High Elo vs Low Elo: Champion Preferences and Performance Analysis
Hello summoners!
I recently analyzed champion data comparing Diamond+ players (High Elo) with the entire player base (Low Elo). I focused on three key aspects: Win Rate, Pick Rate, and Ban Rate, using both Diff (difference) and Ratio metrics to highlight how preferences and performance change across skill levels. I’ve included visualizations to make the trends clearer.
Data: Lolalytics, Patch 15.3, All regions
1. Win Rate Differences: Who Performs Better at High Elo?
Champions like Rengar (+7.26%), Nidalee (+6.06%), and Zoe (+5.79%) show significantly better win rates in Diamond+ games. In contrast, Zyra (-0.26%) and Yorick (-0.41%) are among the few who perform worse at higher tiers.
I personally think these results are closely tied to a champion’s mechanical difficulty and macro requirements. I’d love to hear your thoughts.
Note: The average Win Rate Diff between Diamond+ and All ranks is +2.40%. Keep this in mind when interpreting the data. For example, a champion with a +1.5% difference may appear to be performing better in higher Elo, but considering the average increase, this actually suggests the champion might be more suited for lower Elo players.
2. Popularity vs Performance Scatter plot
This scatter plot visualizes the relationship between a champion's popularity and performance across different Elo levels:
X-axis (Pick+Ban Rate Diff) represents how much a champion's pick and ban rate changes from low Elo to high Elo. A positive value means the champion is more popular in Diamond+, while a negative value means they are favored more in lower ranks.
Y-axis (Win Rate Diff) shows how much a champion's win rate changes from low Elo to high Elo. A positive value indicates better performance in Diamond+, whereas a negative value suggests a drop in effectiveness.
The chart is divided into four quadrants. For example, the upper-right quadrant represents champions that are both popular and perform well in High Elo compared to Low Elo.
Results: The average Win Rate Diff sits at +2.40%, highlighting that many champions perform better at higher tiers. Additionally, there is a slight positive correlation between Popularity and Performance, meaning that champions who gain more traction in higher ranks also tend to perform better. This suggests that High Elo players are generally better at identifying and utilizing strong picks.
3. Pick Rate Analysis
Understanding Diff and Ratio:
Diff (Difference): This represents the absolute difference in pick rates between Diamond+ and all ranks. For example, a +5% Diff means that a champion is picked 5% more in Diamond+ compared to the overall player base.
Ratio (Change Rate): This shows the relative change in pick rate, calculated by dividing the Diamond+ pick rate by the pick rate across all ranks. For instance, a ratio of x2.0 means the champion’s pick rate in Diamond+ is double that in lower ranks.
I decided to present both Diff and Ratio because relying solely on absolute differences may not fully capture the significance of pick rate changes. For example, champions like Rengar may have a very low pick rate in both tiers. Even a slight increase in their pick rate in Diamond+ can result in a large ratio value. Thus, combining both metrics provides a more comprehensive understanding of champion popularity shifts.
4. Ban Rate Analysis
The analysis procedure and the meaning of Diff and Ratio here are the same as in section 3.
Results: Pyke (+14.39%) and Lulu (+13.72%) top the ban charts. Shaco (-16.15%) and Morgana (-15.42%) are banned far less at higher ranks. Also, Karthus (+5.18x) sees his ban rate quintuple in Diamond+.
5. Final Thoughts
This entire data analysis came simply from my curiosity and interest in understanding champion trends across different skill levels. I’m still a beginner when it comes to data analysis and presentation, so if you notice any points that seem off or have any feedback, please feel free to share your thoughts in the comments. I would also love to hear your opinions and reactions after seeing these results. Let me know what you think—it would be awesome if more people could see and discuss this analysis :D
If you’re interested in exploring the data in more detail, feel free to check out this Google Drive link: Google Spreadsheet