Spurious correlation #2,329 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Boston Celtics' NBA season loss count and the second variable is Liquefied petroleum gas used in Portugal. The chart goes from 1980 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 Boston Celtics' NBA season loss count and the second variable is Liquefied petroleum gas used in Portugal. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

This bizarre connection can be attributed to the little-known fact that every time the Boston Celtics lose a game, a fan in Portugal has made a bet with a friend that involves grilling copious amounts of food to drown their sorrows in. As the Celtics' losses mount, so does the consumption of LPG in Portugal, leading to a peculiar and inexplicable correlation. It just goes to show, when it comes to the Celtics and LPG in Portugal, there's more than just a game at stake!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Create a vibrant pop art-inspired image of a cozy Portuguese kitchen, with a family gathered around a stove cooking a traditional meal. The stove should be the central focus, with flames leaping from the burners. The family should be engaged in cooking, cheering for the Boston Celtics game playing on the TV in the background. The colors should be bold and dynamic, with exaggerated contrasts to evoke the energetic atmosphere. The meter above the stove tracking Liquefied Petroleum Gas usage should stand out and convey a sense of motion. The Celtics' season loss count on the TV should be prominent, contributing to the overall lively and expressive pop art style. 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: Boston Celtics' NBA season loss count and Liquefied petroleum gas used in Portugal. 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: Concept art Parallax art Cybernetic art Macabre Pop art
ChatGPT: Pop art

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 Boston Celtics' NBA season loss count and Liquefied petroleum gas used in Portugal together. Keep the description simple.
ChatGPT: In a cozy Portuguese kitchen, a family gathers around a stove cooking a traditional meal while watching the Boston Celtics game on TV. Above the stove, a meter ticks up as the family uses Liquefied Petroleum Gas for cooking. On the television, the Celtics' season loss count flashes on the screen, reminding the family of the team's performance as they enjoy their meal together.

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: [ Pop art ] and depicting this scene: In a cozy Portuguese kitchen, a family gathers around a stove cooking a traditional meal while watching the Boston Celtics game on TV. Above the stove, a meter ticks up as the family uses Liquefied Petroleum Gas for cooking. On the television, the Celtics' season loss count flashes on the screen, reminding the family of the team's performance as they enjoy their meal together. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Create a vibrant pop art-inspired image of a cozy Portuguese kitchen, with a family gathered around a stove cooking a traditional meal. The stove should be the central focus, with flames leaping from the burners. The family should be engaged in cooking, cheering for the Boston Celtics game playing on the TV in the background. The colors should be bold and dynamic, with exaggerated contrasts to evoke the energetic atmosphere. The meter above the stove tracking Liquefied Petroleum Gas usage should stand out and convey a sense of motion. The Celtics' season loss count on the TV should be prominent, contributing to the overall lively and expressive pop art style.

*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 Boston Celtics' NBA season loss count positively influenced Liquefied petroleum gas used in Portugal.

AI academic paper

(Because p < 0.01)

Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal

The Journal of Unusual Correlations in Sports and Energy Consumption

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 Boston Celtics' NBA season loss count and Liquefied petroleum gas used in Portugal. Make lots of jokes, goofy observations, and puns.

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]]
"Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal"

[[ABSTRACT]]
In this study, we delved into the unlikely coupling of the Boston Celtics' performance in the NBA and the consumption of Liquefied Petroleum Gas (LPG) in Portugal. While this pairing might seem as odd as wearing a basketball jersey to a gas station, our findings reveal a surprising connection that is as captivating as a buzzer-beater shot. By analyzing data from Wikipedia and the Energy Information Administration, we uncovered a correlation coefficient of 0.6596735 and p < 0.01 from 1980 to 2022, demonstrating a statistically significant relationship between these seemingly unrelated entities. Our results defy conventional wisdom, prompting us to ponder the whimsical ways in which sports and energy consumption may intertwine. So, whether you're a hoops fan or a gas enthusiast, this study is sure to provide some unexpected food for thought – or should we say, "fuel for thought."

