NFC West: Measuring “Rivalry” Through Twitter

How do you measure a “rivalry”?  Is it how much you hate someone?  Is it how often you have competed head-to-head for an important goal?  Is it how often you spend your time talking about someone?  As in previous studies, we decided to use Twitter to quantify the level of rivalry between teams in the same division in the NFL.  We are starting with the teams in the NFC West: The Seattle Seahawks, the San Francisco 49ers, the Arizona Cardinals, and the St. Louis Rams.

NFC West Talk MatrixOur methodology is straightforward.  We are measuring the intensity of a “rivalry” by the number of tweets mentioning a non-home team in the home team’s market.  For example, we look at the number of tweets mentioning the 49ers, Cardinals, and Rams in the Seattle market.  These tweets represent the relative intensity of rivalry of each team with the Seahawks fan base.  We realize that a limitation of this method is that some of these tweets could be from 49ers, Cardinals, or Rams fans that live in Seattle.   For each market, we index the tweets relative to the team with the most tweets (e.g. if the 49ers have the most tweets in the Seattle area, we divide the number of tweets for each team by the number of tweets that mention the 49ers).  We perform this analysis for a four year period and for just the 2013 regular season, so we can capture established rivalries and the recent trend.

It is interesting to note that in both analyses, the 49ers and Seahawks are each other’s primary rival and the intensities of the secondary and tertiary rivalries are not even close.  Over a four year period, the 49ers are the primary rivals for all of the teams in the NFC West, but in just the 2013 regular season analysis, the Seahawks took over as the primary rivals of the Cardinals, and are barely behind the 49ers in terms of the intensity of the Rams’ rivalries.

Mike Lewis & Manish Tripathi, Emory University 2014

Are They Really Mad Bro? Twittersphere Reaction to Sherman’s Post-Game Interview

ShermanAndrewsRichard Sherman’s post-game interview with Erin Andrews seems to have created a huge response on social media, as well as with sports columnists and talk-radio.  While it’s easy to pick out a few tweets from prominent Twitter accounts that say Mr. Sherman is “classless”, “vile”, or worse (there is a lot or worse in this case), we were interested to determine the overall post-game Twitter sentiment towards Mr. Sherman.

Our analysis is quite straightforward.  We first collected all tweets that were tweeted in the ten-hour period following the end of the NFC Championship game.  From this collection of tweets, we selected any tweet that contained “Seattle”, “Seahawks”, or “Sherman”.  These selected tweets were then coded as having “positive”, “negative”, or “neutral” sentiment.

It is interesting to note that overall there are as many positive tweets mentioning Sherman as there are negative tweets.  However, while “Seattle” and “Seahawks” tweets had a 1:1 (Positive:Negative) ratio outside of the state of Washington, “Sherman” had a 1:9 ratio outside the state of Washington (shockingly, the 49ers home state of California had the highest ratio of negative tweets).  Perhaps Sherman really has been driving a lot of the outside of Seattle Twitter hate towards the Seahawks that we previously documented.

ShermanSeahawksTable

Full disclosure, from a marketing perspective, we are fascinated by Richard Sherman.  He has done a remarkable job building his social media following; he has more Twitter followers than the official Seattle Seahawks Twitter account.  Perhaps Sherman’s engagement with his followers has insulated him from the rest of the Twittersphere, since post-game tweets that mentioned “@RSherman_25” had a 2:1 (Positive:Negative) ratio.  We look forward to seeing what he does next in the build-up to the Super Bowl.

Mike Lewis & Manish Tripathi, Emory University 2014.

U MAD BRO? Twitter Sentiment for NFL Teams In and Out of Their Markets

In and Out MarketAt Emory Sports Marketing Analytics, we often use Twitter as a marketing research tool that helps us understand the mood and loyalty of fan bases.  Recently, we decided to compare the sentiment of tweets about a NFL football team that originate from the team’s home market with the sentiment of tweets coming from outside the home market.  For example, are tweets mentioning the Cowboys more positive if initiated in the Dallas/Fort Worth Metroplex than if tweeted from elsewhere;  if so, how much more positive?  Furthermore, how does this compare to the other thirty-one teams in the NFL?

