Auto-play: A data mining approach to ODI cricket simulation and prediction
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Citations
Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach.
A team recommendation system and outcome prediction for the game of cricket
Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods
Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning
An Analysis of Bangladesh One Day International Cricket Data: A Machine Learning Approach
References
Application of Association Rule Mining: A case study on team India
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.