Auto-play: A data mining approach to ODI cricket simulation and prediction
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Citations
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References
Ridge Regression in Practice
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
A fair method for resetting the target in interrupted one-day cricket matches
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data
Predicting the match outcome in one day international cricket matches, while the game is in progress
Related Papers (5)
Predicting the match outcome in one day international cricket matches, while the game is in progress
Frequently Asked Questions (8)
Q2. What is the term for the period during which TeamA bats?
The period during which TeamA bats is called innings1, in which TeamA has 50 overs to score as many runs as possible, while TeamB tries to minimize the scoring by getting TeamA’s batsmen out (more commonly referred to as taking wickets).
Q3. How many runs are predicted in attribute bagging method?
It can be observed that, for 50% of matches, prediction error has a maximum of 16 runs in Attribute bagging method, while for nearest neighbor method, it is close to 30 runs.
Q4. What is the intuition behind using nearest-neighbor algorithm?
The intuition behind using nearest-neighbor algorithm is that information from similar match situations can be “borrowed” from the training dataset.
Q5. How many powerplays are required in a match?
The first 10 overs of the game are mandatory powerplays, with two more instances of powerplay periods arbitrarily chosen by the batting and bowling team each, to occur at any point in the game up to the 45th over.
Q6. How do the authors predict the number of home runs in a segment?
4.1 Home-Run Prediction Model Using the historical and non-historical features discussed above, the authors predict the number of home runs ĤRi for a segment Si, using attribute bagging ensemble method [5] with nearest-neighbor clustering.
Q7. How do the authors get the total predicted score at the end of the innings?
Using these predictions, the total predicted score at the end of the innings can be obtained as(3.1) R̂eoi = Rknown + n∑ i=n+1 R̂iIf an innings has not commenced, as a special case, n = 0, Rknown = 0 and Wknown = 0.
Q8. What are the key features for predicting runs for the first segment?
Of these, historical features are critical for predicting runs for the first segment, since by definition, no instantaneous match data is available before the first segment.