1,879 research outputs found
Predicting In-game Actions from Interviews of NBA Players
Sports competitions are widely researched in computer and social science,
with the goal of understanding how players act under uncertainty. While there
is an abundance of computational work on player metrics prediction based on
past performance, very few attempts to incorporate out-of-game signals have
been made. Specifically, it was previously unclear whether linguistic signals
gathered from players' interviews can add information which does not appear in
performance metrics. To bridge that gap, we define text classification tasks of
predicting deviations from mean in NBA players' in-game actions, which are
associated with strategic choices, player behavior and risk, using their choice
of language prior to the game. We collected a dataset of transcripts from key
NBA players' pre-game interviews and their in-game performance metrics,
totalling in 5,226 interview-metric pairs. We design neural models for players'
action prediction based on increasingly more complex aspects of the language
signals in their open-ended interviews. Our models can make their predictions
based on the textual signal alone, or on a combination with signals from
past-performance metrics. Our text-based models outperform strong baselines
trained on performance metrics only, demonstrating the importance of language
usage for action prediction. Moreover, the models that employ both textual
input and past-performance metrics produced the best results. Finally, as
neural networks are notoriously difficult to interpret, we propose a method for
gaining further insight into what our models have learned. Particularly, we
present an LDA-based analysis, where we interpret model predictions in terms of
correlated topics. We find that our best performing textual model is most
associated with topics that are intuitively related to each prediction task and
that better models yield higher correlation with more informative topics.Comment: First two authors contributed equally. To be published in the
Computational Linguistics journal. Code is available at:
https://github.com/nadavo/moo
Optimaztion of Fantasy Basketball Lineups via Machine Learning
Machine learning is providing a way to glean never before known insights from the data that gets recorded every day. This paper examines the application of machine learning to the novel field of Daily Fantasy Basketball. The particularities of the fantasy basketball ruleset and playstyle are discussed, and then the results of a data science case study are reviewed. The data set consists of player performance statistics as well as Fantasy Points, implied team total, DvP, and player status. The end goal is to evaluate how accurately the computer can predict a player’s fantasy performance based off a chosen feature set, selection algorithm, and probabilistic methods
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories
Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare actions like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces (1) a new
language for describing individual player actions on the pitch and (2) a
framework for valuing any type of player action based on its impact on the game
outcome while accounting for the context in which the action happened. By
aggregating soccer players' action values, their total offensive and defensive
contributions to their team can be quantified. We show how our approach
considers relevant contextual information that traditional player evaluation
metrics ignore and present a number of use cases related to scouting and
playing style characterization in the 2016/2017 and 2017/2018 seasons in
Europe's top competitions.Comment: Significant update of the paper. The same core idea, but with a
clearer methodology, applied on a different data set, and more extensive
experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201
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