Data Collection and Analysis

I gathered a comprehensive list of award nominees and winners from 1973 onward using the Internet Movie Database (IMDb), concentrating on the following categories: Best Performance by an Actor in a Leading Role, Best Performance by an Actress in a Leading Role, Best Performance by an Actor in a Supporting Role, and Best Performance by an Actress in a Supporting Role. This resulted in a dataset that included 1,200 nominations, 200 Oscar awards, and 548 artists.

Subsequently, I collected the individual filmographies of these artists utilizing the IMDbPY package in Python, yielding approximately 40,000 titles. After filtering out generic titles and compilations such as The Oscars and The EE British Academy Film Awards, I was left with around 35,000 titles.

Oscar Distribution Insights

After compiling my dataset of over 500 artists and 35,000 titles, I began to explore the Oscar landscape to identify notable figures and intriguing facts. For instance, when organizing individuals by their nominations and wins, Meryl Streep stands out with an impressive 21 nominations and three wins. Jack Nicholson also reached similar heights, with three awards from ten nominations since 1973. Similarly, Daniel Day-Lewis and Frances McDormand each won three Oscars, having received six nominations each. Notably, McDormand contributed to the film Nomadland (2020), which earned her a Best Motion Picture Award, although this category is not included in the current analysis.

The dataset reveals a handful of double winners who secured Oscars on both of their nominations, including Christoph Waltz, Hilary Swank, Kevin Spacey, and Mahershala Ali. However, securing multiple awards is quite rare; in fact, the 200 Oscars have been awarded to 174 artists, with only 22 having multiple wins. This indicates that 68% of nominees have never received an Oscar. Notably, Glenn Close has been nominated eight times, and Amy Adams six, yet both left empty-handed each time. In contrast, Al Pacino, with nine nominations, finally won for his role in The Godfather and The Irishman, while Leonardo DiCaprio earned six nominations before winning his Oscar in 2015.

Network Analysis of Oscar Winners

While examining individual awards offers valuable insights, it's also crucial to consider how collaboration and networking influence award outcomes. The Oscars are determined by approximately 7,000 members of the Academy of Motion Picture Arts and Sciences, and prior studies have identified several network effects on cinematic success. To explore this, I constructed and analyzed the social networks of winners and nominees.

In this network, each node represents an Oscar nominee or winner (the 548 artists), connected if they share a film cast (i.e., they have overlapping filmographies). The frequency of shared projects strengthens their connection. After processing all films and filmographies, I created a densely interconnected network of 546 nodes and 17,140 links, indicating the need for further data refinement. Despite filtering out several titles, including the 76th Annual Academy Awards, certain entries remained in the dataset that were generic collections rather than actual films.

To enhance the network's accuracy, I opted to clean the data at the network level, filtering out statistically insignificant edges while preserving as many nodes as possible to minimize information loss. Using a previously established method for cleaning noisy networks, I ended up with a graph comprising 526 connected nodes and 1,299 links.

This video delves into the dynamics of actors aged 50 and above, exploring their contributions and collaborations over the years.

Network Characteristics and Trends

The most notable aspect of this network is the absence of clearly defined communities. Instead, there are two primary clusters within the network, with denser areas on the left (featuring actors like Christoph Waltz) and right (including Jodie Foster), while the middle section is relatively sparse with fewer names. Additionally, the most prominent nodes in the network are distributed fairly evenly, suggesting that awards are allocated across various groups rather than concentrated within a specific clique.

This observation is supported by low values of the clustering coefficient and the rich-club coefficient, indicating a lack of a centralized core where top nodes connect. When I colored the nodes by the debut years of the artists relative to their Oscar nominations, a clear trend emerged. The color transition from bright (past) to dark (present) illustrates the evolution of filmmaking, emphasizing that while a few all-time stars exist, new trends and names consistently emerge, expanding the pool of actors.

This video reviews the highs and lows of the 2024 Oscars, highlighting key moments and award achievements.

Individual Networking and Oscar Performance

Having analyzed the overall network, it is valuable to zoom in on individual artists to identify the top networkers and whether their networking intensity correlates with their award success. To quantify this, I calculated each artist's node Degree (the number of connections) and Weighted Degree (the total edge weights for a node). The top ten artists based on these metrics are listed, with Robert De Niro, Diane Keaton, and Burgess Meredith leading the pack. Their network metrics exhibit a wide range of wins and nominations, from zero to two wins and one to eight nominations.

Do these network metrics indicate a connection to Oscar success? A correlation analysis suggests otherwise, as the correlation values are relatively low. Moreover, the average Weighted Degree remains consistent among those with zero to two Oscars, dropping only for the small group with three wins. Further analysis using more complex network centrality measures revealed similar findings.

Conclusion and Future Considerations

In this analysis, I initially examined the top Oscar winners and then considered whether their success was influenced by strong networking ties within Oscar-centric communities. Surprisingly, the findings indicated a low correlation between networking patterns and Oscar success.

However, this analysis has notable limitations. A more comprehensive view would emerge if I compared winners and nominees with actors who have not received any nominations, establishing a clearer baseline. Additionally, defining the collaboration network based solely on shared films does not guarantee that two actors are friends or collaborators. Future studies could enhance this dimension by incorporating social media interactions or news articles.