WNBA Social Media Equity Rankings

We begin our summer of fan base rankings with a project done by one of our favorite Emory students – Ilene Tsao.  Ilene presents a multi-dimensional analysis of the WNBA across Facebook, Twitter and Instagram.  The first set of rankings speak to the current state of affairs.  Seattle leads the way followed by LA and Atlanta.  In the second analysis, Ilene takes a look at what is possible in each market (by controlling for time in market and championships).  In this analysis the Atlanta Dream lead the way followed by Minnesota and Chicago.

The teams in the WNBA are constantly looking for ways to improve their brand and continue to expand their fan base. Social media provides a way to measure fan loyalty and support. In order to calculate WNBA teams’ social media equity, we collected data on each team’s followers across the three main social media platforms of Facebook, Twitter, and Instagram. We then ran a regression model to help predict followers for each platform as a function of factors such as metropolitan populations, number of professional teams, team winning percentages, and playoff achievements. After creating this model, we used the predicted number of followers and compared it to each team’s actual number of social media followers.  Our goal is to see who “over” or “under” achieves based on social media followers on average. We then ranked the WNBA teams based on the results.

The first model only used the metropolitan population and winning percentage of each team. After taking the average of the Facebook, Twitter, and Instagram rankings, we found the Seattle Storm had the best performance. The Connecticut Sun and Washington Mystics consistently ranked as the bottom two teams across all three platforms, but teams like the Los Angeles Sparks and Atlanta Dream had more variation. The Dream ranked 6th for Twitter, but 1st for Instagram while the Sparks ranked 1st for Twitter and 6th for Instagram. This could be because both Instagram and the Dream recently joined the social media world and the WNBA, while the Sparks and Twitter have been around for longer. Based on raw numbers, the New York Liberty has high performance in terms of social media followers, but when we adjust for market size and winning percentage, the team does poorly.

Rankings for Facebook, Twitter, and Instagram based on the metropolitan population and the teams’ winning percentages:

WNBA Social Media 1

The second model extended the previous analysis by incorporating the number of other professional teams in the area and number of WNBA championships won into the regression analysis. This model seemed to be a better fit for our data and resulted in small adjustments in the rankings. After taking the average of all three rankings with the new factors, the Atlanta Dream was ranked first while passing the Seattle Storm and Los Angeles Sparks. The Mystics were no longer consistently the worst team, but were still in the bottom half of the rankings.

Rankings based on metropolitan population, winning percentage, number of other professional teams, and number of WNBA championships:

WNBA Social Media 2Ilene Tsao, Emory University, 2015.

2015 NFL Draft Efficiency: A Good Sign of Things to Come for Gator fans?

The first three rounds of the 2015 NFL Draft concluded last night. While there was no Twitter-breaking Manziel event like last year, the event was once again a marketing success for the NFL.

For the past two years, we have examined the NFL draft from a unique perspective.  We analyze the process of taking high school talent and converting it into NFL draft picks.  In other words, we want to understand how efficient are colleges at transforming their available high school human capital into NFL draft picks?

Our approach is fairly simple. Each year, every FBS football program has an incoming class. The players in the class have been evaluated by several national recruiting/ranking companies (e.g. Rivals, Scout, etc.). In theory, these evaluations provide a measure of the player’s talent or quality. Each year, we also observe which players get drafted by the NFL. Thus, we can measure conversion rates over time for each college. Conversion rates may be indicative of the school’s ability to coach-up talent, to identify talent, or to invest in players. These rates may also depend on the talent composition of all of the players on the team. This last factor is particularly important from a recruiting standpoint. Should players flock to places that other highly ranked players have selected?

 How did you compute the conversion rate?

The conversion rate for each school is defined as (Sum of draft picks for the first three rounds of 2015 Draft)/(Weighted Recruiting Talent). Weighted Recruiting Talent is determined by summing the recruiting “points” for the relevant eligible class for the 2015 NFL Draft for each program (this can include eligible juniors as well as fifth year seniors). These “points” are computed by weighting each recruit by the overall population average probability of being drafted in the first three rounds for recruits at that corresponding talent level over the last three years. For example, a five-star recruit is much more likely to get drafted than a four or three-star recruit. We are using ratings data from Rivals.com.

2015 nfl draftThe table above shows the results of our analysis of the first three rounds of the draft.  Colorado State had two draft picks in the first three rounds that were both 3-stars or below coming out of high school.  It will intersting to see how Jim McElwain will be able to shape the higher level of talent he will most likely attract at the University of Florida.  Please note that we did not include schools that only had one player drafted in the first three rounds, as that could be considered an aberration. Of course, a similar argument could be made that one draft is too small of a sample to rate the efficiency of a college. Thus, the table below represents results from the last 4 years of drafts (2012-2015).

2012-2015 NFL DraftThe school that really stands out over the last four years with respect to the development of talent is Stanford University.  While Connecticut and Boise State may be rated higher, Stanford has produced more than double the number of draft picks of the other two schools.

