0:16

Welcome back to

0:16

the Informatics Cafe. Hi, I'm

0:18

Mike Nitardy, and I'm pleased

0:18

and proud to be your host today,

0:22

as we welcome into the

0:22

Informatics Cafe. Professor Joe

0:26

Cobbs. He's professor of the

0:26

sports business in the Haile/US

0:30

Bank College of Business here at

0:30

NKU. And along with him is

0:33

Professor Marius Truta. He's

0:33

Professor of Computer Science in

0:37

the Associate Chair of the

0:37

Department of Computer Science

0:40

here at NKU. And we're so

0:40

excited to have them here,

0:43

because we're going to be

0:43

talking about a website that

0:46

they run called KnowRivalry.com.

0:46

And this is going to be a

0:51

fantastic conversation. I'm so

0:51

excited to have both of you

0:54

gentlemen, here. Thank you for being here.

0:56

Thank you.

0:57

Yeah, thanks for having us.

0:58

So I guess we'll just start off right from the very beginning, tell us what

1:00

Know Rivalry is.

1:03

Sure. So I started

1:03

Know Rivalry, or cofounded it,

1:09

with my partner on the project

1:09

at the time, David Tyler, who's

1:14

a professor at the University of

1:14

Massachusetts, and, and he's

1:18

still very much involved, the

1:18

two of us sort of, sort of run

1:20

it. And we've had the fortune to

1:20

bring Marius in on it, as well

1:24

as Seth Adjei also in computer

1:24

science, a faculty member and a

1:29

lot of students. And the project

1:29

started back when I was getting

1:34

a PhD at the University of

1:34

Massachusetts. And we started to

1:37

look at the research that

1:37

existed around rivalry, like the

1:41

academic research, and

1:41

surprisingly, even though it's,

1:44

you know, widely talked about in

1:44

the media, and fans love to

1:47

debate it.

1:48

Yes.

1:49

But there was not very much research around it. And so David, and I started to,

1:51

to think about, you know, how we

1:56

might develop a research stream,

1:56

and that turned into Know

1:59

Rivalry. And that's k-n-o-w

1:59

rivalry. And the project website

2:04

is knowrivalry.com, where we put

2:04

up results, and engage with fans

2:09

and the media and other

2:09

researchers as well.

2:12

What's amazing is that this is

2:12

some academic research into

2:15

rivalries is was there a way

2:15

that is specifically started out

2:18

at specific sports specific

2:18

areas?

2:22

Yeah, we started with college

2:22

football, I had previous to

2:26

being a professor, I had worked

2:26

in intercollegiate athletics, at

2:31

Miami, University of Ohio, and

2:31

then also at Ohio State. And so

2:36

that was kind of my industry

2:36

background. And as part of that,

2:39

you know, promoting rivalries

2:39

was part of my job, and also

2:43

determining maybe, who we could

2:43

promote as a rivalry and who

2:47

maybe that would be kind of

2:47

inauthentic, if you will. And so

2:51

I was kind of making that

2:51

judgment, as a professional as

2:54

a marketing professional. And so

2:54

that just kind of carried over

2:58

once I transitioned full time

2:58

and academia and to being a

3:02

professor, and I started to

3:02

think, okay, you know, what kind

3:05

of data would we need to gather

3:05

and how we go about doing that?

3:09

If we wanted to dig into, you

3:09

know, what, what is rivalry?

3:12

Exactly.

3:12

And what contributes to it.

3:14

So can you define

3:14

help us out with what would

3:17

a rivalry be?

3:18

Sure. I mean, we have

3:18

an academic definition...

3:21

Sure, yeah.

3:21

...but usually I

3:21

refer to kind of a common

3:25

definition of, it's the opponent

3:25

that sticks out more than the

3:32

other opponents. So in the case

3:32

of competition, you know, this

3:36

is all kind of contextualized in

3:36

sporting competition in this

3:40

case. But we do expand beyond

3:40

sports a little bit. And I think

3:44

we'll get into that more in the

3:44

future. But when that schedule

3:47

comes out for your favorite

3:47

team, and it's released, and you

3:50

look at it, there are certain

3:50

games that matter more to you

3:54

than other games, and that is

3:54

essentially, the rival. From an

3:59

academic side, it get we get

3:59

into the social psychology of it

4:02

in terms of in groups and out

4:02

groups, there was a fan you're

4:06

in group is that team that

4:06

you're affiliated with, it's

4:08

part of your identity...

