This pie chart shows the distribution of players across different categories based on their activity. The categories are determined by factors such as the number of matches played and consistency of play. We only consider ranked team matches.
The calculations behind this chart involve several key metrics:
Consistency Score = Total Matches / Active Period (in days)
where Active Period is the time between a player's first and last match.
Using these metrics, we categorize players as follows:
Total Matches >= 50
Consistency Score > 0.7
Total Matches >= 20
Consistency Score > 0.5
Total Matches >= 5
Consistency Score > 0.3
Total Matches >= 1
The pie chart visualizes the proportion of players in each category. For each category, we calculate:
Percentage in Category = (Number of Players in Category / Total Number of Players) * 100%
This percentage determines the size of each slice in the pie chart.
The retention rate line chart shows how well players from different categories (Core Gamer, Dedicated Player, Casual Enthusiast, and Occasional Visitor) continue to engage with the game over time. The x-axis represents the player categories, while the y-axis shows the percentage of players retained. Each line on the chart represents a different retention period (7 days, 30 days, 90 days, and 180 days). This allows us to compare both short-term and long-term retention across different player segments.
The chart is constructed by calculating the proportion of players in each category who remain active after specific time intervals. A player is considered "retained" if their active period (the time between their first and last match) exceeds the specified retention period. For example, the 30-day retention rate for Core Gamers would be the percentage of Core Gamers who played at least one match more than 30 days after their first match. This calculation is repeated for each player category and each retention period, creating a comprehensive view of player retention patterns.
This chart gives an insight into the effectiveness of player engagement strategies across different player segments. For instance, if Core Gamers show high retention rates across all time periods but Casual Enthusiasts drop off sharply after 30 days, it might indicate a need for features or incentives to keep casual players engaged in the long term. Additionally, comparing retention rates between categories can help identify which types of players are most likely to become long-term players.
This box plot shows the distribution of skill gaps in matches. A smaller gap indicates more balanced matchmaking.
The skill gap is calculated as the difference between the maximum and minimum player skills (mu) in each match. A smaller skill gap indicates that players of similar skill levels are matched together, which generally reflects better matchmaking quality, while a larger gap suggests more disparity in player skills.
From the provided statistics, the mean skill gap is approximately 19.58. The median skill gap (50th percentile) is close to the mean at 19.96, suggesting that the data is relatively symmetrically distributed around the average. The 25th percentile (12.16) shows that the lower quarter of matches has skill gaps smaller than this value, while the 75th percentile (26.32) shows that the upper quarter of matches has skill gaps larger than this value. The maximum skill gap is 72.57, indicating that there are some matches with a substantial difference in player skill levels, although such cases are relatively rare.
This graph displays the average number of matches played per hour and per day of the week. By calculating the averages, we normalize the engagement data to account for the total number of days or weeks in the dataset.
Average Matches per Hour: This part of the graph shows how many matches are played on average during each hour of the day. It helps identify peak playing times, such as when most players are active or when there is the least activity.
Average Matches per Day of the Week: This part of the graph illustrates the average number of matches played on each day of the week. It provides insight into daily engagement patterns.