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.

The “Smartest” NHL Teams

Continuing our series on win efficiency (which is also part of our class on predictive sports analytics) we now turn to the NHL.  We offer the NHL rankings largely without comment.  At the top we have the Red Wings, Sharks and Canucks.  At the bottom it’s Edmonton, Florida and Columbus.  Okay, maybe one comment.  Based on all of our NHL analyses over the last year, I think we can say that the best place to be a hockey fan is Detroit.

Rank

Team

1

Detroit

2

San Jose

3

Vancouver

4

Ottawa

5

Boston

6

Nashville

7

New Jersey

8

Anaheim

9

Pittsburgh

10

Minnesota

11

Chicago

12

Dallas

13

Montreal

14

St. Louis

15

Buffalo

16

Arizona

17

Washington

18

Colorado

19

Philadelphia

20

NY Islanders

21

Tampa Bay

22

Calgary

23

Los Angeles

24

Carolina

25

Winnipeg

26

Toronto

27

NY Rangers

28

Edmonton

29

Florida

30

Columbus

 

The “Smartest” MLB Teams

We had a couple of reasons for the previous post regarding the relationship between spending and winning across leagues.   The post was intended as a discussion point for our class – “Predictive Sports Analytics”.  But, the post was also background for our next series of analyses that focus on identifying the “smartest” or most efficient spenders in each of the four major professional leagues.

Today, we take a look at Major League Baseball.  The analysis itself is fairly simple.  We use the last 12 years of data on spending and winning to estimate a linear regression model.  We also include fixed effects for each team in this equation.  As a side note, an examination of residual trends might be a little better as management teams tend to change over time.

The fixed effects or team level intercepts provide an indication of whether teams over or under perform relative to what they invest.  The analysis is kept simple since it is for class, but it could easily be extended to include non-linear effects, or perhaps the dependent variable could be post-season qualification models as a binary logit.

The results of our analysis are both as expected in many ways and surprising in others.  At the top of the list we have the early adopter of analytics, the Oakland A’s.  We will take this as evidence of the value of investing in analytical capabilities.  At number two on the list we have the Atlanta Braves.  In positions three through five we have the Cardinals, Yankees and Giants.  The Yankees came as a bit of a surprise given their enormous payrolls.

At the bottom of the list we have Baltimore (note that this current season data is not used).  To some extent, the simplicity of the model might be unfairly penalizing Baltimore.  We would explain why, but this seems like a good question for class!  The other notable but unsurprising member of the bottom five is the Chicago Cubs.  This pains Professor Lewis too much to write about.

1

Oakland

2

Atlanta

3

St Louis

4

NY Yankees

5

San Francisco

6

Boston

7

LA Angels

8

Philadelphia

9

Cleveland

10

Minnesota

11

Chicago White Sox

12

Texas

13

Cincinnati

14

LA Dodgers

15

Toronto

16

Arizona

17

San Diego

18

Miami

19

Houston

20

Tampa Bay

21

Milwaukee

22

Seattle

23

NY Mets

24

Washington

25

Colorado

26

Detroit

27

Chicago Cubs

28

Pittsburgh

29

Kansas City

30

Baltimore

 Mike Lewis & Manish Tripathi, Emory University, 2014.

 

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