Spurious correlation #3,989 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is NYSE Composite Index Annual Percentage Change and the second variable is Frank Lampard's appearances for the England national team. The chart goes from 1999 to 2016, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is NYSE Composite Index Annual Percentage Change and the second variable is Frank Lampard's appearances for the England national team. The chart goes from 1999 to 2016, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the NYSE Composite Index Annual Percentage Change increased, so did the collective financial confidence. This led to heightened national morale, making the English players feel like they were on top of the world. Frank Lampard, feeding off this economic optimism, found the net with ease and secured his spot on the national team. When stocks were up, so was his game!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a Discovery magazine cover featuring a bustling city skyline with towering skyscrapers. In the foreground, display the NYSE Composite Index Annual Percentage Change graph ascending steeply, with upward arrows and numbers climbing high. Simultaneously, depict a soccer field with Frank Lampard wearing the England national team jersey, celebrating as he scores a goal, with his arms raised in triumph and the crowd roaring in the background. The soaring stocks and Lampard's victorious goal should seamlessly symbolize the simultaneous rise in financial market numbers and his playing time for the national team, revealing the synergy between market success and Lampard's on-field prowess. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Minimalism Charcoal Voxel art A Discovery magazine cover image Cute cartoon
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team, in the following situation: Because as the NYSE Composite Index Annual Percentage Change rose, so did Frank Lampard's appearances for the England national team. As the stocks soared, so did Lampard's playing time, proving that when the market is bullish, there's more bullish play on the field. It's as if the financial market numbers whispered trade secrets to Lampard, inspiring him to score big on the soccer field. So, next time you see the market booming, just know that's when Lampard was lighting up the pitch.. Keep the description simple.
ChatGPT: In a bustling city skyline, the NYSE Composite Index Annual Percentage Change graph ascends steeply, with upward arrows and numbers climbing high. At the same time, a soccer field comes into view and Frank Lampard, wearing the England national team jersey, is scoring a goal, his arms raised in triumph as the crowd roars in the background. The soaring stocks and Lampard's victorious goal perfectly symbolize the simultaneous rise in financial market numbers and his playing time for the national team, revealing the synergy between market success and Lampard's on-field prowess.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: In a bustling city skyline, the NYSE Composite Index Annual Percentage Change graph ascends steeply, with upward arrows and numbers climbing high. At the same time, a soccer field comes into view and Frank Lampard, wearing the England national team jersey, is scoring a goal, his arms raised in triumph as the crowd roars in the background. The soaring stocks and Lampard's victorious goal perfectly symbolize the simultaneous rise in financial market numbers and his playing time for the national team, revealing the synergy between market success and Lampard's on-field prowess. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a Discovery magazine cover featuring a bustling city skyline with towering skyscrapers. In the foreground, display the NYSE Composite Index Annual Percentage Change graph ascending steeply, with upward arrows and numbers climbing high. Simultaneously, depict a soccer field with Frank Lampard wearing the England national team jersey, celebrating as he scores a goal, with his arms raised in triumph and the crowd roaring in the background. The soaring stocks and Lampard's victorious goal should seamlessly symbolize the simultaneous rise in financial market numbers and his playing time for the national team, revealing the synergy between market success and Lampard's on-field prowess.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in NYSE Composite Index Annual Percentage Change positively influenced Frank Lampard's appearances for the England national team.

AI academic paper

(Because p < 0.05)

A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team

The Journal of Sports Economics and Social Sciences

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team"

[[ABSTRACT]]
This study delves into the nuanced connection between the NYSE Composite Index Annual Percentage Change and the appearances of the esteemed Frank Lampard for the England national team. The data for this research was sourced from 1stock1 and Wikipedia and covers the period from 1999 to 2016. Through rigorous statistical analysis, a correlation coefficient of 0.5095091 was calculated, with a significance level of p < 0.05, indicating a discernible relationship between these seemingly disparate variables.

In exploring this relationship, we uncover the unexpected and often overlooked interplay between sports and the stock market. Our findings suggest that Frank Lampard's tenure with the England national team may have exerted a notable influence on the NYSE Composite Index, providing a whimsical twist to the world of economic indicators. As the saying goes, "When it comes to market analysis, it's all fun and games until someone brings in soccer statistics!"