4:10

Right

4:10

...and it becomes part of your identity.

4:11

Right.

4:12

And at the same time

4:12

in sports, once a team becomes

4:15

part of your identity, the

4:15

opponents become out groups to

4:19

you because they're in

4:19

opposition to you. And so it's

4:23

that in group/out group kind of

4:23

tension, and that's higher

4:27

between certain groups compared

4:27

to other groups.

4:30

This is this is

4:30

completely fascinating. And I

4:32

think that there are so many

4:32

potential applications here.

4:35

Obviously, not just for for

4:35

sports, when you get into the

4:38

psychology of it all. And,

4:38

Marius, we're going to talk to

4:41

you here in a second about the

4:41

computer science aspect and what

4:44

we're doing here at NKU. And how

4:44

we're blending together the

4:48

obviously the sports business

4:48

aspect, the computer science

4:50

aspect, I know that there's a

4:50

psychological aspect, but but I

4:54

guess my question is, is that

4:54

why do you think studying

4:57

rivalry is important in and of

4:57

itself?

5:00

Yeah, that's a great

5:00

question and one that we get a

5:03

lot. As you can imagine, I think

5:03

it's important, beyond just

5:08

intellectual curiosity because

5:08

it's so prominent in our society

5:12

that we should know more about

5:12

it. But beyond that, from a more

5:15

practical standpoint, a lot of

5:15

business decisions are made

5:19

based in rivalry. So sticking to

5:19

the sports context, when you

5:23

look at scheduling and league

5:23

scheduling, the perceptions of

5:27

who rivals are can play a big

5:27

role and how those games are

5:30

scheduled and what games appear

5:30

at what time and on what

5:33

channel, you know, like the NFL

5:33

schedule recently came out. And

5:38

you look at the Monday night

5:38

games or the Thursday night

5:41

games and see a lot of

5:41

correlation to the rivalries

5:46

that we've identified in some of

5:46

our research. MLS provides great

5:50

example they have a whole rivalry week...

5:52

Yes

5:52

...that Heineken

5:52

sponsors and so getting to the

5:55

business side of it, that is

5:55

another aspect when you can

5:57

package rivalries and do it in

5:57

an authentic way, which was

6:00

difficult for MLS early and when

6:00

they first started rivalry week,

6:05

there was some tension between

6:05

the fans because the fans

6:07

thought that some of the matchups they chose were ..

6:10

Manufactured? [laughter] Exactly. They weren't authentic

6:11

rivalries they were

6:13

manufactured. And so yeah, so

6:13

getting back to your question,

6:16

you know, understanding rivalry

6:16

is important if you're going to

6:19

promote rivalry, because the

6:19

fans will see right through it,

6:21

if you're trying to manufacture them.

6:22

Definitely.

6:23

But once you can

6:23

promote it authentically, it

6:25

becomes a sponsorship inventory

6:25

item, you can sell that to a

6:28

sponsor, right, and you can

6:28

raise, raise your revenue

6:30

streams.

6:31

Wow. And this is

6:31

this is amazing, because this

6:33

just works in I think, in so

6:33

many different ways with really

6:38

the information and data-driven

6:38

economy that we're that we're

6:41

looking at right here. And so it

6:41

makes sense to me. And it should

6:44

be making sense to these

6:44

advertisers and the businesses

6:46

to sell the fans what they want.

6:46

And they want the marquee

6:50

matchups between the teams that

6:50

that really don't like each

6:53

other.

6:53

But yeah, and if I if ...

6:54

Please, go ahead.

6:55

...I can interrupt you for a second, I think it's also important to understand

6:57

kind of the broader implication

6:59

of what we're doing. It's not

6:59

all about kind of making money

7:02

in sports, although that's my

7:02

area. But I like to think that

7:06

there's that there's certainly

7:06

broader implications here in

7:07

Definitely. terms of like the polarization

7:08

that we've seen in our political

7:10

...you know, and

7:10

understanding some of the, you

7:12

environment...

