Player Ranking: A Solution to the Duckworth/ Lewis Method Problems

2018 ◽  
Author(s):  
Fayyaz Ali ◽  
Shah Khusro
Keyword(s):  
Player Ranking

scholarly journals A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League

2021 ◽  
Author(s):  
Tim D. Smithies ◽  
Mark J. Campbell ◽  
Niall Ramsbottom ◽  
Adam J. Toth
Keyword(s):  
Random Forest ◽  
Performance Indicators ◽  
Outcome Measure ◽  
Supersonic Speed ◽  
Notational Analysis ◽  
Forest Models ◽  
Traditional Sports ◽  
Random Forest Models ◽  
Player Ranking ◽  
Outcome Performance

Abstract Notational analysis is a popular tool for understanding what constitutes optimal performance in traditional sports. However, this approach has been seldom used in esports. The popular esport “Rocket League” is an ideal candidate for notational analysis due to the availability of an online repository containing data from millions of matches. The purpose of this study was to use Random Forest models to identify in-match metrics that predicted match outcome (performance indicators or “PIs”) and/or in-game player rank (rank indicators or “RIs”). We evaluated match data from 21,588 Rocket League matches involving players from four different ranks. Upon identifying goal difference (GD) as a suitable outcome measure for Rocket League match performance, Random Forest models were used alongside accompanying variable importance methods to identify metrics that were PIs or RIs. We found shots taken, shots conceded, saves made, and time spent goalside of the ball to be the most important PIs, and time spent at supersonic speed, time spent on the ground, shots conceded and time spent goalside of the ball to be the most important RIs. This work is the first to use Random Forest learning algorithms to highlight the most critical PIs and RIs in a prominent esport.

scholarly journals Hierarchical Macro Strategy Model for MOBA Game AI

2019 ◽  
Vol 33 ◽  
pp. 1206-1213 ◽  
Author(s):  
Bin Wu
Keyword(s):  
Strategy Model ◽  
Game Ai ◽  
Great Progress ◽  
Micro Level ◽  
Comprehensive Evaluations ◽  
Player Ranking ◽  
Partially Observable ◽  
Complete Solutions ◽  
Made In ◽  
Human Player

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated crossagent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.

scholarly journals Searching for the GOAT of tennis win prediction

2016 ◽  
Vol 12 (3) ◽  
Author(s):  
Stephanie Ann Kovalchik
Keyword(s):  
Paired Comparison ◽  
Model Development ◽  
Predictive Performance ◽  
Comparative Performance ◽  
Forecasting Models ◽  
Data Regression ◽  
Model Predictions ◽  
Professional Tennis ◽  
Performance Benchmark ◽  
Player Ranking

AbstractSports forecasting models – beyond their interest to bettors – are important resources for sports analysts and coaches. Like the best athletes, the best forecasting models should be rigorously tested and judged by how well their performance holds up against top competitors. Although a number of models have been proposed for predicting match outcomes in professional tennis, their comparative performance is largely unknown. The present paper tests the predictive performance of 11 published forecasting models for predicting the outcomes of 2395 singles matches during the 2014 season of the Association of Tennis Professionals Tour. The evaluated models fall into three categories: regression-based, point-based, and paired comparison models. Bookmaker predictions were used as a performance benchmark. Using only 1 year of prior performance data, regression models based on player ranking and an Elo approach developed by FiveThirtyEight were the most accurate approaches. The FiveThirtyEight model predictions had an accuracy of 75% for matches of the most highly-ranked players, which was competitive with the bookmakers. The inclusion of career-to-date improved the FiveThirtyEight model predictions for lower-ranked players (from 59% to 64%) but did not change the performance for higher-ranked players. All models were 10–20 percentage points less accurate at predicting match outcomes among lower-ranked players than matches with the top players in the sport. The gap in performance according to player ranking and the simplicity of the information used in Elo ratings highlight directions for further model development that could improve the practical utility and generalizability of forecasting in tennis.

scholarly journals Video Sequence Analysis for On-Table Tennis Player Ranking and Analysis

2022 ◽  
Vol 13 (2) ◽  
pp. 1-9
Author(s):  
Xiaoni Wei
Keyword(s):  
Video Sequence ◽  
Information Science ◽  
Big Data Analytics ◽  
Table Tennis ◽  
Tennis Players ◽  
Daily Training ◽  
Relationship Network ◽  
Player Ranking ◽  
Video Sequence Analysis ◽  
Table Tennis Player

With the rapidly developing of the scientific research in the field of sports, big data analytics and information science are used to carry out technical and tactical statistical analysis of competition or training videos. The table tennis is a skill oriented sport. The technique and tactics in table tennis are the core factors to win the game. With the endlessly emerging innovative playing techniques and tactics, the players have their own competition styles. According to the competition events among athletes, the athletes’ competition relationship network is constructed and the players’ ranking is established. The ranking can be used to help table tennis players improve daily training and understand their ability. In this paper, the table tennis players’ ranking is established their competition videos and their prestige scores in the table tennis players’ competition relationship network.

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