Brawl Stars Analysis

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Backstory: In Brawl Stars, balance updates are like a reset button for the game's ecosystem. With every patch, some brawlers get stronger, some get weaker, causing players to adapt. The November "Angels vs. Demons" patch brought some big changes to brawler health stats, sparking conversations in the community about which characters are now must-picks.

Why This Project? I wanted to break down these health changes visually so it’s easier to see who got buffed, who got nerfed, and how those changes might shake up team strategies. Raw numbers can be hard to interpret, so this project turns them into visuals that tell a clearer story.

Personal Motivation: I started this project because I believe Brawl Stars patch notes could be more impactful. Right now, they mainly present raw numbers, which can feel abstract and hard to grasp. By visualizing the data, I aim to bridge that gap—transforming those numbers into clear insights that help players truly understand the magnitude of these changes and how they might affect gameplay.

This project aims to create a data visualization that highlights the impact of the November patch update on brawlers' health. The visualization will help players better understand the significance of these changes—insights that may not be immediately clear from raw data alone.

Note: I grabbed all my data for the project here , selecting only the data points titled "Health" as these specifically illustrate the brawlers whose health changed.

#Necessary Imports for Project import numpy as np import pandas as pd import matplotlib.pyplot as plt import plotly.graph_objects as go

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# Use pip install to add the missing dependency. #Had to install this to read Excel files !pip install openpyxl

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df = pd.read_csv("Proj1_BS_Data.csv") df.head()

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# Create Interactive Plot fig = go.Figure() for i in range(len(df)): fig.add_trace(go.Scatter( x = ['Previous Patch', 'Current Patch'], y = [df['Health (Previous Patch)'][i], df['Health (Current Patch)'][i]], mode = 'lines+markers+text', text = [f"{df['Health (Previous Patch)'][i]}", f"{df['Health (Current Patch)'][i]}"], textposition = 'top center', name = df['Brawler'][i], hoverinfo ='text+name', line = dict( width=2, shape='linear', dash='dash' # Change this to 'dot', 'dashdot', or 'solid' ), marker = dict(size = 8), visible = 'legendonly', )) # Manually set the y-axis range y_min = df[['Health (Previous Patch)', 'Health (Current Patch)']].min().min() - 400 y_max = df[['Health (Previous Patch)', 'Health (Current Patch)']].max().max() + 300 # Add Brawler Icons as Annotations for i, brawler in enumerate(df['Brawler']): fig.add_layout_image( dict( source = 'Larry.png', x=1.05, # Position slightly outside plot area y=1 - (i * 0.1), # Spaced vertically per brawler xref="paper", yref="paper", xanchor="left", yanchor="middle", sizex=0.08, sizey=0.08, layer="above" ) ) # Plot Details fig.update_layout( title=dict( text='Brawler Health Changes', x=0.5, # Horizontal centering xanchor='center', y=0.90, # Vertical position (90% of the plot area) yanchor='top' ), yaxis=dict( title='Brawler Health', range=[y_min, y_max] # Ensures y-axis range is consistent ), hovermode='x unified' ) fig.show()

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Using the Graph:

To interactive with the graph, click on each of the Brawler's name in the legend. A line graph will display for each respective brawler. Unclick to remove the graph(s).

The recent health adjustments in the November patch may influence players' brawler choices, especially for those prioritizing health points above other stats. For example, players who typically main Larry might consider switching to Brock or Spike if their main objective is to use brawlers with higher health. It’s important to note that this analysis focuses solely on health points and excludes other critical factors like base damage, special abilities, or damage boosts.

Player playstyles play a significant role in how impactful these changes will be. For instance, Brock, a long-range brawler with a less aggressive playstyle, may remain a consistent choice for players who prefer maintaining distance from opponents. In contrast, Spike—a close-combat brawler—might still be played as a long-range option by some players, despite his intended design. This highlights that brawler health changes may not drastically alter player preferences if those preferences are rooted in playstyle rather than raw stats.

Larry's situation is particularly noteworthy. As a versatile brawler with both close and mid-range combat potential and fast movement speed, Larry's 400 health reduction is significant. Previously, this extra health acted as a “buffer” or pseudo-shield, allowing Larry to sustain damage while closing gaps for attacks. With this health loss, Larry becomes more vulnerable, potentially leading to quicker eliminations in skirmishes. This vulnerability could have ripple effects on team dynamics, as losing a key brawler early in a match can disrupt overall team performance.

Ultimately, while these health changes might seem straightforward numerically, their real impact depends heavily on each player's unique playstyle, strategic choices, and adaptability in the current meta.

Patch notes can significantly impact players' game-play strategies. As mentioned in the "Analysis" section, a player's choice of brawler and playstyle can influence whether they decide to continue using certain brawlers during a patch. Recommended strategies I’d implement are as follows:

1. Adapt to the Meta: Learn another brawler that's easy to pick up during the current patch. This allows you to remain competitive in the meta while waiting for your main brawler's health or stats to improve.

2. Pair Strategically: If a brawler you use during this patch has experienced a health decline, pair it with another brawler that complements its strengths. Even with reduced health, the right pairing can still make it effective with proper team support.

3. Adapt Game-Play: If a brawler has experienced an increase or decrease in health, adjusting your gameplay style to account for these health changes can be beneficial.

I strongly believe that implementing data visualizations in Brawl Stars patch notes for health changes would significantly enhance players' understanding of balance updates. By presenting these changes visually, players can quickly grasp which game-play strategies to adapt, helping them make the most of each patch.

Throughout this project, I began by collecting and preparing data from the patch notes, ensuring that every health change was accurately captured. I then moved on to visualizing the data through interactive graphs, allowing users to clearly see which brawlers were buffed or nerfed.

Finally, in the analysis section, I interpreted these patterns, explaining how the health changes could influence gameplay strategies.This type of visualization is especially valuable for competitive players who are often short on time while grinding ranks. Instead of sifting through dense, raw data, an interactive visualization can present key insights in a more engaging and actionable format. This not only saves players time but also empowers them to refine their strategies efficiently and return to the game with a clear path to achieving their rank goals.