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Video: Chris Draft, Founder & President, The Chris Draft Family Foundation

Chris Draft, Founder & President of the Chris Draft Family Foundation, joined us to discuss how athletes can build their personal brand.

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Video: On building and using a personal brand

Twitter Analysis: Which NFL Markets Are Most and Least Receptive to Michael Sam?

Top Twitter Michael SamMichael Sam’s announcement has engendered several reports in the media regarding how accepting NFL management and players would be to an openly gay player.  We were interested in looking at how the fans in NFL cities feel about Michael Sam.  In order to do this, we collected all tweets mentioning “Michael Sam” in the 31 NFL markets for the past 2 days (2/9 morning – 2/11 morning).  The tweets were sorted by market, and analyzed for positive, negative, or neutral sentiment.  Looking at the ratio of positive, negative, and neutral tweets allowed us to compare Twitter sentiment for Michael Sam across NFL Markets.

We present the top ten and the bottom seven markets in the NFL.  It is interesting to note that a lot of the tweets in St. Louis and Kansas City that mention Michael Sam also reference the University of Missouri.  The most negative Twitter sentiment toward Michael Sam seems to be in the Nashville market. Worst Twitter Michael Sam

Michael Lewis and Manish Tripathi, Emory University 2014

Despite Media Efforts, Twitterverse Loves Beast Mode

Marshawn SentimentThere has been a lot of discussion about Seattle Seahawks RB Marshawn Lynch’s reluctance to speak with the media.  He spoke only for six minutes on Media Day (January 28th).  The appearance was dubbed as “Least Mode”.  Sites like Deadspin have documented the reaction of football writers to Mr. Lynch’s behavior, and it seems a lot of the reaction has been negative.  However, as the chart above shows, since Media Day, there has been a surge in positive tweets about Marshawn Lynch.

Related Articles:

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

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

Mike Lewis & Manish Tripathi, Emory University 2014. 

Pre-releasing Super Bowl Ads: Tease or Full Monty?

sodastreamIn the last few years, the trend of releasing Super Bowl ads online in advance of the Super Bowl has been well documented.  Super Bowl advertisers can choose to pre-release a full ad, preview an ad, or wait until the Super Bowl to unveil their ad.  There is a belief among many advertisers that previewing or fully releasing a commercial online before the Super Bowl will generate online exposure and buzz at a much lower cost than running the ad on TV during the Super Bowl.  Since the majority of the pre-Super Bowl advertising activity is being done online, we decided to study 2013 Super Bowl ads using Twitter.  We realize that we could also look at online views of an ad, but we believe that tweets do a good job of capturing the buzz around an ad.  We were interested in investigating how the decision to preview or fully pre-release a Super Bowl ad impacts the pre-game online buzz.   Also, we wanted to determine the difference in long-term online impact of previewing or fully releasing a Super Bowl ad online in advance.  Our key insights from this study were:

1.    Previewing or teasing a commercial online increases the pre-game “buzz” at higher percentage than revealing the entire commercial online.

2.    Releasing the full commercial beforehand seems to have a long-term effect on online exposure, whereas previewing the commercial does not.

Now, for some more details on our study.  First, we coded each of the advertisers for the 2013 Super Bowl as releasing the full commercial in advance (Full), previewing the commercial (Preview), or doing neither (Neither).  We then used Topsy Pro to collect all tweets that mentioned the advertised brands for a period two months before and after the Super Bowl (February 3, 2013).  We summed up the total number of tweets mentioning a brand on a daily basis.  We averaged the number of daily tweets per brand over several different time periods.

Pre-Game Chatter of Brand

Pre-Game Twitter BuzzThe first thing we examined was how the pre-releasing of Super Bowl ads online affects the pre-game tweeting regarding the advertised brand.  The key metric we examined was the percentage increase in average daily mentions of a brand in the two-week period before the Super Bowl (the time period in which the advance release typically occurs) as compared to the two weeks before that.  We pool the data across the three types of ads: Full, Preview, and Neither.  The chart on the right displays the average percentage increase in the three categories.

Interestingly, it is not the full commercials, but the teased commercials that show the largest percentage increase in online chatter.  It’s possible that the full commercials get more online views, but the teasing nature of the previewed commercial might be building up some excitement, that is being captured through the increased Twitter activity.

Short Term Effects

Two Week Before and AfterNext, we wanted to look at how the actual airing of the Super Bowl ad on TV interacted with the pre-release decision of the firm.  The key metric we examined was the percentage increase in average daily mentions of a brand in the two week period after the Super Bowl as compared to the two week period before the Super Bowl.  The firms that teased their commercials beforehand experienced the largest increase in the two-week period after the Super Bowl as compared to the two-week period before the Super Bowl.  The increase is compounded if you consider that the same type of advertised brands experienced the largest growth in tweets in the two-week period before the Super Bowl!

