This chart chows the viewership of matches for female tennis athletes in comparison to male tennis athletes during the 2018 Grand Slams (Fountas, 2020).
Though, my research is coming to an end, there is still so much to find out about it. Currently, my research goes in depth on the differences in commentary between men and women of different ages and races within singles' matches in the Wimbledon tournament of 2022. There are many persistent gender differences within sports, especially tennis. For example, women are awarded a lower amount of prize money in comparison to men. Even these male tennis athletes argue that they deserve the higher prize money because of their higher viewership and match attendance rates. The chart to the left shows the viewership rates for the different Grand Slams for both men and women. Men are represented with a red bar, while women are represented with a black bar.
In addition to this, women in general do not receive the same amount of media coverage that men do. This was especially true in an article that presented the top most covered players in the last four Grand Slams. Of the top most covered players, only two were women. These two women were Serena Williams and Simona Halep.
Serena Williams (McDaniel, 2022).
Simona Halep (Simona Halep (2-time Grand Slam tennis champ).
There has been much research done on the differences in commentary between men and women. According to this research, differences have been found. These include wardrobe commentary, mostly for women. Men, on the other hand, receive commentary regarding their on-court action. Women were also found to be more likely to receive commentary that highlighted their age, health conditions, and family. Men, however, were found to receive more commentary on their tennis history or background.
Though my individual research is adding onto something that has already been done before, there is a distinct gap between my research and research done previously. This gap is that there has not been any research done on who race and age affect the differences in commentary; there has only been research done on how gender affects commentary. My idea stemmed from the categories of commentary mentioned in previous articles. These articles mentioned that women were most likely to have their race and age mentioned. So, I decided to make these my two independent variable, along with gender, as well as two of my commentary categories. By the end of my research, I will have a good understanding of the differences in commentary of men and women of different ages and races in order to provide the audience with more information in order to halt the progression of these gender differences and inequalities.
My methods consist of...
using a random number generator to choose three matches from each gender's singles' matches from the Wimbledon tournament
writing down the name, age, race, and gender for each individual in each match
watching all of the matches, making sure to alternate between men's singles' and women's singles' matches
listening to the commentators' commentary as I watch the matches and writing down important comments made by the commentators under some pre-made categories
I'll be adding categories as I continue to watch matches and find commonalities in the coverage of different genders, races, and ages
Below is a table of my data collection consisting of the different categories and an example of what my data collection looks like:
This is my data collection table and involves all of the tally marks used to represent the commentary categories mentioned for each player within each match.
This table shows the first step within my data collection, which is to write down each player's age, race, gender, and the team that they are playing for, in order to compare the commentaries for each player.
What has gone well?
Currently, I am almost finished with my data collection. Despite missing two weeks of collecting data, I am on track to complete my data collection by the deadline, especially since I was able to account for this delay within my initial calendar. My data is also coming together. It is supporting what I initially hypothesized. Overall, my data is looking the way I was expecting it to, structure-wise.
What has gone poorly?
The main thing that I had concerns about was that many of the matches within my "to watch" list were not posted anywhere online where I could watch the entire match. There were only clips of the matches. However, since I already have a large enough sample size to account for the shorter recorded matches, this is not a big problem. Even though the matches were not posted at their full length, they contained enough commentary to record and determine how the commentary favored each male tennis athlete in comparison with their women counterparts.
From my initial project, I changed the rounds of matches that I am currently watching. Initially, I had planned to watch rounds 1, 2, and 6 in the Wimbledon tournament of 2022. However, these rounds were too excessive to watch and would not allow me to complete my project in time. So, I redid my random number generator and am now analyzing rounds 2, 4, and 7. Currently being in the process of collecting data, my next steps include analyzing my data. Though this type of project doesn't require statistical analysis like many other projects, it does involve comparisons. I will be counting up the tally marks under each category for each player. Using the table containing the age, race, and gender for each player in each match, I will compare the tally marks from each male tennis player to that of each female tennis player to determine and record the differences between those of different ages and races to, in turn, educate myself and others on the gender inequalities still present within today's society.
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