[[LITERATURE REVIEW]]
The relationship between the Boston Celtics' NBA performance and Liquefied Petroleum Gas (LPG) consumption in Portugal may seem as improbable as finding Bigfoot's shoe collection, but a surprising body of literature supports the notion that these seemingly disparate entities might have more in common than meets the eye.
Smith et al. (2015) conducted a comprehensive analysis of NBA team performance and energy consumption patterns across various countries, albeit without specifically focusing on the Celtics or Portugal. Their findings offered a broader perspective on the potential links between sports outcomes and societal energy usage, paving the way for our more specialized investigation.
Moving on from the serious scholarship, Doe and Jones (2018) unearthed intriguing parallels between sports fan enthusiasm and fuel preferences in their seminal work on "Sports Fandom and Energy Affinity." Their qualitative study delved into the subconscious associations between team loyalty and energy choices, delving into the peculiar phenomenon of fans lighting up their grills to celebrate a win or drown their sorrows in LPG-induced comfort.
As we turn the pages from the scholarly to the more wide-ranging, it's worth noting the relevance of non-fiction works such as "The Economics of Sports" by Michael Leeds and Peter Von Allmen, and "Energy Transitions: History, Requirements, Prospects" by Vaclav Smil, which offer valuable insights into the intersection of sports and energy dynamics. These titles might not directly address our specific inquiry, but the juxtaposition of sports and energy sources certainly lingers in the minds of these authors.
From the land of fiction, we draw parallels to our investigation in "The Crossover" by Kwame Alexander, a novel that beautifully encapsulates the essence of basketball and the unexpected twists of life. While the storyline may not directly involve gas consumption or Portugal, the spirit of sportsmanship and unpredictability aligns with our exploration of unlikely connections.
And who can overlook the timeless classic "Around the World in Eighty Days" by Jules Verne? While Phileas Fogg's journey didn't involve tracking NBA game outcomes or monitoring LPG usage, the globe-trotting adventure resonates with our endeavor to bridge geographical and thematic gaps through our investigation.
Furthermore, in the realm of internet culture, the "Uncomfortable Situation Seal" meme humorously captures the bewildering juxtaposition of improbable scenarios – much like the initial reaction to our research hypothesis. Likewise, the "Confused Nick Young" meme perfectly encapsulates the widespread disbelief that often greets unconventional ideas, mirroring the initial skepticism towards our study's premise.
In our quest to unravel this enigmatic correlation, we journey through the pages of academic literature and beyond, turning over unexpected connections and delving into uncharted territories. As we navigate this unorthodox path, brace yourselves for a rollercoaster ride of statistical analysis and whimsical observations. After all, who would have thought that the Celtics' wins and losses could intersect with the aroma of LPG in Portugal, creating a conundrum both mystifying and captivating? So, let's lace up our sneakers, turn on the gas, and embark on this unanticipated odyssey of research – because as we've learned, truth is often stranger than fiction, and statistical correlations can be as unpredictable as a basketball game in double overtime.

[[RESULTS]]
Our analysis of the data revealed a surprising correlation between the Boston Celtics' NBA season loss count and the consumption of Liquefied Petroleum Gas (LPG) in Portugal from 1980 to 2022. The correlation coefficient of 0.6596735 suggests a moderately strong positive relationship between these two seemingly unrelated variables. In other words, as the Celtics' losses increased, so did the consumption of LPG in Portugal. It's as if the Celtics' defeats were somehow fueling the demand for LPG across the Atlantic – talk about a transatlantic power play!
The r-squared value of 0.4351692 indicates that 43.52% of the variation in LPG consumption in Portugal can be explained by the variability in the Boston Celtics' loss count. This finding provides compelling evidence for the influence of basketball performance on energy choices in a way that transcends borders and ball courts.
The p-value of less than 0.01 further strengthens the validity of our results, indicating a statistically significant correlation. This p-value is so small, it's almost as rare as a perfect underhand free throw – something truly remarkable in the world of statistical analysis.
In our visually stimulating scatterplot (Fig. 1), the data points form a clear pattern, resembling the trajectory of a well-executed jump shot. With each additional loss for the Celtics, there is a discernible uptick in LPG consumption in Portugal, painting a picture that is as intriguing as a game-winning buzzer-beater.
These findings challenge traditional notions of sports and energy consumption, opening the door to a world of possibilities in the realm of statistical analysis and athletic influence. It's like discovering that dribbling a basketball can somehow impact the boiling point of LPG – who would have thought?
In conclusion, our results support the existence of a notable association between the Boston Celtics' NBA season loss count and Liquefied Petroleum Gas consumption in Portugal. This unexpected linkage invites further investigation into the nuanced dynamics of sports performance and energy utilization, leaving us with a hoop dreams-meets-gasoline scenario that is as perplexing as it is amusing. Who knew that the Celtics' rollercoaster season could have an impact on heating and cooking preferences in Portugal? The world of statistical research is indeed filled with as many surprises as an overtime thriller, and this correlation is proof that sometimes, the most unexpected connections can emerge from the most unlikely pairings.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings offer compelling support for the unorthodox yet undeniably captivating relationship between the Boston Celtics' NBA season loss count and Liquefied Petroleum Gas (LPG) consumption in Portugal. While it may seem as improbable as a three-pointer from half court, our statistical analysis has unveiled a correlation that is as intriguing as a last-second alley-oop.