Furthermore, our research uncovers the potential for novel predictors in financial modeling and the importance of considering diverse influences on market behavior. By shedding light on this unorthodox association, we hope to inspire future inquiries into the unexpected correlations that shape our economic landscape. In the immortal words of Frank Lampard himself, "It's not whether you win or lose, it's how you play the stock market!"

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of economics and sports often seem as different as night and day. One is filled with charts, graphs, and discussions of monetary policy, while the other involves cheering, jeering, and athletic prowess. However, as with any good dad joke, there may be a surprisingly fitting punchline lurking just beneath the surface. In this study, we delve into the unexpected connection between the NYSE Composite Index Annual Percentage Change and the renowned English footballer, Frank Lampard.
As the data unfolded, we uncovered a correlation coefficient that left us feeling as pleasantly surprised as a well-timed punchline at a scientific conference. This subtle relationship between a footballer's appearances for the England national team and the gyrations of the stock market paints a picture as unexpected and amusing as a scientist's attempt at stand-up comedy.
By unearthing this correlation, we force the stock market to come to terms with the hidden impact of England football matches and the indefatigable Frank Lampard. It’s as if the market exclaimed, "You can't be serious!" when confronted with the connection between England’s football hero and its financial performance.
As researchers, we are reminded that in the grand symphony of economic indicators, there may be room for a whimsical flute solo, played by none other than the appearances of Frank Lampard in an England jersey. This study invites us to look beyond the predictable metrics and embrace the unexpected quirks and crannies of our financial landscape, much like a good dad joke sneaking up on an unsuspecting audience.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

A number of significant studies have sought to elucidate the relationship between stock market performance and external factors. Smith et al. (2010) found relationships between market fluctuations and various socioeconomic factors. Similarly, Doe and Jones (2015) explored the influence of international sporting events on stock market behavior, unveiling intriguing connections.

Turning our attention to more specific influences, the literature also speaks to the potential impact of individual athletes on market dynamics. In "Business and Balls: Exploring Sports Influences on Market Movements," the authors delve into the unorthodox intersections of sports and finance, shedding light on the unexpected role of athletes in economic trends. Additionally, "The Economics of Sports: How Revenue Streams Tackle Stock Markets" offers insights into the unexplored connections between sporting events and market activity, sending ripples through traditional economic paradigms.

But let us not forget the more unconventional sources of inspiration that have guided our inquiries. "The Stock Market and Soccer: A Story of Unlikely Bedfellows" explores the whimsical world of economic indicators and the unexpected correlations flowing through it, offering an enriching perspective on the fusion of sports and finance. Furthermore, "The Footballer's Dilemma: A Tale of Market Influence" brings to light the curious juxtaposition of athletic prowess and market quirkiness, challenging traditional economic dogmas in a lighthearted yet thought-provoking manner.

In addition to these academic and practical sources, our investigations have been shaped by the unconventional wisdom of fictional works. "The Ball and the Bull: A Tale of Stock Market Strikers" presents a captivating narrative that touches upon the subtle interplay between sports and market dynamics, inviting readers to contemplate the peculiar dance of financial variables. Likewise, "Market Masters: The Game of Stocks and Soccer" spins a riveting yarn that mirrors the whimsical nature of our research, igniting the imagination and teasing out unexpected parallels between the trading floor and the football pitch.

Inspiration can also be found in the unlikeliest of places, such as the world of board games. A game like "Stock Strikers: The Soccer Stock Exchange" tickles the mind with its playful exploration of the intersection between sports and market forces, offering a quirky yet insightful reflection on the intricate dance between financial indicators and athletic performances. Similarly, "Monopoly: Soccer Superstars Edition" weaves a captivating narrative of economic intrigue and sporting prowess, challenging players to navigate the whimsical landscape of financial ups and downs in the context of the soccer world.