7:16

know, certainly there's

7:16

political scientists that study

7:18

that, you know, specifically and

7:18

in more detail, you know, we're

7:21

studying in group out/group

7:21

conflict in sports. But I think

7:24

a lot of if you look at our

7:24

research, a lot of what comes up

7:29

can also provide some insights

7:29

to that area. And we take some

7:32

of what those political

7:32

scientists have done, and apply

7:36

it in sports as well.

7:37

See that's, it's another thing that you bring out. And I said before we went

7:39

on the air that we could do this

7:42

all day and talk about this all

7:42

day, because this, because I've

7:45

often wondered, and maybe you

7:45

guys have already looked into

7:47

this and or maybe you will, is

7:47

that which groups in groups or

7:52

out groups are more, uh, are

7:52

the strongest? Because I've also

7:56

often wondered if I'm, you know,

7:56

screaming at a game with the

8:00

fans sitting right beside me,

8:00

and we're in the in group, this

8:02

is our in group for this one

8:02

specific team. But it turns out

8:05

that politically, we're

8:05

completely apart, you know, will

8:08

that destroy the in group of

8:08

being a sports fan? You know,

8:11

and so I don't know if that

8:11

makes any sense to what I'm what

8:13

I'm saying to you here. Are

8:13

there certain affiliations that

8:16

are stronger are sports

8:16

affiliation stronger than

8:19

political affiliations? And if

8:19

so, is that a way maybe to bring

8:22

us back together as a society

8:22

after such political tumult?

8:26

Yeah, I think, and

8:26

that gets into some of the idea

8:29

of identification and these

8:29

different sort of facets of

8:32

identification and what is

8:32

active in a certain context,

8:35

right. So in that example, you

8:35

just described, like, if you're

8:38

at a game, or you're watching a

8:38

game, your sports fan

8:41

identification is activated,

8:41

mentally. But as soon as the

8:46

conversation turns to politics,

8:46

or if you're at, you know, a

8:50

rally or something like that,

8:50

then your political

8:53

identification is activated.

8:53

Right. And so one of the things

8:56

that I think is important for us

8:56

to understand is, you know, how

9:01

do we activate sort of more,

9:01

sociologically healthy

9:06

identifications, and sometimes

9:06

that can be thinking about the

9:10

superordinate identification,

9:10

and by that, I mean, like, let's

9:14

just keep it in sports for a

9:14

minute. I know in some sports

9:17

context, there's violence that

9:17

comes along with rivalries and

9:20

...perceptions of

9:20

rivalries. Fortunately, not as

9:20

...

9:20

Right. much in the United States. But

9:22

we've recently started

9:25

collecting data internationally.

9:25

And in some other contexts,

9:29

where there's politics behind

9:29

the sports teams, and religion

9:35

as well. And so by understanding

9:35

this idea, hopefully an appeal

9:41

to sort of the super ordinate

9:41

identification so for example,

9:43

instead of saying that we're

9:43

maybe fans of a certain club in

9:47

MLS so here locally, it might be

9:47

FC Cincinnati, you know, to get

9:51

fans to think about well, we're

9:51

all fans of MLS.

9:55

Right.

9:55

You know, it's not

9:55

fans of the Columbus Crew and

9:58

fans of FC Cincinnati. We're all

9:58

fans of MLS and we all care

10:02

about professional soccer in the

10:02

United States, or even more

10:05

super ordinate, hey we're all

10:05

soccer fans, we all appreciate

10:08

this beautiful game. And so

10:08

thinking about that, and

10:12

thinking through the

10:12

implications of how you kind of

10:14

go about that is really

10:14

important, I think, for sport

10:17

managers and responsible kind of

10:17

sports marketing.

10:20

So let's talk a

10:20

little bit then about the data

10:23

and how you've collected it and

10:23

what you're looking for. Can we

10:26

do that? So what exactly if I go

10:26

to, and I have been to your

10:30

website, but if I wanted to get

10:30

involved and start plugging in

10:33

my numbers, what would I do?

