Shhhh….heard that? Or maybe I am re-living some of my football memories from back in the days. The sound of a 95,000 celebration is not something you can easily forget. We were all glued to our screens during the first World Cup played on African soil, in the land of samina mina....
Tshabalala's left strike straight into the back of the net sent shivers to all the other teams who thought African teams would be a walk in the park. Further more, giants like Etoo, Asamoah ,Mariga have literally carried their respective African countries on their shoulder
Away from Africa, English premier league on its own is a closely followed debacle that occurs once a year, when TV remotes are no longer available and most guys are no longer interested in Sunday road trips but very keen to be home for the games. Most diehard fans are Chelsea fans: beating PSG is no walk in the park.....or is it?......anyways, I digress.....
Due to the huge financial allocations and investments by the owners of these clubs, data driven prediction is paramount in establishing which new players to buy, players performances and realtime analysis based on the historical data accrued over the years.
Python as a tool can be used to predict and write code to get the probabilities of winning the league. I pulled data from the website https://www.football-data.org/client/home through its API and wrote a pull request to retrieve this data for analysis.
I thereafter got a code to clean the data, convert it to a data frame that python understands then worked out the probabilities for winning. The probabilities based on the games played and games won was then drafted and shared as a list. This just goes on to show the power of Python in using predictive analysis to get probabilities from sets of data.
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