NHL Fan Analysis Part 5: Defining Fan-Team Relationships with Social Media

Note: This is Part V of our study of NHL Fan Quality.  This week we will be ranking NHL teams/fans on the following dimensions: Fan Equity, Social Media Equity, Fan Equity Growth, Price Elasticity, Win Elasticity, and Social Media based Personality.  For more details on our measures of quality, please click here.  For Part I, click here.  For Part II click here.  For Part III click here.  For Part IV click here.

Social media is increasingly being used as a market research tool, and we believe that it provides opportunities to develop some richer descriptions of NHL fan bases.  The foundation for today’s analysis is something known as social media sentiment.  The idea behind sentiment is that we look at the “tone” of tweets surrounding each team.  In this study, we are examining the distribution of positive versus negative tweets for each team over the past three years.

Our actual approach uses a variety of statistics used to characterize distributions (e.g. mean, variance, skewness, kurtosis, etc.…), and then we employ a technique known as cluster analysis.  We will avoid the details (feel free to contact us) but the general idea is to find teams that have similar distributions of social media sentiment.  We use cluster analysis on team social media sentiment on Twitter over the past three seasons to dynamically segment fan bases (we allow fan bases to move across clusters over time).  Perhaps, it is more accurate to describe what we are doing as segmenting the types of relationships fans have with their teams.  Do fans have unconditional love for their team?  Do they have violent mood swings?*

Based on our dynamic cluster analysis of Twitter sentiment, we are able to describe each NHL fan base.  The chart below summarizes the social media “personality” of most NHL fan bases over the past three seasons.

Twitter Based NHL Personalities

*One caveat to this study is that since this is all based on Twitter data, the results reflect the opinions of fans on SOCIAL MEDIA only.  Also, please note that unlike our previous study of NHL social media equity that was based on the size of each team’s following, this analysis is based on sentiment or tone.

Mike Lewis & Manish Tripathi, Emory University 2014.

NHL Fan Analysis Part 4: Social Media Equity

Note: This is Part IV of our study of NHL Fan Quality.  This week we will be ranking NHL teams/fans on the following dimensions: Fan Equity, Social Media Equity, Fan Equity Growth, Price Elasticity, Win Elasticity, and Social Media based Personality.  For more details on our measures of quality, please click here.  For Part I, click here.  For Part II click here.  For Part III click here.

NHL 2014 Social EquityToday we continue our analyses of NHL fan bases with something thoroughly modern: Social Media Equity.  In this analysis, we look at how teams’ combined social media following on Facebook and Twitter compares to teams that have similar records and populations.  Social Media Equity has some significant pluses in that it is not constrained by stadium capacity, and allows for including non-local fan support.  Social Media Equity may also be a forward-looking metric since social media is more prevalent among younger consumers.

The social media rankings are dominated by the traditional NHL powers.  Detroit is first followed by Boston, New York (Rangers), Pittsburgh and Chicago.  A significant difference between the revenue premium based brand equity ranking and the social media based rankings is the relative position of US and Canadian teams.  The US teams dominate the social media rankings while the Canadian teams dominate the Fan Equity rankings.  At the bottom of the rankings we have Anaheim, Columbus, Tampa Bay, Phoenix and St. Louis.  These tend to be the teams that struggle on many of our fan metrics.

Mike Lewis & Manish Tripathi, Emory University 2014.

Impact of NBA Draft Day on Social Media Following

Social Media is of course a popular medium for athletes to build their brand.  Two popular platforms are Twitter and Instagram.   I tracked the Twitter and Instagram followers for the top 100 draft prospects in the weeks leading up to the draft, and the morning after the draft.   The chart below presents the growth in followers for the lottery picks.

Akash Lottery

It is interesting to see how the following of second-round picks of the teams that had lottery picks as well was affected by the draft.  The chart below documents the social media presence of some of these players.

Akash Non LotteryNote: Gary Harris should have 35,265 Twitter followers on June 13

Guest Entry By Akash Mishra, 2014.

MLB Fan Analysis Part 3: Defining Fan-Team Relationships with Social Media

Social media is increasingly being used as a market research tool, and we believe that it provides opportunities to develop some richer descriptions of MLB fan bases.  The foundation for today’s analysis is something known as social media sentiment.  The idea behind sentiment is that we look at the “tone” of tweets surrounding each team.  In this inaugural version, we are examining the distribution of positive versus negative tweets for each team over the past couple of years.