In order to answer these questions, we used Topsy Pro, a platform that allowed us to collect all tweets mentioning NFL teams from June 1, 2009 to January 1, 2014.  We then sorted the tweets as originating from inside or outside the team’s market.  Next, the content of the tweets was analyzed and the tweets were marked as having positive, negative, or neutral sentiment.  Using this data, we were able to create a “sentiment” index which was simply the ratio of positive to negative tweets.  The chart above graphs the difference between the sentiment index for in-market tweets and out of market tweets for each NFL team.  The Seattle Seahawks have the biggest difference between how positively they are perceived in their home market versus outside their home market.

There are several factors that can drive this difference between in and out of market sentiment, including:

  • Polarizing team brand (e.g. Dallas Cowboys)
  • Polarizing personalities on a team (e.g. Richard Sherman & Jim Irsay)
  • Off the Field Scandals (e.g. Miami Dolphins & Kansas City Chiefs)
  • On the Field Performance (e.g. Seattle Seahawks & Houston Texans)

umadbroA deeper look at tweets mentioning the Seahawks seems to indicate that in the Seattle area, the Seahawks are beloved on Twitter due to the fact that they have been winning over the past few years, and because of outspoken personalities like Richard Sherman.  These same factors seem to be driving much of the hate for the Seahawks on Twitter outside of Seattle.  The Green Bay Packers are an exception to the factors listed above.  In the case of the Packers, their sentiment index is ridiculously high in the state of Wisconsin.  Even though they also have a high sentiment index outside of Wisconsin, it’s just that no team is close to being as beloved in their home market as the Packers.

It is interesting to note that there are teams that have more positive sentiment outside their home market than within the market.  For the Patriots, Raiders, Bears, Giants, Broncos, and Steelers, this phenomenon seems to be partially due to having a large widespread national fan base that is actually less critical of the team than the fans that still live in the home market.

Mike Lewis & Manish Tripathi, Emory University 2014.

Twitter College Football Review: A Tale of Two Heisman Winners

College football is a business and college football fans are a vocal group of customers.  Like many businesses, college football has a new opportunity to track customer opinion: The Twitterverse.  For a bit of a of “Holiday diversion” we are going to take a look at several college football stories from the past season using Twitter as the data.Manziel vs Winston

The first story is a look at the past two Heisman trophy winners.  The chart shows something call Twitter sentiment for Johnny Manziel and Jameis Winston.  Sentiment analysis is basically the ratio of positive to negative Tweets. The higher the score the more positive the Twitterverse is about a subject.

What we have done is track sentiment for a one year period for each player. The blue line shows the weekly sentiment for Johnny Manziel.  This starts off very high following his historic Heisman victory.  But there are some ups or downs in the off season.  The downs can be linked to some of the news stories about Manziel gambling in casinos or showing up at high profile events.

The big dip for Manziel occurred in June.  Following his comments about being eager to leave College Station, his sentiment dropped from 93 to 20.  “Johnny Football” did recover much of the lost sentiment but then dropped to an even lower score of 18 following the mini-controversies surrounding his appearances at UT frat parties, and leaving the Manning passing academy early.  Interestingly, his autograph controversy in August did not generate an equal backlash as his sentiment bottomed out at around 30.

From a marketing perspective, this is a fascinating story.  The biggest damage to his brand occurred when he offended the locals by talking bad about College Station and partying in Austin.  The impact of breaking NCAA rules was not as large an issue.  It’s one thing to break a rule, but it’s much worse for a brand to insult it’s primary customers.

