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

Identifying significant features for Player Evaluation in NFL comparing ANNs and Traditional Models

The evaluation of player performance in sports is popular and important in modern sports, enabling teams to use real data in the construction of their rosters. This dissertation proposes to apply machine learning algorithms to predicting the player evaluations from a leading NFL analytics company who use a combination of statistics and expert evaluation. In addition, it will investigate what features are significant in the evaluation of a position. Data for the dissertation is obtained from multiple online sources - Pro Football Reference and Pro Football Focus (the the NFL analytics company). These data sets are combined and analysed before applying six different approaches to the problem. The use of Neural Networks (both Single and Multi Layer) as an approach is evaluated against the other approaches of Support Vector Regression (SVR), Linear Regression, Decision Trees and XGBoost. They will be evaluated using accuracy, root mean squared error and the p-value from a t-test. Wrapper methods of Sequential Feature Selection and Permutation Importance are both used to discover relevant features. SVR was the best performing approach with 74% accuracy for QB, 76% accuracy for WR and 59% for RB. Both XGBoost and the Neural Network implementations performed well in comparison. The relevant features that were uncovered fell into two distinct categories. First is a measure of the ability of the player to make an impact on the game when they are involved and receive the ball. The second is a highlight of the importance of solid foundations and basics

Social Norms and Behavior in the Local Commons Through the Lens of Field Experiments

Behavior in the local commons is usually embedded in a context of regulations and social norms that the group of users face. Such norms and rules affect how individuals value material and non-material incentives and therefore determine their decision to cooperate or over extract the resources from the common-pool. This paper discusses the importance of social norms in shaping behavior in the commons through the lens of experiments, and in particular experiments conducted in the field with people that usually face these social dilemmas in their daily life. Through a large sample of experimental sessions with around one thousand people between villagers and students, I test some hypothesis about behavior in the commons when regulations and social norms constrain the choices of people. The results suggest that people evaluate several components of the intrinsic and material motivations in their decision to cooperate. While responding in the expected direction to a imperfectly monitored fine on over extraction, the expected cost of the regulation is not a sufficient explanatory factor for the changes in behavior by the participants in the experiments. Even with zero cost of violations, people can respond positively to an external regulator that issues a normative statement about a rule that is aimed at solving the social dilemma.Key words: social norms, regulations, cooperation, collective action, common-pool resources, experiemental economics, field experiments

ESTA: An Esports Trajectory and Action Dataset

Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models. However, data from conventional sports is often unsuitable for research use due to its size, veracity, and accessibility. To address these issues, we turn to esports, a growing domain that encompasses video games played in a capacity similar to conventional sports. Since esports data is acquired through server logs rather than peripheral sensors, esports provides a unique opportunity to obtain a massive collection of clean and detailed spatiotemporal data, similar to those collected in conventional sports. To parse esports data, we develop awpy, an open-source esports game log parsing library that can extract player trajectories and actions from game logs. Using awpy, we parse 8.6m actions, 7.9m game frames, and 417k trajectories from 1,558 game logs from professional Counter-Strike tournaments to create the Esports Trajectory and Actions (ESTA) dataset. ESTA is one of the largest and most granular publicly available sports data sets to date. We use ESTA to develop benchmarks for win prediction using player-specific information. The ESTA data is available at https://github.com/pnxenopoulos/esta and awpy is made public through PyPI

Game analytics - maximizing the value of player data

During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.peer-reviewe

Using Geographic Information to Explore Player-Specific Movement and its Effects on Play Success in the NFL

American Football is a billion-dollar industry in the United States. The analytical aspect of the sport is an ever-growing domain, with open-source competitions like the NFL Big Data Bowl accelerating this growth. With the amount of player movement during each play, tracking data can prove valuable in many areas of football analytics. While concussion detection, catch recognition, and completion percentage prediction are all existing use cases for this data, player-specific movement attributes, such as speed and agility, may be helpful in predicting play success. This research calculates player-specific speed and agility attributes from tracking data and supplements them with descriptive factors to produce a quality data set that, with machine learning models, can lead to accurate predictions of success on a play-by-play basis. A neural network was trained to predict play success with an F1 score of 40%. Therefore, the true effect of the inclusion of player movement attributes in predicting play success appears to have a minimal effect, but additional data and future research may be needed to confirm that

Sports Data Mining Technology Used in Basketball Outcome Prediction

Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to complete a convincing result, data of 5 regular NBA seasons was collected for model training and data of 1 NBA regular season was used as scoring dataset. After processes of automated data collection and cloud techniques enabled data management, a data mart containing NBA statistics data is built. Then machine learning models mentioned above is trained and tested by consuming data in the data mart. After applying scoring dataset to evaluate the model accuracy, Simple Logistics Classifier finally yields the best result with an accuracy of 69.67%. The results obtained are compared to other methods from different source. It was found that results of this project are more persuasive since such a vast quantity of data was applied in this project. Meanwhile, it can be referenced for the future work