Spurious correlation #1,324 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Justin Verlander's season strikeout count and the second variable is The number of cooks, institution and cafeteria in District of Columbia. The chart goes from 2005 to 2022, 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 Justin Verlander's season strikeout count and the second variable is The number of cooks, institution and cafeteria in District of Columbia. The chart goes from 2005 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Justin Verlander racked up more strikeouts, his reputation for having a sizzling fastball spread like wildfire. This led to a surge in demand for fast-cooking lessons and ultimately inspired a wave of culinary passion across the District of Columbia. Who knew that his pitches were the secret ingredient to a cooking frenzy?

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a bustling kitchen in Washington, D.C., filled with energetic chefs and cooks working diligently at their stations. The atmosphere is vibrant and charged with excitement as they prepare dishes with precision and enthusiasm. Baseball memorabilia adorns the walls, and a large screen displays highlights of Justin Verlander's striking out opposing batters. The chefs are all wearing Verlander jerseys and can be seen high-fiving each other in celebration. The aroma of delicious food fills the air, and satisfied customers can be seen dining with contented smiles, savoring the inspired culinary creations. 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 situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: "Justin Verlander's Season Strikeout Count Causes Surge in DC Cooks! Experts Say His Pitching Dominance Has Stirred Up a Recipe for Success, Inspiring More Culinary Enthusiasts. Local Cafeterias and Institutions Now Boasting a Full Roster of Cooks. Baseball Fans and Foodies Rejoice as DC Embraces the 'Verlander Effect' in the Kitchen!". Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Symbolism 3D Pixar Style Low poly Cubism Parallax art
ChatGPT: Symbolism

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. "Justin Verlander's Season Strikeout Count Causes Surge in DC Cooks! Experts Say His Pitching Dominance Has Stirred Up a Recipe for Success, Inspiring More Culinary Enthusiasts. Local Cafeterias and Institutions Now Boasting a Full Roster of Cooks. Baseball Fans and Foodies Rejoice as DC Embraces the 'Verlander Effect' in the Kitchen!"
ChatGPT: A bustling kitchen in DC is shown, with chefs and cooks working energetically at various stations. The atmosphere is charged with excitement as they prepare dishes with precision and enthusiasm. The walls are adorned with baseball memorabilia, and a large screen displays highlights of Justin Verlander's striking out opposing batters. The chefs are wearing Verlander jerseys and high-fiving each other in celebration. The aroma of delicious food fills the air, and customers dine with contented smiles, enjoying the fruits of the inspired culinary creations.

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: [ Symbolism ] and depicting this scene: A bustling kitchen in DC is shown, with chefs and cooks working energetically at various stations. The atmosphere is charged with excitement as they prepare dishes with precision and enthusiasm. The walls are adorned with baseball memorabilia, and a large screen displays highlights of Justin Verlander's striking out opposing batters. The chefs are wearing Verlander jerseys and high-fiving each other in celebration. The aroma of delicious food fills the air, and customers dine with contented smiles, enjoying the fruits of the inspired culinary creations. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling kitchen in Washington, D.C., filled with energetic chefs and cooks working diligently at their stations. The atmosphere is vibrant and charged with excitement as they prepare dishes with precision and enthusiasm. Baseball memorabilia adorns the walls, and a large screen displays highlights of Justin Verlander's striking out opposing batters. The chefs are all wearing Verlander jerseys and can be seen high-fiving each other in celebration. The aroma of delicious food fills the air, and satisfied customers can be seen dining with contented smiles, savoring the inspired culinary creations.

*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 an increase in Justin Verlander's season strikeout count caused The number of cooks, institution and cafeteria in District of Columbia to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia

The Journal of Sports Economics and Culinary Sociology

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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Justin Verlander's season strikeout count and The number of cooks, institution and cafeteria in District of Columbia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia"

[[ABSTRACT]]
In this study, we sought to investigate a rather unconventional relationship between a professional baseball player's performance and the culinary landscape of a specific region. After meticulous data collection and statistical analysis, we discovered a remarkably strong correlation between Justin Verlander's season strikeout count and the number of cooks, institution, and cafeteria in the District of Columbia. Our findings suggest a robust relationship, with a correlation coefficient of 0.8419076 and statistically significant p-value of less than 0.01 for the years spanning from 2005 to 2022.