Harkening back to the literature review, the parallels drawn by Smith et al. (2015) between NBA team performance and energy consumption across countries have found resonance in our specialized investigation. We have not only substantiated their broader perspective but also steered the spotlight onto the specific interplay of the Celtics' outcomes and LPG demand in Portugal. It's as if our research has set the stage for a slam dunk of statistical validation.

Doe and Jones (2018) would be delighted to see their musings on the subconscious links between sports fan enthusiasm and fuel preferences find empirical grounding in our correlation between the Celtics' losses and LPG consumption. The qualitative speculations of fans finding solace in LPG-induced comfort now align with quantitative evidence, painting a picture as bright as a stadium floodlight.

Moving on from the serious scholarship, we must also acknowledge the unexpected resonance of "The Crossover" by Kwame Alexander in our investigation. While the novel may not have directly involved gas consumption or Portugal, the spirit of unpredictability and the game of basketball have found a home in our research, much like a basketball finding the net.

Rendering homage to Jules Verne's "Around the World in Eighty Days," our findings traverse geographical and thematic gaps, uncovering the extraordinary linkage between the Celtics' performance and Portuguese LPG consumption. Our statistical journey mirrors Fogg's adventure in its unpredictability and surprising connections, making us feel like we've circled the globe of statistical analysis in eighty days.

The "Uncomfortable Situation Seal" meme aptly captures the bewildering yet fascinating juxtaposition of our investigation's initial premise, now validated by empirical evidence. Similarly, the "Confused Nick Young" meme's widespread disbelief now gives way to a delightfully confounded acceptance of our unexpected but established correlation.

Our results have not only validated prior conjectures but have also unveiled a connection so robust that it may change the game in both sports analytics and energy research. It's like discovering a new play in the basketball playbook that involves shooting a free throw blindfolded and still making the shot.

In essence, our findings cement the playful yet powerful bond between the Celtics' on-court performance and the aroma of LPG wafting through Portuguese kitchens. This correlation invites further exploration into the whimsical ways in which sports and energy intertwine, painting a picture that is as colorful as a team's jersey and as captivating as a buzzer-beating victory. In the world of statistical analysis, truth may indeed be stranger than fiction, and our research stands as a testament to the whimsical yet unequivocal connections that statistical correlations can uncover. After all, who would have thought that tracking the Celtics' game outcomes could lead us to discover a trail of LPG all the way to Portugal? The unlikely pairings in our findings serve as a reminder that statistical research, much like a basketball game, is filled with as many surprises as an overtime thriller.

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 Boston Celtics' NBA season loss count and Liquefied petroleum gas used in Portugal. Make lots of jokes, goofy observations, and puns.

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]]
"Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal"

[[ABSTRACT]]
In this study, we delved into the unlikely coupling of the Boston Celtics' performance in the NBA and the consumption of Liquefied Petroleum Gas (LPG) in Portugal. While this pairing might seem as odd as wearing a basketball jersey to a gas station, our findings reveal a surprising connection that is as captivating as a buzzer-beater shot. By analyzing data from Wikipedia and the Energy Information Administration, we uncovered a correlation coefficient of 0.6596735 and p < 0.01 from 1980 to 2022, demonstrating a statistically significant relationship between these seemingly unrelated entities. Our results defy conventional wisdom, prompting us to ponder the whimsical ways in which sports and energy consumption may intertwine. So, whether you're a hoops fan or a gas enthusiast, this study is sure to provide some unexpected food for thought – or should we say, "fuel for thought."

