A Meta-Analysis of the Gender Wage Gap in Professional Wrestling

Dr. Rex Kwondo1

1 Department of Applied Gender Studies in Mixed Martial Arts, Cranberry-Lemon University, Pittsburgh, PA, USA, and part time Taekwondo Instructor

Abstract

There isn’t an institution that takes gender inequality more seriously than the WWE. Because of the complicated nature of a typical career in professional wrestling, it’s often extremely difficult to even understand where disparities occur. Even measuring the wage gap can be impossibly complex in between the physical athleticism required, the acting acumen, the art of professional wrestling life immersion known as Kayfabe, and even the amount of money spent on elaborate entrances. In this paper, the complicated and often misunderstood factors which predict success in a modern professional wrestling career will be modeled to not only attempt to understand and quantify but fix the gender wage gap in this predominantly male field.  This meta-analysis will incorporate data from American, Mexican, and Japanese Professional Wrestling datasets. By carefully developing our professional wrestling career feature space, our model has determined that we need more data and have no idea how to fix sexism. 

Keywords: Professional Wrestling, WWE, Gender Wage Gap, Data Analysis, pairwise Scatterplot

1. Introduction

Professional wrestling, while primarily being more than 90% male, has a history of professional women wrestlers making it big. To this day, female professional wrestlers are an integral part of the industry. They provide more interesting story arcs, more flexible moves, and are fantastic wrestlers. Women professional wrestlers can often be perfect Faces for underdog stories and absolutely ruthless heels. Unfortunately due to societal and systematic issues, these women wrestlers are often paid less than their male counterparts. 

1.1 Background

While the suffragette movement had won the right to rumble, it did not become widespread until the 1940s. Women’s professional wrestling truly began during WWII when all of the male wrestlers enlisted to pile drive Germany back to Berlin. During that time, women made due by creating female leagues so that someone at home could entertain their children and educate them on the true meaning of pain with an effectively executed flying suplex. When the men came back from WWII, the women’s leagues disbanded before they would be labeled a communist by McArthur for not making more babies at home to fight communism in Korea [1].  

Early Days of Wrestling
Figure 1: Early Days of Wrestling

In the 1980s, the United States decided to fight communism by exporting culture and Coca Cola products instead of bullets and the women’s wrestling movement was rebirthed in earnest [2]. It even caught on in Japan and Mexico in the 1990s. Pretty soon, it was very normal to see a young girl playing with a hulk hogan doll instead of a barbie doll. 

1.2 Purpose

Despite the resurgence of women’s professional wrestling, there still appears to be some sort of a wage gap. The WWE and other major American wrestling programs have been working hard to address this, unfortunately the comprehensive studies on the subject often do not address the complexities of a professional wrestling career and just boring things like child care and institutional sexism. Because most income in the field comes from TV appearances, endorsements, individual wrestling events and non-salaried work, it is difficult to evaluate and correct the gender wage gap [3]. Additionally, with such an emphasis on child care in correcting for the wage gap, this could be the scientific study we need to justify the WWE day care program after the recent controversy [4]. While the news is often sensationalized, there was no way of knowing the teachers knew they bought the wrong Cats DVD. 

2. Data Collection and Cleaning

Data was collected from 1980 to 2020 with 2020 being by far the most asterixed year. Using Cranberry-Lemon’s famous team of unpaid interns, wrestling footage was analyzed across the careers of thousands of wrestlers. During the data collection, wrestler moves were notated by intensity, complexity, type, and bump to pain efficiency. The interns trained in the complete catalogue of Pro-Wrestling moves in physical demonstrations to include holds, throws, strikes, pins, aerial techniques, and even double-team maneuvers. 

Next commercial record was analyzed to determine and track the storylines of each Pro-Wrestler’s story arc tracking how long each character is a heel, face, wins a belt, has a dramatic turn, or is out for an injury and plotting vengeance. Each character story arch career is quantized using TV appearances, entrance time length and elaborateness while grading each character gimmick on an overly extensive and arbitrary grading scale which will never be discussed in full detail later on in this paper.  

Finally, salary data was collected to track overall salary to include wrestling event performances, endorsements, breakfast cereal deals, general merchandising, and Hollywood Cameos for the more successful. 

