CSGO analyzer

our project provides a easy way to view the comparison between 2 players we can compare the fields like kills, deaths, damage dealt and damage taken these data is plotted in the frontend with chart js

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Inspiration

The inspiration for our project is the need for statistical analysis in the field of esports and compared to other physical sporting events esports platforms doesn't have a lot of descriptive and statistical form of viewing the data

What it does

Our project extracts the values from the provided CSGO dataset and the filters out the data which is required for the plotting the comparison between 2 players this data is passed to chart.js in a dictionary format and the chart.js will plot this data in the frontend where the user could visualize the descriptive statistics of the players data

How we built it

first we used the pandas dataframe to extract the details from the jsonl file and then we extracted the events column in the file and then in the events column we segregated the values of "game-ended-round" using this we got a dataframe about the teams details where we extracted the columns called "targetstateteams" and within that column we had another nested column called "targetstateteamplayers" which is the table which we used for building our project this dataframe has been separated for all 10 players with their stats and these dataframes is now converted into a nested dictionary fromat so that this data is passed to chart.js to plot the graph.

Challenges we ran into

The challenge that we found in the process was interpreting the data as the data didn't have any schema it was difficult to find the relations between diffrent tables and as the jsonl were too is was very difficult to get a picture about the data

Accomplishments that we're proud of

The accomplishments that we're proud of is the idea that we got to extract the data as we have not worked with such a large and complex dataset the way at least how we separated the data makes us feel proud

What we learned

The things that we have learnt are working with large data and working with realtime data and processing schema less data, working with various plots and graphs, sending data to frontend and save it in state to give graph and preforming EDA with various high level data and using power bi plots to visualize the data

What's next for CSGO analyzer

Our time management was awful now we try to improve of work on the project by planning correctly and the next step which we are planning is to provide a ML model to forecast a rate a winning a series by using the round data for certain limit (like lets consider we can give forecast after the completion of 9 rounds) and we also like to provide descriptive gun usage plots as well. These are the plans that we have in our mind right now and as ideas are always born guess we get many more ideas and use cases in the following days and hope we will develope an even better analyzer

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