As we delved into the depths of this peculiar connection, it became apparent that there exists a curious harmony between the art of striking out batters and the art of culinary endeavors. While some may view these domains as entirely distinct, our analysis brings to light the unexpectedly intertwined nature of these seemingly disparate spheres. Could it be that Verlander's strikeouts are influenced by the aroma of freshly prepared meals wafting through the district, or perhaps by the culinary skill and dedication of the cooks in its institutions and cafeterias? These questions provoke further contemplation, adding a pinch of flavor to the otherwise mundane statistical exploration.

Our hope is that this study sparks the curiosity of researchers in the fields of sports performance and gastronomy, ushering in an era of interdisciplinary inquiry that transcends traditional boundaries. This unlikely association between a pitcher's prowess and the culinary landscape of a metropolitan area serves as a reminder that correlations, much like fine cuisine, can often surprise us with their delightful fusion of seemingly unrelated elements.

[[LITERATURE REVIEW]]
The exploration of unconventional associations and interdisciplinary dynamics has often led researchers to unexpected discoveries. As we venture into the realm of peculiar correlations, our quest for understanding intertwining spheres of human activity brings us to the unlikely nexus of professional baseball and the culinary landscape of the District of Columbia. What follows is a review of pertinent literature that has, in one way or another, paved the way for our investigation into the connection between Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the nation's capital.
In "The Art of the Strikeout" by Smith, the authors find an in-depth analysis of pitching techniques and the psychological factors that contribute to a pitcher's success in striking out batters. While this seminal work delves into the intricacies of the sport, it inadvertently inspires contemplation of the potential influences beyond the baseball diamond. Could the sensory experiences of the pitcher, perhaps even the aroma of nearby culinary delights, play a role in his performance?
Doe's comprehensive study, "Culinary Landscapes of Urban Centers," offers a detailed examination of culinary trends and the prevalence of eateries in metropolitan areas. While its primary focus lies in the realm of food anthropology, the correlations drawn between societal demographics and culinary diversity invite speculation about the impact of such variables on seemingly unrelated domains, including athletic feats on the baseball field.
Jones' "Statistical Analysis in Sports Performance" provides a methodological framework for identifying and analyzing patterns in athletic achievements. However, as we perused the pages of this scholarly work, it became evident that statistical analyses, while inherently objective, can often illuminate unexpected associations that extend beyond the boundaries of traditional sports research.
Turning our attention to the broader implications of culinary environments, "Fast Food Nation" by Schlosser and "The Omnivore's Dilemma" by Pollan shed light on the intricate relationships between food production, consumption patterns, and societal values. While their narratives primarily revolve around food systems and dietary habits, the subtle parallels between culinary landscapes and the performance of professional athletes begin to surface, nudging us toward the unexplored terrain of our peculiar inquiry.
In a more lighthearted foray into the realm of fictional literature, works such as "The Hunger Games" by Collins and "The Restaurant at the End of the Universe" by Adams, though ostensibly unrelated to our academic pursuits, nonetheless beckon us to consider the captivating interplay of competition, sustenance, and unforeseen connections. After all, as we journey deeper into the quirky juncture of sports and food, a sprinkle of literary whimsy might just infuse our pursuit with a dash of humor and wry contemplation.
Continuing our pursuit of diverse sources, we occasionally indulged in the unorthodox approach of perusing information beyond scholarly texts, including but not limited to the backs of cereal boxes, restaurant menus, and even shampoo bottles. While these unconventional sources raised an eyebrow or two in academic circles, they did serve to remind us that insight can be gleaned from the unlikeliest of places, a notion that aligns seamlessly with the unconventional nature of our investigative venture.
In synthesizing the threads of literature that have informed our exploration, it becomes evident that the seemingly disparate realms of baseball and cuisine may indeed share underlying commonalities that beckon us to delve deeper, entertaining and enlightening ourselves with the unexpected symphony of striking out batters and the sizzle of culinary mastery.

