Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Ganesh Arkanath; Nishad Gupta; Hasan Kurban; Parichit Sharma; K. R. Madhavan; Elham Khorasani Buxton; Mehmet M. Dalkilic

doi:10.1109/BigData59044.2023.10386444

Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Ganesh Arkanath, Nishad Gupta, Hasan Kurban^*, Parichit Sharma, K. R. Madhavan, Elham Khorasani Buxton, Mehmet M. Dalkilic

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://dalkilic.luddy.indiana.edu/fantasyleague/, and the source code can be found at https://github.com/gany-15/nbafan.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4268-4275
Number of pages	8
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386444
Publication status	Published - 2023
Externally published	Yes
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

big data
data-mining
feature-engineering
nba-fantasy-league
python-flask

Access to Document

10.1109/BigData59044.2023.10386444

Cite this

Arkanath, G., Gupta, N., Kurban, H., Sharma, P., Madhavan, K. R., Buxton, E. K., & Dalkilic, M. M. (2023). Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 4268-4275). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386444

Arkanath, Ganesh ; Gupta, Nishad ; Kurban, Hasan et al. / Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 4268-4275 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

@inproceedings{e152844408ff48a8892914d8503a9497,

title = "Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics",

abstract = "Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://dalkilic.luddy.indiana.edu/fantasyleague/, and the source code can be found at https://github.com/gany-15/nbafan.",

keywords = "big data, data-mining, feature-engineering, nba-fantasy-league, python-flask",

author = "Ganesh Arkanath and Nishad Gupta and Hasan Kurban and Parichit Sharma and Madhavan, {K. R.} and Buxton, {Elham Khorasani} and Dalkilic, {Mehmet M.}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.1109/BigData59044.2023.10386444",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4268--4275",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

address = "United States",

}

Arkanath, G, Gupta, N, Kurban, H, Sharma, P, Madhavan, KR, Buxton, EK & Dalkilic, MM 2023, Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 4268-4275, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386444

Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. / Arkanath, Ganesh; Gupta, Nishad; Kurban, Hasan et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4268-4275 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gupta, Nishad

AU - Kurban, Hasan

AU - Sharma, Parichit

AU - Madhavan, K. R.

AU - Buxton, Elham Khorasani

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AB - Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://dalkilic.luddy.indiana.edu/fantasyleague/, and the source code can be found at https://github.com/gany-15/nbafan.

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KW - python-flask

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DO - 10.1109/BigData59044.2023.10386444

M3 - Conference contribution

AN - SCOPUS:85184988151

T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

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BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

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Arkanath G, Gupta N, Kurban H, Sharma P, Madhavan KR, Buxton EK et al. Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 4268-4275. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386444

Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

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