Mike Lewis & Manish Tripathi, Emory University, 2015.

CNBC: A Blunder Proof Brand: Can Anything Hurt the NFL

CNBC: A Blunder Proof Brand: Can Anything Hurt the NFL

Based on viewership and sponsorship interest, the NFL is flourishing through a series of highly-publicized fiascoes that would have wreaked havoc on many organizations with weaker brands, experts told CNBC.
“Just the strength of that product and fan loyalty and fan interest is so great that it’s really kind of bulletproof at this point,” said Michael Lewis, a marketing professor at Emory University who specializes in sports.

Charlotte Business Journal: Panthers Playoffs – Building Brand Loyalty

Charlotte Business Journal: Panthers Playoffs – Building Brand Loyalty

Two marketing professors at the Goizueta Business School at Emory University study sports franchises and fan interest by looking at whether fans are willing to invest financially (tickets, souvenirs and so on) and in terms of social media chatter. In both instances, Carolina ranks in the bottom third (23rd in financial willingness and 30th in social media equity).

“There is an opportunity to get better here,” Manish Tripathi, one of the Emory marketing professors who works on the studies, told me. “Post-season success has a strong impact in the NFL, especially for building the younger fan base.”

Washington Post: Fans starting to dislike Redskins’ Coach Jay Gruden as much as owner Daniel Snyder

Washington Post: Fans starting to dislike Redskins’ Coach Jay Gruden as much as owner Daniel Snyder

Kirk Cousins had a short stint as Redskins quarterback, but it was clear after his four-interception performance against the Giants on Sept. 25 that fans were ready to move on.

However, no matter what the outcome of the game, one thing was certain: fans were most negative about owner Daniel Snyder week by week. Until now.

For the first time this season, the negative sentiment on Twitter was as high for Coach Jay Gruden as it was for the team’s owner.

Daily Knicks: “Real Fan?” “Bandwagoner” “Does it Matter?”

Daily Knicks: “Real Fan?” “Bandwagoner” “Does it Matter?”

As a result of Linsanity, people that had little to no interest in basketball became fans of the Knicks and, more importantly, fans of NBA basketball in general. I couldn’t give two craps about the “real” fan aspect during a time of when the Knicks were in dire need of a point guard that wasn’t an injured, decrepit Baron Davis. And then, when Lin left, (I’m assuming) fans of Lin and the Knicks shifted over to Houston, because their basketball hero went elsewhere, prompting people calling Knick fans some of the worst fans in the league. But, however, Emory University’s sports marketing analytics department disagree based off of the field of finances, at least.

How Much Do NFL Stadiums Matter?

MLB Ballpark factorsWhere a game takes place hugely impacts performance, without even taking home field advantage into account.  In the MLB, there are “ballpark factors” which provide data as to how much more or less likely an event (e.g. double, home run, etc.) is in a particular ballpark relative to the league average.  These “ballpark factors” are a concept that most die-hard baseball fans know very well, and something all fantasy baseball players should be familiar with (especially for daily sites, like FanDuel).  ESPN provides a table to all its readers like the one shown on the left. The table reads as follows: for every one run scored in a league average MLB park, 1.501 runs will be scored in Colorado and .825 runs will be scored in Seattle. These factors are not the be-all and end-all when it comes to explaining player performance, but it’s another predictive tool to add to your tool belt.

Two recent examples show its application quite nicely: the performance decline of Robinson Cano after he decided to move to Seattle this past season, and the reasoning (or lack thereof) behind the Mets free agent signing of Michael Cuddyer this offseason.  In his seven full seasons with the Yankees, Cano averaged 24 home runs in 160 games per year.  His first year in Seattle he hit 14 in 157 games. Using only ballpark factors we would have predicted that he would hit 16 – not bad for just one calculation.  Michael Cuddyer has played his last three seasons with Colorado, hitting .307 with 15 home runs in only 93 games per year.  Again using only ballpark factors, in 93 games next year he should hit .254 with 12 home runs.

Being the fantasy sports aficionado that I am, I wanted to apply the same idea behind these ballpark factors to NFL data. However, much to my dismay, there was no NFL equivalent to be found. So, I decided to create NFL Stadium factors based on data from 2010 to 2013. The result is the table seen below.

Stadium FactorsExcludes SF & MN

Just as with MLB “ballpark factors”, the numbers in this table are just another piece in the puzzle of football analytics.  Unlike baseball, however, the NFL Stadium Factors are a slightly more effective tool on a team-by-team basis rather than for individual performance. For example, consider the trade rumors hovering around the weeks leading up the NFL draft this past year. “Brady to Houston,” the headlines read. On the surface this looks like a no-doubter for the Texans, but has New England’s stadium been augmenting Tom Brady’s statistics over these many years? In fact, a quick look at the table on the left makes me wonder if the Patriot offensive juggernaut as a whole has benefited by playing in Foxboro.  New England’s passing attack has averaged 276 yards per game and 33 touchdowns in the air over the past five years. If they had been playing in Houston’s stadium over that time span, stadium factors suggest those number would have plummeted to 252 and 29.

