Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. Due to these frequent changes to the parameters of the game, esport analytics models can have a short life-spam, a problem which is largely ignored within the literature. This paper extracts information from game design (i.e. patch notes) and utilises clustering techniques to propose a new form of character representation. As a case study, a neural network model is trained to predict the number of kills in a Dota 2 match utilising this novel character representation technique. The performance of this model is then evaluated against two distinct baselines, including conventional techniques. Not only did the model significantly outperform the baselines in terms of accuracy (85% AUC), but the model also maintains the accuracy in two newer iterations of the game that introduced one new character and a brand new character type. These changes introduced to the design of the game would typically break conventional techniques that are commonly used within the literature. Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature

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

ON PATTERNS FOR THE USE OF RAILWAY STATIONS

Transport (smart transport) is one of the main components of the Smart City. Accordingly, much attention is paid to the analysis and planning of transport (traffic flows) in cities. Naturally, any analysis should be based on some collected (measured) data. In this article, information about the use of railway stations by passengers is used as such data. This is data on the validation (checking) of travel documents at the entrance to the station and at the exit from it. For each station, the data includes time, the characteristics of the travel document, as well as information on the starting and ending station of the trip. The article is based on the results of work on the design of a new system of urban railways, and this design involves analyzing data on the use of railway stations, both within the city and in the urban metropolitan area. In the paper, the patterns (models) of the use of railway stations are considered. An understanding of how a station is used by passengers is necessary to assess the traffic (passenger traffic) of the transport system, which in turn is the main task at the design stage. Another important point is that usage patterns reflect the current state of the transport system and the urban environment. Accordingly, these patterns (models) can be used in urban analytics and act as indicators and metrics of changes in the urban environment

Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems

The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological conditions. The results revealed that the ML models accurately captured the wettability behavior at different geo-storage conditions by yielding less than 5% mean absolute percent error and above 0.95 coefficient of determination values. The partial dependency or sensitivity plots were generated to evaluate the impact of individual features on the trained models. These plots revealed that the models accurately captured the physics behind the problem. Furthermore, a mathematical equation is derived from the trained ML model to predict the wettability behavior without using any ML software. The accuracy of the predictions of the ML model can be beneficial for exactly predicting the H2 geo-storage capacities and assessing of H2 containment security of storage and caprocks for large-scale geo-storage projects

Diseño de un Sistema Inteligente para la predicción de partidas en League Of Legends

Este proyecto presenta un sistema inteligente diseñado para predecir los resultados de partidas en el videojuego League of Legends usando datos previos al inicio de la partida. Se experimenta con varios modelos de predicción: KNN, SVM y ANN, utilizando un dataset creado a través de la API de Riot Games y técnicas de Web Scraping. Los resultados de las pruebas muestran F1-Scores de 0.94 para KNN, 0.95 para SVM, y 0.93 para ANN, considerando factores externos que pueden afectar las partidas.Número de páginas: 13