In contrast, there isn’t much to say about Winston during the off season.  While his sentiment also bounces around, this is more of a problem of small numbers (minimal data) rather than anything else.  Manziel’s Twitter traffic absolutely dwarfed Winston’s.  In marketing language we would say that Manziel had much higher brand awareness.  This is important in the context of the Heisman race since publicity matters quite a bit.

winston_manzielDuring the season, we see a steady climb for Winston and an up and down pattern for Manziel.  These patterns obviously have a lot to do with how the teams played.  For example, A&M’s late season losses dropped Manziel’s sentiment to the mid twenties.   Departing from college football for a moment, think about what this means for companies interested in tracking customer satisfaction!  The Twitter data almost tells us exactly what happened in games each week.   We say almost because one of Manziel’s biggest gaining weeks followed a close loss to Alabama.  In other words, Twitter gives us instant feedback about team quality.

Just like Manziel, Winston also had a huge late season drop in popularity.  He bottomed out with a sentiment score of 13 in the middle of November.  In Winston’s case, the drop can be attributed to legal issues.  The interesting thing about Winston’s drop is how quickly the public forgave.  The combination of “no charges” and the Heisman victory drove Winston’s sentiment to 97 in December.  Winston also finally passed Manziel in terms of total Twitter activity in December as well.

In terms of the final word – a look at these two brands / quarterbacks over time says something profound (and obvious) about sports brands: If you win then the public will be very forgiving.

Mike Lewis & Manish Tripathi, Emory University 2013.

What if the Heisman Trophy Really Was A Popularity Contest?

Ballots for the Heisman Trophy were due yesterday.  Ostensibly, the Heisman Trophy “annually recognizes the outstanding college football player whose performance best exhibits the pursuit of excellence with integrity”.  However, many have argued throughout the years that the Heisman is essentially a large popularity contest.  This view is supported by the millions of dollars annually spent by universities on publicity campaigns for their Heisman candidates.  There are 928 voters for the Heisman Trophy.  This includes members of the media, former winners, and 1 “fan vote” that represents the public at large.  We were curious to see what would happen if the general public was completely responsible for determining the winner of the Heisman Trophy.  As with past studies, we decided to use Twitter as a proxy for the views of the public.  Below, we present our methodology and results.

The first thing to consider is how does one define “Popularity” on Twitter.  Often, studies will use the volume/number of mentions on Twitter as a proxy for popularity.  However, this measure does not account for sentiment (positive, negative, or neutral), which could be important in the decision to vote for someone.  So, we constructed a daily “popularity” measure that is the product of the volume of tweets mentioning a candidate and the average sentiment of those tweets (Note: we tried several specifications of the “popularity” measure, but the rankings were robust).

Once we had a method for determining popularity, we decided to look at the six Heisman Trophy finalists: Johnny Manziel, Jameis Winston, A.J. McCarron, Tre Mason, Jordan Lynch, and Andre Williams.  The pie chart on the left looks at the sum of the popularity measure for each candidate over the entire season (mid-August to Dec 9th).  Johnny Manziel is by far the leader of the pack.  This could potentially be attributed to the stellar start of his season, as well as his huge following.  Heisman-favorite Jameis Winston is in second place, and A.J. McCarron is third.  It’s incredible that Manziel leads Winston by more than a 2:1 margin.  We realize that Heisman voters mark their ballots for 1st, 2nd, and 3rd place, and we are simply looking at most “popular”.

We performed a similar analysis, looking at only the last month and looking at only the last week.   It’s remarkable to see the variation in “popularity” over time.  Tre Mason had a relative 5% popularity if you look at the full season, but 11% over the last month, and 24% over the last week.  In the analysis of popularity over the last week, James Winston barely edges out Manziel for 1st place.  To better understand the factors behind these movements in popularity, we would have to perform content analysis on the tweets to determine what topics were being discussed with respect to these athletes; that is left for a future study.