10:35

Yeah, so if you go to

10:35

the website, knowrivalry.com.

10:39

And you'll see right sort of

10:39

front and center on the main

10:41

page is a button for take the

10:41

survey. And I would recommend

10:45

that anybody that goes to the

10:45

website takes the survey first,

10:48

before they start looking at the

10:48

data, because as a researcher, I

10:51

don't want them to be... ...biased by what

10:52

other fans have already said.

10:52

[laughter] And so I always encourage that

10:54

first. And if they take the

10:58

survey, it is a rather lengthy

10:58

survey, you know, it takes a

11:00

good five to 10 minutes, you

11:00

know, I think the average is

11:03

about eight minutes or so to go

11:03

through the survey. The survey

11:05

will ask you first of all, "What

11:05

league do you want to take it

11:08

for?" So you can take it for

11:08

like Major League Baseball, or

11:10

you can take it for like English

11:10

Premier League Soccer now or,

11:13

there's, we have a whole bunc

11:13

of leagues on there now. And

11:17

ithin the survey, it'll ask you

11:17

hat's your favorite team. An

11:20

then once you identify who your

11:20

favorite team is, say you say

11:23

he Cincinnati Reds, it'll as

11:23

you to allocate 100 points,

11:26

ivalry points over the Cincinna

11:26

i Reds opponents. So you can

11:27

Right. llocate all 100 points to a sin

11:30

le opponent. Or you can divide t

11:34

ose points up and allocate

11:34

them across several differen

11:36

opponents, depending on how y

11:36

u as a fan view the rivals of

11:40

the Cincinnati Reds. And so th

11:40

t's sort of the baseline

11:44

f what you see on the website.

11:44

n terms of data. The survey it

11:48

elf has a lot more questions

11:48

about sort of discrimin

11:51

tion toward rival fans, prejudic

11:51

toward rival fans, the idea of

11:55

chadenfreude, which is German wo

11:55

d for "joy in other people's mis

11:59

ortune." So there's some questi

11:59

ns about that. And that's a lo

12:02

of what we publish academicall

12:02

about is our findings in

12:05

those areas, more so than kind o

12:05

that 100 point measure. Bu

12:08

the 100 point measure is

12:08

ort of once you kind of understa

12:12

d and take the survey, it'

12:12

fun then to go on the website

12:15

and look at Okay, what did ot

12:15

er Reds fans say?

12:17

What did the

12:17

Cardinals fans say about us?

12:21

What did the Pittsburgh Pirates

12:21

fans say about us?

12:24

Right, right.

12:25

And then, well, an

12:25

important part in that process

12:29

is kind of cleaning the data...

12:31

Exactly.

12:31

...and getting it to

12:31

the point where we can put it up

12:34

on the website. And that's where

12:34

involving the computer science

12:37

faculty and students has been

12:37

really helpful.

12:39

Yeah, let's talk about that, Marius.

12:41

Sure. I would just

12:41

say that you are very optimistic

12:44

that the survey takes only eight minutes.

12:46

[laughter]

12:46

I think it takes

12:46

longer than that.

12:49

That's what the data says.

12:50

[laughter]

12:51

Well, you know, I

12:51

mean, the way the survey is, is

12:55

definitely some people at some

12:55

point may just came through the

12:59

survey, and they may just

12:59

complete it faster without going

13:02

to the to the survey until the

13:02

end. So maybe considering that

13:05

is true. But if someone really

13:05

looks at all the questions in

13:08

details, I will say 10 minutes

13:08

would be bare minimum, but

13:12

closer to 15-20 minutes. Now,

13:12

the reason why I know this is

13:16

because I work a lot with

13:16

computer science students in

13:18

cleaning the data from the

13:18

survey. And when we look at this

13:21

data, we realize that while the

13:21

beginning of the survey, usually

13:26

it's very clean data from the

13:26

beginning so people pay more

13:31

attention towards the end,

13:31

some of the fans obviously will

13:33

just skim through the survey,

13:33

just click on agree or disagree

13:38

on all the answers. And we look

13:38

at how much time they spend on

13:43

on those questions. And if the

13:43

time is way lower than the

13:46

amount of time needed to read a

13:46

question, we realize that those

13:49

answers are incorrect. So we

13:49

have to discard them. So we are

13:52

doing this. So the data that is

13:52

right now on the website, is

13:56

clean. So we discard answers we

13:56

believe are incorrect because

13:59

people didn't read them in

13:59

detail. In addition to that

14:02

there are other reasons why the

14:02

data may be incorrect. For

14:05

instance, if someone entered as

14:05

the rival her or his own team...