As we consider these diverse sources of insight, we are reminded of the enriching and often delightfully unexpected perspectives that can inform our understanding of the world of economics and sports. Just as in life, it is the unexpected turns that often provide the most engaging and thought-provoking twists, much like a good dad joke delivered with impeccable timing.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from 1stock1 and Wikipedia . You used data from 1999 to 2016

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team"

[[ABSTRACT]]
This study delves into the nuanced connection between the NYSE Composite Index Annual Percentage Change and the appearances of the esteemed Frank Lampard for the England national team. The data for this research was sourced from 1stock1 and Wikipedia and covers the period from 1999 to 2016. Through rigorous statistical analysis, a correlation coefficient of 0.5095091 was calculated, with a significance level of p < 0.05, indicating a discernible relationship between these seemingly disparate variables.

In exploring this relationship, we uncover the unexpected and often overlooked interplay between sports and the stock market. Our findings suggest that Frank Lampard's tenure with the England national team may have exerted a notable influence on the NYSE Composite Index, providing a whimsical twist to the world of economic indicators. As the saying goes, "When it comes to market analysis, it's all fun and games until someone brings in soccer statistics!"

Furthermore, our research uncovers the potential for novel predictors in financial modeling and the importance of considering diverse influences on market behavior. By shedding light on this unorthodox association, we hope to inspire future inquiries into the unexpected correlations that shape our economic landscape. In the immortal words of Frank Lampard himself, "It's not whether you win or lose, it's how you play the stock market!"

[[INTRODUCTION]]
The world of economics and sports often seem as different as night and day. One is filled with charts, graphs, and discussions of monetary policy, while the other involves cheering, jeering, and athletic prowess. However, as with any good dad joke, there may be a surprisingly fitting punchline lurking just beneath the surface. In this study, we delve into the unexpected connection between the NYSE Composite Index Annual Percentage Change and the renowned English footballer, Frank Lampard.
As the data unfolded, we uncovered a correlation coefficient that left us feeling as pleasantly surprised as a well-timed punchline at a scientific conference. This subtle relationship between a footballer's appearances for the England national team and the gyrations of the stock market paints a picture as unexpected and amusing as a scientist's attempt at stand-up comedy.
By unearthing this correlation, we force the stock market to come to terms with the hidden impact of England football matches and the indefatigable Frank Lampard. It’s as if the market exclaimed, "You can't be serious!" when confronted with the connection between England’s football hero and its financial performance.
As researchers, we are reminded that in the grand symphony of economic indicators, there may be room for a whimsical flute solo, played by none other than the appearances of Frank Lampard in an England jersey. This study invites us to look beyond the predictable metrics and embrace the unexpected quirks and crannies of our financial landscape, much like a good dad joke sneaking up on an unsuspecting audience.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this research endeavor aimed to rigorously examine the relationship between the NYSE Composite Index Annual Percentage Change and the number of appearances by Frank Lampard for the England national team. The primary data for this investigation were sourced from 1stock1 and Wikipedia, with a focus on the period spanning from 1999 to 2016. The utilization of data from multiple sources reflects the eclectic nature of this research, much like incorporating a variety of instruments to create a harmonious melody.

To begin, the data on Frank Lampard's appearances for England was meticulously collected from reputable soccer statistics databases and official match records. The approach to collecting this data was as meticulous as a scientist attempting to capture an elusive particle, leaving no statistical stone unturned. Additionally, the NYSE Composite Index Annual Percentage Change data was obtained from comprehensive financial repositories to ensure the reliability and accuracy of the information, akin to sifting through a treasure trove of economic data to uncover hidden gems.

The collected data was then subjected to a series of statistical analyses, including correlation and regression techniques, to elucidate the potential relationship between the variables under investigation. This research journey involved navigating through the statistical landscape with the precision of a seasoned explorer, meticulously charting the terrain to uncover potential correlations. Furthermore, the application of robust statistical methodologies aimed to capture the nuances of the relationship, ensuring that no statistical subtlety was overlooked.

The statistical analysis was conducted using specialized software, allowing for the implementation of sophisticated models and algorithms to tease out the interplay between Frank Lampard's England appearances and the fluctuations of the NYSE Composite Index. This approach to data analysis was as meticulous as a chef crafting an intricate recipe, ensuring that each statistical ingredient was carefully added to the analytical concoction.

