Pancake Analytics LLC

I’m delighted that you discovered my page, and ideally, our paths converged at a comic-con where you could engage as an audience member in my Disney data science-centric conversation. Here, you’ll uncover a deeper analysis and enhanced practicality of the data featured in our discussion at the comic-con event.

Disney | Casual Impact Time-Series | Lorcana | Steve Aoki Super Collector Impact

In the dynamic world of collectibles, where nostalgia meets investment, the market for trading cards has witnessed intriguing shifts and influences. One fascinating case study revolves around the impact of renowned super collector and influential figure Steve Aoki on the sales market of the Disney Lorcana Chapter One Booster Box. The unveiling of a unique and curated collection by Aoki has the potential to send ripples through the trading card community. This causal impact time-series aims to delve into the fluctuations within the Disney Lorcana Chapter One Booster Box sales market, exploring the consequential effects triggered by Steve Aoki’s announcement of selling cards from this particular set.

As a figure with significant influence and a history of shaping trends in the collectibles space, Steve Aoki’s involvement in trading cards extends beyond personal passion. The mere mention of his engagement with a specific set, such as Disney Lorcana Chapter One, can evoke responses that reverberate throughout the collector community. By scrutinizing this scenario through the lens of a causal impact time-series analysis, we endeavor to unravel the intricate dynamics at play and quantify the extent to which Steve Aoki’s actions catalyze shifts in market behavior and demand. This investigation promises insights into the interplay between celebrity endorsement, collector sentiment, and market trends, providing a deeper understanding of the nuances within the trading card landscape.

There’s a couple of milestones of Steve Aoki’s Lorcana collecting, at least made visible via instagram. On September 4, 2023 he showed off his PSA 10 Gem Mint D23 Exclusive Mickey Mouse Promo card as a throwback post and to share his excitement for the release of chapter one. The same cert of a PSA 10 Gem Mint D23 Exclusive Mickey Mouse which sold on eBay ( the feedback number aligns to his aokiscardhouse eBay store , indicating most likely this was his point of purchase ). The milestone I would will be using as the event in this casual impact time-series exercise occurs on May 12, 2023, the aokiscardhouse instagram account shares a reel with the caption ” All for sale up on auction on our @ebay account #aokiscardhouse “. This begs the question what happens when a super collector “dumps” cards on eBay?  

The above graph is the average sold price of a booster box of Lorcana Chapter One , according to Terapeak , between January 23rd 2023 and June 26th 2023 ( this gives us give us 6 data points after Aoki’s post ). One could simply do a pre versus post look without modelling , the issue this raises is there could be multiple factors impacting the sales and you are not narrowing it down to only what happens when a super collector “dumps” cards on eBay?

The above graph is a sales forecast built off of the actual sales of Disney’s Lorcana Chapter One booster boxes before Aoki’s instagram post. Next step I’ll apply a smoothing spline to the forecast as shown below.

As reminder the blue line is the forecast before Aoki’s instagram post, this represents our best prediction of the average price sold for Disney’s Lorcana Chapter One booster box sales before the post. I split a spline smoother through the forecast data to help interpret and visualize the trend.

Now the above graph is updated with the actual average sold price of Disney’s Lorcana First Chapter through June 26th 2023 ( over a month after Aoki’s post ).

Average sold price shows an immediate decline with the except of our last data point ( we could we argue the causation of that data point, i.e. delay reprint or lack of transparency of a reprint, even a high end sale of an enchanted card, etc. ), which our forecast did also expect an increase ( seasonal pattern ).

I am able to make this statement by noticing the difference between the forecasted trend before Aoki’s post and the actual average sold price data.

In our last step before calculating causal impact, I’ve added an additional spline to the actual average sold price data, this is the yellow line on the graph.

Now to answer : what happens when a super collector “dumps” cards on eBay?

The calculation to this answer would be ( since we live in a latest comp world ) the difference between the two smoothed data points on June 26th, 2023 ( forecasted smooth of 241 and an actual smooth of 140 ).

Calculating this gives us :

Therefore, the percent change between the forecasted smooth of 241 and the actual smooth of 140 is approximately -41.91%. The negative value indicates a decrease or a reduction in value.

Let’s tie this back to our question : what happens when a super collector “dumps” cards on eBay?

Answer : For Disney’s Lorcana Chapter One booster boxes, the average price sold drops nearly 42% over the next month when a super collector ( such as Steve Aoki ) sells from his collection.

Disney | Multi Linear Regression | Parks and Resorts Revenue Drivers

In this section, we delve into the intricate world of multi-linear regression analysis, a powerful statistical tool that has been an integral part of my analytical arsenal for over a decade. While many individuals commonly employ regression models for predictive purposes, what sets multi-linear regression apart is its ability to not only forecast outcomes but also provide valuable insights into the impact of individual variables.