Our actual approach uses a variety of statistics used to characterize distributions (e.g. mean, variance, skewness, kurtosis, etc.…), and then we employ a couple of techniques known as factor analysis and cluster analysis.  We will avoid the details (feel free to contact us) but the general idea is to find teams that have similar distributions of social media sentiment.  We use factor and cluster analysis on team social media sentiment on Twitter over the past two seasons to segment fan bases into four types.  Perhaps, it is more accurate to describe what we are doing as segmenting the types of relationships fans have with their teams.  Do fans have unconditional love for their team?  Do they have violent mood swings?

One caveat to this study is that since this is all based on Twitter data, the results reflect the opinions of fans on SOCIAL MEDIA only.  Also, please note that unlike our previous study of social media equity that was based on the size of each team’s following, this analysis is based on sentiment or tone.

Segment 1: Loving Stable Relationships

Our analysis suggests that the Atlanta and St. Louis teams have enviable fan bases.  Braves and Cardinals fans are both very happy and stable.  Whatever these teams are doing, the end result is fans that adore their teams, and tend not to vary in their feelings.  These are fans that love their teams, and mostly overlook their club’s faults.

Segment 2: Generally Happy but Volatile

The second cluster is the largest segment.  This group of fan bases is generally positive but volatile.  Meaning that on average, these fans are happy but they have mood swings.  This group is the largest, and includes the fans of the Cubs, Orioles, Reds, Indians, Tigers, Marlins, Astros, Royals, Phillies, Pirates, Mariners, Giants, Rangers, and Blue Jays.  These seem to be the “normal” fan bases.

Segment 3: Miserable Marriages

This is where the analysis becomes fun.  The third segment is made up of fan bases that are generally unhappy but stable.  These are fans that don’t get a lot of joy from their teams.  In addition, these feelings don’t seem to change much.  This group includes a diverse set of teams.  These are the fans of the Diamondbacks, Angels, White Sox, Rockies, Brewers, Padres, Nationals, and BOTH New York teams.

Segment 4: Depression with a Bit of Mania

This is Professor Lewis’ personal favorite segment.  Fan bases that are generally VERY unhappy but have a few instances of extreme joy.  We think we can also say that these are the teams with the most challenging fan bases to manage.  Again, we have a diverse group.  We have the small market fans of the Twins (what happened to Minnesota nice?), the A’s (Moneyball doesn’t create happiness?), and the Rays (probably the Florida heat).  In terms of the large markets, we have Boston (probably the most unsurprising result) and the LA Dodgers.

Social Media Based Cluster of MLB

Mike Lewis & Manish Tripathi, Emory University 2014.

MLB Fan Analysis Part 1: Fan & Social Media Equity

Who are the best fan bases in Major League Baseball?  A quick Google search of “best MLB fan bases” produces more than a million results.  Specific rankings are published by entities ranging from news organizations to ticket brokers.  In general, these rankings are based more on subjective opinion than data and analysis.  In contrast, we take a 100% data-driven approach.

That said, we readily acknowledge that fan base analysis is a complex topic.  Our core metric is something we term “fan equity.” This metric is based created using a revenue-premium model of brand equity.  This model is driven by the financial support shown by fans conditional on team performance and market characteristics.  This approach has significant advantages in that it is based on spending behavior and not driven by short variations in winning.  But, the revenue-premium approach is not perfect.  Therefore, this year we will be publishing a number of rankings (and providing descriptions of the strengths and weaknesses of each approach).  Click here for an overview of each method.

Today, we present three analyses of MLB fan bases.  We begin with the fan equity / revenue-premium model (based on the last three years), a trend analysis of fan equity growth over the past 15 seasons, and an analysis of each team’s social media equity.

