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D A T A & A N A L Y T I C S

Thomas H. Davenport

What Businesses Can Learn From Sports Analytics The use of analytics in the sports world has much to teach

managers about alignment, performance improvement and

business ecosystems.

Research Highlight June 3, 2014 Reprint #55410 http://mitsmr.com/1h4FHgs

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I N T E L L I G E N C E

[ANALYTICS]

What Businesses Can Learn From Sports Analytics The use of analytics in the sports world has much to teach managers about alignment, performance improvement and business ecosystems. BY THOMAS H. DAVENPORT

Sports analytics are all the rage now. The

Moneyball story about the Oakland A’s use

of analytics has made its way into the collec-

tive consciousness, and the appetite for

more knowledge about the field has steadily

increased year by year. The MIT Sloan

Sports Analytics Conference, for example,

has grown from about 175 attendees in its

first year in 2007 to more than 2,000 in

2014. Called the “Super Bowl of sports

analytics” and “TED talks in cleats,” the

conference has catalyzed academics, profes-

sional and college teams, and the press to

focus much more heavily on analytics to

understand various aspects of sports per-

formance and business. Almost ever y

professional baseball team now has at least

one professional quantitative analyst on

staff, and many basketball, football and soc-

cer teams do, too. Even some high school

teams now employ quantitative analysts.

In general, however, sports teams are

still lagging behind businesses in their use

of analytics. For one thing, even the most

successful pro teams are still relatively

small businesses that can’t feasibly employ

hundreds of analysts like a large bank or

retailer can. Also, many old-line coaches,

managers and executives don’t trust or un-

derstand sophisticated sports analytics.

And, as far as its application on real teams

is concerned, the discipline is still in its

infancy. The Moneyball story about the

SUMMER 2014 MIT SLOAN MANAGEMENT REVIEW 11SLOANREVIEW.MIT.EDU

Oakland A’s took place in 2002, when

sports analytics was quite new. In contrast,

the first analytics group I have found in

businesses dates from 1954 at United Par-

cel Service (UPS).

Despite this, businesses can still learn

much from the use of analytics in the sports

world. I recently interviewed more than

30 representatives of teams, sports analytics

vendors and consultants for a report on

the state of the art in sports analytics.

(See “Further Reading.”) I focused on three

different areas of activity, each of which

is growing rapidly. In order of decreasing

prevalence, they are: team and player

performance analytics, sports business

analytics, and health and injury prevention

analytics. In this article, I describe five key

lessons from that research that almost any

business could adopt.

1. Align leadership at multiple levels. In sports, key decisions — which players to

acquire, how much to pay them, and which

strategies to adopt for better athletic and

business performance — must be made

and overseen at multiple levels. As a result,

alignment along different management

levels is crucial.

Consider the Dallas Mavericks, a team

in the National Basketball Association

(NBA). Owner Mark Cuban and coach

Rick Carlisle are strong supporters and

users of analytics, and they’ve hired the

well-known analyst Roland Beech, who

actually sits on the bench during games.

After the Mavericks won the NBA Cham-

pionship in 2011, Cuban, a former Internet

entrepreneur, commented to ESPN that,

“Roland was a key part to all this. I give a

lot of credit to Coach Carlisle for putting

Roland on the bench and interfacing with

him, and making sure we understood ex-

actly what was going on: knowing what

lineups work, what the issues were in terms

of play calls and training.”

The business equivalent to the Maver-

icks would be for CEOs, middle managers

and analytical specialists to be working

closely together and consulting frequently

with each other on key decisions. A few

companies like Procter & Gamble have

placed “embedded” analysts from a cen-

tralized analytical group in close proximity

to executives (in effect, placing them on

the bench with the coach), but, in business,

that is far more the exception than the rule.

2. Focus on the human dimension. Sports teams realize that their players are

both their most important and expensive

resources. (Businesses might mouth the

same sentiments, but they don’t act on

them in the same fashion.) Professional

sports teams focus on the human dimen-

sion of performance in a variety of ways.

First, they address individual-level

game performance by monitoring points

scored, rebounds gathered, batting aver-

ages and other increasingly sophisticated

measures of both offensive and defensive

performance. Indeed, analysts employed

by teams (and quantitative-minded fans)

are constantly inventing new performance

metrics, some involving video and loca-

tion-based tools such as GPS and Wi-Fi.

