An IPL captain taught me the 4 types of Data Analytics without ever mentioning analytics.

I was watching an IPL match a few weeks ago and it made me think about Data Analytics in a completely different way.

The batting team needed around 45 runs from the last 4 overs.

The bowling side still had their star bowler left.

Naturally, everyone expected the captain to give him the ball.

But what if the data said otherwise?

Imagine the analysts showed the captain something like this:

* The star bowler had been expensive in the death overs this season.

* Left-handed batsmen were scoring heavily against him.

* Another less popular bowler actually had much better numbers in similar situations.

Now the captain has a decision to make.

Go with his instincts?

Or trust what the data is telling him?

Let's say he picks the second bowler.

The over goes well, the pressure builds, and eventually the team wins.

What I found interesting is that this one decision contains all four types of Data Analytics.

First, the team looks at past performance data.

Then they try to understand why certain bowlers perform better in certain situations.

Next, they estimate what is likely to happen if a particular bowler is used.

Finally, they decide what action to take.

That's basically Data Analytics.

Not dashboards.

Not fancy charts.

Not SQL queries.

Just using information to make better decisions when the stakes are high.

The funny thing is that most of us watch these moments in sports without realizing how much data is influencing them behind the scenes.