[[RESULTS]]
The results of our investigation into the interconnected realms of Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the District of Columbia yielded an unexpected yet compelling correlation. The Pearson correlation coefficient between these seemingly unrelated variables was calculated to be 0.8419076, indicating a remarkably strong positive relationship. This correlation is further substantiated by an r-squared value of 0.7088083, suggesting that approximately 70.88% of the variability in Verlander's season strikeout count can be explained by the number of cooks, institutions, and cafeterias in the District of Columbia.
In the world of statistical analysis, a correlation coefficient of this magnitude is nothing short of striking, much like one of Verlander's well-executed fastballs. The p-value of less than 0.01 underscores the statistical significance of this relationship, affirming that the observed correlation is highly unlikely to have occurred by mere chance. It seems that the universe, or perhaps the baseball diamond, has conspired to intertwine the art of pitching with the culinary craft in a manner that elicits both surprise and intrigue.
Remarkably, the robust association between Verlander's performance on the mound and the culinary landscape of the nation's capital spans a notable period from 2005 to 2022. As we reflect on these findings, it is worth considering the broader implications of such an unexpected correlation. Could it be that the tantalizing aromas of culinary creations in the District of Columbia have unknowingly served as a seasoning of inspiration for Verlander's pitching prowess? Or perhaps there exists an unspoken kinship between the precision of a fastball and the precision of a culinary masterpiece? These contemplative musings lend a dash of whimsy to what might otherwise be seen as a purely empirical study.
To visually depict the compelling correlation unearthed in our analysis, we present Figure 1, a scatterplot that encapsulates the strong relationship between Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the District of Columbia. This graphical representation offers a tangible glimpse into the unexpected harmony between these ostensibly divergent domains, inviting the viewer to ponder the enigmatic forces at play.
In conclusion, our research has unveiled an intriguing link between a renowned pitcher's performance and the culinary fabric of a distinct geographic area. What started as a whimsical exploration has culminated in a statistically robust revelation, underscoring the often unforeseen connections that permeate the tapestry of human endeavors. As we consider the implications of this unlikely correlation, we are reminded of the delightful surprises that await those willing to venture beyond the confines of traditional disciplinary boundaries. The convergence of athletic prowess and gastronomic allure continues to be a source of wonder and inspiration, offering a reminder that statistical analysis, much like a well-crafted dish, can present unexpected flavors and aromas that captivate the senses.
Our hope is that this study ignites further curiosity and inquiry, prompting scholars and enthusiasts alike to embrace the serendipitous correlations that lie hidden beneath the surface of ostensibly unrelated domains. For in the realm of statistical exploration, as in the culinary arts, there exists a richness of discovery that transcends the limitations of conventional wisdom.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

The results of our study have illuminated a remarkably strong correlation between Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the District of Columbia. Our findings not only corroborate the earlier research but also serve to highlight the unexpected nature of this relationship, much like stumbling upon a gourmet meal in the most unlikely of places.

In our review of the literature, we observed that Smith's comprehensive analysis of pitching techniques inadvertently raised the tantalizing question of whether sensory experiences, such as the aroma of nearby culinary delights, could influence a pitcher's performance. Lo and behold, our study's findings seem to support this notion, suggesting that the culinary landscape of the District of Columbia may indeed have an impact on Verlander's prowess on the baseball field. Furthermore, Doe's exploration of culinary trends in urban centers beckoned us to consider the broader implications of such variables on seemingly unrelated domains, providing a backdrop for our investigation into this perplexing connection.

Our statistically significant correlation coefficient of 0.8419076 and an r-squared value of 0.7088083 provide compelling evidence for the unexpected relationship between these variables. It appears that approximately 70.88% of the variability in Verlander's season strikeout count can be attributed to the number of cooks, institutions, and cafeterias in the District of Columbia, a finding that undoubtedly raises eyebrows in both the sports and culinary communities.

The p-value of less than 0.01 further cements the significance of this association, serving as a reminder that sometimes, in the world of statistics, the most extraordinary connections can emerge from the most unassuming circumstances. This finding is as unexpected as finding a perfectly ripe avocado when you weren't even in the produce section of the grocery store.

As we consider the implications of this correlation, we are struck by the realization that the universe, much like a well-crafted dish, often presents us with flavors and aromas that we least expect. Our study, much like a carefully prepared meal, urges us to savor the delightful surprises that lie at the confluence of athletics and gastronomy, reminding us that the most tantalizing discoveries may indeed unfold in the most unexpected places.

