This project focuses on solving the classification Problem of what team wins in a competitive e-sports matches. Dota2 and CSGO were analyzed.

• BettingFrontend: A frontend to visualize data from predictions and evaluation of the data.
• BettingRestAPI: A rest api to gather matches continuously and evaluate the prediction results
• CSGO Data Analysis: Includes a scrapy crawler to crawl data from hltv.org, store it in csv files and evaluate it with a svm model and a tensorflow model implemented with keras.
• DotaDataAnalysis: Includes a scrapy crawler to crawl data from https://api.opendota.com/api/proMatches , store it in csv files and evaluate it with a svm model.

I have gathered data from csgo matches from the last four years which resulted in a sample of around 60k matches. Each match consists of two teams playing vs each other. Each team has five players and each player has five statistics about his playing style. I have restricted the stats to death per round, Kast, average damage per round, kills per round and his rating.
The target is to predict the outcome of a match given its players. So I used the player stats as features and the outcome of the match as labels (0 and 1). Given this thoughts each model takes 5 (Players) * 5 (Player stats) * 2 (Teams) as input and predict the match output.
I have evaluated different models and it seems like a svm with a poly kernel and a degree of 10 gets the best results for machine learning model. As neuronal network a simple model of one input layer, two hidden layers and one output layer works quite good. The described SVM achieved an accuracy of 69% on 48k training data sample and 12k test data samples. The neuronal network achieved 71% accuracy on the same samples.

After the results on all pro matches that I have gathered I want to evaluate the model results on recent pro matches and see if it can actually make money by betting on the predicted matches. I have build a frontend and backend for that reason. A scrapy crawler is crawling recent matches with its betting odds, on these matches are predictions made and finally evaluated with the actual match outcome.

As you can see not all bets are profitable. It looks like the neuronal network works really good on best of 3 games since the roi is 2% on 1688 games.

If we filter all games and restrict the results by picked odds higher than a certain threshold an higher roi can be achieved. In this examples the neuronal network could make about 8% roi when we filter bets that has higher odds than 1.6 from picked games by the model. In absolute numbers that mean the model could achieve about 60 buy ins.

1.Install node modules in the frontend

cd BettingFrontend && npm i

2.Run the frontend

cd BettingFrontend && npm run serve

3.Install python modules

cd BettingRestAPI && pip install -r requirements.txt

4.Run the django server

cd BettingRestAPI && python manage.py runserver

5.Or run with docker

docker-compose up -d --build

6.(Optional) Load sample data

cd BettingRestAPI && python manage.py loaddata db_prod.json

pyhton manage.py test

Go to

http://localhost:8000/swagger

or

http://localhost:8000/redoc