Sentiment analysis helps detect NBA players’
pre-game moods from their tweets
and predict their on-court performance.
BY CHENYAN XU, YANG YU, AND CHUN-KEUNG HOI
THE SPORTS INDUSTRY is big business globally and
domestically; 10 for example, the New York Knicks
of the National Basketball Association (NBA)
generated $287 million in revenue in 2013.2 In order
for sports organizations to maximize their financial
performance, they must win on the court. The
operational staffs, including coaches and general
managers, must consistently make the right decisions
despite many constraints, including a league-imposed
salary cap and team budgets. Sports analytics plays an
increasingly important role in such decisions.
Sports analytics traditionally involves statistical
techniques for analyzing historical player performance.
General managers have used it to build their rosters
and coaches have used it in conjunction with their
domain knowledge to adjust lineups and improve
players’ on-court performance. Though
ongoing sports analytics research and
practices center mostly on the struc-
tured data of player profiles and histori-
cal performance, 1 this article explores
the extent NBA teams can use “unstruc-
tured” social media data to further
their sports analytics efforts. This novel
focus is motivated by the prevalence
of social media analytics in all kinds
of business domains over the past five
years. Specifically, our objective is to
show how NBA players’ pre-game emo-
tional state, as captured through their
tweets, or the messages they post on
Twitter, before a game can help predict
on-court performance in the game.
The framework we propose here can
be used to inform decisions regarding
game-day player assessment, lineup
changes, and on-court strategy. Given
NBA teams’ longstanding positive attitude toward and practice of sports
analytics, the league is an appropriate
context for illustrating the new role we
envision for sports analytics.
We begin by exploring the widespread use of Twitter among NBA players, then how our framework could
benefit sports analysts in the NBA
and their potential motivation for
adopting the approach. This discussion includes justification for why the
framework and potential future relevant work could interest analysts and
motivate software developers to add
processing of unstructured data (such
as social media content) to their commercial products. We next detail how
social media analytics can be incorporated into analysts’ decision-making
processes and follow with a case study
in NBA Players’
˽ Sentiment analysis, an emerging
text-mining technique, can help
analyze the tweets of NBA players.
˽ Coaches, general managers, and other
staff of NBA teams can use it to discern and
address players’ moods before games.
˽ Our study shows players’ before-game
mood, as captured through the sentiment
they reveal in their tweets, is positively
associated with their on-court performance.