Three Surprising Ways IPL Teams Leverage Data Analytics during Auctions

Cricket commentators are often found explaining a player’s performance, his strike rate against a particular bowler and even match winning percentages based on tons and tons of match data collected over time.

The trend is an indication of how important data science has become in team strategic decisions. Nowadays, players are using their personal data analytics to improve performance, and every team is using big data analytics to handpick their dream squad in IPL auctions.

IPL has ten years of every player’s ball to ball performance data that can offer strategic insights in creating the dream team.  Back in 2014, SAP’s auction analytics helped KKR draw the best lot at the auction and emerge as the winning team claiming the trophy. There are several game and performance analytic firms that are hired by IPL teams to calculate their path towards victory right from team selection to game strategy and fan engagement.

The auction this year too witnessed the use of data analytics and AI to maximize the winning chances even before the game starts. IPL teams have been able to come up with novel strategies by leveraging big data and often creating possibilities for young players to become match-winning IPL heroes.

Also, the IPL 2018 auction which was conducted last week, witnessed some surprises. There is little doubt now that big data played a key role in making these surprising decisions! So let’s find out how IPL team utilize the power of data science.

Catching them early

In IPL, once a player gets fame, his price goes sky high and teams find it difficult to get such players again. It becomes important for teams to use analytics and grab emerging players early.

By establishing a player’s strength and weak spots, analyzing performance statistics against different cricketing conditions, and studying the current form, IPL teams are able to choose the right player for the team and train him with the right skills needed for the team.

Choosing right player over best player

Many big players like Malinga did not hear the knock of a hammer this year, while Chris Gayle was picked at the last moment of the auction by Kings XI Punjab. Uncapped players, on the other hand, rocked the show. Rahul Tripathi, Krunal Pandya, Deepak Hooda and Shubman Gill are some of the youngsters favoured by auction analytics against inconsistent celebrity players. This was thanks to their impressive performance figures through recent IPL performances.

Value for Money

While Rajasthan Royals took the most lavish move by bagging Ben Stokes, the most expensive player out of all 169 players bought in the auction, many uncapped players earned more than their base value. The dynamic and unpredictable nature of IPL auction makes data metrics, as well as statistical analysis, essential tools for strategic auction day planning. Player performance profiling allows franchises to make team-oriented decisions during auctions. Teams keep multiple player options with the same strengths to bid for a particular team position.

Auction analytics provide real-time decisions based on player performance, current bid value and his strategic importance in team composition. In IPL auction 2018, Yuzvendra Chahal was favoured by game analytics as a match-winning asset and he closed the deal for 6 crores.

Big data, bigger possibilities

Big data and data analytics have immense scope in virtually every field. Big data is leveraged by production houses, healthcare institutes, insurance sector and finance firms to create meaningful strategies derived from consumer insights. Data science training from a reputed brand like Manipal ProLearn can help you start a career in big data.

Written By

Aditi Bhat