Evaluating Go Game Records for Prediction of Player Attributes

We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs

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

Exploratory research about playing styles and performance patterns in football teams

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMThe sports industry has grown exponentially over the years and is becoming an increasingly attractive market due to high levels of investment. In this way, sports institutions seek to obtain a competitive advantage both outside and inside the playing field. One of the strategic approaches to obtain this competitive advantage is through the study of performance data collected from athletes and teams. In this study, through exploratory research, a performance analysis model is developed where the main objective is the identification of various styles of play and team-level performance patterns regarding technical aspects of the games across the Bundesliga during the 2020/2021 season. The analysis model consisted of unsupervised methods with the application of a Factor Analysis that allowed the characterization of teams in terms of playing styles and the identification of various performance patterns through the application of clustering algorithms

Projecting the Future Individual Contributions of NHL Players

Professional sports are a multibillion-dollar industry with millions of people invested in the outcomes of games and seasons. Owners, management, and fans sit on the edges of their seats wondering what will happen next. Lots of work has been done forecasting success at the team level across a variety of sports, but player level predictions are less common. Predictive work related to the NHL is even rarer. This thesis explores the ability to predict NHL player performance in a given season using publicly available information via statistical learning methods. Data featured in the analysis includes play-by-play and shift information, box score statistics, a variety of composite and catch-all statistics, injury information, and player biographical information. Data was compiled and analyzed to find meaningful relationships between past and future performance. The results of the analysis found the most predictive values in . season’s raw numbers can be supplemented with more information to improve predictive power

The transformative potential of reflective diaries for elite English cricketers

The sport of cricket has a history of its players suffering from mental health issues. The psychological study of cricket and, in particular, the attendant demands of participating at an elite level has not previously received rigorous academic attention. This study explored ten elite male cricketers’ experiences of keeping a daily reflective diary for one month during the competitive season. The aim was to assess how valuable qualitative diaries are in this field. Participants were interviewed regarding their appraisal of the methodology as a self‐help tool that could assist coping with performance pressures and wider life challenges. Three outcomes were revealed: first, that diary keeping was an effective opportunity to reflect upon the past and enhance one’s self (both as an individual and a performer); second, that diary keeping acted as a form of release that allowed participants to progress; and third, that diary keeping allowed participants to discover personal patterns of success that increased the likeliness of optimum performance

Competitive Edge Teaching: A Comparison of Differentiated Reading Instruction in the K-3 Elementary Classroom to the Sports Psychology Behind High School Athletic Coaching Methods

This study explored the similarities between the philosophies and techniques used by select high school athletic coaches and select elementary reading teachers. The study shows parallels between the psychology behind coaching methodology within high school athletics and differentiated instruction within the elementary reading classroom. The purpose of this research was to develop a pathway to influence the increased implementation of differentiated instruction in elementary schools by determining and highlighting these parallels. The design of the study is a triangular analysis of interview questions conducted in a face-to-face interview format, document analysis, and surveys to show the parallels between the planning and implementation approaches. The data gathered from these measures generated patterns and identified strong parallels of structure between instructional delivery in the two areas. We learned that with a better focus on aligning prioritization within these parallels, school leaders have the opportunity to shed new light on differentiated instruction, grounded in the UDL model, to better promote and foster student success in the classrooms. Perception is reality, and it is the goal that this study provides a positive perception of differentiated instruction