An important consideration in this study was the potential influence of extraneous variables, such as market dynamics and sporting events, which could confound the observed relationship. Therefore, measures were taken to control for these variables through the application of advanced statistical techniques, similar to untangling a complex web of influences to reveal the underlying patterns.

The culmination of these methodical procedures resulted in the quantification of the relationship between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. The unveiling of this unexpected correlation was as surprising as a punchline delivered in the midst of a scientific symposium, adding a touch of whimsy to the world of economic indicators.

In the language of statistics, "A good dataset is like a good joke - it should never be wasted. But remember, a correlation does not imply causation; much like how a well-timed pun may be coincidental rather than intentional."

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team"

[[ABSTRACT]]
This study delves into the nuanced connection between the NYSE Composite Index Annual Percentage Change and the appearances of the esteemed Frank Lampard for the England national team. The data for this research was sourced from 1stock1 and Wikipedia and covers the period from 1999 to 2016. Through rigorous statistical analysis, a correlation coefficient of 0.5095091 was calculated, with a significance level of p < 0.05, indicating a discernible relationship between these seemingly disparate variables.

In exploring this relationship, we uncover the unexpected and often overlooked interplay between sports and the stock market. Our findings suggest that Frank Lampard's tenure with the England national team may have exerted a notable influence on the NYSE Composite Index, providing a whimsical twist to the world of economic indicators. As the saying goes, "When it comes to market analysis, it's all fun and games until someone brings in soccer statistics!"

Furthermore, our research uncovers the potential for novel predictors in financial modeling and the importance of considering diverse influences on market behavior. By shedding light on this unorthodox association, we hope to inspire future inquiries into the unexpected correlations that shape our economic landscape. In the immortal words of Frank Lampard himself, "It's not whether you win or lose, it's how you play the stock market!"

[[LITERATURE REVIEW]]
A number of significant studies have sought to elucidate the relationship between stock market performance and external factors. Smith et al. (2010) found relationships between market fluctuations and various socioeconomic factors. Similarly, Doe and Jones (2015) explored the influence of international sporting events on stock market behavior, unveiling intriguing connections.
Turning our attention to more specific influences, the literature also speaks to the potential impact of individual athletes on market dynamics. In "Business and Balls: Exploring Sports Influences on Market Movements," the authors delve into the unorthodox intersections of sports and finance, shedding light on the unexpected role of athletes in economic trends. Additionally, "The Economics of Sports: How Revenue Streams Tackle Stock Markets" offers insights into the unexplored connections between sporting events and market activity, sending ripples through traditional economic paradigms.
But let us not forget the more unconventional sources of inspiration that have guided our inquiries. "The Stock Market and Soccer: A Story of Unlikely Bedfellows" explores the whimsical world of economic indicators and the unexpected correlations flowing through it, offering an enriching perspective on the fusion of sports and finance. Furthermore, "The Footballer's Dilemma: A Tale of Market Influence" brings to light the curious juxtaposition of athletic prowess and market quirkiness, challenging traditional economic dogmas in a lighthearted yet thought-provoking manner.
In addition to these academic and practical sources, our investigations have been shaped by the unconventional wisdom of fictional works. "The Ball and the Bull: A Tale of Stock Market Strikers" presents a captivating narrative that touches upon the subtle interplay between sports and market dynamics, inviting readers to contemplate the peculiar dance of financial variables. Likewise, "Market Masters: The Game of Stocks and Soccer" spins a riveting yarn that mirrors the whimsical nature of our research, igniting the imagination and teasing out unexpected parallels between the trading floor and the football pitch.
Inspiration can also be found in the unlikeliest of places, such as the world of board games. A game like "Stock Strikers: The Soccer Stock Exchange" tickles the mind with its playful exploration of the intersection between sports and market forces, offering a quirky yet insightful reflection on the intricate dance between financial indicators and athletic performances. Similarly, "Monopoly: Soccer Superstars Edition" weaves a captivating narrative of economic intrigue and sporting prowess, challenging players to navigate the whimsical landscape of financial ups and downs in the context of the soccer world.
As we consider these diverse sources of insight, we are reminded of the enriching and often delightfully unexpected perspectives that can inform our understanding of the world of economics and sports. Just as in life, it is the unexpected turns that often provide the most engaging and thought-provoking twists, much like a good dad joke delivered with impeccable timing.

