Current Climate of Sports
Analytics
Despite the increased availability to data and analysis
techniques that can potentially aid sports management organizations of
decisions, many teams do not use the tools available to them.
Interest in the sports analytics field has been rapidly
growing as of late, highlighted by the book and film, Moneyball, which not only
grossed over $75 million but also drew attention to the significant potential
digital analytics holds within the sporting environment.
While the term ‘Moneyball’ was coined by author Michael
Lewis in his 2003 book to describe a strategy that utilizes analytics, “Sports
Analytics” involves the gathering and collection of data, data management,
statistical analysis, data visualization and information systems to deliver
better information more efficiently to decision makers within an organization.
The technology behind these tools has advanced rapidly in the last decade,
making access to immense amounts of data more readily available than ever
before. [1]
Evolution of Analytics within
Sports
The digital analytics field itself is still a developing industry
therefore asking executives, managers, coaches and players to embrace
analytical tools and techniques is a difficult task within the high pressure
field of professional sports. Organizations are hesitant to expand their investment
in a sports analytics program without an understanding of a clear proven way
forward and sense of potential value. While sports analytics will continue to
evolve as a field the following are challenges facing adoption of these tools
and techniques: [1]
·
Natural
inclination to resist change - Individuals and organizations both internal
and external to the sporting environment are naturally inclined to resist
change, making the shift toward basing major decisions on data and analysis
driven models a slow moving process.
·
Distrust
of the unfamiliar - Team executives are unfamiliar with statistical
modeling, therefore have a predisposed distrust or discomfort with analytic
tools and techniques.
·
Old-school
traditional decision making vs. progressive data driven decision making – Similar
to the business world, decision making in the sports industry is intuitive and instinctual.
Shot-calling executives may have started as scouts or coaches, which had to rely
on personal experience to make decisions. These same decision makers would view
the use of digital analytics and data models as non-credible resources. As this
video clip from Moneyball
shows, organizational friction can occur in regards to how analytics should be
utilized and trusted when making player personnel decisions.
·
Technical
Barriers – General communication barrier between analysts and front office
of a sports franchise, as typically those with decision making power in an organization
are not familiar with the language of analytics.
·
Limited
financial resources to spend on analytics
·
Implementation
- Turning the data gathered from statistical tools into useful data is the
main challenge teams now face as they implement new data collection and
analysis methods. Then a team must determine what tools and techniques create
the best value to help the team win. [2]
The pace of adoption and evolution in sports analytics will
depend on how quickly leaders are aware that significant investments into
analytics will deliver a true competitive advantage. Within the past few years
increased knowledge sharing and development of these analytic programs have
come to the forefront of the sporting environment. Global conferences and
college courses aid in improving communication skills between sports, analytics
and data sciences.
The ‘Who’, ‘What’, and ‘Why’ of
Sports Analytics
The expansion into sports analytics territory also comes
with many questions. Understanding the strengths and weaknesses in your
organizations can help guide how sports analytics are best utilized. Being able
to harness its analytic capabilities and work past obstacles can create a
significant competitive advantage on the field
Who can use
Analytics?
·
General
Manager and Executives – making player personnel decisions
·
Coaches
– reinforces gut instinct or uncovers something not previously seen, whether
this be tactically or personnel related. As one NBA head coach noted “It’s a
good backup for what your eyes see, but we can’t make all your decisions based
on it; the tools can’t measure heart, chemistry and personality.”
·
Players
-
o
Assists in injury analysis - understanding when
to taper off individual training schedules to avoid overuse injuries and ensure
players are kept fresh for game day
o
Performance analysis – objectively know players
are in optimal condition going into games by monitoring training load and determine
who isn’t working hard enough
o
Technical analysis – tracking individual
movements to improve tactical analysis [2]
·
Referees
- Improve
officiating of games by using analytics to determine if a refs positioning and
site lines were appropriate based on the calls which were made
·
Fans –
Digital analytics can also help improve fan/consumer experience, by putting
meaningful numbers in front of fans and GMs alike, rather than outdated meaning
less statistics. [3]
What is
used to gather sports analytic data?
The following companies are changing the way data is
collected as well as the types of data available for consumption:
·
STATS LLC –
Utilizes cameras and optical tracing technology to capture the positioning of
everything that moves on a court or field of play, from the players to the ball
to the referees. This data can be captured at a rate of 25 times a second,
critical in high movement sports. [5]
·
Catapult Sports – Utilizes GPS, accelerometers and other wearable technology to
track players movements and physical characteristics such as heart rates.[4]
Video: How does the Catapult system collect data from athletes?
Why are
sports analytics effective?
Digital analytics are causing a major shift in the type of
data available in sports from specific court/field actions (attempted shots,
passes, rebounds etc.) to data drawn from continuous movements of every element
within a play. Analytics also play a large role in front-office player
personnel decisions.
Examples:
·
The Rockets signed Carlos Delfino last season
in part because the camera data revealed he grabbed an unusually large
percentage of rebounds that fell near him, very valuable for a player in his
position. [3]
·
Regarding the analysis of officiating, “We will use whatever data and means we
can to improve our referees,” says Steve Hellmuth, the NBA’s executive vice
president of operations and technology. “The refs haven’t been tracked before.
Now for the first time, they will be.” [3]
·
The Moneyball strategy is a prime example of how
sports analytics can be utilized, implemented and developed into a value added
investment. The Moneyball strategy is a concept used to identify undervalued
players, so that teams with lower payrolls can still compete at a high level.
Currently both the Oakland A’s and the Tampa Bay Rays have followed this
strategy to success, making the 2013 playoffs despite being in the bottom 5 payrolls
league-wide. [6]
·
Data visualization showing advanced player
statistics and movement tracking
Sports Analytics Overview
With today’s speed of computing, increase in processing power, as
well as economies of scale sport organizations are more able than ever to move
toward implementation of a data-driven sports analytics model, with the end
goal being able to create a competitive advantage on the field of play.
Along with the significant leap forward in technology in terms of
data gathering, through companies such as Stats LLC and Catapult Sports, more
knowledge is readily available for those franchises willing to move forward into
this field. Benjamin Alamar, an industry leading sports analytics consultant,
speaker and author of “Sports Analytics”, has successfully has helped teams,
businesses and individuals establish analytic systems – translating facts,
figures and other data – into usable information.
“The
franchises who are successful in truly leveraging analytics will be those that
come to see data and model results as the mechanism through which information
(unstructured text as well structured data) is transformed to deliver insight
to decision makers in a well-contextualized format” - Ben Alamar, whose clients
include San Francisco 49ers, ESPN, Portland Trailblazers, OKC Thunder, among
many others. [7]
Building more effective and unified communication channels
between digital analytics and the sporting environment is vital to the success
of a sports analytic program. This will enable the analysts, coaches, players,
scouts and general managers to work with one another to improve the team and
the ultimate goal: Wins.
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[3] Lowe, Zach. “Seven Ways the NBA’s Camera System Can
Change the Future of Baseketball.” http://grantland.com/the-triangle/seven-ways-the-nbas-new-camera-system-can-change-the-future-of-basketball/.
4 Sept 2013. Web. Feb 2014.
[7] Alamar, Benjamin. “Sports Analytics”. http://www.alamarsportsanalytics.com.
Aug 2013. Web. Feb 18th.