It is interesting to note that in the their final straw poll, Heisman Pundit has the following ranking: 1) Winston, 2) Lynch, 3) Manziel, 4) Mason, and 5) McCarron.  The “popularity” measure for over the last week gives the ranking: 1) Winston, 2) Manziel, 3) Mason, 4) McCarron, and 5) Lynch.  Jordan Lynch is the only player of these top 5 that plays for a “Non-AQ” school (Northern Illinois).  Perhaps Lynch in second place is evidence that voters look at performance on the field, and not just popularity, however if Heisman Pundit’s straw poll is correct, it seems a lot can be explained by recent popularity.

Mike Lewis & Manish Tripathi, Emory University 2013.

 

 

Twitter Analysis: Who Really Talks About Their Rivals?

It’s rivalry week, and while there is much debate about the best rivalry in college football, it is generally agreed that the Iron Bowl (Auburn versus Alabama) and Ohio State versus Michigan are two of the top rivalry games in college football.  While both sides in these rivalries seem to hate each other, we were curious to determine if the level of vitriol was even or more one-sided in these two storied matchups.  What we found was interesting:  1) discussion around Michigan football seems to encompass A LOT more of the general conversation in Columbus than discussion of Buckeye football in Ann Arbor and 2) after accounting for where the game is being played, the relative level of discussion about the rival school is fairly even in Auburn and Tuscaloosa.

Similar to previous studies, we used geo-coded data from Twitter to serve as a proxy for fan conversation.  We collected all Twitter conversation in Ann Arbor, Columbus, Auburn, and Tuscaloosa for the Monday before the rivalry game in 2010, 2011, 2012, and 2013.  We then calculated the percentage of tweets in that city that were about the opposing school’s football team (“Rival Team Share of Twitter Voice”).   Thus, we had a metric for how much of the conversation in a city was about the rival team.  It is interesting to note that we also determined the average sentiment for tweets in a city that were about the rival football team.  The average sentiment was very negative, but similar across years and cities (translation:  the toxicity of the comments about rivals is the same whether you are in Columbus, Ann Arbor, Auburn, or Tuscaloosa).

We would expect that a rivalry where both local fan bases hated (or were obsessed with) each other at a similar level would have relatively similar “Rival Team Share of Twitter Voice”.  However, we found that in the past four years, regardless of where the game is played, or who won the previous year, the percentage of conversation in Columbus regarding Michigan Football is at least twice the percentage of conversation in Ann Arbor regarding Ohio State football.  Thus, there seems to be a bit of an asymmetric rivalry here with respect to how much one of local fan bases spends its time talking about their rival.  It should be noted that 7% of the population of Columbus are Ohio State students (57,466 out of  809,798) while 37% of the population of Ann Arbor are Michigan students (43,426 out of 116,121).

The Auburn-Alabama rivalry seems to be more even with respect to the level of conversation regarding one’s rivals.  We found that the site of the game seems to change the direction of the ratio of the “Rival Team Share of Twitter Voice”.  If the game is in Tuscaloosa, then local Alabama fans spend more of their time talking about Auburn football than local Auburn fans spend discussing Alabama football.  If the game is in Auburn, then that trend is reversed.  Perhaps the Iron Bowl being played in their hometown adds some more desire to trash talk for the local fans.  It should be noted that 45% of the population of Auburn are Auburn students (25,469 out of 56,908) while 37% of the population of Tuscaloosa are Alabama students (34,852 out of 93,357).

Mike Lewis & Manish Tripathi, Emory University 2013.

Twitter Analysis: The Seat is Warm for Philbin & Shanahan

During the NFL season, columnists & “insiders” provide their speculation on coaches that are on the proverbial “hot seat”.  It seems like coaches can be on the “hot seat” before the season even starts, and they can jump on and off the seat on a weekly basis.  We assume that the columnists & “insiders” are basing their speculation on institutional knowledge.  While Emory Sports Marketing Analytics does not have access to NFL team management, we do have the ability to gauge fan/customer opinion through Twitter.  We would like to present the NFL Coaching Hot Seat from the fan perspective.