[[INTRDUCTION]]
The world of sports and the realm of energy consumption may seem as compatible as a pineapple on a pizza, but in this study, we aim to demonstrate that the connection between the performance of the Boston Celtics in the NBA and the consumption of Liquefied Petroleum Gas (LPG) in Portugal is more than just a shot in the dark. While most researchers might not have thought to link the Celtics' wins and losses with the use of LPG in a distant country, we are not your average researchers. We prefer to take the road less traveled, which in this case could be the less traveled path to the NBA locker room via a Portuguese gas station.
As we navigate through the maze of data analysis and statistical methods, we invite you to join us in this unorthodox exploration, which promises to be as entertaining as watching a double-overtime game – with even more surprising twists and turns. We hope that by the end of this paper, you'll be as captivated by the unexpected correlation as you would be by a halftime trampoline dunk show.
With that in mind, let's dribble into the fascinating world of statistical correlation, athletic prowess, and the aromatic allure of LPG, because what could be more exhilarating than unpacking the mystery behind the Celtics' wins and losses and the consumption of LPG in Portugal? Let's step onto this unconventional court of research and see where the data takes us. And who knows? We might just score a statistical slam dunk – or at the very least, a comedic alley-oop.

[[RESULTS]]
Our analysis of the data revealed a surprising correlation between the Boston Celtics' NBA season loss count and the consumption of Liquefied Petroleum Gas (LPG) in Portugal from 1980 to 2022. The correlation coefficient of 0.6596735 suggests a moderately strong positive relationship between these two seemingly unrelated variables. In other words, as the Celtics' losses increased, so did the consumption of LPG in Portugal. It's as if the Celtics' defeats were somehow fueling the demand for LPG across the Atlantic – talk about a transatlantic power play!
The r-squared value of 0.4351692 indicates that 43.52% of the variation in LPG consumption in Portugal can be explained by the variability in the Boston Celtics' loss count. This finding provides compelling evidence for the influence of basketball performance on energy choices in a way that transcends borders and ball courts.
The p-value of less than 0.01 further strengthens the validity of our results, indicating a statistically significant correlation. This p-value is so small, it's almost as rare as a perfect underhand free throw – something truly remarkable in the world of statistical analysis.
In our visually stimulating scatterplot (Fig. 1), the data points form a clear pattern, resembling the trajectory of a well-executed jump shot. With each additional loss for the Celtics, there is a discernible uptick in LPG consumption in Portugal, painting a picture that is as intriguing as a game-winning buzzer-beater.
These findings challenge traditional notions of sports and energy consumption, opening the door to a world of possibilities in the realm of statistical analysis and athletic influence. It's like discovering that dribbling a basketball can somehow impact the boiling point of LPG – who would have thought?
In conclusion, our results support the existence of a notable association between the Boston Celtics' NBA season loss count and Liquefied Petroleum Gas consumption in Portugal. This unexpected linkage invites further investigation into the nuanced dynamics of sports performance and energy utilization, leaving us with a hoop dreams-meets-gasoline scenario that is as perplexing as it is amusing. Who knew that the Celtics' rollercoaster season could have an impact on heating and cooking preferences in Portugal? The world of statistical research is indeed filled with as many surprises as an overtime thriller, and this correlation is proof that sometimes, the most unexpected connections can emerge from the most unlikely pairings.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In closing, our research has unveiled a correlation between the Boston Celtics' NBA season loss count and Liquefied Petroleum Gas (LPG) consumption in Portugal that is as puzzling as a basketball player attempting to shoot hoops while wearing scuba flippers. Our study has not only shattered the backboard of conventional wisdom but has also delivered a slam dunk of statistical significance. It's like witnessing a three-pointer from half court with the entire stadium holding their collective breath – unexpected, exhilarating, and downright miraculous.

This fascinating connection between the Celtics' performance and LPG usage in Portugal is akin to finding out that the trajectory of a free throw somehow influences the rate of photosynthesis in ferns – utterly mind-boggling. It's as if the outcome of the Celtics' games exerts a gravitational pull on LPG consumption across the ocean, creating a transcontinental tug of war that is as captivating as it is confounding.

With these results in hand, we encourage future researchers to explore similarly whimsical connections between sports and seemingly unrelated variables. Perhaps a study on the correlation between the New York Knicks' win streaks and the sales of yak milk in Mongolia could be the next frontier in unconventional research. Who knows what unexpected revelations might emerge from such an inquiry – the possibilities are as vast as a basketball court and as surprising as a mid-game mascot dance-off.

In sum, our findings point to a compelling relationship between the Celtics' losses and LPG consumption in Portugal, leaving the world of statistical analysis and sports influence richer for the discovery. We can confidently assert that further research in this field is as unnecessary as a referee at a game of pickup basketball. It's time to hang up our lab coats and bask in the glory of this remarkable discovery, much like a winning team revels in the glow of victory. No more research is needed in this area – it's time to celebrate this statistical slam dunk and let this unlikely pairing of basketball and LPG consumption bask in its quirky, unexplainable glory.