Some of the brands that experienced the largest increase in tweet activity in this two week post Super Bowl period included Skechers & E*Trade.  While all three categories understandably experienced dramatic growth in online chatter, brands that had released the full commercial in advance had the least growth.  There are several potential explanations for this phenomenon, including less of a surprise factor, since the full commercial was already known to consumers.

Longer Term Effects

Long Term Twitter IncreaseTo better understand the lasting impact of a commercial, we decided to compare the average daily mentions of a brand for a three-week period a month AFTER the Super Bowl with a three-week period a month BEFORE the Super Bowl.  Looking at these periods would hopefully remove some of the short-term buzz, and allow us to see if there was a more permanent level of change to the Twitter activity surrounding a brand.  We realize that there could be other actions that could influence tweet activity besides the Super Bowl.  However, surprisingly, there is relatively low level of variability within members of the three types of advertisers.

Only the companies that showed the full ad before the Super Bowl manifested a “long” term increase on average in tweets mentioning the brand.   The two big winners with respect to long-term impact were SodaStream and Speed Stick.  Perhaps it was the repeated exposure to the full commercial that left a longer lasting impression on consumers.

2014 Super Bowl

So, what does this mean for the 2014 Super Bowl?  Our study only looked at data from one Super Bowl, but it will be interesting to see if commercials follow a similar pattern this year.  We are seeing more companies release their full commercials in advance this year.  We are also seeing firms with multiple spots preview one spot and fully release another spot.  The brands showing the largest increase in pre-game Twitter activity include: SodaStream, Squarespace, Oikos, & Butterfinger.

Mike Lewis & Manish Tripathi, Emory 2014.

2014 Pro Bowl: A Twitter Success

NFL-Pro-Bowl-Draft-2014The Pro Bowl decided to change its format this year, ostensibly to boost interest in a game that according to many has been on a decline over the last few years.  We were interested to see how this year’s Pro Bowl did in terms of Twitter activity compared to the Pro Bowl last year.  Specifically, we wanted to capture the “Twitter Share of Voice” of the Pro Bowl.  This is simply the number of tweets that mention the Pro Bowl divided by the total number of tweets over a given time period.  We believe this is a better metric than comparing the year over year number of tweets mentioning the Pro Bowl, since overall activity on Twitter is growing over time.

Pro Bowl 2014 Share of VoiceWe did not expect a large growth in Pro Bowl Twitter Share of Voice, largely because this year (unlike last year) the Pro Bowl was on at the same time as the Grammy Awards.  As anyone on Twitter can attest, it seems every other tweet last night was about the Grammy Awards.  Thus, we were surprised when we found a 100% increase in Twitter Share of Voice for the Pro Bowl as compared to last year!  Our study looked at a 24 hour period starting at 10am EST on the day of the Pro Bowl.  The overall sentiment of the Pro Bowl tweets remained unchanged from last year (A 3:1 ratio of Postive to negative tweets).  It should be noted that there were still more than fifteen times the number of tweets mentioning the Grammy Awards than the Pro Bowl.  The chart on the right breakdowns the top 10 Twitter Share of Voice for the 2014 Pro Bowl at the state-level.

 

Mike Lewis & Manish Tripathi, Emory University 2014.

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.

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.

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.

Don’t Want to Get Fired? Best and Worst Cities for Firing Professional Coaches

Mike_Shanahan_RedskinsIt’s “Black Monday” in the NFL.  The Vikings, Redskins, Lions, and Bucs have already fired their coaches today, and more firings are possible before the day is done.  There are many variables that can affect the firing of a coach in professional sports.  Of course, three easily observable factors are the performance of the coach (winning percentage, playoff appearances, and championships), the investment by the ownership (team payroll), and the sports league (NFL, MLB, NBA, and NHL).   There are also intangible factors endemic to each city in America and Canada with a professional sports team that can influence the probability of a coach getting fired.

We decided to estimate a logistic regression model that could explain the probability of getting fired as a function of performance, investment by ownership, and professional league affiliation.  We looked at data from all four professional sports leagues over the last twelve years.  We then compared the predicted probability from our model of getting fired with the actual firings in each city.  In theory, cities with intangible characteristics that make it more likely for a coach to get fired would have actual firings at a higher probability than predicted through our model of performance and investment.  We tried several specifications of our model, and these rankings are robust.

Based on our study, the Top 8 Worst Cities (Highest probability for getting fired above predicted) are:

  1. Orlando
  2. San Francisco
  3. Montreal
  4. Sacramento
  5. Milwaukee
  6. Oklahoma City
  7. Jacksonville
  8. Miami

The Top 8 Best Cities (Lowest probability for getting fired below predicted) are:

  1. Winnipeg
  2. Nashville
  3. Salt Lake City
  4. Memphis
  5. Los Angeles
  6. Portland
  7. Buffalo
  8. Minneapolis

It’s interesting to note that the top 8 worst cities does not include big media markets like New York, LA or Chicago, where one might think there is large expectation for winning.

Mike Lewis & Manish Tripathi, Emory University 2013.