Data Annotation And AI-Powered Sports Analytics

1
Data Annotation And AI-Powered Sports Analytics
In the Hollywood movie Moneyball, Billy Beane,
the manager of the Oakland As uses statistical
analysis and previously unused metrics to build
his team based upon identifying players that were
undervalued relative to their impact on winning.
The result his team became more competitive and
ultimately went on a 20-game winning streak.
2

• Can analytics be used to improve the performance
  of individual athletes or entire teams? Yes, and
  the domain of sports analytics has experienced
  somewhat explosive growth over the past decade.
• Sports analytics is now being used to
• Increase revenue
• Improve player skills and performance
• Enhance teams quality of play and win/loss
  performance
• Prevent major injuries, extend careers, and
  minimize games missed
• Thanks to the intense competition among leading
  teams across sports, theres a growing demand for
  sports analytics (and analysts). In fact, the
  global market for Sports Analytics is expected to
  grow to upwards of 4.5 billion by 2025.
• Statistics have played a central role in modern
  sports for quite a while. More recently, the use
  of Artificial Intelligence (AI) in the sports
  domain is further transforming the way teams
  identify opportunities and develop winning
  strategies.
• How do sports analytics and AI together add value
  for teams? Lets take a look.
• 1. Player Scouting
• Depending on the sport, scouting for and
  optimizing talent is costly and time-consuming.
  Team owners and managers need to ensure they have
  the right mix of players in their squad while
  managing their overall team salaries. AI, and
  more specifically, machine learning
  algorithms, analyze player-related data
  including past performances, playing videos,
  and skills, to make the right personnel
  decisions.

3

• One recent example is Sean Durzi, a 19-year-old
  defenseman playing in the Ontario Hockey League
  was found as a result of using Assistive AI
  and sports analytics, which ranked Durzi as one
  of the top available players for the NHL draft.
  Durzi was subsequently drafted and is now playing
  on the roster of the LA Kings.
• Performance Improvement
• AI-driven sports analytics is also being
  leveraged to improve individual player
  performance by providing real-time feedback and
  helping to build personalized training programs.
  Overall, team performance depends upon pre-game
  and in-game strategies and tactics. For example,
  AI and sports analytics can help determine team
  selection (or lineups) based on prevailing
  conditions, the opposition team, and individual
  player matchups.
• AI can also be used to improve sports performance
  by understanding player metrics. For example,
  ball handling, release time, vertical jump, and
  shot accuracy for basketball. Similarly, in
  cricket, players are using sensor-enabled bats to
  measure shot quality and detect the point of
  impact, and the speed of a delivered ball (in a
  simulated mode).
• Further, team coaches and support staff are using
  data to make micro-decisions about workload
  management and game strategies. For
  individual players, wearable sensors in the form
  of smart clothing or fitness bands can measure
  and track their progress. For instance, wearables
  can now measure the players breathing and heart
  rate along with their hydration levels and body
  temperature, which can determine individual
  player-level training and nourishment needs.
• Predictive Analytics
• AI-based predictive models can analyze matchups
  and predict outcomes. A well-known example of the
  use of predictive analysis in sports is that of
  the Boston Red Sox baseball team. By predicting
  the performance of its players, this team managed
  to win three World Series titles after 86 years
  of heartache.

4

• ML models can also predict which baseball player
  can perform better at a given position on match
  day. These models are built on player parameters
  such as
• Previous player performances against a team (or
  individual)
• Match conditions on the day like weather and
  playing surface
• Player statistics at home or away
• Another classic example of predictive analytics
  in sports is that of Formula 1 racing, where
  participating teams integrate telemetry data from
  thousands of sensors generating millions of data
  points every second to optimize in-race
  strategies like the number of pit stops, tire
  changes, and overtakes.
• Every professional team in the NBA now has
  computer vision systems to track the movement of
  the ball and players moving around the court.
  They analyze this data using machine learning to
  identify player effectiveness on both offense and
  defense and in specific circumstances like
  pick-and-roll execution.
• Fan Engagement
• Sports fans today want to experience more than
  simply watching their favorite team (or player)
  in action. They are now looking for more
  immersive and engaging experiences. To meet that
  demand, sports teams can track fan movement and
  purchase patterns during home games and create
  opportunities for on-site engagement based on
  that information. In addition, teams can
  enable personalized engagement with their most
  loyal fans through social media.
• Sports Analytics (along with AI
  recommendation engines) can help sports
  franchises determine
• What kind of merchandise fans prefer?
• Which events and games are they more likely to
  attend?
• Who are their favorite players?

5

• The Complexities Of Sports Analytics And The
  Role Of Data Annotation
• For effective analytics, sports teams need to
  crunch massive amounts of data from diverse
  sources. Millions of fans attend sporting events
  each year. For example, the regular-season games
  of all National Football League (NFL) teams were
  attended by a total of 16.67 million spectators
  in 2019. This generates a large amount of data
  available for analytics.
• That said, data sources in the Sports domain can
  be extremely diverse and complex as it is flowing
  through IoT-enabled devices, social media
  platforms, and various sports apps. Concerns
  about data privacy or data storage add complexity
  to the analytics processes.
• This is where data annotation can help simplify
  massive volumes of data and make it
  analytics-ready. Some examples include
• The use of bounding box annotation helps
  capture pictures and provide training datasets
  for AI-based models.
• Video games can be enhanced by analyzing and
  annotating human actions and postures.
• Annotation of recorded sporting events provides
  detailed and labeled information which can then
  be used for advanced analytics.
• Conclusion
• There is tremendous potential to leverage sports
  analytics to improve player and team performance.
  Data-driven insights are growing the number of
  use cases and applications, leading to improved
  decisions by professional sports teams. To handle
  complexities associated with sports analytics,
  data annotation can help deliver relevant data
  and accurate insights to decision-makers.

6
EnFuse Solutions has significant experience in
the field of data annotation in support of AI and
data analytics, helping companies reap maximum
gains on their initiatives. If you are looking
for assistance in the area of sports analytics,
reach out to us to learn how we can help you
succeed. Read More Key Skills That Data
Annotation Experts Must Possess