2014 MLB Fan Equity

The winners in the fan equity analysis include the Red Sox, Yankees, Cubs, Phillies, Cardinals and Twins.  The Red Sox and Yankees placing at the top of the list is simultaneously unsurprising and interesting.  It is unsurprising because these are two of the league’s most prominent teams, and interesting because the two teams are bitter rivals.  The intense competition between these two teams provides an added factor that may be lacking for teams like the Cubs or the Phillies.  And yes, we do know that Cardinals fans love to beat the Cubs. (Click here for more details on our methodology for fan and social equity)

At the bottom of the list, we have teams in cities with great weather (or maybe summers that are too hot) and teams that are generally regarded as number two in their markets.  The bottom five are the White Sox, Angels, A’s, Mets and Rays.  As an aside, how about the “Portland A’s”?

We know the winners and the losers, but fan bases are not static entities.  As teams win, lose or market themselves, their fan equity evolves.  As a second analysis, we examined fan equity trends over the past 15 years.  This analysis revealed that MLB’s high equity teams are tending to even greater levels of fan support.  In this analysis, the Yankees finished first followed by the Red Sox, Cubs, Nats, Phillies, Dodgers and Giants.  This list of teams is overwhelmingly concentrated in the largest markets.  At the bottom of the list, we have teams like the Diamondbacks, Indians, Orioles, Padres and Rays.

2014 MLB Trend

The last analysis for today is something we term social media equity.  This analysis looks at each team’s social media following (again controlling for market size and winning).  Social media equity is important because it is unconstrained by stadium size, unaffected by a team’s pricing decisions and provides a measure of national following. It may also be a forward looking indicator if social media participants are younger than those fans who attend games.

2014 MLB Social Equity

The social media ranking is fairly different.  While the Yankees are number one, the top five also includes the Padres, Brewers, Rangers and Pirates.  Perhaps, the revenue-premium measure is picking up the economics of the big markets while the social media metric is best for identifying current interest.  However, the bottom of the social media list is consistent with the bottom of the fan equity list with teams like the Mets, A’s and Angels.

In our next post, we will present analyses of fan base sensitivity to winning and pricing.

Mike Lewis & Manish Tripathi, Emory University 2014.

Fan Rankings 2014

Evaluating sports brands, or any brands, is a complicated endeavor.  The fundamental issue is that a brand is an intangible asset so the analyst must rely on indirect measures of the brand.  Last year, we introduced a measure of fan loyalty that we termed “fan equity.”  This measure was based on the degree to which fans were willing to support a franchise after controlling for factors such as population and winning percentage.  We also explored a social media based metric that used a similar approach to evaluate a team’s success in building a social media footprint.

This summer, we are updating our analyses across the four major sports leagues (NFL, NBA, MLB, & NHL) and the two major college sports (football & basketball).  We are also including several additional analyses that further illuminate fan support and brand equity.  Shifting to multiple measures of “fan support” provides significant benefits.  First, using multiple measures allows for a form of triangulation, since we expect that a great fan base will excel on most or all of the measures.  The second benefit is that since each measure has some unique elements, the construction of multiple measures allows for a richer description of each fan base.  Next, we provide basic descriptions and critiques of each of the metrics to be published.

Fan Equity

Our baseline concept of fan quality is something we term fan equity.  This is similar in spirit to “brand equity” but is adapted to focus specifically on the intensity of customer preference (rather than to consider market coverage or awareness).  We calculate fan equity using a revenue-premium model.  The basic approach is to develop a statistical model of team revenues based on team performance and market characteristics.  We then compare the forecasted revenues from this model for each team to actual revenues.  When teams actual revenues exceed predicted revenues, we take this as evidence of superior fan support.

The fan equity measure has some significant benefits.  First, since it is calculated using revenues, it is based on actual fan spending decisions.  In general, measures based on actual purchasing are preferred to survey based data.  The other prime benefit is that a statistical model is used to control for factors such as market size, and short variations in team performance.  This allows the measure to reflect true preference levels for a team rather than effects due to a team playing in a large market, or because a team is currently a winner. However, the fan equity measure also has a couple of potential issues.  First, one of the distinguishing features of sports is capacity constraints.  Measures of attendance or revenues may therefore underestimate true consumer demand simply because we do not observe demand above stadium capacity.  The second issue relates to owner pricing decisions.  An implicit assumption in the revenue-premium model is that teams are revenue maximizers.

Social Media Equity

Our social media equity metric is similar in spirit to our fan equity measure, but rather than focus on revenues we use social community size as the key dependent measure.  The calculation of social media equity involves a statistical model that predicts social media community size as a function of market characteristics and current season performance.  Social media equity is then based on a comparison of actual versus predicted social media following.

