Auto-play: A data mining approach to ODI cricket simulation and prediction
Citations
40 citations
Cites methods from "Auto-play: A data mining approach t..."
...[11] uses a combination of linear regression and nearest-neighbor clustering algorithms to predict the outcome of a match....
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...For instance, we do not have the details on the timings of the matches (day/night) as used by [10], and the instantaneous state of the matches at multiple stages as used by [11]....
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...The only obstacle we faced while evaluating our approach is the inability to compare against previous models like [10] and [11], due to the different underlying datasets used....
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34 citations
Cites methods from "Auto-play: A data mining approach t..."
...Historic features extracted from the previous matches is combined with the ongoing match features like a number of wickets and runs scored are used in prediction Sankaranarayanan et al. (2014)....
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30 citations
Cites background from "Auto-play: A data mining approach t..."
...[21] build a prediction system that analyzes historical Cricket match data and the instantaneous state of a match to predict game progression and the outcome of ODI match....
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...Cricket is the second most popular sport in the world after Soccer with two to three billion fans [21]....
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15 citations
Cites background from "Auto-play: A data mining approach t..."
...Similarly [18] discusses modeling home-runs and non-home runs prediction algorithms and considers taking runs, wickets, frequency of being all-out as historical features into their prediction model....
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13 citations
Cites methods from "Auto-play: A data mining approach t..."
...[1] used 6 features and got the accuracy of between 68% and 70% almost....
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References
742 citations
"Auto-play: A data mining approach t..." refers methods in this paper
...4.2 Non-Home-Run Prediction Using the same historical and instantaneous features, non-home runs of segment Si, ˆNHRi is predicted by means of Ridge Regression [13]....
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...2 Non-Home-Run Prediction Using the same historical and instantaneous features, non-home runs of segment Si, ˆ NHRi is predicted by means of Ridge Regression [13]....
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...Ridge Regression and attribute bagging algorithms are used on the features to incrementally predict the runs scored in the innings....
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...(Line 6) Using the same features Θ and ∆i−1, non-home runs ˆNHRi are predicted using Ridge Regression as mentioned earlier in this section (line 7)....
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484 citations
"Auto-play: A data mining approach t..." refers methods in this paper
...1 Home-Run Prediction Model Using the historical and non-historical features discussed above, we predict the number of home runs ĤRi for a segment Si, using attribute bagging ensemble method [5] with nearest-neighbor clustering....
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181 citations
"Auto-play: A data mining approach t..." refers background or methods in this paper
...2 Academic Interest in Cricket One of the earliest and pioneering works in cricket was by Duckworth and Lewis [6] where they introduce the DuckworthLewis or D-L method, which allows fair adjustment of scores in proportion to the time lost due to match interruption (often due to adverse weather conditions such as rain, poor visibility etc....
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...The method proposed in [6], and subsequently adapted by [14], for capturing the resources of a team during the progression of a match has found independent use in subsequent work in cricket modeling and mining [14][2]....
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...2.2 Academic Interest in Cricket One of the earliest and pioneering works in cricket was by Duckworth and Lewis [6] where they introduce the DuckworthLewis or D-L method, which allows fair adjustment of scores in proportion to the time lost due to match interruption (often due to adverse weather conditions such as rain, poor visibility etc.)....
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...Lewis [10], Lemmer [9], Alsopp and Clarke [1], and Beaudoin [3] develop new performance measures to rate teams and to find the most valuable players....
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147 citations
Additional excerpts
...[4] developed the Advanced Scout system for discovering interesting patterns from basketball games, which has is now used by the NBA teams....
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80 citations
"Auto-play: A data mining approach t..." refers background or methods in this paper
...Bailey and Clarke [2] use historical match data and predict the total score of an innings using linear regression....
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...Lewis [10], Lemmer [9], Alsopp and Clarke [1], and Beaudoin [3] develop new performance measures to rate teams and to find the most valuable players....
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...[2] propose a model that predicts the R̂eoi of a game in progress which is used to analyze the sensitivity of betting markets....
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...Figure 8: Mean absolute error in R̂eoi prediction for innings 1 (top) and innings 2 (bottom) for both [2] and our model....
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...Bailey and Clarke [2] use historical match data and predict the total score of an innings using linear regression....
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Related Papers (5)
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.