A Mathematical Model to Predict Award Winners

Sparks, Rebecca; Abrahamson, D. L.

doi:10.1080/10724117.2005.12021813

Cited by 5 publications

(2 citation statements)

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“…They predicted the output of One Day International (ODI) cricket during the match using statistical models. Sparks et al [16] used Machine learning to predict the winning prize for cricketers. He predicted the best man of the match by using the previous record.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

Nasim¹,

Yousaf²,

Masood³

et al. 2023

IASC

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Batsmen are the backbone of any cricket team and their selection is very critical to the team's success. A good batsman not only scores run but also provides stability to the team's innings. The most important factor in selecting a batsman is their ability to score runs. It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record. This hypothesis is based on the fact that a player's batting average is generally considered to be a good indicator of their future performance. We proposed a data-driven probabilistic system for batsman performance prediction in the game of cricket. It captures the dependencies between the runs scored by a batsman in consecutive balls. The system is evaluated using a dataset extracted from the Cricinfo website. The system is based on a Hidden Markov model (HMM). HMM is used to generate the prediction model to foresee players' upcoming performances. The first-order Markov chain assumes that the probability of a batsman scoring runs in the next ball is only dependent on how many runs he scored in the current ball. We use a data-driven approach to learn the parameters of the HMM from data. A probabilistic matrix is made that predicts what scores the batter can do on the upcoming balls. The results show that the system can accurately predict the runs scored by a batsman in a ball.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

Nasim¹,

Yousaf²,

Masood³

et al. 2023

IASC

View full text Add to dashboard Cite

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“…For starting pitchers, the most commonly cited statistics are innings pitched per game, earned run average (ERA), strikeouts per 9 innings pitched (K/9), and walk plus hit per inning pitched (WHIP) [1][2][8][9], so we will include them in this study. We want to calculate by AHP and TOPSIS, one whose parameters as criteria are familiar to all fans, so only those four statistics are used: innings pitched per game (IPG), earned run average (ERA), strikeouts per 9 innings (K/9) and walks plus hits per inning pitched (WHIP).…”

Section: Datamentioning

confidence: 99%

Selection Best Starting Pitcher of the Chinese Professional Baseball League in 2010 Using AHP and TOPSIS Methods

Chen

Lin

Lee

et al. 2012

Frontiers in Computer Education

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Abstract. The selecting of the best starting pitchers from many alternatives is a multi-criteria decision-making (MCDM) problem. This study develops an evaluation model, based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to the ideal solution (TOPSIS), to evaluate the best starting pitchers in CPBL. The AHP is used to analyze the structure of the starting pitcher selection problem and to determine weights of the criteria, while the TOPSIS method is used to make the final ranking. An empirical analysis is conducted to illustrate the utilization of the model for the best starting pitcher selection problem. Results indicate that Ken Ray, Jim Magrane and Orlando Roman were ranked top three among starting pitchers of the CPBL in the 2010 season.

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A data‐driven approach to predicting the most valuable player in a game

Romero

Lozano-Murcia

López-Gómez

et al. 2021

Comp and Math Methods

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The identification of outstanding behaviors is a matter of essential importance in sports analytics. However, analyzing how human experts select each match's most valuable player (MVP) according to objective and subjective factors is a great challenge. This article proposes a data-driven approach for sports team performance based on the weighted aggregation of statistical indicators. The proposal is divided into two approaches: The first conducts a principal component analysis to examine the relationship between each game's statistical indicators.The other addresses a meta-heuristic analysis to weight the attributes and choose the MVPs optimally. Finally, we apply the proposed approach to the 2018 European Men's Handball Championship and take the "Player of the Match" of each game as an example to illustrate its usefulness and efficacy. We perform multiple analyses, including a comparison with a fuzzy multi-criteria decision-making method that show that the data-driven approach can predict the "Player of the Match" in most matches. It also allows us to estimate and quantify the expert evaluations, which are often difficult to obtain in a disaggregated form.

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A Mathematical Model to Predict Award Winners

Cited by 5 publications

References 0 publications

Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

Selection Best Starting Pitcher of the Chinese Professional Baseball League in 2010 Using AHP and TOPSIS Methods

A data‐driven approach to predicting the most valuable player in a game

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