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 Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Unusual Correlations in Sports and Energy Consumption

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 Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Intercontinental Energy Sports and Environmental Studies

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 Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal with an abstract of In this study, we delved into the unlikely coupling of the Boston Celtics' performance in the NBA and the consumption of Liquefied Petroleum Gas (LPG) in Portugal. While this pairing might seem as odd as wearing a basketball jersey to a gas station, our findings reveal a surprising connection that is as captivating as a buzzer-beater shot. By analyzing data from Wikipedia and the Energy Information Administration, we uncovered a correlation coefficient of 0.6596735 and p < 0.01 from 1980 to 2022, demonstrating a statistically significant relationship between these seemingly unrelated entities. Our results defy conventional wisdom, prompting us to ponder the whimsical ways in which sports and energy consumption may intertwine. So, whether you're a hoops fan or a gas enthusiast, this study is sure to provide some unexpected food for thought – or should we say, "fuel for thought.

ChatGPT:

Boston Celtics, NBA, loss count, Liquefied Petroleum Gas consumption, Portugal, correlation, Energy Information Administration, statistical analysis, sports performance, energy consumption, unusual correlations

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

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Data details

Boston Celtics' NBA season loss count
Detailed data title: Boston Celtics' NBA session match loss count
Source: Wikipedia
See what else correlates with Boston Celtics' NBA season loss count

Liquefied petroleum gas used in Portugal
Detailed data title: Volume of liquefied petroleum gas used consumed in Portugal in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Liquefied petroleum gas used in Portugal

Correlation r = 0.6596735 (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 = 0.4351692 (Coefficient of determination)
This means 43.5% of the change in the one variable (i.e., Liquefied petroleum gas used in Portugal) is predictable based on the change in the other (i.e., Boston Celtics' NBA season loss count) over the 43 years from 1980 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.5E-6.^Show 0.0000014983684765170090000000
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 0.66 in 0.00015% of random cases. Said differently, if you correlated 667,393 random variables^Note You don't actually need 667 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 42 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 42 because we have two variables measured over a period of 43 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.

[ 0.45, 0.8 ] 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.

	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Boston Celtics' NBA season loss count (Match lost)	21	20	19	26	20	19	15	23	25	40	30	26	31	34	50	47	49	67	46	31	47	46	33	38	46	37	49	58	16	20	32	26	27	40	57	42	34	29	27	33	24	36	31
Liquefied petroleum gas used in Portugal (Million Barrels/Day)	0.3	0	0	0	16.7377	17.0082	17.7644	19.2384	21.3743	22.2192	24.8932	26.4247	28.2732	30.389	32.6438	34.1671	37.1803	36.9644	34.6767	34.7726	32.9973	32.5123	32.3836	29.8712	29.7623	28.1534	27.326	27.9699	27.3525	23.4548	23.4877	22.3781	18.5437	26.0274	31.6247	26.8247	25.1612	23.3288	19.6685	25.3863	25.6776	23.8274	22.5562

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([21,20,19,26,20,19,15,23,25,40,30,26,31,34,50,47,49,67,46,31,47,46,33,38,46,37,49,58,16,20,32,26,27,40,57,42,34,29,27,33,24,36,31,])
array_2 = np.array([0.3,0,0,0,16.7377,17.0082,17.7644,19.2384,21.3743,22.2192,24.8932,26.4247,28.2732,30.389,32.6438,34.1671,37.1803,36.9644,34.6767,34.7726,32.9973,32.5123,32.3836,29.8712,29.7623,28.1534,27.326,27.9699,27.3525,23.4548,23.4877,22.3781,18.5437,26.0274,31.6247,26.8247,25.1612,23.3288,19.6685,25.3863,25.6776,23.8274,22.5562,])
array_1_name = "Boston Celtics' NBA season loss count"
array_2_name = "Liquefied petroleum gas used in Portugal"

# 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 Boston Celtics' NBA season loss count
Line chart for only Liquefied petroleum gas used in Portugal
AI-generated correlation image
The spurious research paper: Gasping for Victory: Exploring the Correlation Between Boston Celtics' NBA Loss Count and Liquefied Petroleum Gas Consumption in Portugal

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Correlation ID: 2329 · Black Variable ID: 79 · Red Variable ID: 24826


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