Conclusion

Questions From Introduction

How does the factors of a game correlate with its user score and meta score ranking?

According to the metacritic(https://www.metacritic.com/browse/game/pc/all/2021/metascore/?platform=pc&page=1), and the visualization, From a qualitative perspective, one might hypothesize that games with higher Metascores may attract more followers due to their perceived quality, critical acclaim, and the publicity that comes with high scores. But the order of the followers does not perfectly match with the metascore order. Because the number of followers could be influenced by other factors such as the game's community, marketing strategies, or previous success of the game series or its developers.

What trends emerge when comparing the release dates of different genres?

Usually, the main factor that can influence game release dates are marketing strategies, significant sale periods like the holiday season and events within the industry such as gaming conventions and expos. But unfortunately, several conventions like PAX East and multiple Comicpalooza events were canceled or postponed because of the Covid-19.

For the biggest game conventions in 2021 are :

EGX Rezzed 2021 took place from July 15–17, 2021 at the Tobacco Dock in London, United Kingdom.
The 2021 Gaming Community Expo (GCX) took place online from June 17–20, 2021 at Rosen Shingle Creek in Orlando, Florida.
E3 Canceled
Pax East Canceled

Because Pax is usually held in March and E3 are scheduled in June to September, the peek of below visualization is showing the impact of events.

Can we identify any outliers or anomalies in the playtime distribution among the top-ranked games?

Unfortunately, playtimes in our dataset was mostly null value or 0:00. So, we are not able to found outliers about it with specific values.

How do independent developers fare compared to major studios in terms of user engagement and sales?

By looking at these graphs on below, we can combine some hypothesis to get the results. From third graph, we could know most of the developer produce one games in 2021. That means those followers and ownership are mostly from the game they released in 2021. Some titles from independent developers show high ownership, implying successful sales. However, major studios generally show higher ownership, indicating strong sales performance likely due to broader marketing reach and established franchises.

Is there a correlation between the number of owners and the median playtime for a game?

Unfortunately, playtimes in our dataset was mostly null value or 0:00. So, we are not able to found outliers about it with specific values.

What insights can be gained by comparing the scores given by users and professional critics?

From the first question, We know Metascore and Userscore is not matching at all. Critics' scores are sometimes higher than users', which has been attributed to the belief that professional reviewers might not reflect the opinion of the "average gamer." On the other hand, some speculate that user reviews can be skewed by factors not directly related to the game's content or quality, such as disappointment with certain business decisions made by the game's publishers or developers. There's also a point of view suggesting that while critic reviews are considered more informed and holistic assessments of games, user reviews can sometimes be extreme and less reliable due to the potential of being influenced by mass sentiment or campaigns, rather than individual, thoughtful experiences.

Are there discernible patterns in the choice of publishers for high-scoring games?

High-scoring games were often published by well-established companies known for their consistent output of quality titles. Publishers like 'Sony Interactive Entertainment' and 'Xbox Game Studios' featured prominently, with successful titles like "Deathloop" and "Psychonauts 2." Smaller publishers like 'Hazelight' made a significant impact with critically acclaimed games such as "It Takes Two". It shows that while major publishers often have a number of high-scoring games due to their resources and experience, independent publishers can also achieve critical success with standout titles

How does playtime vary across different age groups of players?

Unfortunately, playtimes in our dataset was mostly null value or 0:00. So, we are not able to found outliers about it with specific values. Also, There was not enough data about age groups.

Can we identify any geographical patterns in the distribution of game ownership?

Geographical patterns in game ownership can be influenced by a range of factors including regional preferences for game genres, platform availability, and the socio-economic landscape. For instance, different gaming platforms may be more popular in certain regions, and cultural preferences can affect the popularity of game genres.

Are there significant differences in the statistics between single-player and multiplayer games?

There's a notable preference for solo gameplay among gamers, with a majority indicating they prefer single-player games. This preference is largely due to the enjoyment of tackling challenges independently and the satisfaction derived from the accomplishment.

Conclusion

When it comes to the gaming world, people tend to have their own favorites. Many enjoy the solo journey, diving into stories and challenges on their own. It’s a bit like reading a good book—you’re the hero, and it’s all about your adventure. Then there are those who love the buzz of multiplayer action, where you can team up or compete with friends and even make new ones along the way. It's like a team sport; you're all in it together. Both styles have their charm and attract different crowds, but they share one thing—they're about having a good time. Whether it’s going solo or going social, games offer a world for everyone.

Similarly, in analyzing the dataset with various statistical models, we observed a clear trend in performance related to the complexity of the models used. Simpler models, such as Linear Regression, demonstrated higher errors in their predictions, indicating they might be too basic to capture the nuances of the data effectively. As we moved to more sophisticated models, the accuracy of predictions improved significantly.

The Artificial Neural Network (ANN) model and Random Forest Regressor stood out as the most effective, achieving the lowest errors. These models are capable of understanding complex patterns and relationships within the data, which allows for much more accurate predictions compared to simpler models like Linear Regression and Support Vector Regression (SVR).

This analysis highlights the importance of choosing the right model based on the specific characteristics and complexity of the dataset. For datasets with complex relationships and patterns, investing in more advanced models such as Random Forest and ANN can lead to more reliable and accurate predictions, making them preferable choices in scenarios where precision is critical. Just like in gaming, where the choice between solo and multiplayer can define the experience, the selection of an appropriate statistical model can significantly influence the outcomes of data analysis.

Future Works

Enhanced Feature Engineering: There may be more predictors that have a major impact on game ownership that can be found with more investigation and improvement of feature engineering methodologies. To further understand player preferences, this can entail gathering more detailed information from game platforms or adding user engagement measures.
Deep Learning Architectures: Investigating more complex deep learning architectures, like recurrent neural networks (RNNs) or convolutional neural networks (CNNs), may provide light on how to best capture spatial or temporal correlations in game data. These architectural designs exhibit efficacy in representing spatial or sequential data and have the potential to yield additional enhancements in prediction precision.
Interpretability and Explainability: The incorporation of interpretability and explainability techniques into predictive models has the potential to augment their transparency and reliability in the gaming sector. Techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enable stakeholders to adopt actionable insights by offering insights into the factors influencing model predictions.
User Segmentation and Personalization: It can be possible to create customized marketing plans and recommendations for various player segments by looking into user segmentation strategies and personalized modeling methodologies. Personalized models can improve user experience and engagement by identifying unique player cohorts based on behavior and interests.
Ethical Considerations: It is imperative that ethical concerns such as algorithmic fairness, bias mitigation, and data privacy be taken into account while developing and implementing predictive models in the gaming sector. In order to guarantee the ethical and fair application of predictive analytics in gaming, future research should give special attention to ethical frameworks and principles.

Reference

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