Second, teams are beginning to assess

not just individual performance, but per-

formance in context. In basketball, for

instance, analysts can determine how a

team performs with or without a particu-

lar player. This is called “plus/minus”

analysis. So, even if a particular player

doesn’t generate impressive individual sta-

tistics, he may still be invaluable in a game

if the team tends to perform much better

when he’s playing. And it’s also possible to

assess a team’s performance with and

without combinations of players. Shane

Battier, who now plays for the Miami Heat,

is a notable plus/minus hero: His team

simply plays better when he’s on the court.

In most businesses, analytics have typi-

cally focused on operational or marketing

issues and not on the human dimension of

performance. Even when companies do

employ human resource analytics, their

approaches are not as sophisticated as those

of sports teams, and thus far they have been

applied only to individuals. But assessing

employees by investigating group perfor-

mance with or without a particular person’s

presence could be a valuable technique. The

effectiveness of a large company’s B2B sales

teams, for example, could be evaluated

across different team compositions for

various customers to identify the “Shane

Battiers” in the sales organization. Teams in

retail stores or bank branches could also be

analyzed with such plus/minus approaches.

3. Exploit video and locational data. In Major League Soccer (MLS), players wear a

GPS-based locational device that captures

all movements around the field. In the NBA,

six cameras in the ceiling of each arena cap-

ture all movements of the players and ball.

All Major League Baseball (MLB) stadiums

have cameras that track every pitch, and

many teams also track every hit and fielding

play with video cameras. Armed with such

data, the New York Yankees — latecomers to

Moneyball-style analytics usage, but now

aggressive adopters — can now predict

which players would likely succeed in Yan-

kee Stadium. After analyzing the trajectory

of the fly balls hit by Atlanta Braves catcher

Brian McCann, for instance, the team deter-

mined that many of them would have been

home runs at the Yankees’ home field, and

the Yankees signed him to a five-year,

$85-million contract.

Businesses are likewise beginning to

make use of video and GPS-based location

data. Transport companies such as United

Parcel Service (UPS) and Schneider Na-

tional, for example, are already reaping

FURTHER READING �T.H. Davenport, “Analytics in Sports: The New Science of Winning,” white paper available with registration at www.sas.com/sportsreport.

(Continued on page 12)

12 MIT SLOAN MANAGEMENT REVIEW SUMMER 2014 SLOANREVIEW.MIT.EDU

I N T E L L I G E N C E

substantial savings from optimizing their

routes based on such information. And re-

tailers, banks and lodging companies are

beginning to analyze videos of customer

lines in order to minimize waiting times to

improve customer satisfaction. But such

efforts are only scratching the surface of

possibilities. Retailers could, for example,

analyze video to determine what customers

shop for before they buy and what promo-

tions catch their attention. In such ways,

video could give “bricks-and-mortar”

stores the kind of detailed information

online retailers have.

4. Work within a broader ecosystem. Professional sports teams are relatively

small businesses, with much of their reve-

nue going toward player salaries, leaving

just nominal funds for any data and ana-

lytics projects. As a result, teams often need

to work within a broader ecosystem of

data, software and services providers.

The key in these partnerships is to draw

as much as possible from the partner while

maintaining key internal capabilities. At the

Orlando Magic, for example, the analytics

and strategy function has established a close

relationship with a software vendor for ana-

lytical software and services, but the team’s

eight analysts maintain expertise on such

topics as basketball and business operations,

dynamic ticket pricing, fan promotions, dig-

ital strateg y and so forth. The Magic

organization has also learned a great deal

from its partnership with the Walt Disney

Co. — which has a strong analytics group in

Orlando — for joint promotions to resi-

dents and visitors in the area.

Of course, working within an ecosystem

often means sharing ideas, and there’s a

trade-off between the benefits of broad

adoption among competitors and gaining

competitive advantage through exclusive

early adoption. Now that entire leagues such

as the NBA, MLB and MLS have adopted

new data technologies, the vendors of those

technologies can help the individual teams

by producing standardized and customized

reports. (Fan access to many of these reports

also helps build interest in those sports.)

Even then, teams that were early adopters

feel that they can maintain an advantage

even after broad adoption. The Houston

Rockets, for example, were among the earli-

est teams to adopt cameras in their arena.

