With so many awards shows taking place in the months leading up to the greatest of them all, the Academy Awards, The Reckoner spares you the excitement of waiting with a prediction of this year’s winners. For our analysis technique, please see the “Method” section below.
Best Picture: Three Billboards Outside Ebbing, Missouri
Having already won the BAFTA award for Best Screenplay and the Golden Globes award for Best Motion Picture, it is no wonder that Three Billboards Outside Ebbing, Missouri has the highest normal scores of all the films. The collective awards won by its cast and crew for the film were also taken into account to compute the normal scores for best picture nominees. Past years have shown a positive correlation between the talent working on a film, assuming that talent can be measured by the amount of awards won, and the film’s chances at winning the Academy Award for Best Picture.
Best Director: Guillermo del Toro
Guillermo del Toro, one of the driving forces behind Pan’s Labyrinth, brought us the similarly mystical The Shape of Water last year. He has already won the Golden Globe award and the BAFTA award for best director. Combined with his previous nominations and awards for Pan’s Labyrinth, he has the highest normal score for both total nominations and total awards. Although none of the other nominees are new to the industry, their major award histories are lacking in comparison.
Best Actor: Daniel Day-Lewis
Daniel Day-Lewis acted in the leading role for his final time as a fashion designer in The Phantom Thread. With his long career and with his long list of nominations and awards, this will likely be his last Academy Award as he retires. Denzel Washington follows closely behind Day-Lewis with his own streak of nominations, but less wins. Gary Oldman, who acted as Winston Churchill in Darkest Hour, received the Golden Globe, BAFTA, and SAG awards for best leading actor, and follows closely behind them. However, the case for best actor exemplifies one of the main problems with using past awards and nominations to predict future winners. On one hand we have established, consistent talent. On the other, we have inspirational performances that are a product of both the talent of the actor and the power of the story. Oldman’s role as Winston Churchill may have been one of those performances.
Best Actress: Frances McDormand
As the lead role in Three Billboards Outside Ebbing, Missouri, Frances McDormand has already won the Golden Globe, BAFTA, and SAG award for best lead actress. With Meryl Streep on the line, McDormand would have been our second choice for best actress. However, as we did last year, Meryl Streep was removed from the running. Her long career and numerous awards give her an obvious advantage over other nominees. It is another case of one actor’s record of excellence versus another actor’s exceptional performance. It is not as if McDormand does not have her fair share of accolades, having won an Oscar for her role in Fargo, but it is a reminder of how even in statistical analysis, judgement is needed to eliminate outliers.
We analyzed data from the 2007 – 2018 Academy Awards. For each prediction category – Best Picture, Best Director, Best Actor, and Best Actress – we compiled data that may be relevant to success in the Academy Awards. We looked at British Film Academy Awards (BAFTA) Nominations, BAFTA wins, Screen Actors Guild Awards (SAG) nominations, SAG wins, Golden Globe nominations, Golden Globe wins, and Oscar nominations. Award records were gathered from the Internet Movie Database (IMDb). 
In the data analysis of the nominees, one-variable statistics were used to compare candidates of the same year. We took the arithmetic mean and standard deviation of the categories’ number of BAFTA Nominations, BAFTA wins, SAG nominations, SAG wins, Golden Globe nominations, Golden Globe wins, and Oscar nominations to compare the nominees. The categories were selected as BAFTA, SAGs, and Golden Globes as these are the most prestigious awards recognizing achievement in film and television. They are shown to be indicative of a nominee’s success and are used to help predict the winner of this year’s Oscars. A normal score was calculated using the following formula:
z = (x – μ) / σ .
Where x represents a value in the data set, is the mean of the data set, and is the standard deviation. This results in a normalized score in which higher values indicate better standing among the nominees of the year.
The normal scores of the winners of the 2007 – 2017 Oscars were calculated for each category. The normal score for BAFTA nominations, SAG nominations, and Golden Globe nominations were averaged. The normal score for BAFTA wins, SAG wins, and Golden Globe wins were also averaged. In the majority of years, the winners had high average win normal scores. The compiled data is available for viewing online. 
In all four categories that we analyzed, we based our predictions on three awards shows: the BAFTAs, SAGs, and Golden Globes. The three awards ceremonies are typically held a month before the Academy Awards and are a strong indicator of who the outstanding Academy nominees are. BAFTA’s purpose is to recognize excellence in the film, television, and video game industry. They are open to all nationalities, with the exception of a few UK-only awards. The SAGs focus on outstanding performances – as such, directors are not recognized here and this category was excluded in the analysis. These awards have various categories such as Outstanding Performance by a Male Actor in a Leading role, Outstanding Performance by a Male Actor in a Drama Series, and Outstanding Performance by a Male Actor in a Comedy Series. This provides a better view of a nominees overall ability in the performance arts. Similarly, the Golden Globes also offer many categories. Instead of just a Best Actor category, the Globes offer both a Best Actor of a Musical or Comedy award and a Best Actor of a Drama award. By analyzing performance record through various prestigious awards shows in the months preceding the Oscars, we can predict the winner of the Academy.