Have you ever been to a meeting, where everyone in the room has good stats to share, but no one knows how to use them? I am sure, you have! Don’t worry, you are not alone and by end of this article, you will have some tips to make your analysis better.
In this article, I will challenge how analytics is being showcased during the cricket matches. While the display is a good way to evangelize use of analytics on grand scale, it is missing the very point it should be proving!
The message I want to leave through this article is far broader than just application to cricket. Example of cricket is being used only as a case study.
P.S. If you don’t follow cricket, you can still follow the article for the points I am trying to tell. Here is a beginner’s guide to Cricket.
Example of insights shown during a cricket match:
Here is an example of what spectators glued to the screen would typically see at the start of the match:
- Keys to success for Team A:
- Team A wins 85% of matches when Player X scores more than 80 runs
- If there are 2 partnerships of more than 50 runs, team A wins in 72% matches
- Team A wins 70% matches, if bowler Y picks up 4 wickets
There will be similar set of so called “insights” for the second team as well. The analysts performing this analysis have also attached probabilities with each event!
Here is a live example, which was shown in a recent match (South Africa vs. New Zealand):
These look good and exciting! What is the problem with this? Can you spot problems with these so called keys to success?
The problem with current insights:
While these insights are good to see, they do not help the teams play better. Let me explain – an insight saying that team A wins 85% of matches when Player X scores more than 80 runs is useless to the team coach and the captain.
It is an insight, but not an actionable insight!
The team management would want analytics to do much more than just pulling out this insight and then praying that Player X has a good outing in every match!
The current insights provide no help to the team management to utilize this information.
If I was the analyst running this model – I would go further and say what is the best strategy to make sure Player X goes on to make a big score – Which position should he bat at? What kind of areas and which bowlers should he target? Which bowlers should he negotiate?
Let’s take another example – analysts have mapped out the strong zone and the weak zone for each batsman. You would think this is clearly actionable. The bowler just needs to ball in the right areas.
But it isn’t! Why? Strong zone and weak zone for a batsman would change from bowler to bowler, with different field settings and different whether conditions. It would also depend on the current form of both – the bowler and the batsman.
Other way to look at these insights is this – they have a lot of numbers and stats, but don’t really tell what they mean.
Ways to improve insights (in Cricket matches):
As shown already, the current level of analysis shown during cricket matches is rudimentary at best! There are tons of ways to improve this analysis. I’ll share a few high level thoughts, which, when implemented would surely provide better use of analytics:
- Right level of granularity: Some of the insights shown in these matches are very high level in nature to be useful. The analyst who created the key to success in above case study has not done the analysis at the right level of granularity. For example, if you look at the keys to success for South Africa – the first point mentioned is that they should score more than 280 runs! Let’s look at the granularity here – all matches played outside Asia since 2011? Even a lay man would say that the pitches prepared for World Cup have favored batsman and 300 has been a par score, which is usually not the case. The analyst did not look at the right level of granularity to take into account the right nature of pitch.
- Focus on creating actionable insights from user perspective: Like any other piece of analysis, the focus of the analysis has to be the end user. If the analysis is for team management, then we need to bring out insights to improve performance of the players. If it is the audience watching on the TV, then it has to answer the questions which would be going on in mind of the audience. For example, there can be widgets showing the probabilities of win for each team.
- Additional features / variables should be considered: Sadly, in almost all these insights, there are a lot of features which are missing. For example: current form of the players, the way in which a batsman paces his innings are missing from the current analysis. Needless to say, the more feature we add, the more insightful and helpful the analysis would be.
- What if scenarios and alternate strategies: Finally, in order to make this analysis more useful and fun, there could be displays of what-if scenarios. So, what do the analysts predict, if bowler X comes up to bowl vs. if bowler Y comes to bowl. Similarly for batting as well – how does the scenario likely to emerge if the captain decides to send player X instead of Y on fall of next wicket!
There are many more ways to improve, but this should hopefully help you to understand what I mean by actionable insights.
The idea behind this article was to bring out some ways in which you can improve your analysis and to show case them through a real life case study. I have learnt some of these practices the hard way over time, but you don’t need to do that! A single minded focus on yielding actionable insights for your users can completely change the way analytics can add value to them. On the other hand, a sketchy job can lead to wrong outcomes and mis-guided views. All the best for the next piece of analysis you do.
Disclaimer: I have shared some of the gaps on the analysis showcased to the audience during recent cricket matches. I am sure individual teams in the tournament would be taking help of analysts for more sophisticated analysis. I do not have access to that analysis and hence do not know, how many of the shortcomings mentioned here get covered through those pieces of analytics.