Predicting the number of viewers of an NFL game: a Fantastic new approach!

TLDR: What can you get with $700,000? How about a 30 second commercial during Sunday Night Football. With such a large price tag, it is imperative that businesses accurately estimate how many eyeballs are expected to see their pricey Sunday night advertisement.

I find the best method of forecasting NFL viewers is not by using a measure of the featured teams' quality. Instead, I find that the share of Fantasy Football ownership can help to explain around 73% of the variation in NFL viewership: 12.5% better than team quality!

And now that I have begun to provide readers with the NFL nationally broadcast game viewership data, you too can experiment with these types of analyses!

$700,000 can buy you a lot of things: a house, a used 100-foot yacht, or a 30 second commercial during Sunday Night Football. And while it is nowhere near the $5 million price tag for a Super Bowl commercial, I imagine most businesses find it important to accurately estimate how many eyeballs are expected to see their pricey Sunday night advertisement.

So how would a business go about predicting the number of viewers for a National League Football (NFL) broadcast? They would likely need to begin with some background information.

First, the NFL regular-season consists of 17 weeks. There are three three nationally broadcast games in a typical NFL week: the aptly name Thursday, Sunday, and Monday Night Football based on the weekday the game is played, abbreviated to TNF, SNF, and MNF, respectively.

Second, Nielsen, a media company, provides data on the number of people who tune in to each of the three nationally broadcast games each week. Many readers have likely heard the term ratings, which is the number of televisions tuned to a specific broadcast divided by the total number of televisions. Instead, a business may want to focus on the audience, or the total number of people exposed to their advertisement.

Nielsen estimates for the size of the audience and/or ratings are then used by television stations to set the price of advertising. Since SNF usually has the most viewers, it is the most costly of the three nationally broadcast games, but TNF and MNF are not much cheaper and will cost around 385,000 to 500,000 for 30 seconds.

With all of this information in mind, the next step is to predict the number of viewers using a multivariate regression model. I use the Nielsen estimated audience size as the independent variable and consider many different explanatory variables in attempt to explain the variation in the audience size. The most logical suggestion for an explanatory variable would be a measure of the quality of each team featured in the match, such as FiveThirtyEight's ELO. While this is a good start, is team quality the best explanatory variable of viewership?

Instead, I suggest looking at data from an industry that has seen a growth of +370% from 2005 to 2017: Fantasy Football! For those who are not familiar, Fantasy Football is a game played among viewers wherein each individual becomes a manager of a hypothetical football team in a league made up of their friends, colleagues, co-workers, etc. At the beginning of each actual NFL season, Fantasy Football managers create their own teams wherein each actual NFL player can be owned by no more than one Fantasy Football manager within each Fantasy Football league. The performance of the actual NFL players contribute to the points to the Fantasy Football team that own said player. And while the advent of technology has made tracking the points of each Fantasy Football team much easier, watching the players owned by one's Fantasy Football team adds to the excitement of the NFL viewing experience.

And surprisingly, Fantasy Football also adds significant value to a model for predicting the number of viewers of a NFL game!

I have considered several multivariate models in order to predict NFL viewers, each varying by only a single regressor. First, I use the aforementioned ELO, or the quality of each team featured in the broadcast. I then turn to the Fantasy Football ownership of the NFL players that are featured in the game-broadcast; Quarterback (QB); Running Back (RB); Tight End (TE); Wide Receiver (WR), and; overall. Ownership is defined as the percent of Fantasy leagues where the player is owned and is provided by ESPN. I then sum the two values for ownership of Team A's player and Team B's player when Team A is playing Team B on a nationally broadcast NFL game.

Below is a graphical depiction of the results:

At 64%, ELO (team quality) does quite a great job at explaining the variation of the viewership (i.e., a relatively high R-squared). However, it does not outperform other measures...

Instead, turning to the Fantasy Football ownership, the ownership of Wide Receivers helps explain 73% of the variation in the viewership. Trailing just slightly, overall ownership of all the positions between the two teams explains 72%. Quarterback ownership does about as good of a job as ELO and Tight End ownership does not quite outperform ELO. Running Back ownership does little in terms of explaining the audience at all: I suspect this is because Fantasy Football managers are required to have at least two Running Backs on their team and the ownership of first-string Running Backs in the NFL is above 90% for 30 of the NFL's 32 teams.

While this is an interesting find, it also has quite practical purposes: from fans to advertisers to league organisers, it is in everyone's best interest to offer the most exciting and interesting games from a viewers perspective. And now that I have begun to provide readers with the NFL nationally broadcast game viewership data, you too can experiment with these types of analyses!