Evaluating Player Strategies in the Design of a Hot Hand Game

Abstract

The user’s strategy and their approach to decisionmaking
are two important concerns when designing user-centric
software. While decision-making and strategy are key factors in a
wide range of business systems from stock market trading to
medical diagnosis, in this paper we focus on the role these factors
play in a serious computer game. Players may adopt individual
strategies when playing a computer game. Furthermore, different
approaches to playing the game may impact on the effectiveness
of the core mechanics designed into the game play. In this paper
we investigate player strategy in relation to two serious games
designed for studying the ‘hot hand’. The ‘hot hand’ is an
interesting psychological phenomenon originally studied in sports
such as basketball. The study of ‘hot hand’ promises to shed
further light on cognitive decision-making tasks applicable to
domains beyond sport. The ‘hot hand’ suggests that players
sometimes display above average performance, get on a hot
streak, or develop ‘hot hands’. Although this is a widely held
belief, analysis of data in a number of sports has produced mixed
findings. While this lack of evidence may indicate belief in the hot
hand is a cognitive fallacy, alternate views have suggested that
the player’s strategy, confidence, and risk-taking may account
for the difficulty of measuring the hot hand. Unfortunately, it is
difficult to objectively measure and quantify the amount of risk
taking in a sporting contest. Therefore to investigate this
phenomenon more closely we developed novel, tailor-made
computer games that allow rigorous empirical study of ‘hot
hands’. The design of such games has some specific design
requirements. The gameplay needs to allow players to perform a
sequence of repeated challenges, where they either fail or succeed
with about equal likelihood. Importantly the design also needs to
allow players to choose a strategy entailing more or less risk in
response to their current performance. In this paper we compare
two hot hand game designs by collecting empirical data that
captures player performance in terms of success and level of
difficulty (as gauged by response time). We then use a variety of
analytical and visualization techniques to study player strategies
in these games. This allows us to detect a key design flaw the first
game and validate the design of the second game for use in
further studies of the hot hand phenomenon.