841 research outputs found

    Predicting the performance of batsmen in test cricket

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    Cricket is one of the team games played over 50 countries in different levels. Though the performance of each batsman in the team can be easily quantified, the prediction of player performance is arduous. This paper demonstrates a methodology to predict the performance of cricket batsman in test-match series. In this study, longitudinal test cricket data have been collected over five years of period. A model is developed to predict the player performance as a function of certain characteristics related to the player, the team and the match series. Due to the hierarchical nature of the collected cricket data, a three stage hierarchical linear model is proposed in this investigation. According to the outcome of the analysis, the handedness of the player (batsman) and the rank of the team significantly influence player performance. Finally, an accurate prediction of player performance is conducted using the proposed model

    On the development of a soccer player performance rating system for the English premier league

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    The EA Sports Player Performance Index is a rating system for soccer players used in the top two tiers of soccer in England—the Premier League and the Championship. Its development was a collaboration among professional soccer leagues, a news media association, and academia. In this paper, we describe the index and its construction. The novelty of the index lies in its attempts to rate all players using a single score, regardless of their playing specialty, based on player contributions to winning performances. As one might expect, players from leading teams lead the index, although surprises happen

    The Probabilistic Final Standing Calculator: a fair stochastic tool to handle abruptly stopped football seasons

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    The COVID-19 pandemic has left its marks in the sports world, forcing the full-stop of all sports-related activities in the first half of 2020. Football leagues were suddenly stopped and each country was hesitating between a relaunch of the competition and a premature ending. Some opted for the latter option, and took as the final standing of the season the ranking from the moment the competition got interrupted. This decision has been perceived as unfair, especially by those teams who had remaining matches against easier opponents. In this paper, we introduce a tool to calculate in a fairer way the final standings of domestic leagues that have to stop prematurely: our Probabilistic Final Standing Calculator (PFSC). It is based on a stochastic model taking into account the results of the matches played and simulating the remaining matches, yielding the probabilities for the various possible final rankings. We have compared our PFSC with state-of-the-art prediction models, using previous seasons which we pretend to stop at different points in time. We illustrate our PFSC by showing how a probabilistic ranking of the French Ligue 1 in the stopped 2019-2020 season could have led to alternative, potentially fairer, decisions on the final standing.Comment: 4 tables, 2 figure

    Rating players in test match cricket

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    In general, the evaluation of player performance in test cricket is based on measures such as batting and bowling averages. These measures have a number of limitations, among which is that they fail to take into account the context in which runs are made or conceded and wickets are taken or given away. Furthermore, batting and bowling averages do not allow the comparison of performances in these two disciplines; this is because batting and bowling performances are measured using different metrics. With these issues in mind, we develop a new player rating system for test cricket. We use multinomial logistic regression to model match outcome probabilities session by session. We then use these probabilities to measure the overall contribution of players to the match outcome based upon their individual batting, bowling and fielding contributions during each session. Our measure of contribution has the potential for rating players through over time and for determining the “best” player in a match, a series, or a calendar year. We use results from 104 matches (in 2010, 2011 and 2012) to illustrate the method

    An analysis of the efficiency of player performance at the 2011 Cricket World Cup

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    In limited overs cricket, efficiency plays a significant role in team success. Batsmen especially are under pressure to score quickly rather than in large quantities because only 50 overs are available per innings. This paper uses data envelopment analysis (DEA) and stochastic multicriteria acceptability analysis (SMAA) to assess the efficiency with which players at the 2011 Cricket World Cup converted inputs (balls faced or bowled) into performance outputs. The effect that non-discretionary variables like the cricketing resources available to a player have on his efficiency is controlled for, allowing for a fairer assessment across players from different countries

    The power of sport: Building social bridges and breaking down cultural barriers

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    Is sport effective at breaking down cultural barriers within sporting communities for Indigenous Australians and people from Culturally and Linguistically Diverse backgrounds? Can it build social bridges by contributing to wider social issues? Drawing upon insights from those in this field, this thesis finds that sport is not the magic 'cure-all' that some assume. However, if managed correctly, sport can be an excellent medium for encouraging valuable debate, and can assist with positive social change

    Predicting players’ performance in the game of cricket using machine learning

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    Player selection is one of the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select eleven players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This thesis attempts to predict the performance of players as how many runs each batsman will score and how many wickets each bowler will take for both teams in one-day international cricket matches. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used NaĂŻve Bayes, Random Forest, multiclass SVM and Decision Tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both problems.Master of Science (MSc) in Computational Science

    Roster-Based Optimisation for Limited Overs Cricket

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    The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection
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