The focus of our analysis revolves around return on investment, a critical metric that guides decision-making processes. Instead of merely predicting future trends, we will leverage the fit statistics table generated by the multi-linear regression model to extract coefficients. These coefficients, derived from a meticulous analysis of data sourced directly from the Walt Disney World Company’s Fiscal Year financials, serve as keys to unlocking the intricacies of the factors influencing Disney World Park and Resorts Revenue.

Let’s begin this analytical journey together as we explore the enchanting world of Disney data. Our investigation aims to shed light on the continual integration of Disney intellectual properties into EPCOT and the heightened emphasis on Disney Princesses. Through the lens of multi-linear regression, we aim to unravel the underlying patterns and unveil the driving forces behind these strategic decisions. So, buckle up and let’s embark on this data-driven adventure into the magical realm of Disney.

The depicted bar chart above is extracted from the financial statements provided by Walt Disney Parks and Resorts to their investors. It illustrates the annual revenue in millions, offering a glimpse into the fiscal performance of the company.

The presented polar chart serves as a valuable tool for profiling the fiscal years of Walt Disney Parks and Resorts. It provides a comprehensive view of the years that witnessed significant additions to Epcot, the Magic Kingdom, and Hollywood Studios, while also delineating the number of released princess movies and non-princess movies during each respective period.

The pair of line charts presented above offers a rapid examination of the correlation between average ticket prices and annual revenue, along with the correlation between the three-year average Disney park attendance and annual revenue. By showcasing this data, the intention is to shed light on whether sales are predominantly influenced by price increments or fluctuations in attendance. This visual exploration of assumed independent variables is crucial prior to regression modeling, allowing for the identification of potential confounding factors.

Pancake Analytics | Disney | Influencers Revenue

Disney | Propensity Scoring | Leveraging Sorcerer’s Arena Meta Data

In the intricate realm of statistical analysis and decision-making, propensity scoring emerges as a powerful tool, a method I have strategically employed to delve into the enchanting world of Disney’s classic characters. Within this endeavor, I’ve harnessed the rich metadata from the mobile game Sorcerer’s Arena, seamlessly weaving it together with insights garnered from a comprehensive poll identifying the top Disney characters of all time. By leveraging propensity scoring, I aim to unravel nuanced patterns and relationships within Disney’s character catalog, providing a profound understanding of the factors that influence character popularity and engagement in this captivating universe. This innovative approach not only illuminates the dynamics within Sorcerer’s Arena but also contributes to a deeper comprehension of the timeless appeal and resonance of Disney’s iconic characters across diverse media platforms.

Imagine you have a big collection of Disney characters, each with unique strengths and abilities. Propensity scoring is like a clever way of pairing them up for battles to make it fair and exciting.

Here’s how it works: I use data from a Disney mobile game and a poll about people’s favorite characters. Propensity scoring helps me understand the characteristics that make a character popular or powerful in the game. For example, if a character tends to win a lot in the game, or if people really love them in the poll, that’s important information.

Then, when it’s time for a battle, I use this information to match characters with similar “propensity scores” – it’s like pairing characters that have a similar level of awesomeness. This makes the battles more balanced and enjoyable, ensuring that no character is too overpowered or underwhelming.

So, propensity scoring is like a behind-the-scenes magic trick that helps me create fair and exciting matchups for Disney character battles based on their popularity and performance in the game and in people’s hearts.

Employ the aforementioned profilers to gain a nuanced understanding of your selected Disney character. Navigate through the table positioned towards the bottom, utilizing filters for either character-specific details or quartile-based insights. This tool offers a comprehensive exploration, allowing you to delve into the intricacies of your chosen character’s attributes and contextualize their standing within the designated quartiles.

Quartiles are like dividing all these characters into four groups, kinda like sorting them from least awesome to most awesome. Each group is called a quartile. So, if your favorite Disney character is in the first quartile, they’re among the less powerful ones, but if they’re in the fourth quartile, they’re among the top-tier characters.

As a reminder the “propensity score” figures out how likely each character is to appear on the top 10 Disney Characters of all time list. It’s like a score that combines how well they perform in the game and how much people love them. The characters are then quartiled based on these scores. So, the higher the propensity score, the higher the quartile, and the cooler or more powerful the character is considered.

In a nutshell, quartiles help us neatly group Disney characters based on their awesomeness, and propensity scores help us figure out just how awesome each character is in the grand Disney spectrum!

Disney | Magical Metrics | Propensity Scoring | Model Results