In the grand scheme of scholarly inquiry, our work piques the curiosity of both sports enthusiasts and culinary aficionados, inviting them to embrace the serendipitous correlations that underpin human endeavors. For just as a master chef seamlessly blends ingredients to create a harmonious dish, our research has revealed an unforeseen harmony between Verlander's athletic acumen and the culinary tapestry of the nation's capital.

With Figure 1, our scatterplot visually encapsulates the artful interplay between these domains, beckoning the viewer to contemplate the intriguing forces at play. Indeed, the visualization of this unexpected association serves as a delightful appetizer to the complex feast of statistical exploration, as we invite scholars and enthusiasts alike to indulge in the richness of discovery that lies beyond the boundaries of conventional wisdom.

Thus, as we leave the table, we invite our fellow researchers to savor the taste of interdisciplinary inquiry and to relish the flavors of unexpected connections, for in the world of statistical exploration, as in the culinary arts, the most extraordinary discoveries often emerge from the unlikeliest of sources. After all, much like an exquisite dish, the beauty of knowledge lies in its ability to surprise, captivate, and nourish the inquisitive soul.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Justin Verlander's season strikeout count and The number of cooks, institution and cafeteria in District of Columbia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia"

[[ABSTRACT]]
In this study, we sought to investigate a rather unconventional relationship between a professional baseball player's performance and the culinary landscape of a specific region. After meticulous data collection and statistical analysis, we discovered a remarkably strong correlation between Justin Verlander's season strikeout count and the number of cooks, institution, and cafeteria in the District of Columbia. Our findings suggest a robust relationship, with a correlation coefficient of 0.8419076 and statistically significant p-value of less than 0.01 for the years spanning from 2005 to 2022.

As we delved into the depths of this peculiar connection, it became apparent that there exists a curious harmony between the art of striking out batters and the art of culinary endeavors. While some may view these domains as entirely distinct, our analysis brings to light the unexpectedly intertwined nature of these seemingly disparate spheres. Could it be that Verlander's strikeouts are influenced by the aroma of freshly prepared meals wafting through the district, or perhaps by the culinary skill and dedication of the cooks in its institutions and cafeterias? These questions provoke further contemplation, adding a pinch of flavor to the otherwise mundane statistical exploration.

Our hope is that this study sparks the curiosity of researchers in the fields of sports performance and gastronomy, ushering in an era of interdisciplinary inquiry that transcends traditional boundaries. This unlikely association between a pitcher's prowess and the culinary landscape of a metropolitan area serves as a reminder that correlations, much like fine cuisine, can often surprise us with their delightful fusion of seemingly unrelated elements.

[[INTRDUCTION]]
The world of sports and the world of cuisine may appear to inhabit separate orbits, but as we embark on this investigation, we are compelled to consider the possibility of an unseen gravitational pull between the two. Justin Verlander, a distinguished figure in the realm of professional baseball, has captured the attention of statisticians and culinary enthusiasts alike with his remarkable talent for strikeouts. Likewise, the District of Columbia's culinary landscape, consisting of cooks, institutions, and cafeterias, has drawn our curiosity as we endeavor to uncover the hidden connections that underpin seemingly unrelated spheres of human endeavor.
As we set out to explore the potential relationship between Verlander's season strikeout count and the culinary establishments in the District of Columbia, it is important to emphasize the inherently unconventional nature of this inquiry. The hallowed halls of academia may more commonly witness studies of financial markets, healthcare systems, or ecological phenomena, but here we stand, poised to examine the intriguing intersection of America's favorite pastime and the art of gastronomy.
While the uninitiated onlooker might raise an eyebrow at the juxtaposition of these seemingly disparate pursuits, we find it fitting to heed the words of George Bernard Shaw: "There is no love sincerer than the love of food." Indeed, it is with this profound sentiment as our guiding light that we venture forth into the uncharted terrain of statistical analysis, seeking to shed light on the curious interplay between Verlander's pitching prowess and the culinary expertise nestled within the nation's capital.
In the spirit of scientific inquiry and a dash of whimsy, we invite the reader to join us on this odyssey of discovery. As we harness the power of data and statistical methods, let us not squander the opportunity to uncover the tantalizing mysteries that lie at the nexus of strikeouts and sizzling stovetops. Together, we embark on a journey that may elicit a wry smile from the stoic face of conventional wisdom, for in the unraveling of statistical relationships, as in life, there exists the potential for delightful surprises that defy expectation.
So, with a nod to both the precision of science and the artful flair of cuisine, we begin our expedition into the curious correlation between athletic prowess and gastronomic ingenuity. Let the game begin.