The social media equity metric provides two key advantages relative to the revenue-premium metric.  Since social media following is not constrained by stadium size and does not require fans to make a financial sacrifice, this metric provides 1) a measure of unconstrained demand and 2) avoids assumptions about owner’s pricing decisions.  On the negative side, the social media equity does not differentiate between passive and engaged fans.  Following of a team on Facebook or Twitter requires a minimal, one time effort.

Trend Analysis (Fan Equity Growth)

A key issue in evaluating fan or brand equity is the time horizon used in the analysis.  The methods described above produce an estimate of “equity” for each season.  The dilemma is in determining how many years should be used to construct rankings.  The shorter the time horizon used, the more likely the results are to be biased by random fluctuations or one-time events.  On the other hand, using a long time horizon is problematic because fan equity is likely to evolve over time.  This year, we present an analysis of each team’s fan equity trajectory.

Price Elasticity and Win Elasticity

This year we are adding analyses that look at the sensitivity of attendance to winning and price at the team-level.  This is accomplished by estimating a model of attendance (demand) as a function of various factors such as price, population, and winning rates.  The key thing about this model specification is that we include team level dummy variables and interactions between the team dummies and the focal variables of winning and price.

The win elasticity provides a measure of the importance of quality in driving demand.  For example, if the statistical model finds that a team’s demand is unrelated to winning rate, then the implication is that fans have so much of a preference for the team that winning and losing don’t matter.  For a weaker team (brand) the model would produce a strong relationship between demand and winning.

This benefit of this measure is that the results come directly from data.  A possible issue with this analysis is that the results may be driven by omitted variables.  For example, prior to conducting the analysis we might speculate that demand for the Chicago Cubs might only be slightly related to the team’s winning percentage.  This speculation is based on the fact that the Cubs never seem to win but always seem to have a loyal following.  Our finding would, however, need to be evaluated with care since the “Cub” effect is perfectly correlated with a “Wrigleyville Neighborhood” effect.

Social Media Based Personality

This year we are adding another new analysis that uses social media (Twitter) data to evaluate the personality of different fan bases.  The foundation for this analysis is information on “sentiment.”  Sentiment is basically a measure of the tone of the conversation about a team.  To understand fan personality, we examine how Twitter sentiment varies over time.  We do comparisons of how much sentiment varies across teams.  This tells us if some fan bases are even-keeled while other are more volatile.  We can also look at whether some teams tend have higher highs or lower lows.  These analyses are based on the distribution of sentiment scores over a multiple year period.

Twitter based sentiment has both positives and negatives.  On the positive side, Twitter conversations are useful because they represent the unfiltered opinions of fans.  Fans are free to be as happy or as distraught as they want to be.  The availability of sentiment over time is also useful as it allows for the capture of how opinion changes over time.  On the downside, Twitter sentiment scores are only as good as the algorithm used to evaluate each Tweet.  Twitter data may also be a bit biased towards the opinions of younger fans.

Mike Lewis & Manish Tripathi, Emory University 2014.

Major League Soccer (MLS) Social Media Equity Rankings: Sporting Kansas City & Seattle Sounders FC on Top

MLS Social Media Equity RankingsFan base evaluation has always been a topic of interest for sports researchers.  The world of social media provides an opportunity to look at fan base support/loyalty without worrying about capacity constraints, pricing, or revenue data issues.  To calculate MLS teams’ “social media equity” we collected social media engagement metrics (Twitter mentions of the club, both with and without hashtags).  We then created a statistical model that predicts these measures of social media engagement as a function of factors such as market size, official club tweeting activity, team payroll, and team performance for this past season.  We then compared each team’s actual social media engagement against the model predictions.

To examine the social media equity of MLS teams, we collected tweets for each team from 2009 to 2013 and built a statistical model.  The logic behind this model is that social media engagement from fans is driven by a bunch of factors like team performance, city demographics, etc.  Unlike other factors, social media equity is not directly measurable.  So we can attribute the contribution from social media equity to model residuals after controlling for measureable factors like team performance.

