          <dbl> 397818, 817868, 246949, 3059353, 83913, NA, NA, 99612…
$ exp_women        <dbl> NA, 742460, 251184, NA, NA, 432648, 340259, 113886, 3…
Code 
summary(data)
      year      institution_name      sports          ef_male_count  
 Min.   :2015   Length:132327      Length:132327      Min.   :    0  
 1st Qu.:2016   Class :character   Class :character   1st Qu.:  513  
 Median :2018   Mode  :character   Mode  :character   Median :  986  
 Mean   :2018                                         Mean   : 2126  
 3rd Qu.:2019                                         3rd Qu.: 2385  
 Max.   :2019                                         Max.   :35954  
                                                                     
 ef_female_count sum_partic_men   sum_partic_women    rev_men         
 Min.   :    0   Min.   :  0.00   Min.   :  0.00   Min.   :       65  
 1st Qu.:  652   1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:    63428  
 Median : 1248   Median :  0.00   Median :  6.00   Median :   158126  
 Mean   : 2496   Mean   : 14.49   Mean   : 10.86   Mean   :   809011  
 3rd Qu.: 2860   3rd Qu.: 20.00   3rd Qu.: 17.00   3rd Qu.:   400604  
 Max.   :30325   Max.   :331.00   Max.   :327.00   Max.   :156147208  
                                                   NA's   :70462      
   rev_women           exp_men           exp_women      
 Min.   :       0   Min.   :      65   Min.   :     65  
 1st Qu.:   58746   1st Qu.:   63062   1st Qu.:  59301  
 Median :  138318   Median :  159666   Median : 141800  
 Mean   :  279346   Mean   :  662386   Mean   : 331594  
 3rd Qu.:  331120   3rd Qu.:  424025   3rd Qu.: 361860  
 Max.   :21440365   Max.   :69718059   Max.   :9485162  
 NA's   :63444      NA's   :70462      NA's   :63442

Scatter plots comparing Institution Attendance against Participation by Gender

Code 
plot(attendance_data$ef_male_count, attendance_data$sum_partic_men)

Code 
plot(attendance_data$ef_female_count, attendance_data$sum_partic_women)

Scatter plots comparing basketball Participation against Expenditures by Gender

Code 
plot(basketball$sum_partic_men, basketball$exp_men)

Code 
plot(basketball$sum_partic_women, basketball$exp_women)

For the dataset, I could extrapolate my variables of interest as seen here: <https://github.com/rfordatascience/tidytuesday/blob/master/data/2022/2022-03-29/readme.md>.

My critical variables of interest are the following items:

  • year: Period year

  • institution name: School name

  • sports: Sport name

  • ef_male_count: Total male population

  • ef_female_count: Total female population

  • sum_partic_men: Total male participation

  • sum_partic_women: Total female participation

  • rev_men: Revenue in USD for men

  • rev_women: Revenue in USD for women

  • exp_men: Expenditures in USD for men

  • exp_women: Expenditures in USD for women

References

  • Blinder, A. (2021, August 3). Report: N.C.A.A. Prioritized Men’s Basketball ‘Over Everything Else.’ The New York Times. Retrieved April 12, 2023, from https://www.nytimes.com/2021/08/03/sports/ncaabasketball/ncaa-gender-equity-investigation.html?partner=slack&smid=sl-share.

  • Feinberg, D., & Hunzinger, E. (2021, October 26). Second NCAA Gender Equity Report Shows Spending Disparities. US News. Retrieved April 11, 2023, from https://www.usnews.com/news/sports/articles/2021-10-26/second-ncaa-gender-equity-report-shows-spending-disparities#:~:text=The%20NCAA%20spent%20%244%2C285%20per,championships%20than%20for%20the%20women%27s.

  • Mock, J. T. (2022, March 29). rfordatascience/tidytuesday. GitHub. Retrieved April 10, 2023, from https://github.com/rfordatascience/tidytuesday/blob/master/data/2022/2022-03-29/readme.md.

  • Zimbalist, A. (2022, October 12). Female Athletes Are Undervalued, In Both Money And Media Terms. Forbes. Retrieved April 12, 2023, from https://www.forbes.com/sites/andrewzimbalist/2019/04/10/female-athletes-are-undervalued-in-both-money-and-media-terms/?sh=5006015513ed.