[[RESULTS]]
The statistical analysis revealed a correlation coefficient of 0.5095091 between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. This finding suggests a moderate positive relationship between these two seemingly unrelated variables. It seems as though the stock market may have been as swayed by Frank Lampard's performances as an audience member at a stand-up comedy show – a surprising twist indeed.
The calculated r-squared value of 0.2595995 indicates that approximately 25.96% of the variability in the NYSE Composite Index Annual Percentage Change can be explained by Frank Lampard's appearances for the England national team. In other words, the influence of this football icon on the stock market may have been more substantial than initially presumed, much like a well-timed punchline that catches everyone off guard.
Furthermore, the significance level of p < 0.05 indicates that the observed correlation is statistically significant, thus rejecting the null hypothesis of no relationship between the two variables. This result provides robust evidence supporting the notion that Frank Lampard's involvement with the England national team did indeed have a discernible impact on the NYSE Composite Index, offering a comedic twist to the world of economic indicators.
The scatterplot (Fig. 1) visually portrays the strong correlation between the annual percentage change of the NYSE Composite Index and the number of appearances made by Frank Lampard for the England national team. The data points form a pattern as striking as a well-crafted pun, further highlighting the unexpected yet tangible association between these disparate realms.
In summary, our findings uncover a whimsical thread woven between the performances of a footballer and the gyrations of the stock market, providing a lighthearted yet thought-provoking addition to the tapestry of economic research. With this revelation, it seems that the world of finance and sports may share more in common than meets the eye, reminiscent of a clever dad joke that leaves one simultaneously bemused and enlightened.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The correlation found in this study between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team supports previous research that has highlighted the influence of unexpected factors on stock market behavior. Much like a well-timed dad joke catching us off guard, the statistical relationship discovered here adds a whimsical twist to the world of economic indicators.

The literature review provided insight into the potential impact of individual athletes on market dynamics, and this investigation has shed light on the influence exerted by Frank Lampard's presence on the NYSE Composite Index. Our findings mirror those of previous studies that have uncovered intriguing connections between sports and market behavior, reaffirming that the world of finance is not immune to the infectious enthusiasm of sports. It appears that the stock market may have been as captivated by Lampard's performances as an audience member at a comedy show – a surprising turn of events indeed.

The robust evidence of a statistically significant correlation challenges traditional economic dogmas, much like an unexpected punchline that defies conventional comedic structures. The visual representation of the correlation in the scatterplot is as striking as a cleverly constructed pun, further accentuating the unexpected yet tangible association between these seemingly disparate realms. This connection between sports and financial indicators beckons us to contemplate the broader forces at play in shaping market movements, reminding us that market analysis can be as whimsical as a good dad joke delivered with impeccable timing.

By unveiling this unorthodox relationship, this study underscores the importance of considering diverse influences on market behavior, much like a well-crafted dad joke enriching an otherwise serious conversation. The implications of this research extend beyond the realm of sports economics, urging economists to recognize the potential for novel predictors in financial modeling and to embrace the unexpected correlations that shape our economic landscape. As Frank Lampard himself once said, "It's not whether you win or lose, it's how you play the stock market!"

In conclusion, the association between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team adds a whimsical yet thought-provoking dimension to economic research. Much like a clever dad joke that leaves one simultaneously bemused and enlightened, this discovery invites us to entertain the unexpected influences that may shape our financial world.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team"

[[ABSTRACT]]
This study delves into the nuanced connection between the NYSE Composite Index Annual Percentage Change and the appearances of the esteemed Frank Lampard for the England national team. The data for this research was sourced from 1stock1 and Wikipedia and covers the period from 1999 to 2016. Through rigorous statistical analysis, a correlation coefficient of 0.5095091 was calculated, with a significance level of p < 0.05, indicating a discernible relationship between these seemingly disparate variables.

In exploring this relationship, we uncover the unexpected and often overlooked interplay between sports and the stock market. Our findings suggest that Frank Lampard's tenure with the England national team may have exerted a notable influence on the NYSE Composite Index, providing a whimsical twist to the world of economic indicators. As the saying goes, "When it comes to market analysis, it's all fun and games until someone brings in soccer statistics!"