Daryl Morey, the team’s general manager,

admits that other teams have now caught up

after the league-wide adoption of arena

cameras (and he appreciates now being able

to get data from all teams and games). But he

still feels that the Rockets have an edge be-

cause they have more analysts, experience

and motivation in using the data.

Working in a broader analytic ecosystem

might be particularly important for small to

medium-sized businesses, but it is also rele-

vant to large companies. There are just too

many different techniques, types of data and

other aspects of analytics to exploit, and even

the largest corporation can’t excel on its own.

Procter & Gamble, for example, has built

close partnerships with several key vendors

of data and analytical software and services.

Together, P&G and its vendor partners have

codeveloped “business sphere” rooms for re-

viewing and acting on data and analyses;

there are now more than 50 such rooms in

different P&G facilities. The company has

also shared its approaches to analytics with

other leading companies like BP, Boeing,

Disney, General Electric and FedEx. Both the

vendors and the peer companies meet at an

annual conference P&G calls “Goldmine.”

5. Support “analytical amateurs.” Some professional athletes have begun to analyze

their own performance in depth using pub-

lic or team data and reports. Specifically, a

number of soccer and football players have

become assiduous reviewers of their video

and GPS data, although the most frequent

users have been professional baseball play-

ers, particularly pitchers.

Consider Brandon McCarthy, who cur-

rently plays for the Arizona Diamondbacks.

The 30-year-old pitcher was previously with

the Texas Rangers, where in 2009 he had a bit

of an analytical conversion. Before then, few

would have labeled McCarthy a statistical

geek; he was drafted out of high school

and never went to college. But during a

three-month rehab for a shoulder injury,

McCarthy began studying his data relative

to more successful pitchers. His particular

focus was on the Fielding Independent

Pitching (FIP) metric and the ratio of

ground balls to fly balls. He realized that his

pitching style led to too many fly balls, which

created more than twice the expected value

of runs than ground balls. Thus, according

to ESPN Magazine, he began to work on a

two-seam fastball, which induces grounders

at a higher rate. McCarthy’s performance

improved dramatically: For the 2011 season,

he had the lowest FIP for a starting pitcher in

the American League. He was throwing sub-

stantially fewer pitches, and hitters were

hitting fewer fly balls. Although the num-

bers of such “analy tical amateurs” in

professional sports are not yet huge, several

players like McCarthy have made substantial

improvements in their performance with

the help of analytics.

Business managers and professionals

may not have as much data available on their

performance as professional athletes do, but

What Businesses Can Learn From Sports Analytics (Continued from page 11)

If, for instance, the most successful sales professionals tend to spend at least 10% of their time on lead generation, then average and low performers could adjust their daily work routines accordingly.

SUMMER 2014 MIT SLOAN MANAGEMENT REVIEW 13SLOANREVIEW.MIT.EDU

they could still benefit from becoming ana-

lytical amateurs. Many companies have

evaluation and compensation models with

measurement along particular criteria. Mo-

tivated employees could keep track of their

own scores on these measures and use that

information to improve their performance.

Analytics-minded salespeople and manag-

ers could, for example, use the extensive data

from customer relationship management

(CRM) and sales management systems to

assess and improve their performance. If, for

instance, the most successful sales profes-

sionals tend to spend at least 10% of their

time on lead generation, then average and

low performers could adjust their daily work

routines accordingly to ensure that this im-

portant task isn’t given short shrift.

PROFESSIONAL SPORTS TEAMS and leagues

have emulated businesses’ analy tical

approaches in various ways, including

identifying and rewarding the best custom-

ers (typically season-ticket holders) and

optimizing ticket prices (a practice that air-

lines started more than two decades ago).

But now, as sports analytics becomes in-

creasingly sophisticated, companies can, in

turn, learn much from the successful ana-

lytical teams like the Boston Red Sox, Dallas

Mavericks and San Francisco 49ers. Indeed,

in the past, many managers would use

sports metaphors in a figurative sense (“We

need to swing for the fences on this proj-

ect”); today the lessons from the sports

world have become far more literal — and

analytical.

Thomas H. Davenport is the President’s Distinguished Professor of IT and Manage- ment at Babson College in Wellesley, Massachusetts, as well as a research fellow at the MIT Center for Digital Business and a senior advisor to Deloitte Analytics. Com- ment on this article at http://sloanreview .mit.edu/x/55410, or contact the author at [email protected].

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