Cricket Analytics Starter Kit — Data Science Projects — Statistical Arbitrage


  • The tools rely on a D/L based index which combines strike rate and runs for batsmen.
  • The same index works in an opposite direction and combines economy/wickets for bowlers.
  • The idea is to bring contribution/effectiveness to a single number and to be able to compare them.
  • The paper and some of the resources are mentioned towards the end.
  • The tools can be used separately to explore batsmen and bowlers. The data though is currently, only from IPL matches of the last few years. It might be from 2017 onwards and might have a few missing values.


Before you can play around with the data, the first question is where do you get it. Having similarity with Baseball which has a whole branch of analytics called Sabermetrics, Cricket analytics is still in very early stages.

Courses for Cricket Analytics

There are a couple of Sabermetrics courses online that should be able to give an idea or impetus around getting started with Cricket Analytics. How to define KPIs and think about performance analysis in general.


These are some blogs you can refer to, to get an idea about the work already done. The approaches that were taken and the challenges with analysis and otherwise.

Data & Processing

The most important part of being able to do any good analytics is dependent on the quality and breadth of data available. However the only freely available data sets are by Irish & English gentlemen. It is ironic as we know India is home to IPL.

Paid Historical Data

Source: Agaram Infotech

Paid Streaming Data

Source: Cricket API

Stack & Resources

It can be inferred from the two courses mentioned that SQL for data storage & R for basic statistical analysis is more than enough for standalone reporting.

  • The ball update typically had a delay of 5 seconds which in rare cases would extend to 15 sec or more. This delay was incredibly volatile and made building a live analytical engine difficult.
  • The data quality in streaming services has its own challenges involving frequent errors which would later be corrected.

Use Cases & Stakeholders

The entire idea behind carrying out this analysis is to be able to use them for some purpose. The numbers crunched can be consumed by :

  • Fans: Analytical reports can be a source of engaging news and alternate medium for fans to ponder on. This is something along the lines of FiveThirtyEight.
  • League Teams: IPL franchises and other T20 leagues are a ripe customer for such analytics. Though analytics is still prevalent, it is largely driven by video analysts who or were largely ex-cricketers. They have no statistical backgrounds resulting in the same old domain knowledge being circulated around.
  • Media/ Agencies: Fan engagement numbers and even player performance forecasts etc can be incredibly useful for advertising agencies and celebrity management firms. However, they can better price their associated players. Firms looking to advertise can make a more scientific assessment of their marketing spends.

Landscape & Opportunities

Despite the growth in tech in recent times, the majority of stakeholders who run the show(BCCI, IPL Teams) have been very slow to adopt and less willing to bet on newer possibilities. Though, it has to be mentioned that both HotStar and Dream11 have made some serious strategic moves backed by sound technical expertise.

  • You have Cricbuzz & Cricinfo dominating the content landscape. They have the largest volume of visits but suffer from poor engagement time and the fact that their offering has no direct monetisation.
  • Dream11 has the numbers in terms of paying user base and very fast-growing one but poor engagement numbers seeing the nature of their static game. The next logical step is to go for some sort of streaming.
  • HotStar has the best of both worlds, official streaming partners so not only high engagement numbers but given their recent foray into fantasy, they might eat into Dream11’s pie.



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