Player Ratings Research Articles

Skill-Based Matchmaking for Competitive Two-Player Games

Skill-based matchmaking is a crucial component of competitive multiplayer games and it is directly tied to how the players would enjoy the game. We present a simple matchmaking algorithm that aims to achieve a target win rate for all players, making long win/loss streaks less probable. It is based on the estimated skill levels of players. Therefore, we also present a rating estimation for players that does not require any game-specific information and purely relies on game outcomes. Our evaluation shows that our methods are effective in estimating a player's rating, responding to changes in rating, and achieving a desirable win rate that avoids long win/loss streaks in competitive two-player games.

Modelling the sequential behaviours of simultaneous predator and prey patch use

Models investigating predator–prey games often fix one player's behaviour while allowing the other to make patch use choices. Here, we use adaptive dynamics to model changes in behavioural rates of a predator and prey, while explicitly allowing both to make decisions simultaneously. Our model focuses on a two-patch situation. The prey can move between an open patch, allowing for energy intake but also predation risk, and a refuge with no possibility of energy intake or predation risk. The predator can move between that patch, where it can attack prey, and an alternative patch that provides a fixed rate of energy intake, and consequently imposes a missed opportunity cost (MOC) of using the focal patch. The model predicts how the rates of movement by the players are influenced by specific parameters, including the predator's lethality and MOC, as well as the information available to the players. For example, our model predicts that the time a prey takes to emerge from its refuge and the time a predator waits for the prey to emerge depend on the sequence of events leading to the prey entering the refuge. For instance, the predator would leave the patch more quickly and the prey would emerge from the refuge more quickly if the prey entered the refuge after a failed attack rather than as part of its time allocation. • We use adaptive dynamics to model behavioural changes in predator–prey patch use. • We model a two-patch choice: open vs environment (predator); open vs refuge (prey). • Players can make decisions simultaneously and use information from recent encounters. • Specific parameters (risk, opportunity) affect each player's rate of movements. • Adaptive movement rules depend on predator lethality and missed opportunity costs.

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

SummaryManagers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

Personalized Interactive Narratives via Sequential Recommendation of Plot Points

In story-based games or other interactive systems, a drama manager (DM) is an omniscient agent that acts to bring about a particular sequence of plot points for the player to experience. Traditionally, the DM's narrative evaluation criteria are solely derived from a human designer. We present a DM that learns a model of the player's storytelling preferences and automatically recommends a narrative experience that is predicted to optimize the player's experience while conforming to the human designer's storytelling intentions. Our DM is also capable of manipulating the space of narrative trajectories such that the player is more likely to make choices that result in the recommended experience. Our DM uses a novel algorithm, called prefix-based collaborative filtering (PBCF), that solves the sequential recommendation problem to find a sequence of plot points that maximizes the player's rating of his or her experience. We evaluate our DM in an interactive storytelling environment based on choose-your-own-adventure novels. Our experiments show that our algorithms can improve the player's experience over the designer's storytelling intentions alone and can deliver more personalized experiences than other interactive narrative systems while preserving players' agency.

A new sports ratings system: The tiddlywinks world ratings

A system for calculating relative playing strengths of tiddlywinks players is described. The method can also be used for other sports. It is specifically designed to handle cases where the number of games played in a season varies greatly between players, and thus the confidence that one can have in an assigned rating also varies greatly between players. In addition, the method is designed to handle situations in which some games in the tournament are played as individuals ("singles'), while others are played with a partner ("pairs'). These factors make application of some statistical treatments, such as the Elo rating system used in chess, difficult to apply. The new method characterizes each player's ability by a numerical rating together with an associated uncertainty in that player's rating. After each tournament, a "tournament rating' is calculated for each player based on how many points the player achieved and the relative strength of partner(s) and opponent(s). Statistical analysis is then used to estimate the likely error in the calculated tournament rating. Both the tournament rating and its estimated error are used in the calculation of new ratings. The method has been applied to calculate tiddlywinks world ratings based on over 13 r 000 national tournament games in Britain and the USA going back to 1985.

A practical method of estimating energy expenditure during tennis play

This study aimed to develop a practical method of estimating energy expenditure (EE) during tennis. Twenty-four elite female tennis players first completed a tennis-specific graded test in which five different Intensity levels were applied randomly. Each intensity level was intended to simulate a "game" of singles tennis and comprised six 14 s periods of activity alternated with 20 s of active rest. Oxygen consumption (VO2) and heart rate (HR) were measured continuously and each player's rate of perceived exertion (RPE) was recorded at the end of each intensity level. Rate of energy expenditure (EE(VO2)) during the test was calculated using the sum of VO2 during play and the 'O2 debt' during recovery, divided by the duration of the activity. There were significant individual linear relationships between EE(VO2) and RPE, EE(VO2) and HR (r > or = 0.89 & r > or = 0.93; p < 0.05). On a second occasion, six players completed a 60-min singles tennis match during which VO2, HR and RPE were recorded; EE(VO2) was compared with EE predicted from the previously derived RPE and HR regression equations. Analysis found that EE(VO2) was overestimated by EE(RPE) (92 +/- 76 kJ x h(-1)) and EE(HR) (435 +/- 678 kJ x h(-1)), but the error of estimation for EE(RPE) (t = -3.01; p = 0.03) was less than 5% whereas for EE(HR) such error was 20.7%. The results of the study show that RPE can be used to estimate the energetic cost of playing tennis.

Junket Reps and Casino Marketing

All-expense-paid trips for gamblers, known as junkets, are part of casinos' marketing strategies. Independent "junket reps" act as the liaison between casinos and proven premium players, making all the arrangements and accompanying the players. Casinos assign a rating to each premium player based on the equation: (casino's theoretical win) = (player's average bet) x (hands per hour) x (hours played) x (house advantage). A player's rating is used in several ways to figure the rep's commission: flat fee, percentage of front money, commission on dead chips, commission on live chips, percentage of player's loss, or percentage of casino's theoretical win. Some casinos operate their own junkets, but junkets can be unprofitable or marginally profitable. Junkets may not be needed in peak seasons and on weekends; they can mean more bookkeeping and staff; and, as airfares rise, marginal costs increase. As competition intensifies and marketing strategies become more sophisticated, however, the competition for premium players will ensure the survival of junkets in some form.