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

In-Game, In-Room, In-World: Reconnecting Video Game Play to the Rest of Kids' Lives

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and Learning The focus of this chapter is on how young people learn to play video games. We have approached this question ethnographically, studying young people playing in their own homes among friends and family. The primary data analyzed for the chapter are videorecordings of play from two perspectives -- in-game and in-room -- which we synchronized into a single side-by-side video record. By looking at in-room actions along with in-game actions, the chapter expands on a separate worlds view that holds video games as a world apart from the rest of kids' lives. Our case material shows instead how game play is quite tangled up with young people's lives, including relations with siblings and parents, patterns of learning at home and school, as well their own imagined futures. Our analysis also documents a remarkable diversity of what we call learning arrangements that young people create among themselves while playing together

Thereʼs no ʻIʼ in ʻEmergency Management Team:ʼ designing and evaluating a serious game for training emergency managers in group decision making skills

Serious games are games that are designed to educate rather than entertain. The game outlined and evaluated here was commissioned and designed as a tool to improve the group decision making skills of people who manage real-world emergencies such as floods, fires, volcanoes and chemical spills. The game design exploits research on decision making groups and applies pedagogically sound games design principles. An evaluation of the game design was carried out based on a paper prototype. Eight participants were recruited and assigned to two groups of four participants each. These groups were video recorded while playing the game and the video was analysed in terms of game actions and member participation. Results indicate that the group who behaved in a more appropriate manner for a decision making group were rewarded with more positive feedback from the game state. These findings suggest that the game itself delivers appropriate feedback to players on their collaborative behaviour and is thus fit for the purposes intended in the current project

How players exploit variability and regularity of game actions in female volleyball teams

Variability analysis has been used to understand how competitive constraints shape different behaviours in team sports. In this study, we analysed and compared variability of tactical performance indices in players within complex I at two different competitive levels in volleyball. We also examined whether variability was influenced by set type and period. Eight matches from the 2012 Olympics competition and from the Portuguese national league in the 2014–2015 season were analysed (1496 rallies). Variability of setting conditions, attack zone, attack tempo and block opposition was assessed using Shannon entropy measures. Magnitude-based inferences were used to analyse the practical significance of compared values of selected variables. Results showed differences between elite and national teams for all variables, which were co-adapted to the competitive constraints of set type and set periods. Elite teams exploited system stability in setting conditions and block opposition, but greater unpredictability in zone and tempo of attack. These findings suggest that uncertainty in attacking actions was a key factor that could only be achieved with greater performance stability in other game actions. Data suggested how coaches could help setters develop the capacity to play at faster tempos, diversifying attack zones, especially at critical moments in competition

Factors Used to Make Appropriate Decisions in Youth Categories in Volleyball

Acknowledgments: This article will be part of the Doctoral Thesis titled: “Study of cognitive skills in volleyball players in Spain and Brazil”, by Manuel Conejero Suárez, at the University of Extremadura. This work was conducted with thanks to the Fernando Valhondo Calaff Foundation for the contribution of predoctoral contracts to young researchers.The study aim was to examine the associations between the category of play and the factors athletes use to make appropriate decisions. We observed 6567 game actions performed by 144 athletes. All game actions involved appropriate decisions. The study variables were factors on which appropriate decision-making is based (for five game actions in volleyball: serve, reception, setting, attack, block) and game category (Under-14, Under-16, Under-19). Our analysis—using contingency tables, the Chi-square test, and Cramer’s V—revealed a significant association between the two variables across the five actions. In the U-14 category, and sometimes in the U-16 category, it was more frequent than the expected random frequency that appropriate decisions were of low tactical complexity, focused on the performance of the skill, with an attentional focus on close elements, of low risk, and with actions of reduced difficulty and precision. For the U-19 category, it was more frequent than the expected random frequency that decisions were of greater tactical complexity, with an attentional focus on the opposing team, considering more relevant stimuli, with greater risk, and with greater time pressure. There is, therefore, a need for coaches to understand the decision-making skills of athletes from early on, as this will allow them to develop tasks and apply cognitive strategies that are adapted to the level of the athlete and that can ultimately improve decision-making further.Catedra del Real Madrid-European University 2017/RM02Consejeria de Economia e Infraectructuras de la Junta de Extremadura (Spain) through the European Regional Development fund: A way to make Europe GR1812

Indirect Match Highlights Detection with Deep Convolutional Neural Networks

Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find automatically highlights, in order to automatize the otherwise manual editing process. Most of the state-of-the-art approaches try to solve the problem by training a classifier using the information extracted on the tv-like framing of players playing on the game pitch, learning to detect game actions which are labeled by human observers according to their perception of highlight. Obviously, this is a long and expensive work. In this paper, we reverse the paradigm: instead of looking at the gameplay, inferring what could be exciting for the audience, we directly analyze the audience behavior, which we assume is triggered by events happening during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event. Outputs of the crops belonging to the same frame are then accumulated to produce a value indicating the Highlight Likelihood (HL) which is then used to discriminate between positive (i.e. when a highlight occurs) and negative samples (i.e. standard play or time-outs). Experimental results on a public dataset of ice-hockey matches demonstrate the effectiveness of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with ICIAP 201

AnswerTree – a hyperplace-based game for collaborative mobile learning

In this paper we present AnswerTree, a collaborative mobile location-based educational game designed to teach 8-12 year olds about trees and wildlife within the University of Nottingham campus. The activity is designed around collecting virtual cards (similar in nature to the popular Top TrumpsTM games) containing graphics and information about notable trees. Each player begins by collecting one card from a game location, but then he or she can only collect further cards by answering questions – whose solutions are obtainable through sharing knowledge with other cardholders. This ostensibly allows each player to become a subject expert at the start of the game, encouraging collaborative interaction for the game to be successfully completed. In this initial paper we will outline the structure and background of this location based game. AnswerTree has been authored within the Hyperplace framework, and is a first implementation of a wider process to develop a flexible, multi-purpose platform for both individual and group location-based mobile learning