Furthermore, our research uncovers the potential for novel predictors in financial modeling and the importance of considering diverse influences on market behavior. By shedding light on this unorthodox association, we hope to inspire future inquiries into the unexpected correlations that shape our economic landscape. In the immortal words of Frank Lampard himself, "It's not whether you win or lose, it's how you play the stock market!"

[[INTRDUCTION]]
The world of economics and sports often seem as different as night and day. One is filled with charts, graphs, and discussions of monetary policy, while the other involves cheering, jeering, and athletic prowess. However, as with any good dad joke, there may be a surprisingly fitting punchline lurking just beneath the surface. In this study, we delve into the unexpected connection between the NYSE Composite Index Annual Percentage Change and the renowned English footballer, Frank Lampard.
As the data unfolded, we uncovered a correlation coefficient that left us feeling as pleasantly surprised as a well-timed punchline at a scientific conference. This subtle relationship between a footballer's appearances for the England national team and the gyrations of the stock market paints a picture as unexpected and amusing as a scientist's attempt at stand-up comedy.
By unearthing this correlation, we force the stock market to come to terms with the hidden impact of England football matches and the indefatigable Frank Lampard. It’s as if the market exclaimed, "You can't be serious!" when confronted with the connection between England’s football hero and its financial performance.
As researchers, we are reminded that in the grand symphony of economic indicators, there may be room for a whimsical flute solo, played by none other than the appearances of Frank Lampard in an England jersey. This study invites us to look beyond the predictable metrics and embrace the unexpected quirks and crannies of our financial landscape, much like a good dad joke sneaking up on an unsuspecting audience.

[[RESULTS]]
The statistical analysis revealed a correlation coefficient of 0.5095091 between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. This finding suggests a moderate positive relationship between these two seemingly unrelated variables. It seems as though the stock market may have been as swayed by Frank Lampard's performances as an audience member at a stand-up comedy show – a surprising twist indeed.
The calculated r-squared value of 0.2595995 indicates that approximately 25.96% of the variability in the NYSE Composite Index Annual Percentage Change can be explained by Frank Lampard's appearances for the England national team. In other words, the influence of this football icon on the stock market may have been more substantial than initially presumed, much like a well-timed punchline that catches everyone off guard.
Furthermore, the significance level of p < 0.05 indicates that the observed correlation is statistically significant, thus rejecting the null hypothesis of no relationship between the two variables. This result provides robust evidence supporting the notion that Frank Lampard's involvement with the England national team did indeed have a discernible impact on the NYSE Composite Index, offering a comedic twist to the world of economic indicators.
The scatterplot (Fig. 1) visually portrays the strong correlation between the annual percentage change of the NYSE Composite Index and the number of appearances made by Frank Lampard for the England national team. The data points form a pattern as striking as a well-crafted pun, further highlighting the unexpected yet tangible association between these disparate realms.
In summary, our findings uncover a whimsical thread woven between the performances of a footballer and the gyrations of the stock market, providing a lighthearted yet thought-provoking addition to the tapestry of economic research. With this revelation, it seems that the world of finance and sports may share more in common than meets the eye, reminiscent of a clever dad joke that leaves one simultaneously bemused and enlightened.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has shed light on the remarkable correlation between the NYSE Composite Index Annual Percentage Change and Frank Lampard's appearances for the England national team. The calculated correlation coefficient of 0.5095091 suggests a tangible relationship between these seemingly unrelated variables, leaving the economic community as pleasantly surprised as a well-timed dad joke at a family gathering.

Our findings imply that the stock market may have been as influenced by Frank Lampard's performances as a dad is swayed by a clever pun – a result that would surely make even the sternest economist crack a smile. The r-squared value of 0.2595995 further emphasizes the notable impact of Frank Lampard on market variability, akin to a punchline that unexpectedly steals the show.

Additionally, the significance level of p < 0.05 reinforces the robustness of our results, indicating a statistically significant correlation between these unconventional influencers. It is as if the data is telling us, "Did you hear about the footballer who was such a financial expert? He always knew when to 'pass' on risky investments!"

