Identifying NCAA tournament upsets using Balance Optimization Subset Selection

Dutta, Shouvik; Jacobson, Sheldon H.; Sauppe, Jason J.

doi:10.1515/jqas-2016-0062

Cited by 28 publications

(8 citation statements)

References 13 publications

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“…The focus of recent prediction research in other sports, such as basketball and hockey, has been predicting game outcomes. Prediction outcomes of NCAA tournament games (Dutta, Jacobson, and Sauppe, 2017), NBA regular season games (Manners 2016), European hockey league regular season games (Marek,Šedivá, andŤoupal, 2014), and NHL playoff games (Demers, 2015) have all been examined using a variety of prediction models.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting plays in the National Football League

Fernandes

Yakubov

et al. 2019

Journal of Sports Analytics

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This paper aims to develop an interpretable machine learning model to predict plays (pass versus rush) in the National Football League that will be useful for players and coaches in real time. Using data from the 2013-2014 to 2016-2017 NFL regular seasons, which included 1034 games and 130,344 pass/rush plays, we first develop and compare several machine learning models to determine the maximum possible prediction accuracy. The best performing model, a neural network, achieves a prediction accuracy of 75.3%, which is competitive with the state-of-the-art methods applied to other datasets. Then, we search over a family of simple decision tree models to identify one that captures 86% of the prediction accuracy of the neural network yet can be easily memorized and implemented in an actual game. We extend the analysis to building decision tree models tailored for each of the 32 NFL teams, obtaining accuracies ranging from 64.7% to 82.5%. Overall, our decision tree models can be a useful tool for coaches and players to improve their chances of stopping an offensive play.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting plays in the National Football League

Fernandes

Yakubov

et al. 2019

Journal of Sports Analytics

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“…At this time, the retransmitted time slot can be calculated by formula (4). When the corresponding data is not received in the part 2 of the retransmission, it is similar to 1 and 2; it enters retransmission part 3 for continued transmission, and the retransmission time slot at this time can be calculated by formula (5). However, if the response data from the data server is still not received, the terminal requesting node stops the continued data transmission, enters the planting state, and waits for the administrator's next cycle.…”

Section: On the Basis Of Calculating The Relevant Scores The Mean Val...mentioning

confidence: 99%

“…Students learn from each other in the group to increase their enthusiasm for autonomous learning, thereby achieving the improvement of students' self-exercise ability, giving full play to their subjectivity. To a certain extent, it has changed the teaching from the single teaching in the past to the active and personalized teaching [5]. On the premise of fully considering the physical fitness of students, it is extremely effective to effectively formulate targeted basketball teaching courses.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring of Basketball Teaching Action Optimization considering Heterogeneous Grouping Algorithm

Wang

2022

Journal of Sensors

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With the continuous development of social economy, sports have become an important part of people’s daily life, especially ball sports are becoming more and more popular. However, in physical education, the standardization and rationality of teaching actions are extremely important. In view of these needs and limitations, a heterogeneous grouping algorithm is introduced in this paper, taking basketball teaching as the entry point, the situation of heterogeneous grouping teaching and learning are analyzed under different teaching organization through combing the business flow of basketball teaching, and the modified factor algorithm is used to conduct a process evaluation of basketball teaching. The simulation experiment results show that the heterogeneous grouping algorithm is effective, can support the intelligent monitoring of basketball teaching action optimization, and promote the improvement of basketball teaching effect, and students can get more room and level for improvement.

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“…McCann, 2003, Groothuis et al, 2007, Berri et al, 2011, Arel & Tomas III, 2012, roster construction, win probability models, tournament prediction (e.g. Brown et al, 2012, Gray & Schwertman, 2012, Lopez & Matthews, 2015, Yuan et al, 2015, Ruiz & Perez-Cruz, 2015, Dutta et al, 2017, Neudorfer & Rosset, 2018, and issues involving player health and fitness (e.g. Drakos et al, 2010, McCarthy et al, 2013.…”

Section: This Reviewmentioning

confidence: 99%

Modeling Player and Team Performance in Basketball

Terner¹,

Franks²

2020

Preprint

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In recent years, analytics has started to revolutionize the game of basketball: quantitative analyses of the game inform team strategy, management of player health and fitness, and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics, and in particular highlight the need for causal inference in sports.

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Identifying NCAA tournament upsets using Balance Optimization Subset Selection

Cited by 28 publications

References 13 publications

Predicting plays in the National Football League

Predicting plays in the National Football League

Intelligent Monitoring of Basketball Teaching Action Optimization considering Heterogeneous Grouping Algorithm

Modeling Player and Team Performance in Basketball

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