If you’re a sports fanatic, you must have probably come across the
movie Moneyball, released in 2011. This movie is one perfect example
of how data science and artificial intelligence (AI) technologies can
be used even in the sports industry, and in the online casino industry
too as of late.

Inspired by the book, ‘Moneyball: The Art of Winning an Unfair Game’ authored by Michael Lewis, made a clear concept about how artificial intelligence and data science can easily transform tactics for the sports team.

Both technologies made quite an impact in the sports industry. This is one major reason athletes and teams across the globe are using them to keep pace with the industry trends.

This has become quite the buzz in the market – utilization of data and AI to predict future insights to make competitive strategies. You may also refer to this as ‘sports analytics.’ Sports analytics is used to predict various components in sports such as player’s performance, or recruitment, and even business performance. Such analysis can be used to make better decisions and improve the performance of a particular team.

Data analysis and predictive analysis in the sports industry

We’re all aware of the fact that data science is used to decision making while artificial intelligence (AI) along with machine learning augments valuable predictions based on the current and past information that is gathered.

In simple terms, sports analytics uses this data to help build machine learning models. The data gathered could be related to an individual performance of the player, recent history of the team’s performance, or even the weather forecast of the match day. With the help of the data gathered, the best strategy is then created to boost the overall team’s performance.

More so, in industries related to sports, predictive analysis is used for the evaluation of the team in an attempt for the team to take necessary steps during the game. Cricbuzz and ESPN are among the top sports websites that are extensively using data science and AI to make predictions of various teams playing for different league matches. The models created further helps the team analyze how the opponent might perform during the match. However, below are the top three elements that need to be taken under consideration while performing predictive analysis.

1. Player analysis: In this analysis, the performance of an individual player is evaluated. This helps the player understand what strategies may or may not work for them in comparison to the game previously played.
2. Team analysis: The entire team is evaluated to further develop the base for deep neural networks or machine learning models that will help determine the team to win.
3. The fan management analysis: This analysis does not contribute toward winning or losing of any team. However, it analyzes social media platforms such as Instagram or Twitter in an attempt to spot patterns and trends with the help of clustered algorithms. This analysis is made just to attract fans in selling merchandise.

Influence of big data in sports

Instead of relying on factors such as probability, intuition, or experiences, enthusiasts can now rely on data to make predictions. Some of the added advantages of big data in sports are:

• Boost training results with an improved workout regime
• The broadcast of the match can be personalized
• Provides advanced athlete recovery tracking resulting in an increased winning strategy
• Helps managers make better decisions

In conclusion

In the foreseeable future, the sports industry will hugely depend on technologies like artificial intelligence and data science to analyze and predict game strategy for the team.

However, the sports industry still needs to anticipate steady adoption of AI and data science to succeed in the competitive market.