Bernoulli runs using ‘book cricket’ to evaluate cricketers

Ramalingam, Anand

doi:10.1007/s12045-012-0047-2

Cited by 1 publication

(9 citation statements)

References 2 publications

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“…A similar analysis is done for comparing Pat Cummins' performance over the years. We can see difference in ratings by [2] and our system, which is due to his performance against different oppositions.…”

Section: )mentioning

confidence: 81%

“…We take the idea from [2] and model each delivery as a Bernoulli trial. The two possible outcomes for each Bernoulli trial or a delivery are 'r' runs scored or a wicket, where 'r' is defined as average runs scored by a batsman per ball.…”

Section: Modelling a Matchmentioning

confidence: 99%

“…Figure 2 shows rating of Virat Kohli's performance over years. The Quality Index and rating by [2] were normalised and plotted. Our method gave higher rating to his performance in 2012 as compared to 2016, which [2] rates as highest.…”

Section: )mentioning

confidence: 99%

“…The Quality Index and rating by [2] were normalised and plotted. Our method gave higher rating to his performance in 2012 as compared to 2016, which [2] rates as highest. On a closer look we see that the bowlers he faced in 2012 included experienced players like Lasith Malinga, Nuwan Kulasekara, Umar Gul, Saeed Ajmal, Clinton McKay, etc.…”

Section: )mentioning

confidence: 99%

“…The approach in [2] is completely quantitative and misses an important aspect of quality of opposition. We replace the quantity metrics of 'r' and 'avg' used in [2] with our quality and quantity-based metric which is re-evaluated as follows: - With this method we can consider some important aspects of the match, that are difficult to capture otherwise.…”

Section: Quality Of Runs and Dismissalsmentioning

confidence: 99%

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CRICTRS: Embeddings based Statistical and Semi Supervised Cricket Team Recommendation System

Chhabra¹,

Ali²,

Pudi³

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Team Recommendation has always been a challenging aspect in team sports. Such systems aim to recommend a player combination best suited against the opposition players, resulting in an optimal outcome. In this paper, we propose a semi-supervised statistical approach to build a team recommendation system for cricket by modelling players into embeddings. To build these embeddings, we design a qualitative and quantitative rating system which considers the strength of opposition also for evaluating player's performance. The embeddings obtained, describes the strengths and weaknesses of the players based on past performances of the player. We also embark on a critical aspect of team composition, which includes the number of batsmen and bowlers in the team. The team composition changes over time, depending on different factors which are tough to predict, so we take this input from the user and use the player embeddings to decide the best possible team combination with the given team composition.

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Section: )mentioning

confidence: 81%

Section: Modelling a Matchmentioning

confidence: 99%