It's important to analyze your opponent's strength every day you play. Choosing a winning team is crucial in the forecast. If you think that this key player is going to win the game for the team you're supporting, it's vital to check how his counterpart from the opposing team is doing. The best way to predict the winners is to create your own systems.
Your own predictive models using algorithms. My main goal with this project was not to create the most accurate algorithm ever (no matter how good it was), but to experiment with a bottom-up probabilistic approach to modeling. Instead of trying to predict the results of matches based on historical data, I trained a neural network model to predict the outcome of individual balls and then built a custom Monte Carlo simulation engine to generate tens of thousands of possible matches. Unfortunately, I can't share the data source for this project because I took advantage of my personal sports database, but these types of data are fairly easy to find publicly.
Here are some examples of my home tables to give you an idea of the type of data I was working with (not all the columns are shown). A Medium publication that shares concepts, ideas and codes. The thrill of watching a cricket match live and predicting who will win today's match is unlike any other. Predicting each of the 45 group stage matches leaves England, Australia, India and Pakistan among the four best teams, allowing them to advance to the elimination round, where England is expected to ultimately win.
Then we went on to create a model similar to the WASP model, a tool used to predict the winner and score of a cricket match. In the past, I trained models to predict the results of rugby, tennis and horse racing matches, and even won a lucrative first prize in a data science competition to predict the Australian Open. Now, the conclusion here is not that my model is incredible at predicting the results of T20 matches, but that the results of the T20 matches are inherently difficult to predict. Since Kapil's Indian daredevils won the Cricket World Cup in 1983, the cricket craze among Indians has been unparalleled.
When I compared the predictions in my model with the predictions implicit in the Bet365 odds, I found that my model actually exceeded them. Most likely, the main cause of this is that cricket fields are not regulated, so there can be big differences between different cricket fields. When comparing the predictions with the actual results, most of the incorrect predictions were due to a team with worse statistics defeating a team that was perceived to be better.
