Predicting all star player in the national basketball association using random forest

Soliman, Ghada; El-Nabawy, Ala’a; Misbah, Ahmed; Eldawlatly, Seif

doi:10.1109/intellisys.2017.8324371

Cited by 8 publications

(1 citation statement)

References 8 publications

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“…[28] Data from One Day International (DOI) matches during the time period 2001-2015 for each teamhttp://www.cricinfo.com [32] An NBA men basketball dataset that is publicly available at open source sports in the period 1937 till 2011. [26] - [34] - [25] Data from 500 games from each of the 10 grandmaster chess players, a total of 5000 chess games.…”

Section: [40]mentioning

confidence: 99%

Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

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Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

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Section: [40]mentioning

confidence: 99%

Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

View full text Add to dashboard Cite

show abstract

Athletic signature: predicting the next game lineup in collegiate basketball

Sharma,

Divakaran,

Kaya

et al. 2024

Neural Comput & Applic

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Evaluating the effectiveness of machine learning models for performance forecasting in basketball: a comparative study

Papageorgiou,

Sarlis,

Tjortjis

2024

Knowl Inf Syst

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Sports analytics (SA) incorporate machine learning (ML) techniques and models for performance prediction. Researchers have previously evaluated ML models applied on a variety of basketball statistics. This paper aims to benchmark the forecasting performance of 14 ML models, based on 18 advanced basketball statistics and key performance indicators (KPIs). The models were applied on a filtered pool of 90 high-performance players. This study developed individual forecasting scenarios per player and experimented using all 14 models. The models’ performance ranking was developed using a bespoke evaluation metric, called weighted average percentage error (WAPE), formulated from the weighted mean absolute percentage error (MAPE) evaluation results of each forecasted statistic and model. Moreover, we employed a comprehensive forecasting approach to improve KPI's results. Results showed that Tree-based models, namely Extra Trees, Random Forest, and Decision Tree, are the best performers in most of the forecasted performance indicators, with the best performance achieved by Extra Trees with a WAPE of 34.14%. In conclusion, we achieved a 3.6% MAPE improvement for the selected KPI with our approach on unseen data.

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Predicting all star player in the national basketball association using random forest

Cited by 8 publications

References 8 publications

Computational Intelligence in Sports: A Systematic Literature Review

Computational Intelligence in Sports: A Systematic Literature Review

Athletic signature: predicting the next game lineup in collegiate basketball

Evaluating the effectiveness of machine learning models for performance forecasting in basketball: a comparative study

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