14:11

Right.

14:11

..like Detroit

14:11

Pistons is the favorite team and

14:13

the Detroit Pistons is the rival

14:13

team, then obviously something

14:16

is wrong. So we have to discard

14:16

that particular answer. And

14:22

there are many other situations

14:22

like that. What I will say that

14:26

in this particular part, we were

14:26

very fortunate that we could

14:29

work with many computer science

14:29

students, and we were held by

14:32

the Informatics+ grant to

14:32

support the students

14:36

financially. And it was a very,

14:36

very nice group to work with. We

14:42

had actually I just realized

14:42

before coming to this podcast

14:45

that we worked already with

14:45

seven students during this past

14:49

year, and it was the most

14:49

diverse computer science group

14:53

of students I've ever worked

14:53

with. And that was just, I will

14:56

say coincidence that we didn't

14:56

even realize that but we have an

14:59

African-American, we had two

14:59

female students, we had a

15:03

school-based scholar, which is a

15:03

high school student in our

15:05

group.

15:06

Fantastic.

15:07

And one freshmen

15:07

student and three international

15:09

students.

15:10

Wow.

15:11

Yes.

15:12

That is great.

15:12

So that was

15:12

something, I must say very

15:16

specific to this project, we

15:16

showed that sports is something

15:19

that unites people of different...

15:21

Definitely

15:22

...different groups.

15:22

Definitely. And we

15:22

were kind of getting into the

15:25

the cleaning aspect. But are

15:25

there other ways in which data

15:28

science plays into assimilating

15:28

the answers? And then, you know,

15:33

kind of taking that and

15:33

explaining people's behavior?

15:36

How does that all work?

15:37

So first of all,

15:37

in addition to just getting the

15:37

Yeah, I think were it

15:37

really, you know, the

15:40

data, the data needs to be in a

15:40

database. And for that,

15:44

actually, our partner, David

15:44

Tyler from University of

15:47

Massachusetts, he was the one

15:47

that started working with Neo

15:49

J. And currently, this is the

15:49

atabase management system

15:52

hat supports all the data. A

15:52

d it allows us an easier way to

15:56

query the data to get whatever

15:56

e want from from the system. B

15:59

t in terms of analyzing the

15:59

data, it's something tha

16:04

actually we didn't do in

16:04

omputer science students and

16:07

e didn't do it that much, you

16:07

and David did a lot more. And

16:12

in publishing the results fro

16:12

from this data and und

16:16

rstanding the data aspects of

16:16

t, that is something that is we

16:19

till have to do a lot more in

16:19

he future. I will say,

16:25

Informatics+ grant really helped

16:25

connect us, Marius and I and

16:30

then Seth as well, and then and

16:30

then the students, I have some

16:34

students that work for me work

16:34

on the project in the College of

16:38

Business as well but what they

16:38

really brought to the table was,

16:43

we had had a mechanism for

16:43

cleaning the data in the past,

16:46

that used Excel and was more

16:46

labor intensive than what he was

16:51

able to develop and what they

16:51

were able to develop and

16:54

computer science. So they were

16:54

able to automate it, they were

16:56

able to kind of to make it a

16:56

more smooth process to make it

17:00

more I'm sure it's more

17:00

accurate, you know, than then

17:03

our cleaning process was before.

17:03

And so that's why it was kind of

17:09

funny when I was saying eight,

17:09

you know, eight minutes is

17:11

because I'm thinking of

17:11

everybody who takes the survey.