2.1 Integration of International Datasets

There is a strong, analogous, and similarly diverse professional wrestling community in Mexico and in Japan. These wrestling communities, while not as extensive or action packed or patriotic as America’s, follow the same business model by combining pure athleticism with character drama. Though some data may be lost in google translation, the immense amount of data has proven to be a major boon to the analytics in this now international meta-study. Including international data from countries with different cultures was primarily included in this study in case there is a need for a scapegoat if the results  are unpopular or inconclusive. 

2.2 Missing Data

Unfortunately, there are many unknown elements of each Pro-Wrestlers career and annual salary. Through public channels, much of the personal information needed to complete the study is not legally accessible. Luckily, it has been proven that missing data can be supplemented with synthetic data produced from analogous video games acting as simulators [5]. Thankfully, the professional wrestling community has consistently developed such simulators to create simulated data. The Wrestlemania framework has annually developed simulators with wrestler models across the decades in which we are studying. Though the 1980s simulation software was only compatible with hardware found at a local Chuck E Cheese, most 1980s Wrestlemania simulation software was found to be compatible on most gaming platforms, and PC. This allowed our team to create synthetic or interpolated data instead of throwing out entire wrestlers from our studies. 

Wrestlemania Simulation Hardware
Figure 2: Wrestlemania Simulation Hardware

3. Model Feature Selection

Because of the large feature space of the dataset and the complexities of the field of professional wrestling we were unable to use a trendy ML/AI algorithm and had to pick our feature space by hand. Technically we are still trying the ML/AI approach, but the careers of the professional wrestler, like the Scottish Language, appear to be too different and non-convergent in a Neural Network implementation [6]. In between the limitations in the data and the complex interactions of Pro-wrestling careers, a rudimentary model for Pro-wrestling success was needed just to fit the data.

Wrestler career models were developed across four different factors to include Wrestling Moves, Character Gimmicks, Kayfabe, and each character’s elaborate entrance. Each factor was then pruned down using Leave One Out Cross Validation (LOOcV) metrics as well as a Watanabe-Akaike information Criterion (WAIC). The overall career prediction model is then fit to the overall salary equation below with the below standard letters for things involving modeling a wrestling career. If you don’t know what these letters are already, you might as well not be reading this paper and should go back to school first. 

E1: Overall Salary ~ K1*AAMM + K2*CGM + K3*H + K4*EE

3.1 Wrestling Moves

Because of the many different types of Pro-wrestling moves including pins, strikes, throws, holds, double-team maneuvers, and even aerial techniques, this was the most difficult feature space selection problem. Luckily, there has been plenty of research into the field to guide our search [7][8][9]. Because of the rarity of some complex moves there is not enough data to develop an adequate gender pay gap model across careers for moves such as the spinning headlock elbow drop, the Imploding Corkscrew 450° Splash, or worse the Reverse Frankensteiner

After extensive research, trial, error, and cherry picking, some basic moves and move statistics were selected due to their commonality across Pro-wrestling careers. Pile Driver Efficacy (PDE), Clothes Line Speed (CLS), Half Nelson Mean Squared Error (HNMSE), and Elbow Drop Impact Radius (EDIR) in meters. These Pro-Wrestling Move Metrics (PWMM) have not only been shown by cross validation in previous studies to be quintessential to a Pro-Wrestler but required minimal synthetic data from Wrestlemania simulations to complete the study. 

For more complicated moves, each catalogued wrestling move was categorized and quantified into summary statistics to include Mean Move Bump to Pain Efficiency (MMBtPE), Median Move Complexity (MMC) and of course Air Time (AT). 

Pile Driver Efficacy
Figure 3: Pile Driver Efficacy (Edited Image Mike Kalasnik from Fort Mill, USA, CC BY-SA 2.0, via Wikimedia Commons)
 Clothes Line Speed
Figure 4: Clothes Line Speed Tabercil, CC BY-SA 3.0, via Wikimedia Commons
Elbow Drop Diagram
Figure 5: Elbow Drop Diagram (Edited Image Tabercil, CC BY-SA 3.0, via Wikimedia Commons)

When all of the metrics are included into yet another predictor summary statistic Average Athletic Move Metric (AAMM), career performance can be predicted with the equation 2 below. Because of the complex interaction of CLS and AT on physical damage to wrestlers and Pro-Wrestling partners, the predictors were squared because career success in both male and female careers became so limited if CLS or AT was too high. Using the squared predictors fits the data and the corresponding constants to avoid wrestlers who go too hard. 