It’s easy to take these numbers as they are and just plug them in your statistical analyses; however considering the characteristics of given stadiums in order to understand why certain trends exist in the data is infinitely more useful. The stadium characteristics I looked at are as follows: domes, turf fields, cold weather, noise, and altitude.

  • In stadiums with domes, you see a significant increase in field goals made and a decrease in rush yards gained. This is likely due to the absence of wind and other adverse weather effects, which negatively affects the passing game and field goals. Regardless of whether or not a team has a good rushing attack, if conditions don’t lend themselves to a game plan centered on throwing the ball, then more rush yards will inevitably be gained.
  • On turf fields, the number of successful field goals goes up substantially due to the more consistent footing for kickers. Kickers are much more prone to slipping on the less-secure grass footing of a natural surface.
  • The third characteristic, cold weather, is defined from a list of the 10 coldest and snowiest stadiums in the NFL. From that classification, I found that noticeably fewer points, rush touchdowns, and field goals occur in those stadiums. These outcomes are all fairly logical and can be explained by the unfavorable effects of the cold on the human body. In addition, as the temperature drops the football becomes less elastic. In combination with the dense, cold air inside and outside of the football, this makes field goals a much harder task.
  • In the NFL’s five loudest stadiums, noise was found to lead to fewer points scored per game, most likely because of a two factors – communication and intensity. The louder a stadium gets, the harder it is for an offense to communicate certain blitz protections or other audibles. Secondly, the intensity of a loud crowd leads to more pressure and greater nervousness, which I believe more heavily impacts offensive performance.
  • Finally, altitude is directly and positively correlated to field goals made and rush touchdowns scored. The first part makes perfect sense – things fly further in the lighter air of high altitude. The reason behind the second finding is a little more intricate. Altitude has powerful effects on lung capacity and conditioning levels, so defensive linemen (who aren’t in shape) tend to struggle in places like Mile High Stadium. Rushing touchdown data would specifically reveal this trend, because they often occur at the end of long drives when those in charge of stopping the run (the defensive linemen) are exhausted.

This table is a great starting point in starting to describe the effect that a certain location has on NFL performance. Although the insights behind the aforementioned explanations are my personal opinions, the numbers can be explained logically, and when used in statistical analysis will most definitely lead to improved results.

Michael Byman (@MichaelByman) is a senior at Emory’s Goizueta Business School.  He is a Sport Analytics Research Grant recipient & submarine college pitcher.

Washington Post: Here is when Kirk Cousins lost the Redskins’ fan base

Washington Post: Here is when Kirk Cousins lost the Redskins’ fan base

The Washington Redskins were a team starved for good news, and Sunday’s win over the Tennessee Titans did the trick. It also may have created more questions than answers, which is why the negative sentiment from the Washington area for this team lingers on Twitter.

“Negative is where the action is,” said Manish Tripathi, assistant professor in the practice of marketing at Emory University. “People have a lot more variation into how negative they are about people. This is true in general on Twitter: you always see a lot more variation of negative sentiment. Even when people are doing well you don’t often see spikes in positive, you just just see less negative.”

The “Smartest” NBA Teams

In our “Smartest” Teams series we are using simple statistical models to assess which teams over and under perform on the field, floor, or ice relative to how much they spend.  Thus far we have taken a look at the NHL and MLB.  We now turn to the NBA.

These analyses are in some respects simple, as what we do is estimate linear regression models that predict team performance as a function of team fixed effects and payrolls.  We use a bit more than a decade worth of data.

Astute readers might question the use of fixed effects, since team management (GMs) may change over time, and payrolls may be a point of concern given the prevalence of guaranteed contracts.  Folks might also complain that we don’t consider player ages since rookies are given set dollar value contracts.  Our feeling is that over the course of a decade, these factors (cap management, draft position, etc…) are within the control of teams.

Moving on to the list!  The smartest team in the NBA is San Antonio.  The Spurs are followed by Oklahoma City and the Mavericks.  Houston is a notable 5th.  The top of the list looks very much like a list of successful teams with well-regarded management.

At the other end, we aren’t going to say much.  The bottom two are the Washington Bullets (we are offended by all DC team names so we are going to use whatever we like best) and at the very bottom we have the NY Knicks.  The Knicks are a fascinating team.  They charge the highest prices in the league, have won our most supportive fan base both years, and make the worst player decisions.

1 San Antonio
2 OKC
3 Dallas
4 LA Lakers
5 Houston
6 Phoenix
7 Utah
8 Denver
9 Miami
10 Detroit
11 Indiana
12 Boston
13 Chicago
14 Orlando
15 New Orleans
16 Sacramento
17 LA Clippers
18 Memphis
19 Philadelphia
20 Cleveland
21 Portland
22 Atlanta
23 Milwaukee
24 Brooklyn
25 Toronto
26 Golden State
27 Minnesota
28 Charlotte
29 Washington
30 New York

Mike Lewis & Manish Tripathi, Emory University 2014.

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