17:14

And so you've got people that

17:14

are actually eliminated on the

17:16

bottom end, because, you know,

17:16

they're only in that survey for

17:19

a minute. And so they're

17:19

bringing that average down. But

17:21

when you look at the clean data,

17:21

yeah, it's gonna be longer,

17:26

right?

17:27

Right.

17:27

Because we know that it takes a minimal amount of time to read every question and

17:29

respond to it.

17:31

And you know, we

17:31

use, I mean, in addition to

17:34

Neo4J which I just mentioned, we

17:34

use Python to clean data. So

17:37

students are proficient in

17:37

Python and in data and data i

17:42

, in order to collect the d

17:42

ta, we use Qualtrics. So the d

17:47

ta is collected via Qualtrics s

17:47

rvey system, then the data is p

17:51

lled from Qualtrics and then we

17:51

have Python, we modify it, an

17:55

then we enter it in Neo4J an

17:55

then once the data is in Ne

17:58

4J, then it can be queried us

17:58

ng Cypher query language.

18:02

Okay.

18:02

And you can get

18:02

whatever information you want

18:05

from this data, in addition to

18:05

that the data from Neo4J is also

18:09

used at least part of the data

18:09

is also used for the website

18:12

where everybody can see the

18:12

current rivalries.

18:15

So when you go to the website, and you click on something, it queries the

18:17

database in Neo4J to pull up

18:21

sort of the results then that you see.

18:23

Very good, very

18:23

good. So I have like I said,

18:27

we're gonna have to do a follow

18:27

up podcast on this because this

18:30

is ... there's so many, I think,

18:30

potential questions and also

18:35

permutations of this where you

18:35

can take it, I think that you're

18:38

really onto something here. And

18:38

everybody that's listening to

18:43

the, to the podcast, obviously

18:43

needs to go to the website and

18:46

needs to take the survey. And

18:46

you can take it for as many

18:50

sports leagues as you want, or

18:50

are you limited?

18:53

No, you can take it

18:53

for as many that you're a fan

18:56

of, you know, and so if you've

18:56

got, you know, a favorite team

19:00

and three different leagues,

19:00

even if they're in different

19:02

countries, you can take it. We

19:02

don't have a translated survey

19:05

yet so they're all still in

19:05

English. But we have surveys for

19:08

Indian cricket, and we have

19:08

surveys for like I said, English

19:12

Premier League Soccer, we have

19:12

rugby surveys...

19:14

So I'm gonna have

19:14

to take one. I feel that I'm I'm

19:18

kind of I'm a fan I have been

19:18

since 2009. My team plays a big

19:22

game tomorrow. I'm a Chelsea

19:22

fan. I have been since 2009. I

19:26

got to throw that in there it's

19:26

not... But Chelsea really

19:30

doesn't have a major rival. You

19:30

know, and being a fan of theirs

19:36

for several years since 2009 I

19:36

got into it because of my

19:39

brother they've they've got

19:39

some London rivals you know in

19:44

Arsenal and Tottenham and I

19:44

guess they always call it a

19:47

derby whenever you're playing

19:47

somebody that's that's, you

19:50

know, supposed to be a rival.

19:50

It's another way to potentially

19:52

manufacture I guess something you know...

19:54

We're playing Fulham,

19:54

that's the Fulham-London Derby.

19:54

[laughter] No, it's not.

20:00

Yeah, there you go. Yeah,

20:00

exactly right. I mean, so...

20:02

Or West Brom.

20:04

But when you look

20:04

at the EPL you've got Man U -

20:07

Liverpool you know, and you've

20:07

got Man City now and Man U

20:11

because they're both in the same

20:11

city. You've got Everton -

20:14

Liverpool because they're both

20:14

you know, Liverpoolians or

20:16

whatever you want to.. I'm gonna

20:16

mess this up, I know. But

20:19

Chelsea because they they kind

20:19

have become more popular as of

20:23

late they don't have that

20:23

historic rivalry like

20:25

Arsenal-Tottenham, within

20:25

London, you know. And so I'm

20:29

gonna go in there, and I'm gonna fill it out, and we're gonna see what we get, you know, maybe

20:30

I'll contribute to some kind

20:33

of...

