Analytically Sports… Continued!

FractalAnalytics seems to be a step ahead of me! Here is a news item that covered their prediction on the First Match of World Cup (Cricket), between West Indies and Pakistan. Lo and behold, 2 of the predicted scores match exactly! Nobody would have expected that granular a performance prediction to be correct 75% of the times (as per their claims).

Going back to some of my earlier posts on use of analytics in sports – at Diamond Analytics Blog and here itself, what Fractal has already managed to do is a proof of concept.

The factors that I don’t see them looking at is the location/playground/weather/batting order/ bowling order/ etc., which do have a big impact on performances.

> Under overcast conditions, the chances of Indian batsmen holing out to the wicketkeeper goes up significantly.
>> The chances of genuine swing bowlers running through the side on grassy pitches is high
>> On flat tracks, against minnows, in subcontinent kinda pitches, batsman have a feast day

These are examples of hypotheses that can be tested using data.

It would be interesting to see how teams can use a model like this to decide team composition, play batting orders, etc.!

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