The concept of Data Analytics in Sports was caught by the wider public eye in the Hollywood movie called “Moneyball” (2011), where the protagonist used technology and real-time data to analyze the team’s performance and draft strategies to make a win. With better technology combined with real-time video data capture sports analytics has become one of the most dynamic fields.
For some team sports, the data is measured and collected from the field in real-time and analysis is done off the field post the game. The game is then reviewed and conclusions being made, with the help of analytics, will be implemented during the practice sessions and the following games. The best examples of this scenario are Soccer, Cricket, Basketball, etc.
Why Sports Analytics?
With advances in data collection and management technologies, sports analytics has broadened its scope remarkably. Imbibing data and statistics into sports has become an important part of the game plan in most of the sports. Moreover, the use of analytics contributes to the success of the field and the ticket counter too.
Sports Analytics broadly –
- – Helps teams to enhance their performance.
- – Drives customer engagement data from digital platforms, as well as from the stadium. This data will benefit for
ticket pricing, staffing on the game day, etc..
- – Helps in drafting targeted marketing strategies throughout a broader ecosystem.
- – Improves human resources procurement, supply chain management, and logistics.
Can “one size fits all” applied to Data Analytics for all sports?
Definitely NOT! Each sport is different with different kind of data to be measured and the analysis will vary with the methodology. Every sport will have different testing metrics to measure, like player profiling, distance management, etc. Identifying inventive ways to use these metrics in the most unconventional ways gives the much needed analytical advantage.
The unique element common in all the sports pertains to predictive analytics, requiring more data for better results. With meager data, it will be difficult and not helpful to make predictions about the game.
With less sophisticated data, the team composition may vary. The ideal example would be the Indian Cricket Team. For the selection of the team for World Cup 2019, the Data Analysts pulled out the data of each and every player since 2017. Selectors were provided with deeper insights on every player – like a certain player’s strike-rate during a particular phase of the match, a batsman’s scoring areas in middle overs, a player’s success rate during the final overs, data on the opposition teams and the pitches where the matches were played, etc. Without this level of data, it would have been a grueling task for the selectors to select the best 15. Though the team has lost a spot in the finals, the performance of each and every player was much appreciated.
The Game Ahead
Though the sports industry is gaining tremendously by adopting data analytics, it certainly falls behind when compared to other industries and their analytical capabilities to gain competitive advantage. There certainly are some areas for improvizations in any sport.
- – Drive more investments into analytics. It’s understood that the sports authorities will be budget-crunched and so they will have to compromise on the analytics part to a certain extent, considering that the salaries that need to be paid to the players will be on the higher side, the operational expenditures will be expensive, etc. In such situations, it will be difficult to invest in the latest technology, like video and GPS, to acquire data for analytics.
- – Management and the team should be on the same side of the page and should agree on the fact that analytics will drive major decisions.
- – Stay ahead in gaining the analytical data. Teams that adopt data generating technology early in the cycle can swiftly develop relative analytical capabilities. Analytical abilities are copied quickly and the only way to gain a competitive edge is through innovation in application and execution.
- – Involving players in analytics will be an added advantage as they are the missing link between planning, analytics, and execution. Player-level health and injury analytics will also enable the authorities to make informed decisions about the players.
- – Leaning more towards predictive and prescriptive analytics will be beneficial rather than opting for descriptive analytics. Staying contended with the data gathered from descriptive analytics offers no guide to the future course of action.
Sports analytics have enabled many businesses with better decisions with data and analysis. But, many businesses are more active with analytics and have more analytical capabilities than the sports teams. Therefore, it is time for sports to embrace data!