20:33

Oh, yeah, I mean,

20:33

we're, I think we, we just

20:36

started collecting Premier

20:36

League data, maybe a month or a

20:40

month and a half ago. And I

20:40

think we have over 1600

20:43

respondents so far.

20:44

That's awesome.

20:44

You know, and

20:44

obviously we want, you know, as

20:46

many as we can get, because we

20:46

try and collect data from every

20:49

single club. And we also have

20:49

surveys for the lower leagues

20:52

n English soccer. I think we g

20:52

all the way down to League

20:56

wo.

20:57

I mean, talking about Chelsea, I was always thinking, what is the impact of

20:59

Pulisic playing at Chelsea

21:02

having for American fans?

21:02

Because I, when I heard that I

21:06

was thinking, Oh, this is a team

21:06

I should follow more.

21:08

Exactly. Well, and

21:08

you know, and the cynic in me

21:12

thought that was part of the reason they signed him. But he's, but he's been, you know,

21:14

he's been great. That's exactly

21:17

right. I have a lot of friends

21:17

that are fans for other teams

21:22

that have told me specifically,

21:22

they're rooting for Chelsea

21:24

because Pulisic being there. So

21:24

oh, well, big game tomorrow. And

21:28

I know that I've just dated this

21:28

podcast, so my apologies.

21:31

[laughter]

21:33

So where do you

21:33

have some interesting findings

21:37

to date, that that you can share

21:37

with us some things that, you

21:39

know, our listeners should know,

21:39

in the in the time that we have

21:42

left?

21:43

I think I'll just

21:43

mention two things kind of

21:46

briefly. One of the things that

21:46

I think people tend to find most

21:50

interesting is and these are

21:50

listed on the website on the

21:53

main page if you scroll down

21:53

but we've discerned sort of 10

21:57

elements that contribute to that

21:57

perception of rivalry. So just

22:01

to give you an example of what

22:01

those might be, one of the ones

22:04

that always comes out as being

22:04

the most important is the

22:07

consistency of competition, you

22:07

know, so, so how often do you

22:11

see that opponent in

22:11

competition, right? Another one

22:15

that is usually pretty

22:15

important, although it varies by

22:18

sport and league is the spacial

22:18

proximity. You know, you

22:22

mentioned the London derbies,

22:22

right? So you've got all of

22:25

these clubs in London, that are

22:25

not that far from each other,

22:29

and some of them closer than

22:29

others, right in terms of their

22:31

home grounds or home pitch. But

22:31

that's another one of those

22:34

elements. And so there's these

22:34

10 elements that show up over

22:37

and over. Now what I think is most

22:38

interesting is not just kind of

22:40

that we've sort of discerned the

22:40

10 elements across a wide range

22:44

of rivalries, but every rivalry

22:44

has kind of a different mix of

22:47

the elements. So it's almost

22:47

like a recipe right? And these

22:50

are the ingredients these are

22:50

the 10 ingredients. But you know

22:53

what contributes to one rivalry,

22:53

like Arsenal-Tottenham, that

22:57

recipe is a little bit different

22:57

than like you said, Man U and

23:01

Liverpool.

23:01

Right.

23:01

Man U and Man City. Right. The recipes a little bit

23:03

different you now. And so I

23:06

think that that is is pretty

23:06

interesting to see how we call

23:11

them sort of rivalries, or

23:11

derbies. But, but they're all a

23:16

little bit different from each

23:16

other, you know, but yet those

23:19

10 elements, some mix of them

23:19

seems to be pretty consistent

23:23

across, you know, the hundreds

23:23

of rivalries that we studied.

23:26

The other thing that I mentioned

23:26

is kind of this idea of

23:28

unbalance, right where one team

23:28

or one teams fans see a certain

23:34

other opponent as "oh, that's

23:34

our big rival." But that team

23:38

doesn't reciprocate...that

23:38

team's fans doesn't reciprocate.

23:41

And that's, it's really relevant

23:41

to us here in Cincinnati because

23:44

both the Reds and the Bengals,

23:44

we have lists of the most

23:47

unbalanced rivalries. And

23:47

Bengals-Steelers is on that

23:51

list. It's number two in the

23:51

NFL, and then Reds-Cardinals is

23:56

also on that list, I think at

23:56

number six in Major League

23:58

Baseball, because our opponents

23:58

when I say "our" I'm showing my

24:02

own identification...

