Predicting the Atlanta Falcons Play-Calling Using Discriminant Analysis

Heiny, Erik L.; Blevins, David

doi:10.2202/1559-0410.1230

Cited by 2 publications

(3 citation statements)

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“…For the defense, it is thus of interest to accurately predict the opponent's play. For that purpose, and driven by the availability of play-by-play NFL data, the use of machine learning approaches for play call predictions has been investigated in multiple studies (see, e.g., Heiny and Blevins 2011;Joash Fernandes et al 2020;Wu et al 2021). In particular, Joash Fernandes et al (2020) argue that decision trees are most likely to be adopted in practice, as they are intuitive to interpret (as opposed to alternative, black-box approaches).…”

Section: Application To American Football Datamentioning

confidence: 99%

Markov-switching decision trees

Adam,

Ötting,

Michels

2024

AStA Adv Stat Anal

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Decision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model’s states can be linked to the teams’ strategies. R code that implements the proposed method is available on GitHub.

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Section: Application To American Football Datamentioning

confidence: 99%

Markov-switching decision trees

Adam,

Ötting,

Michels

2024

AStA Adv Stat Anal

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“…They study the coefficients of the regression model to determine which factors affect the final outcome with statistical significance. Heiny and Blevins [34] use discriminant analysis to predict which strategy an American football team will adopt during the game. In soccer, Bialkowski et al [10] analyze data from games to automatically identify the roles of each player, and Lucey et al [52] also use logistic regression to predict the likelihood of a shot scoring a goal.…”

Section: Related Workmentioning

confidence: 99%

“…Such works use an enormous amount of data to make predictions about many popular sports, such as American football [34,67], soccer [10,52] and basketball [51,53]. Clearly, however, these works are not applicable to analyzing the performance of a team of voting agents.…”

Section: Introductionmentioning

confidence: 99%

Every team deserves a second chance: an extended study on predicting team performance

Marcolino

Lakshminarayanan

Nagarajan

et al. 2016

Auton Agent Multi-Agent Syst

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Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high Multi-Agent Syst (2017) 31:1003-1054 quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.

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Predicting the Atlanta Falcons Play-Calling Using Discriminant Analysis

Cited by 2 publications

References 0 publications

Markov-switching decision trees

Markov-switching decision trees

Every team deserves a second chance: an extended study on predicting team performance

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