The visually striking scatterplot (Fig. 1) further accentuates the connection between the NYSE Composite Index and Frank Lampard's England appearances, painting a picture as clear and entertaining as a well-crafted witticism. As we wrap up this analysis, we are reminded of the profound impact of unexpected variables on economic indicators – much like a surprising punchline that leaves a lasting impression.

Ultimately, our research offers a unique perspective on the interplay between sports and finance, adding a lighthearted yet thought-provoking dimension to the world of economic analysis. In the immortal words of Frank Lampard, "It's not whether you win or lose, it's how you play the stock market!" With this, we confidently assert that no further research is needed in this area, paving the way for a new era of interdisciplinary humor-infused analysis in the economic landscape.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Sports Economics and Social Sciences

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Eccentric Economic Inquiry

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team with an abstract of This study delves into the nuanced connection between the NYSE Composite Index Annual Percentage Change and the appearances of the esteemed Frank Lampard for the England national team. The data for this research was sourced from 1stock1 and Wikipedia and covers the period from 1999 to 2016. Through rigorous statistical analysis, a correlation coefficient of 0.5095091 was calculated, with a significance level of p < 0.05, indicating a discernible relationship between these seemingly disparate variables.
In exploring this relationship, we uncover the unexpected and often overlooked interplay between sports and the stock market. Our findings suggest that Frank Lampard's tenure with the England national team may have exerted a notable influence on the NYSE Composite Index, providing a whimsical twist to the world of economic indicators. As the saying goes, "When it comes to market analysis, it's all fun and games until someone brings in soccer statistics!"
Furthermore, our research uncovers the potential for novel predictors in financial modeling and the importance of considering diverse influences on market behavior. By shedding light on this unorthodox association, we hope to inspire future inquiries into the unexpected correlations that shape our economic landscape. In the immortal words of Frank Lampard himself, "It's not whether you win or lose, it's how you play the stock market!

ChatGPT:

NYSE Composite Index, annual percentage change, Frank Lampard, England national team, 1stock1, Wikipedia, statistical analysis, correlation coefficient, significance level, stock market, sports, financial modeling, market behavior, soccer statistics

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

NYSE Composite Index Annual Percentage Change
Source: 1stock1
See what else correlates with NYSE Composite Index Annual Percentage Change

Frank Lampard's appearances for the England national team
Source: Wikipedia
See what else correlates with Frank Lampard's appearances for the England national team

Correlation r = 0.5095091 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.2595995 (Coefficient of determination)
This means 26% of the change in the one variable (i.e., Frank Lampard's appearances for the England national team) is predictable based on the change in the other (i.e., NYSE Composite Index Annual Percentage Change) over the 18 years from 1999 through 2016.

p < 0.05, which statistically significant(Null hypothesis significance test)
The p-value is 0.0308.^Show 0.0307850098933488500000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.51 in 3.08% of random cases. Said differently, if you correlated 32 random variables^Note Which I absolutely did.
with the same 17 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.06, 0.79 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
NYSE Composite Index Annual Percentage Change (Percentage)	9.15	1.01	-10.21	-19.83	28.81	12.57	6.95	17.86	6.58	-40.89	24.8	10.84	-6.11	12.93	23.18	4.22	-6.42	9.01
Frank Lampard's appearances for the England national team (Matches played)	1	0	3	3	9	13	9	13	9	6	10	7	10	11	15	6	6	12

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([9.15,1.01,-10.21,-19.83,28.81,12.57,6.95,17.86,6.58,-40.89,24.8,10.84,-6.11,12.93,23.18,4.22,-6.42,9.01,])
array_2 = np.array([1,0,3,3,9,13,9,13,9,6,10,7,10,11,15,6,6,12,])
array_1_name = "NYSE Composite Index Annual Percentage Change"
array_2_name = "Frank Lampard's appearances for the England national team"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only NYSE Composite Index Annual Percentage Change
Line chart for only Frank Lampard's appearances for the England national team
AI-generated correlation image
The spurious research paper: A Kick in the Stock Market: The Correlation Between NYSE Composite Index Annual Percentage Change and Frank Lampard's Appearances for the England National Team

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Correlation ID: 3989 · Black Variable ID: 315 · Red Variable ID: 99


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