As a publisher of games, we can publish your game across platforms, including the WinZO app. WinZO is a responsible gaming platform and hosts only games of skill with participation fees when played for winnings/rewards. Each game on the platform played with a participation fee for winnings may feature a different kind of skill, however, the underlying element in each contest is a specific skill set that the players demonstrate to play and win
A game of skill primarily depends upon the superior knowledge, training, attention, experience, and adroitness of the player. To assess the skill element games to be onboarded on its platforms, WinZO commissioned a revolutionary mathematical and statistical framework, developed and peer-reviewed by professors across Ivy League universities and leading national and international Institutes (such as Stanford, IIT Delhi, IIT Kanpur, IIT Madras). The empirical tests in this study were carefully chosen based on an extensive literature review backed by WinZO’s domain expertise, access to data from different games, and many other determining parameters. The study provided a set of tests – a combination of which conclusively prove that a game involves a preponderance of skill. WinZO will be applying this groundbreaking framework in the future to assess games and accordingly onboard them subject to other conditions. We encourage all our partner game developers to understand the methodology to independently assess their games.
The comprehensive methodology for analysis of games of skill as identified by the study includes:
- Do players have different expected payoffs when playing the game?
In a game of chance, the anticipated payout for all participants is equivalent.
However, if the observed frequency of successful outcomes deviates significantly across distinct user subsets, we may reasonably infer that factors beyond pure chance have contributed to the contest’s outcome. As such, this test serves as a potent gauge of skill within the game, provided that we can demonstrate a significant divergence in expected payoffs across specific populations.
ANOVA test with the null hypothesis that the average win percentage is the same across all the groups is used for the statistical sufficiency of this test.
- Do there exist predetermined observable characteristics about a player that help one to predict payoffs across players?
In games of chance, predetermined player characteristics should not have any predictive value on the game’s outcome. Hence, if we observe a statistically significant correlation between players with a particular trait and their success rate compared to other players, we can infer that skill has played a pivotal role in determining the outcome rather than mere chance.
In the analysis, players’ win rates were calculated in games after categorizing them in buckets based on a pre-determined characteristic. Correlation coefficients were then derived between them, which if positive suggests that the games are a game of skill.
- Are player returns correlated over time, implying persistence in skill?
To address the question of persistence, the hypothesis was that skill is an intrinsic quality of a player and does not change significantly over the course of the game. If this is the case it is expected to observe a distribution of underlying skill across the playing population in which the win fraction of each individual player in the first half set of games is correlated with that player’s win fraction in the second half. To determine whether this is consistent for WinZO players, the win fraction was plotted for the first half set of games versus the win fraction for the second half for each player.
A F-test was then performed to prove that the variance of the two populations are not equal, thus implying skill.
- Do actions that a player takes in the game have statistically significant impacts on the payoffs achieved?
If skill is part of a game, then evidence of the role of skill should manifest itself in the choices that players make. This test assesses whether the actions that a player takes in a game have a statistically significant impact on the payoffs they receive.
Monte Carlo Simulations would be run by creating such bots of varying difficulties as per the game logic and the win percentages of the bots are computed by competing these bots among themselves / against real humans in a free-play environment. The difference between the win rates among the bots is proven then with statistical significance between these bots thereby proving that the actions that a player takes in the game have a statistically significant impact on the payoffs that are achieved, proving the hypothesis.
- Are players able to intentionally lose the game?
In pure games of chance, intentional ‘bad play’ cannot cause a player to lose, or lose faster. In games of skill, on the other hand, it is possible for a player to play badly, whether intentionally or not, and lose more.
Monte Carlo Simulations are then run by creating such intentionally losing bots as described above and the win percentages of the bots are computed by competing their scores against historical scores and the win rates are then compared against expert level/best strategy players. The win rates of intentionally losing players are then benchmarked against expert players and were found to be significantly different.
- Does players’ performance improve with experience?
In pure games of chance, players’ performance is independent of experience, and not expected to improve with time. In games of skill, players improve their performance with practice. This is a confirmatory test.
WinZO also quantifies the amount of skill required in each game through the R* metric, which can be used as an indicator for the degree of skill present in the game on a scale of 0 to 1. This indicator can be further used to benchmark these games against widely accepted variants of games of skill.
The consistency of the results based on the parameters defined above will further be checked by WinZO through the Bayesian approach. These statistical evaluations reaffirm WinZO’s commitment to ensure responsible gaming practices.
Case study:
WinZO applied this Games of Skill test on the games developed by multiple third-party studios that are published on its App. To test some of the methodologies, studios were asked to develop multiple AIs of different difficulties.
| Game | Test 1 | Test 2 | Test 3 | Test 4 | Test 5 | Bayesian | R* |
| 4 player Card Game | Pass | NA | Pass | Pass | Pass | Consistent | 0.961 |
| Turn-based Casual Game | Pass | Pass | Pass | Pass | Pass | Consistent | 0.747 |
| Sports Game | Pass | Pass | Pass | Pass | Pass | Consistent | 0.780 |
| Racing Game | Pass | Pass | Pass | Pass | Pass | Consistent | 0.710 |
Sample Chart:
