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Pathak & Wadhwa investigated the prediction of the result for cricket matches using data mining techniques. They experimented on predicting the outcome for ODI (One Day International) match format based on various factors such as home ground, toss decision, innings, fitness of team players and other dynamic strategies. In addition to the techniques implemented by , a Support Vector Machine (SVM) method was used to predict the result . Evaluating the accuracy of these techniques, they developed a tool COP (Cricket Outcome Predictor), which gives the probability for winning an ODI match. The data under study was the international cricket (https://aprel-vologda.ru/hack/?patch=3255) match data from 2001 to 2021 for ODI format and scraped from . Results obtained clearly showed that the classifiers derived by the SVM method outperformed those of Naïve Bayes and Random Forests methods [8,5]. SVM produced 62% accuracy, whereas the accuracy rates of the other methods were around 60%. The COP tool developed in R software enabled a user to select the features to predict the match outcome, and the user could change between the classifiers to make multiple predictions. A notable result was observed when COP system was applied on the India vs. Australia series in which Naïve Bayes derived more competitive classifiers in terms of predicting the match outcome.
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The variable ‘Result’ will be the target class for predicting the outcome by classification. Figure 3A and Figure 3B depict the performance results of the machine learning algorithms against the Toss Winner features set. The figures show that the classification accuracy of KNN is 62%, making it a more appropriate model than the other models. Naïve Bayes produced a low accuracy result of around 52%, not a good fit for this type of predictive task.
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R: A language and environment for statistical computing. R Foundation for Statistical Computing.
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The European Cricket T10 series continues with the tournament returning to Italy, Rome. A total of eight teams are taking part in the event that will run from November 3 to November 8. A total of 27 matches will be played across the six days of the event with the teams divided into two groups of four each.
Figure 1 depicts the framework of cricket (https://aprel-vologda.ru/hack/?patch=3979) match intelligent models. Initially, the input dataset is pre-processed by eliminating any incomplete records so that there are no missing values in the dataset. Data with no match result were excluded from the classification (5 instances). More importantly, we eliminate features that have no direct impact on the performance of the training phase by applying feature selection.
The aim of predicting the match result in the first model is to evaluate the impact of home ground advantage. In this experiment, the variable “Result” is derived based on the Home Team (Team 1) winning the match, when the match is played on home ground. For example, it is the frequency of Chennai Super Kings winning the match when it plays in its home ground Chennai. The attribute “Result” will be the target class for predicting the outcome by classification. The format of the tournament is that each team is designated one city as its home ground, and two matches are played by combination of two teams, playing once at the first team’s home ground and once at the other team’s home. In the considered dataset, two seasons were played in a foreign country and for these matches the venue is changed to the home ground of their respective teams. A few teams, such as Kings XI Punjab have different home grounds, but their original home ground is Mohali. For this experiment, only those features that have an impact on the home ground are considered.
Early progress was sedate from the overnight pair of Tendulkar and Jacques Kallis, and they reached the morning drinks interval still together. However, it was Cummins who produced the first breakthrough of the day, trapping Kallis lbw with the second delivery after the break.
With a magnificent 1-3 win in the Test series, England will look to continue their fine form in South Africa when they lock horns with the Proteas in the ODIs and T20Is. The reigning world champions will be the favourites to squash the lowly South Africans in this series but Quinton de Kock and co. may give them a run for their money in the T20I series.
However, with many countries across the world sealing their borders due to the Covid-19 pandemic, including Trinidad & Tobago, where the CPL is being played, the ACB found logistical hurdles in bringing back the players. The six players will thus play the entire CPL now, after which three of them - Rashid Khan, Mohammad Nabi, Mujeeb Ur Rahman - will head directly to the UAE.
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The Matchups suggest not, but you never know. Since, cricket is much much more than matchups. No matter the result, this will be quite a thrilling game nevertheless.
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Machine learning may slightly improve predicting the results based on pre-game conditions but at this stage it cannot be an acceptable solution due to lack of variables in the dataset, which can be considered as one of this research’s limitations. In order for machine learning techniques to be productive, more data including live data streaming and statistics of players are needed. Considering the dynamics of the tournament, team players’ data and statistics are required. It would be advantageous to predict the final score of the innings by analysing the run rate per over and also checking the probability of winning for each team depending on the actual run rate and the required run rate in the second innings.
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Thabtah, F, Cowling, P, Peng, Y, 2005. A study of Predictive Accuracy for Four Associative Classifiers. J. Digital Information Manage, Volume 3:202–205. Digital Information Research Foundation.
For this research, all experiments have been conducted on WEKA (The Waikato Environment for Knowledge Analysis), a machine learning tool which has automated intelligent techniques . To visually explore the IPL data insights, visualisation tool Tableau has been used . The processing machine used to conduct the experiments is an Intel i5 6th generation processor with 8 GB RAM on Windows 10, 64-bit operating system.
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This research attempts to evaluate different machine learning techniques to the problem of predicting the outcome of IPL cricket matches. We design intelligent models to predict a match outcome based on the impact of home ground and toss winner respectively. The team that wins the toss contemplates factors such as weather, pitch and outfield to decide whether to bat or field first, with the intention of securing a strategic advantage. Two models are formulated in this paper, one depicting the impact of home ground and the other considering the effect of toss decision. The former considers 6 variables and the latter, 7 variables.
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Nimmagadda et al. applied statistical techniques to predict a T20 match result while the match is in progress. The authors have designed a model using a statistical approach to achieve the optimum outcome. Firstly, a multiple regression model is tested to develop a prediction model. Using runs scored per over in the first inning and second inning, algorithms such as Logistic Regression with multi-variable linear regression and Random Forest [4,5] are used to predict the final result. The software used for modelling is Anaconda and Python libraries like pandas, NumPy and IPython to work with the data structure and applying algorithms [2,21]. The main result obtained was based on the impact of toss winner and resultant match winner. The predictive model considered the innings score at regular intervals and the final scores to predict the match result. The model predicted score and run rate projected score were quite near to the final score, in particular the score predicted by the model was more accurate to the actual score.
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Once the dataset is split, a number of different learning algorithms are applied on the two feature sets to derive predictive models for the match result. The testing methods used to derive the classifiers is ten-fold cross validation with stratification . These models are then compared to seek the one(s) that can be utilized for forecasting upcoming matches results. The machine learning algorithms that have been implemented to derive the predictive models are Naïve Bayes, Random Forest, K-nearest neighbour and Model Decision Tree. The choice of these methods is based on the diverse learning they adopt to develop the models (Section 4/1 gives more details).
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After the tournament-opener, Dubai will host its first sport tomorrow while Delhi Capitals tackle Kings XI Punjab accompanied through the 0/33 suit on Monday among Sunrisers Hyderabad and Royal Challengers Bangalore. The movement will then shift to Sharjah on September 22 while Rajasthan Royals will host Chennai Super Kings.
West Indies' final tour before IPL 2021 will culminate in the first week of March as they will clash with the Lankan Lions in a brief T20I series. The Caribbean team will start as the favorites to win this series.
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This adds up to a total of 24 games with 20 matches in the group stage and four knockout matches. Five games will be played on the first day, with the competition kicking off with the clash between Frankfurt Cricket (hop over to this site) Club and Turk FC Hattersheim am Main.
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Analysing the results of the models produced for the Toss Winner features set, KNN surprisingly produced the best results. For instance, Precision rate for KNN is high at 64/2% and KNN’s Recall is 58/4%, both reflecting correct classification for the “Win” class label, with a TP rate of 58%. Compared to the Home Team features set, the results from Random Forest are lower, as this algorithm’s FP rate is 48/35% for class ‘Win’. The Decision Tree model’s results are slightly lower for the Toss decision dataset compared to the Home Team features set. The FP rate for the Decision Tree algorithm is around 48% due to 147 instances misclassified as “Win” instead of “Lose”, while 144 instances were incorrectly predicted as “Lose” that were supposed to be “Win” by this algorithm. Similarly, for Naïve Bayes a lower rate was observed in terms of Precision and Recall rates, 49% of “Win” were misclassified leading to the Precision rate being 6% lower compared to that achieved on the Home team subset. KNN performs extremely well when processing the Toss Winner features set.
