Saturday 23 March 2019

A new way to look at bowling economy rates for the IPL

Sunrisers Hyderabad had made a great start, but their innings had started to plateau. At 161/7 off 18 overs they had the opportunity to get a score of 190+, or, if things went really poorly 175. Andre Russell was running into bowl...

He bowled a very good over, removing Braithwaite before only conceding 7 in his final 5 balls. All thoughts of a big finish were gone.

A week later, the Sunrisers were in the qualification final, and things were not going well. After 8 overs they were on 54/4, going at less than 7 an over, and at serious risk of scoring less than 100.

Dwayne Bravo was the bowler this time. He bowled a wide, then a couple of deliveries that Yusuf Pathan managed to hit for 2 each, and ended with a couple of easy singles. It was an over where almost no pressure was put onto the batsmen. And yet, it only went for 7 runs, the same as Andre Russell's excellent over a week earlier.

There's something wrong with any statistic that rates those overs as being of the same value to the team, and yet that's exactly what the traditional Economy Rate does. 7 runs is 7 runs.

As a result, I've come up with an alternative. I call it the Smart Economy Rate. It started with the question "what is the normal number of runs per over for each different over of the match?"

I used the 2018 IPL to create my baseline. I had a suspicion that there would be a difference between the expected scoring rates (because that's hardly news to anyone who's watched cricket) but the actual numbers were not something I intuited. The graph below shows the actual RPO per match over, and then a model that I created that seemed to fit the data well.

This seemed at first to be useful information, but as I thought about it more, another problem appeared.

I would not expect tail-enders to score as quickly as specialist batsmen in the middle overs.

As a result, I went back and tried to recreate the models, adding in the wickets lost in the process.

This was not as simple as it may sound, because there are no examples of 8 wickets falling in the first two overs, and there's very little data with either 6 wickets falling in the first 10 overs, or no wickets being down in the last couple of overs, so the models are inevitable being asked to extrapolate.

The more basic models resulted in negative expected run rates and all sorts of craziness, so it ended up taking me quite a while to find a model that fit the 2018 data well. I had to look at overs remaining, rather than overs bowled before the models started to make any sense.

The final model that I settled on can be seen in the graph to the right.

There were a couple of interesting things here. There most reliable thing in any match is the dip in the scoring rate in over 7, when the fielding restrictions come off. It doesn't always happen, but it almost always happens.

The next interesting thing is the way that the scoring rate levels off with the top order batsmen at the death. This is in part an artifact of the situation that it is most common for teams to be 2 or 3 down at the death when there's a run chase that's easily in hand, and the batsmen are taking fewer risks than normal in order to see their team through.  But it's also true that some batsmen who have been batting for over an hour tend to not have many more gears to go through. They're already scoring about as quickly as they can.

This model allows me to find the expected runs from each ball, and then say if a bowler conceded more or less than that. I could then adjust the economy rate by this to make them comparable.

After doing that, here are the top ten bowlers (min 60 deliveries):
Top 10 bowlers by Smart Economy Rate
Narine and Tye tended to bowl to set batsmen and/or at the death, and so their Smart Economy Rates are lower than their raw RPO, while Sodhi tended to bowl at times of less pressure, so his goes up.

This statistic can also be calculated for the different stages. Who is the most economical at the start, middle and end of an innings:

During the power play:
During the middle overs (middle 8 overs)
And at the death (last 6 overs)

This may be helpful for anyone racing to make their fantasy picks for the first match, but hopefully it's also a better way of looking at bowler's success.

I will be using the Smart Economy Rate statistic in my analysis of this IPL and also the upcoming world cup.


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  3. Very precise insight about the economy rates .. Day by day the technology is getting better to improve statistics and provide the cricket viewers a best insight.

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  5. can you provide the formula for calculating the smart economy rate or to calculate smart runs so that it would be useful for my project

    1. Hi Vinod - these change year by year. Email me michaelwagener at gmail dot com and let me know about your project and I'll see what I can do.

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