Social Media Engagement MLSWe first used data from 2009 to 2012 to calibrate the model, i.e. to estimate the coefficients for the explanatory variables, and then we used these estimates to calculate expected social media engagement using data for explanatory variables in 2013. The difference between our prediction and the true engagement we observed is the social media equity for each team.

Across the various specifications of the model, average attendance was significant.  This means home game attendance is crucial for engaging fans online.  This was also true for championships won by the club.  Social media equity rankings from the models are quite consistent.  Here, we present the ranking from the model whose dependent variable is the sum of all types of mentions on Twitter regarding the club.  It is not surprising to see Sporting Kansas City & Seattle Sounders FC at the top the chart.  Sporting Kansas City has been very strong in recent years, and won the championship in 2013.  Seattle Sounders FC has the highest average attendance among all MLS teams, which is evidence for the enthusiasm of fans.

Zhe Han, PhD Student

The Best Sports Cities: Boston Wins in a Rout; Twin Cities Better than NY & Chicago

Boston InfographicWe started the Emory Sports Marketing Analytics blog back in March of last year.  Our goal was to bring analytics to the world of sports business.  To put a finishing touch on 2013, we are going to present our rankings of the best and worst sports fans by city.  These rankings are based on our revenue premium model of fan equity and our analyses of social media equity.

Phoenix InfographicFor our rankings, we have divided cities into categories based on how many of the four major sports (NFL, NBA, MLB, & NHL) have franchises representing the city.  This categorization does introduce a bit of oddness since Los Angeles becomes a “three-sport” city.  Another tough issue is how to treat teams like the Packers.  Is Green Bay a one-sport city or is Milwaukee as three-sport city (we decided that we would treat Milwaukee as a three-sport city)?

Today we reveal our rankings of the four-sport cities, and a summary of the best and worst markets in the other categories (one, two, & three-sports cities).  Before the actual rankings, a couple of clarifying comments are in order.  The key to our rankings is that we are looking at fan support after controlling for short term variations in team quality and market characteristics.  Basically we create statistical models of revenues as a function of quality measures like winning percentage and market potential factors like population.  This allows our results to speak how much support fans provide as if market size and winning rates were equal.

The number one team on our four-sport city list is Boston; and it wasn’t even all that close.  All of the Boston teams have impressive fan followings.  The Red Sox ranked 1st in terms of fan equity and 1st in social equity. The Celtics finished 3rd in the NBA in both our fan and social media equity rankings.  The Patriots rank 2nd in fan equity and 3rd in social media equity in the NFL.  The Bruins rank relatively low in fan equity (perhaps because they could price higher), but very high in social media equity.  Number two on the list is Philadelphia.  The Eagles, Phillies and Flyers are all very strong fan bases.  The Sixers are weak within the NBA, but the three other sports carry Philly to a second place finish.

The city in third place is likely going to generate Twitter complaints about how clueless we are, and how academics should stay away from sports.  We rank the Twin Cities of Minneapolis and Saint Paul as having the third most supportive fans among the four-sport cities.  Minneapolis/Saint Paul show great support of the Twins and solid support for the Vikings.  The Wild also do surprisingly well in the NHL.

How could Minnesota finish in front of New York and Chicago?  It’s because these cities don’t do a great job in terms of supporting all their teams.  For example, The Brooklyn Nets perform poorly when market size is considered and the White Sox have very poor support on all metrics.  We can hardly wait for the semi-literate Twitter attacks to commence.

At the bottom of the list we have Phoenix.  We should note that the Suns perform well and finish 7th in terms of fan equity in the NBA.  But beyond that, Phoenix sports are a disaster.  In terms of fan equity, the Diamondbacks finish 26th in MLB, the Cardinals 30th in the NFL and the Coyotes 28th in the NHL.  As we have learned over the past year, it seems that weather and tradition are what creates a strong fan culture.  Perhaps the Phoenix teams overall are too new, and the weather is too warm.

Our other winners and losers are given below with linked infographics that summarize raw data and final rankings.

For the three-sport cities, the overall winner is St. Louis, and the worst fan support occurs in Tampa Bay.

For the two-sport markets, the leader in fan support is NashvilleOakland is at the bottom of the rankings.

For the one-sport cities, Portland leads the way, while Memphis trails the field.

Mike Lewis & Manish Tripathi, Emory University 2014.

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.