E2: AAMM ~ C1*PDE + C2*CLS + C3*CLS^2+ C4*HNMSE + C5*EDIR + C6*PWMM + C7*MMBtPE + C8*MMC + C9*AT + C10*AT^2

3.2 Character Gimmick Multiplier

Character Gimmicks was a tricky metric to evaluate. Not only is the importance of character gimmicks in Pro-Wrestling crucial to career importance, it is tough to quantifiably evaluate and it is difficult to measure across genders because of differing societal norms. One female heel may receive a lot of boos from the audience and get a larger salary for being a slut but such a trait could be more of a desirable trait in a male face. 

Even Survey Monkey couldn’t be used to evaluate the hero-villain traits as it could not account for negative stereotypes across time. A islamic terrorist Heel would have only worked after 9/11 and before anti-Iraq war protests ended all of the conflict. Utilizing historic market research, favorability ratings for each Heel-Face character gimmicks were then used  to evaluate the effectiveness for appropriately hating or loving a Pro-wrestler. These points are condensed into a Character Gimmick Multiplier CGM.

3.3 Kayfabe

Kayfabe, or a wrestler’s commitment to their own character is one of the most important aspects in a Professional wrestler’s career. While the idea of Kayfabe is much more nebulous than a gimmick or the types of moves they perform, it is much easier to quantify and model. 

According to a cross-analysis of 140 1990s American wrestlers Kayfabe is easily evaluated and related to performance in [10]. By tabularizing the length of character monologues, public disses and televised promos, the amount of public commitment to a pro-wrestling character roll can then be tracked with a Houts filter, adjusted with Houts parameters, and then normalized with the Houts transformation. This condenses into the Houts Metrix H. This method has been found to be far more effective than using theater critics who tend to call Professional Wrestling “Dumb,” “Not a Sport,” and “Not an artform either.” [11]

3.4 Elaborate Entrances

The easiest feature to model was found to be entrance elaborateness.  Entrance Elaborateness or EE, while a typically subjective metric, has been found to be adequately modeled with the Chauncer method developed in [12]. The Chauncer metric takes into account the amount of time of the entrance, number of props, multiplied by a light induced risk of seizure and then transformed by smoke machine particles per cubic meter. The metric EE is maxed out to 120 Chaunces if the character enters the ring by striking the hero with a folding chair in surprise. 

4. Results

According to the meta analysis, every metric is crucial but also debilitating. While the pure athleticism of the wrestling moves were found to be indicative of a difference in gender pay disparity, it was negligible enough compared to the non athletic elements included in the overall salary model. 

The positive correlation of Elaborate Entrances with an increase in overall salary indicated some level of sex discrimination. Within the meta study, 35% less money was spent on a female professional wrestler’s elaborate entrance and 15% less smoke machine particles per meter. While this could be the Smoking machine gun in the meta study, the positive correlation only accounted for less than 5% of the pay gap disparity. Spending more money increased the overall salary of the individual wrestler but only at a logarithmic scale. 

To analyze the remainder of the data and visualize all of the interactions between the remaining three metrics and historical professional wrestler data, all of the historical meta data was then shown in a pairwise scatter plot shown in figure 6. While more positive correlation was seen between AAMM and the CGM and HM, the CG and HM interactions were less consistent and relatively flat. 

Pairwise Scatterplot of historical Female Wrestler data
Figure 6: Pairwise Scatterplot of historical Female Wrestler data. (Edited Images R-15, CC BY 2.1 JP, via Wikimedia Commons Yuzunet, CC BY-SA 4.0, via Wikimedia Commons Montblanc, CC BY-SA 4.0, via Wikimedia Commons Ed Schipul from Houston, TX, US, CC BY-SA 2.0, via Wikimedia Commons Becca Swanson, CC BY-SA 3.0, via Wikimedia Commons)

According to the intuition from the pairwise scatterplot, while AAMM cannot define a great wrestling career, neither can gimmicks or Kayfabe alone. Each element is crucial to the success of the wrestler. While this may not fully describe what drives the gender pay gap in pro-wrestling it does put to bed an even more important myth than gender equality. Pro-Wrestling is in fact a sport. 

