## NFL Fans at the “Twitter Water Cooler”

Note: This was originally published on September 4, 2013

The start of the NFL regular season is upon us.  In cities across America, NFL fans will engage in the practice of “Monday Morning Quarterbacking,” giving an analysis of their team’s performance on the previous day.  Some fans will of course be delighted after a team victory, while others will be dejected after a crushing defeat.  We decided it would be interesting to analyze how the thirty-two NFL fan bases felt the day(s) after their teams played in the regular season.  While we don’t have the ability to observe the millions of “water cooler” conversations that occur every week, we do have access to millions of Twitter conversations about NFL teams.

We used Twitter data to describe fan base reactions to team wins and losses during the sixteen-game 2012 NFL Regular season.  Our process for data collection can be illustrated with an example using the Buffalo Bills.  Imagine that the Bills played a game on a Sunday.  We recorded whether the Bills won or lost the game.  We then collected all tweets in the Buffalo area that mentioned the words “Buffalo Bills”, “Bills”, or other very frequent terms used to describe the team.  We collected the tweets from Monday (one day after the game), Tuesday (two days after the game), and Wednesday (three days after the game).  We then analyzed each tweet and characterized its content as positive or negative.  Next, we calculated the overall sentiment (roughly the indexed ratio of positive to negative tweets) of the Buffalo Bills related tweets for each of the three days.  We repeated this process for all thirty-two teams, and for all regular season games*.

The chart above displays the average sentiment of fans both after wins and losses.  The chart is based on data from the regular season for all thirty-two teams.  It is interesting to note that by three days after a win or loss, fans on average seem to either come down from their win “high” or recover significantly from their loss “low”.  While the chart above looks at all NFL fan bases in aggregate, we thought it would be interesting to classify each NFL fan base on the following dimensions**:

1)   Happiness After a Win (Highest ratio of positive to negative tweets after win)

2)   Sadness After a Loss (Lowest ratio of positive to negative tweets after loss)

3)   Stability (Least difference in positive to negative tweet ratio between after wins and after losses)

1) Happiness After a Win

The New Orleans Saints’ fans seemed to have just over a 9:1 positive to negative tweet ratio in the two days after the team won a game during 2012 regular season.  We believe that rankings on any of these dimensions are most likely driven by fan expectations (which is in part a function of past and current performance) and by the “expressiveness” of fans.  Since we are presenting descriptive statistics, and not explicitly modeling these drivers, it is tough to make a definitive statement as to why we see this particular order of teams.  Although, is anyone really surprised to see Cleveland or Oakland in the top 5?

2) Sadness After a Loss

The Pittsburgh Steelers’ fans seemed to take losing really badly in the 2012 season.  This could be because of fan expectations.  The Steelers finished 12-4 in 2011, but failed to make the post-season in 2012.  Early losses to the Raiders and Titans produced especially negative Twitter reaction, as did late season losses to the Browns and Bengals.

3) Stability

We measured “stability” by looking at the difference between average sentiment after wins and average sentiment after losses.  Dallas Cowboys’ fans seemed to never get too negative after losses, nor were they tremendously positive after wins.  Colts’ fans were even more understanding after a loss, but more positive on average than Cowboys’ fans after a win.  This could be due to the Colts being a young team that did not have high expectations going into the 2012 season.    The Atlanta Falcons only lost three times during the regular season, and the last loss was meaningless, as the Falcons had already secured home-field advantage throughout the playoffs.  Thus, there was very little negative reaction to the last loss.  The Philadelphia Eagles’ fan base is an interesting story.  It may be surprising to many to see them in the list of “stable” fans.   A better moniker for these fans in 2012 might be “resigned”.  It seems that as Philly began to lose more games, fans started to look forward to the next season, and a new head coach.  A majority of the fan tweets after a game were about changes for the next season, and not about the most recent loss.

The Oakland Raiders’ fan base best resembled “Dr. Jekyll and Mr. Hyde” during the 2012 season.  Fans were extremely happy when the team won, and terribly negative after a loss.  The Raiders were the only team in the top 5 of the “Happy” and “Sad” fan rankings.

Mike Lewis & Manish Tripathi, Emory University 2013.

*There are, of course, several caveats regarding this study.  First, while we only used tweets from the team’s geographic market, there could always be fans of other teams who may have tweeted about the local team.  Similarly, there are fans of the team that do not live in the local market, whose tweets would have been excluded.  Second, though we used terms that were associated/descriptive of the team, there are tweets related to the team that we undoubtedly excluded because they did not mention the terms we were looking for.  Third, the volume of tweets is not the same for each team.  We are confident, however, that a minimum threshold was met for each day, such that the sentiment score was not heavily influenced by a small number of tweets.  Fourth, this study is only over one year; it would be beneficial to perform a multi-year study.  Finally, there was one game in the 2012 season between the San Francisco 49ers and St Louis Rams that ended in a tie.  We have excluded that game from this analysis.  The Twitter data was collected using Topsy Pro Analytics.

**We computed each of these metrics using one day after, an average of one and two days after, and an average of the first three days after.  Since the rankings were fairly robust across these specifications, we only report the average of one and two days after the game.