[[RESULTS]]
The results of our investigation into the interconnected realms of Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the District of Columbia yielded an unexpected yet compelling correlation. The Pearson correlation coefficient between these seemingly unrelated variables was calculated to be 0.8419076, indicating a remarkably strong positive relationship. This correlation is further substantiated by an r-squared value of 0.7088083, suggesting that approximately 70.88% of the variability in Verlander's season strikeout count can be explained by the number of cooks, institutions, and cafeterias in the District of Columbia.
In the world of statistical analysis, a correlation coefficient of this magnitude is nothing short of striking, much like one of Verlander's well-executed fastballs. The p-value of less than 0.01 underscores the statistical significance of this relationship, affirming that the observed correlation is highly unlikely to have occurred by mere chance. It seems that the universe, or perhaps the baseball diamond, has conspired to intertwine the art of pitching with the culinary craft in a manner that elicits both surprise and intrigue.
Remarkably, the robust association between Verlander's performance on the mound and the culinary landscape of the nation's capital spans a notable period from 2005 to 2022. As we reflect on these findings, it is worth considering the broader implications of such an unexpected correlation. Could it be that the tantalizing aromas of culinary creations in the District of Columbia have unknowingly served as a seasoning of inspiration for Verlander's pitching prowess? Or perhaps there exists an unspoken kinship between the precision of a fastball and the precision of a culinary masterpiece? These contemplative musings lend a dash of whimsy to what might otherwise be seen as a purely empirical study.
To visually depict the compelling correlation unearthed in our analysis, we present Figure 1, a scatterplot that encapsulates the strong relationship between Justin Verlander's season strikeout count and the number of cooks, institutions, and cafeterias in the District of Columbia. This graphical representation offers a tangible glimpse into the unexpected harmony between these ostensibly divergent domains, inviting the viewer to ponder the enigmatic forces at play.
In conclusion, our research has unveiled an intriguing link between a renowned pitcher's performance and the culinary fabric of a distinct geographic area. What started as a whimsical exploration has culminated in a statistically robust revelation, underscoring the often unforeseen connections that permeate the tapestry of human endeavors. As we consider the implications of this unlikely correlation, we are reminded of the delightful surprises that await those willing to venture beyond the confines of traditional disciplinary boundaries. The convergence of athletic prowess and gastronomic allure continues to be a source of wonder and inspiration, offering a reminder that statistical analysis, much like a well-crafted dish, can present unexpected flavors and aromas that captivate the senses.
Our hope is that this study ignites further curiosity and inquiry, prompting scholars and enthusiasts alike to embrace the serendipitous correlations that lie hidden beneath the surface of ostensibly unrelated domains. For in the realm of statistical exploration, as in the culinary arts, there exists a richness of discovery that transcends the limitations of conventional wisdom.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In closing, our research has not only shed light on the unusual connection between Justin Verlander's pitching prowess and the culinary landscape of the District of Columbia but has also tantalized the palate of statistical inquiry with the savory flavors of unexpected correlation. As we digest the implications of our findings, it becomes apparent that the artistry of a well-thrown fastball and the artistry of a well-prepared meal may be more intertwined than previously imagined. Could it be that Verlander's strikeouts are seasoned with a sprinkle of culinary inspiration, or are the cooks, institutions, and cafeterias serving up a secret recipe for athletic achievement? These culinary conundrums add a dash of spice to the statistical discourse, reminding us that the world of correlations is a smorgasbord of surprises.

While our investigation has unearthed a statistically robust relationship between these seemingly distinct domains, it is important to note that further research in this area may prove equally delectable, with potential avenues for exploration reaching beyond the confines of baseball and gastronomy. Nevertheless, like a well-cooked dish, our research has marinated in the flavors of statistical significance, becoming a flavorful addition to the buffet of interdisciplinary inquiry.