Different algorithms are adopted to deal with the research problem including Naïve Bayes, Random Forest, K-nearest neighbour and Model Decision Tree [8,5,17,9]. These algorithms are selected as they adopt different learning approaches. The hyperparameter settings are the defaults used in WEKA. The predictive models derived by the machine learning algorithms have been evaluated using various metrics including classification Accuracy, Precision and Recall on the basis of confusion metrics analysis. We used ten-fold cross validation with stratification as a testing method to derive the models . Using this method, the training dataset will be divided into ten folds arbitrary with stratification. Then, the learning algorithm will be trained on nine-fold and then tested on the hold-out fold. The process gets repeated ten times to produce an average predictive accuracy of the model.
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Each team will play against the other five teams once in the group stage. The top four teams would then come up against each other in the semis while the bottom two teams will play in the Shield final. The 3rd and the 2nd placed teams would take on each other in the Bronze Final. The winners of the semis will face each other for one prize - the ECS Cartaxo trophy.
Despite dismissing Marnus Labuschagne, Bumrah was easily negotiated with at the death, as he finished with another identically underwhelming bowling returns: 10-1-79-1. Throughout the game while fielding, Bumrah made his frustrations with his listlessness clear, but Rahul reckoned that was because the fast bowler has always set high standards for himself.
Looking closely at the confusion matrix results, 146 instances which should belong to the “Lose” class have been misclassified by KNN to the “Win” class. This has contributed to a low Precision rate and the reason for low Recall rate by KNN is that 152 instances that should be “Win” are classified as “Lose”. By contrast, for Naïve Bayes, only 136 were wrongly predicted as “Win” instead of “Lose”, that is 47% FP, and 130 instances were misclassified as “Lose” in place of “Win” resulting in 38% FN rate. The results achieved from Random Forest and Naïve Bayes algorithm are quite similar, in terms of Recall rate, as the instances incorrectly classified as “Lose” were 130. The Recall rate for Model Trees was 69% due to 106 instances misclassified as “Lose” instead of “Win” (FNs). When we compare the ratio of misclassified “Win” (FPs) and misclassified “Lose” (FNs) for KNN and Naïve Bayes, the results derived from the latter are more accurate.
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Figure 2B shows that the Recall rate for Model Trees is higher than the other algorithms. The Recall rate obtained by Model Trees was 68/6% as this algorithm achieved low false negatives (FNs), just 106 instances. Precision and Recall rates for KNN is around 55%, which is the worst of the figures obtained by the other machine learning algorithms. Recall rates for Random Forest and Naïve Bayes are the same with 61/5%. Overall, it seems that Random Forest, Model Tree and Naïve Bayes achieved acceptable performance with respect to the Recall metric for this feature set.
A single rotates strike, before Kieron Pollard seals the match with a boundary! Mumbai Indians have beaten the Rajasthan Royals by seven wickets, this has been a really convincing performance from the reigning champions, especially on the back of two defeats in their last two matches.
The last series before IPL 2021 will feature a battle between the Trans-Tasman rivals. After the ODI series in Australia, both the sides will catch a flight to New Zealand and play the T20I series.
The top two teams from both groups will make it to the semis. The winners of the semi-finals will play the summit clash on December 20 at the same venue.
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The classification results derived by the considered machine learning techniques against the Home Team features set are shown in Figures 2A & 2B. Based on Figure 2A, it is apparent that Naive Bayes is the most accurate model to predict the winner. The accuracy of Naïve Bayes is 57% which is relatively low; Random Forest and Model Trees algorithms also produce equally low results with 54% and 56% accuracy respectively. Accuracy produced by KNN is the lowest with only 52%. This means that using the considered features machine learning techniques were unable to improve predictive accuracy as all models showed an unacceptable level of accuracy.