When compared to the data of the male professional wrestlers, the exact same interactions were observed but at much more amplified levels. At most lower levels, male professional wrestlers made only 6% more than their female counterparts. At the higher levels, the pay gap disparity increased into the double digits driving the mean pay gap disparity to 27%. 

Because of the large amount of publicly available data for lower earning pro-wrestlers from Japan compared to the high-earning pro-wrestling data in America, this may on the surface show that America is much more sexist than Japan. This skew may also be explained by the enormous market in Japan for a high population of Otaku (or Weebs in American) who obsess over women dressed like anime characters. This cultural explanation may indeed account for the much lower gender pay disparity in this different market. Even the data availability was a result of in depth internet forums on each woman wrestler. More data will be needed to determine the difference. 

Typical female wrestler in Japan Otaku’s obsess over
Figure 7: Typical female wrestler in Japan Otaku’s obsess over (Yoccy441, CC BY-SA 3.0, via Wikimedia Commons)

There is one other major conclusion in this meta analysis besides wrestling being a real sport. Among all of the studies, wrestling programs with included daycare had a 40% smaller gender pay disparity. In depth analysis wasn’t included in this paper because everyone knows that daycare has that effect in every career and it wouldn’t make for an interesting paper. 

5. Conclusion

As predicted earlier in the paper, the analysis was inconclusive and we blamed the data due to cultural differences between the data sets. The gender pay gap does exist. With less promotion and elaborate entrance money spent on female wrestlers compared to their male counterparts, it is likely due to systemic issues we do not know how to measure. With child care already accounted for by the wonderful and flawless WWE daycare program, the rest of sexism is too complicated to explain with our current data. Two potential solutions would be to market American professional wrestling to weebs. 

6. Conflict of Interest

The author Rex Kwondo goes home to Starla at night and would like for her to get paid her fair share. This paper was also funded by the WWE daycare program. 

References

  1. Rhonda ‘The Destroyer’ Rivers 2015 A Not So Brief History of Women in Professional Wrestling :: Self Published on Amazon
  2. Agent X 1984 Cultural Methods for Defeating the Soviet Union [DECLASSIFIED] :: Internal Memo
  3. Todd ‘The Gorilla Grover’ 2012 Career Modelling for the Modern Professional Wrestler :: Smackdown Magazine 
  4. Jessica Tambers 2021 WWE Daycare Accidentally Shows Cats Movie to Toddlers: 4-7 Year Olds Scared :: Wrestling Times
  5. Bonesaw McGraw 2021 Ancient Moralizing Gods Study to use Age of Mythology Simulation to Supplement Missing Data :: Journal of Astrological Big Data Ecology 
  6. MacGregor, Gregor 2020 There Can be No True Scottish Speech Recognition System :: Journal of Astrological Big Data Ecology 
  7. Jeff ‘The Human Rhinocerous’ Balmer 2008 Effective Piledriver Metrics, An Optimal Approach to Total Domination of the Ring :: International Annals of Westleological Studies
  8. Daniel ‘Flamerlicious’ Johnson 2003 Physics Based Fundamental Techniques for Light Speed Clotheslines:: International Annals of Westleological Studies
  9. Donna ‘Rumble in the Jungle’ Becker 1997 Professional Wrestling Pins for Maximum Pain and Crowd Pleasure :: Journal of Ring Domination
  10. Hugh ‘Clap Man’ Huffner 2004 Kayfabemetrics: An Analytical Approach to your Wrestlesona :: Journal of Wrestleciety
  11. Anna ‘Two Drinks’ Houts 2012 The Houts Metric: A Descriptive Metric for Tracking Public Smack Talk :: International Annals of Smackdownology
  12. Robert ‘Boom Boom’ Chauncer 2015 A Data Driven Approach to Entering the Ring :: International Annals of Westleological Studies

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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