In the spirit of scientific discovery, let us savor the tantalizing mysteries that lie at the intersection of Verlander's strikeouts and the culinary prowess of the District of Columbia. However, in the context of our present inquiry, it is with a sense of both scholarly fulfillment and gastronomic wonder that we assert: no further research is needed at this time.

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 Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Sports Economics and Culinary Sociology

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 Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Culinary and Athletic Analytics

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 Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia with an abstract of In this study, we sought to investigate a rather unconventional relationship between a professional baseball player's performance and the culinary landscape of a specific region. After meticulous data collection and statistical analysis, we discovered a remarkably strong correlation between Justin Verlander's season strikeout count and the number of cooks, institution, and cafeteria in the District of Columbia. Our findings suggest a robust relationship, with a correlation coefficient of 0.8419076 and statistically significant p-value of less than 0.01 for the years spanning from 2005 to 2022.
As we delved into the depths of this peculiar connection, it became apparent that there exists a curious harmony between the art of striking out batters and the art of culinary endeavors. While some may view these domains as entirely distinct, our analysis brings to light the unexpectedly intertwined nature of these seemingly disparate spheres. Could it be that Verlander's strikeouts are influenced by the aroma of freshly prepared meals wafting through the district, or perhaps by the culinary skill and dedication of the cooks in its institutions and cafeterias? These questions provoke further contemplation, adding a pinch of flavor to the otherwise mundane statistical exploration.
Our hope is that this study sparks the curiosity of researchers in the fields of sports performance and gastronomy, ushering in an era of interdisciplinary inquiry that transcends traditional boundaries. This unlikely association between a pitcher's prowess and the culinary landscape of a metropolitan area serves as a reminder that correlations, much like fine cuisine, can often surprise us with their delightful fusion of seemingly unrelated elements.

ChatGPT:

Justin Verlander, season strikeout count, correlation analysis, cooks, institution, cafeteria, District of Columbia, culinary landscape, statistical analysis, sports performance, gastronomy, interdisciplinary inquiry

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

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Data details

Justin Verlander's season strikeout count
Source: Baseball Reference
See what else correlates with Justin Verlander's season strikeout count

The number of cooks, institution and cafeteria in District of Columbia
Detailed data title: BLS estimate of cooks, institution and cafeteria in District of Columbia
Source: Bureau of Larbor Statistics
See what else correlates with The number of cooks, institution and cafeteria in District of Columbia

Correlation r = 0.8419076 (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 = 0.7088083 (Coefficient of determination)
This means 70.9% of the change in the one variable (i.e., The number of cooks, institution and cafeteria in District of Columbia) is predictable based on the change in the other (i.e., Justin Verlander's season strikeout count) over the 18 years from 2005 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.2E-5.^Show 0.0000117980802640770800000000
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 0.84 in 0.0012% of random cases. Said differently, if you correlated 84,760 random variables^Note You don't actually need 84 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
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.

[ 0.62, 0.94 ] 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.

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Justin Verlander's season strikeout count (Strikeouts)	7	124	183	163	269	219	250	239	217	159	113	254	438	290	300	7	0	185
The number of cooks, institution and cafeteria in District of Columbia (Laborers)	1020	1360	1370	1490	1240	1240	1470	1500	1420	1240	1320	1620	1770	1600	1500	1080	1170	1330

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([7,124,183,163,269,219,250,239,217,159,113,254,438,290,300,7,0,185,])
array_2 = np.array([1020,1360,1370,1490,1240,1240,1470,1500,1420,1240,1320,1620,1770,1600,1500,1080,1170,1330,])
array_1_name = "Justin Verlander's season strikeout count"
array_2_name = "The number of cooks, institution and cafeteria in District of Columbia"

# 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 Justin Verlander's season strikeout count
Line chart for only The number of cooks, institution and cafeteria in District of Columbia
The spurious research paper: Swinging for the Fences In and Out of the Ballpark: A Correlative Analysis of Justin Verlander's Season Strikeout Count and the Number of Cooks, Institution, and Cafeteria in District of Columbia

View another random correlation

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Correlation ID: 1324 · Black Variable ID: 247 · Red Variable ID: 6337


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