Simulating a basketball match with a homogeneous Markov model and forecasting the outcome

Štrumbelj, Erik; Vračar, Petar

doi:10.1016/j.ijforecast.2011.01.004

Cited by 61 publications

(28 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They model shot selection and allocation as a dynamic problem, and examine the behavior of professionals relative to theoretical standards of optimality they derive. Finally, Štrumbelj and Vračar (2012) investigate the use of Markov models for basketball match simulation and use a possession-based model to monitor the progress of a basketball match. The transition matrix of their model is estimated directly from NBA play-by-play data and indirectly from the teams' summary statistics.…”

Section: Literature Reviewmentioning

confidence: 99%

Building a basketball game strategy through statistical analysis of data

Nikolaidis

2013

Ann Oper Res

View full text Add to dashboard Cite

Management practices may be based either on manager's intuition or on analytical and objective reasoning. Sports and, particularly, basketball can be considered as an indicative field where these differences in decision-making can be met. This paper has been motivated by the statistical analysis that the author conducted in the past in order to support the management of a Greek basketball team and specifically the decision-making process of its coach regarding the team's strategy during its games. The aim of the paper is on the one hand to present some indicative, simple ideas for the statistical analysis of basketball data, and on the other hand to show that any basketball team can improve significantly its decision-making process if it chooses to be statistically supported. Basketball data is numerous; consequently its elaboration can be extensive and fruitful.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Building a basketball game strategy through statistical analysis of data

Nikolaidis

2013

Ann Oper Res

View full text Add to dashboard Cite

show abstract

“…In addition to the effort of predicting individual performance, there have also been other approaches focusing on teams and other features of the game. Some models using simulation have been developed to forecast the outcome of a basketball match . A comparison between predictions based on NCAAB and NBA match data is discussed in ref.…”

Section: Introductionmentioning

confidence: 99%

Forecasting basketball players' performance using sparse functional data*

Vinué

Epifanio

2019

Statistical Analysis

View full text Add to dashboard Cite

Statistics and analytic methods are becoming increasingly important in basketball. In particular, predicting players' performance using past observations is a considerable challenge. The purpose of this study is to forecast the future behavior of basketball players. The available data are sparse functional data, which are very common in sports. So far, however, no forecasting method designed for sparse functional data has been used in sports. A methodology based on two methods to handle sparse and irregular data, together with the analogous method and functional archetypoid analysis is proposed. Results in comparison with traditional methods show that our approach is competitive and additionally provides prediction intervals. The methodology can also be used in other sports when sparse longitudinal data are available. K E Y W O R D Sarchetypal analysis, basketball, forecasting, functional data analysis, functional sparse data

show abstract

“…His mechanism provided high precision results.Wu [8] performed a study on BasketBall Matches held in High Schools and came to an observation that ranking of teams will always go down with the increase in personal fouls.The Ranking goes up with the acceleration in total rebounds and blocks between playing teams. Performance also goes up with increase in two-point shot hit rate.S trumbelj and Vracar [12] prepared a model for predicting basketball match result.Their results of model came out to be far accurate as compared with commonly used traditional methods.Kvan and Sokol [16] devised a combinational model using Markov chainfor predicting plus using Regressionfor forecasting basketball matches held in colleges. It proved instumentaland gave more precise outcomes as compared to other Mathematical models,existing prediction logics, tournament seeding etc.Mai [9] determined that players must reduce the personal as well as turnover mistakes known as fouls in game to win it.…”

Section: Introductionmentioning

confidence: 99%

Prediction Analysis with Professional Basket Ball Teams Using Historical Data

Chopra¹

2017

IJRASET

View full text Add to dashboard Cite

I.INTRODUCTION Prediction analysis is a field that entertain in extracting information[5] from a given dataset and use it to predict future results and further performance patterns. This technique is usually applied on past or present data whereas the predictionresult is in future. For Instance, recognizing traces after anydamage has been committed,or credit card theft, after it occurred. However, main key factor is, that closeness of results are directly proportional to principle of analysis and accuracy of supposal. Evaluating at a more granular level can be determined as Predictive analytics.Tsai [7] proved that in the field, a team's performance is directly proportional to effectiveness of three major positions (i.eGuard, Forward and Centre).Min et al.[13] provided a mechanism for forecasting Football Match resultsby making use of in-game time series, Bayesian Inference and a rule-based reaasoning. His mechanism provided high precision results.Wu[8] performed a study on BasketBall Matches held in High Schools and came to an observation that ranking of teams will always go down with the increase in personal fouls.The Ranking goes up with the acceleration in total rebounds and blocks between playing teams. Performance also goes up with increase in two-point shot hit rate.S trumbelj and Vracar[12] prepared a model for predicting basketball match result.Their results of model came out to be far accurate as compared with commonly used traditional methods.Kvan and Sokol[16] devised a combinational model using Markov chainfor predicting plus using Regressionfor forecasting basketball matches held in colleges. It proved instumentaland gave more precise outcomes as compared to other Mathematical models,existing prediction logics, tournament seeding etc.Mai [9] determined that players must reduce the personal as well as turnover mistakes known as fouls in game to win it. Kvan and Sokol[10] devised a combinational model using Markov chainfor predicting plus using Regressionfor forecasting basketball matches held in colleges. It proved instumental and gave more precise outcomes as compared to other Mathematical models,existing prediction logics, tournament seeding etc.Mai [14] determined that players must reduce the personal as well as turnover mistakes known as fouls in game to win it. Lee and Worthington[20] made use of data analysis to calculateaccuracy of National Basketball Association guards, their results were greatly remarkable.Chen and Sun[15] prepared a multiple-factor decision-making structure by combiningevaluation laboratory having analytical network process methodsas well as decision making attempts to decipher significant criteriaand importance of individual factor and the surrounding environmental impact on the matchLvankovic et al. [19] detertmined that with the increase in number of features, the classification accuracy is also increased.Liu [11] analysed that a higher level of Offensive capabilityproduces a higher Rank. He also claimed that Predition of Ranking can be easily calculated withthe avera...

show abstract

Simulating a basketball match with a homogeneous Markov model and forecasting the outcome

Cited by 61 publications

References 12 publications

Building a basketball game strategy through statistical analysis of data

Building a basketball game strategy through statistical analysis of data

Forecasting basketball players' performance using sparse functional data*

Prediction Analysis with Professional Basket Ball Teams Using Historical Data

Contact Info

Product

Resources

About