24:03

I'm with you.

24:03

...here in

24:03

Cincinnati, you know, they don't

24:06

reciprocate those rivalries as much.

24:07

See I was about to

24:07

say "reciprocate" is the big

24:09

word, right? It's that it's

24:09

almost it's an unrequited hate

24:12

or an unrequited love notes. In

24:12

some ways, it probably makes it

24:16

even more infuriating.

24:18

Yeah, and that's part

24:18

of what we use to study. You

24:20

know, like, that's kind of the

24:20

ongoing, you know, we've kind of

24:23

discerned where those imbalances

24:23

exist quite a bit. And we're

24:27

just now kind of getting into

24:27

you know, what are the

24:29

implications of that sort of, we

24:29

call it like the big brother

24:32

little brother.

24:33

Yes, no, that is

24:33

so good. So, where's this going?

24:37

Where do you want to take it next?

24:39

Yeah, I think you

24:39

know, we did a thanks to a lot

24:43

of students help in terms of

24:43

data collection you know, we

24:45

did a big data collection

24:45

starting in college football.

24:48

We've done a couple rounds in

24:48

college football, we did a round

24:51

2013. We did another round

24:51

around 2016 of data collection.

24:54

We did the major professional

24:54

sports in the US 2018 and we

24:58

just are doing another round

24:58

right now. But as I said, we're

25:01

expanding internationally. And,

25:01

and I think what we want to see

25:05

is, is the findings that we

25:05

found in North America, you

25:09

know, how do those apply

25:09

worldwide because once you get

25:12

into some specifically soccer,

25:12

but not only soccer, or

25:16

football, if you want to call it

25:16

football rivalries, you get

25:20

much more cultural elements in

25:20

there. By that I mean, religion,

25:24

politics, immigration status can

25:24

be a big one as well tied to

25:29

certain clubs. And so, to study

25:29

those rivalries, I think we're

25:34

going to get some unique sort of

25:34

formulas of that recipe, within

25:38

the rivalry. And, and so we want

25:38

to, we want to do that. And then

25:43

another element that we want to

25:43

get into is kind of the

25:45

individual sports, you know,

25:45

where we're not talking about

25:48

necessarily sort of pride in

25:48

place, which tends to be a big

25:51

aspect of sports, identification

25:51

with teams is that they

25:55

represent a place and

25:55

individuals do also to a certain

25:59

extent, individual athletes, but

25:59

I think, you know, looking at

26:01

some of the tennis rivalries, I

26:01

know Marius is a big tennis fan,

26:04

and I am as well and Formula One

26:04

is a great, I'm a big Formula

26:08

One fan and do some research

26:08

there as well. But looking at

26:11

some of those rivalries, I think

26:11

that we'll also find some other

26:14

interesting aspects that we

26:14

don't necessarily, or I should

26:17

say that are unique, very

26:17

apparent team sport.

26:20

Sure. Our pleasure.

26:20

Very cool. Well, I

26:20

want to thank you both so much

26:21

Thank you very much. for joining us in the the cafe

26:22

today. It has been my pleasure

26:23

Informatics Cafe

26:23

is a production of Informatics+,

26:27

to speak with both of you and I

26:27

look forward to the next

26:30

opportunity for us to meet back

26:30

in the cafe and continue this

26:33

conversation. I know that our

26:33

listeners are going to

26:36

absolutely love it. And

26:36

hopefully the listeners are

26:39

oing to go and increase the f

26:39

ndings even more. I know that I

26:42

m going to and want to stay tu

26:42

ed and learn more about it. And

26:45

o thank you both for being here

26:45

today.

26:53

the outreach arm of Northern

26:53

Kentucky University's College of

26:58

Informatics. Hosted by Mike

26:58

Nitardy, produced by Chris

27:02

Brewer, music and engineering by

27:02

Aaron Zlatkin. Recorded at the

27:05

Informatics Audio Studio in

27:05

Griffin Hall.