The methodology for creating our “hot seat” is straightforward.  Using Topsy Pro, we collected all tweets from the last thirty days that mention a head coach.  Each tweet was then characterized as having positive, negative, or neutral sentiment based on its content.  We computed the ratio of negative to positive tweets for each coach (e.g. If the ratio is 2, the coach has twice as many negative tweets as positive).  We believe that this ratio can serve as a proxy for public sentiment towards a coach.

A quick scan of today’s sports news shows that most columnists & “insiders” believe that Greg Schiano (Tampa Bay) and Leslie Frazier (Minnesota) are receiving the most heat from their coaching seats.  Our analysis of public sentiment over the last thirty days shows that Joe Philbin (Miami) has the highest negative to positive tweet ratio (2.47).  At a distant second is Mike Shanahan (Washington), and Mike Tomlin (Pittsburgh) is in third.  Thus, it seems that if the public were making coaching decisions,  Philbin and Shanahan would be on the hottest seats!  We realize that much of the negative tweeting about Coach Philbin is probably connected to the Martin-Incognito incident, but ultimately this does reflect on Philbin’s job status.

It is interesting to note that in terms of sheer quantity of  tweets (number of mentions), Rex Ryan is the leader in the NFL over the last thirty days.  Schiano  and Philbin are second and third, respectively.

Mike Lewis & Manish Tripathi, Emory 2013

NHL Pricing: A Social Media Based Approach to Assessing Ticket Pricing “Fairness”

Of late we have been looking at value provided by sports franchises in different leagues.  For most of these analyses, we have basically focused on how much fans are asked to pay for each win.  We also make adjustments for factors related to market size, median income and capacity.  Today’s analysis looks at pricing in the NHL.

Of all the pricing analyses we have done, the NHL is the strangest.  The most surprising result is a lack of a positive correlation between winning rates and ticket prices.  Our standard procedure is to develop a model that predicts ticket prices as a function of winning percentage, payroll, market size, median income and other factors that we would expect to be related to demand for tickets.  We do a lot of testing in these models in terms of evaluating different specifications (interactions, nonlinear effects, etc…). In none of these specifications did we find a significant positive relationship between winning rates and prices.  The most powerful predictor was median income.

The other thing that we have been experimenting with in these models is using social media data as an explanatory variable.  The logic is that social media metrics (follows and likes) provide an unconstrained measure of fan support.  This provides a means to assess the relative aggressiveness of how team’s price.

Something to consider in these pricing analysis is the question of how prices are set.  At one extreme, we might suppose that prices are set in order to maximize revenues.  This is a reasonable starting assumption but the implication is that teams are extracting every dollar possible.  On the other hand, teams may price below fan’s reservation prices if the team is trying to build brand loyalty.  The key point is that while consumers might be willing to pay very high prices, if they don’t view the prices as “fair” then loyalty can be adversely affected.  Perhaps the best way to look at our list is that the teams at one extreme price the least aggressively (most benevolently?) while the teams at the other extreme are trying to extract every dollar they can from their fans.

At the top of the list we have Ottawa, Dallas, Boston, San Jose and Chicago.  After adjusting for market sizes, income levels and social media presences we find that these teams underprice. This is an interesting list as it contains both high brand equity teams like the Blackhawks and the Bruins as well as less prominent teams like Dallas and San Jose.  It is also notable that the Blackhawks and Bruins price above the league average while Dallas and Ottawa price near the bottom.  Interestingly, over the past 3 years Ottawa has basically sold out its arena.  The implication is that Ottawa (and the other teams on the list) could likely impose a price increase without too much loss of demand).

At the other extreme we have Philadelphia, Florida, Winnipeg, Toronto and Edmonton.  Again, this list contains both high (Toronto, Philly) and low profile teams (Florida).  Toronto is especially notable as they charge by far the highest prices in the league.  Winnipeg’s price are also extreme as they price higher (according to Team Marketing Report) than teams in New York, Chicago or Los Angeles.

