honestbharani
Whatever it takes!!!
You can't "moneyball" any sport on statistical modelling alone. It's about working out how to solve a problem, using what numbers tell you - go with what history has said will work in the past, we all have memory biases. Of course that's not perfect, but you can live with the decision easier knowing that you got beaten by a person playing better on the night.
And it's no different to other sports - a 25% three point shooter in basketball hitting 67% for the night, for example. If he pulls that out then fair enough, well played.
Yes but there is more of a predictability factor as to what a guy with the ball in a basketball game can do but the sheer size of the cricket ground and the length of the pitch, the width of the crease etc. etc. just makes it so much more difficult to get in that level of predictions, which is why I feel my view on this can be broken down in 3 bits which I mentioned earlier (as it pertains to the usage of stats that goes beyond the basic cricket data that is kept currently and usage of big data, analytical models etc.)
1. It makes sense when it comes to choosing players - basically what the list manager is actually supposed to do. The example of Hastings' replacement is a good one.
2. It is ok as a theory feeder when it comes to working out match ups but I still feel gut feeling and game nous will work better as far as cricket is concerned.
3. Predicting batsmen or bowlers' responses and the amount of trust you can give to that kind of data is where, to me, it will differ from how say sabermetrics is used in baseball.
Vic, think we are just making the same points, but with different words. I already agreed that as something to feed cricket theorists and strategists, I definitely see the value of such data being kept and mined, esp. the T20 leagues.