How did I do it?

- I chose Australia winning a one-day international when chasing runs as an outcome (Win or Loss).
- Using data available from Cricinfo I determined which of the following on its own predicts if Australia will win (ie which predicts the outcome better than just flipping a coin): (1) Who won the toss, (2) whether it is a day or night match, (3) whether it is a home or away match, (4) how many runs the opposition scored.
- As it turned out if Australia lost the toss they were more likely to win (!), and, not surprisingly, the fewer runs the opposition scored the more likely they were to win. I then built a mathematical model. All this means is that I came up with an equation where the inputs were the winning or losing of the toss and the number of runs and the output was the probability of winning. This is called a “reference model.”
- I added to this model Ricky Ponting’s last innings score and recalculatd the probability of Australia winning.
- I then could calculate some numbers which told me that by adding Ricky Ponting’s last innings to the model I improved the model’s ability to predict a win and to predict a loss. Below is a graph which I came up with to illustrate this. I call this a Risk Assessment Plot.

So, when the shrimp hit the barbie, the beers are in the esky, and your mate sends down a flipper you can smack him over the fence for you now know that when Ricky Ponting scored well in his last innings, Australia are more likely to win.

- Pickering JW, Endre ZH. New Metrics for Assessing Diagnostic Potential of Candidate Biomarkers. Clin J Am Soc Nephro 2012;7:1355–64.

Tagged: Australia, australian cricket, Biomarkers, cricinfo, Cricket, innings, last match, one-day, Ricky Ponting, Risk Assessment Plot, risk stratification, sports, Statistics

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