Mike Lewis & Manish Tripathi, Emory University 2013.

Ranking the Most “Volatile” Fans in the SEC: LSU, Ole Miss, & UGA Lead the Way

Last weekend, Georgia beat LSU in a highly entertaining, closely contested football game.  After the game, fans were undoubtedly sad in Baton Rouge and elated in Athens.  These emotions were manifested through the tweeting activity of fans in both cities.  Using data from Topsy Pro, we were able to collect football-related tweets originating from Athens and Baton Rouge after the game.  There were almost twice as many tweets originating from Athens, and the ratio of positive to negative tweets was 9:1 in Athens, whereas the ratio was 1:9 in Baton Rouge.  As transplants who have lived in Atlanta for a few years now, we can attest to the overwhelming passion towards SEC football in the South.  Recently, we used data from Twitter to describe the emotions of NFL football fan bases during the 2012 regular season.  We decided that performing a similar analysis on the SEC football fan bases would be an interesting study.  We decided to empirically determine which SEC football fan bases really “live & die” by the performance of their teams.

The methodology for our study was straightforward.  We considered all of the regular season games from 2012 and the first five weeks of the 2013 season.  For each game, we recorded who won the game, and we collected football-related tweets from all of the SEC college towns for one, two, and three days after the game.  It would be reasonable to ask why we didn’t collect tweets from Atlanta for a UGA game or from all of Kentucky for a UK game.  We were trying to isolate tweets primarily from fans of the SEC team, and we believe that the college town is the best proxy for mainly fans of the college.  Atlanta is full of UGA fans, but there are also Alabama fans, Auburn fans, Florida fans, and pretty much fans of all SEC teams.  We wanted reactions of UGA fans to the UGA games, not the reactions of Auburn fans to the UGA games.  By football-related tweets, we mean tweets that mentioned any words that were commonly related to the particular college football team.  The tweets were coded as positive, negative, or neutral.  We were able to determine the “sentiment” of the collection of tweets as a rough index (1-100) of the ratio of positive to negative tweets.

Thus after each game, we were able to calculate the sentiment of the fan base.  We determined on average how positive a fan base was after a win, and how negative they were after a loss.  To understand the “volatility” of a fan base, we looked at the delta between the average sentiment after a win and the average sentiment after a loss.  In other words, how big is the difference in a fan base’s “high” after a win and “low” after a loss.  We believe that this metric best captures “living & dying” by the performance of your team.  After computing this metric for each fan base, we determined that LSU has the most “volatile” fans in the SEC.

The chart on the left gives the full rankings for the SEC.  It should be noted that these rankings were robust to whether we looked at how fans felt one, two, or three days after a game.  We believe that volatility is in part driven by 1) the expectations of the fan base and 2) the expressiveness of the fan base.  The top three schools in our rankings seem to get to the top for different reasons. The volatility of LSU & UGA fans is driven more by extreme negativity after losses, whereas the volatility of Ole Miss fans is a function of high levels of happiness after wins. This could, of course, in part be due to expectations.  UGA & LSU fans may have higher expectations than Ole Miss fans.  An examination of the data reveals that LSU fans had an extremely negative reaction to the Alabama loss last year and the Georgia loss this year.  These fans even had an overall negative reaction to a close WIN over Auburn last year!  UGA fans spewed a lot of vitriol on Twitter after the loss to Clemson this year.  Ole Miss fans, on the other hand, did not have overly negative reactions to losses, and were very positive after wins (e.g. the win over Texas this year).   It is interesting to note that the Alabama fan base is at the bottom of the volatility list.  Alabama only lost one game during the period of this study (a good reason for publishing this list again next year when we have more data).  But, even after wins, the Alabama fan base is not very positive on Twitter.  There are several tweets that are critical about the margin of victory.  If Alabama does ever go on some type of losing streak in the future (as unlikely as that seems), it will be fascinating to observe the reaction on Twitter.

Mike Lewis & Manish Tripathi, Emory University 2013.