Borussia Dortmund returned to winning ways in the Bundesliga in emphatic style on Matchday 22 with a thumping 4-0 win away to fierce local rivals Schalke, but it was Erling Haaland, and his first of two goals in particular, that grabbed the headlines. BVB have slid down the table in recent weeks, winning just one of their six league outings preceding Saturday's trip to Schalke, but served a reminder to their local neighbours that they remain the Ruhr region's top dogs, scoring twice in each half to condemn the struggling Royal Blues to a 15th defeat of the season. And Haaland was instrumental in that. Schalke had fought doggedly for much of the first half, but Jadon Sancho's opener on 42 minutes had given Dortmund a deserved lead before Haaland set his seal on the game on the stroke of half-time. Standing at 6'4" and weighing 194 pounds it is usually not a fair fight for defenders, but if not all players are created made equal, neither are all goals. Seeing Sancho receive the ball on the left-hand edge of the Schalke penalty area, Haaland instantly began to peel away from his marker, Bastian Oczipka. Sancho spotted it and floated a cross towards his teammate, who hung in the air before executing a sublime sideways scissors-kick with his left foot into the net from 16 yards. "It was a nice goal," Haaland told bundesliga.com "Obviously it was a good assist from Jadon.

Over the last few months I've had the opportunity to talk to a lot of decision-makers about artificial intelligence in general and machine learning in particular. Several of these executives had been asked by their investors about their machine learning (ML) strategies and where they have already implemented ML. So how did this technical subject all of a sudden become a topic of discussion in company boardrooms? Computers are supposed to solve tasks for humans. The traditional approach is to "program" the desired procedure; in other words, we teach the computer a suitable problem-solving algorithm.

Two years ago I asked myself if it in any way would be possible to use Machine Learning techniques to predict the outcome of football matches. To describe the process briefly I started by collecting as much data as I could get hold of. I mined data about old games from every different source and API I could find. Some of the more important ones were Football-data, Everysport and Betfair. I then took all the data for from the old matches, with its corresponding results, quantified it and put it in a database.

This is a guest post by Ola Lidmark Eriksson, CTO at Wide Ideas. Two years ago, I asked myself if it would be possible to use machine learning to better predict the outcome of soccer games. I decided to give it a serious try and today, two years and contextual data from 30,000 soccer games later, I've gained lots of interesting insights. To begin with, I harvested as many data points as possible. I mined old game data from every different source and API I could find.

The past few weeks we've talked a lot about the brand new algorithm that we have designed for Wide Ideas. The story behind Score, which is the name of the new functionality, is a bit interesting. Two years ago I asked myself if it in any way would be possible to use Machine Learning techniques to predict the outcome of football matches. Data mining To describe the process briefly I started by collecting as much data as I could get hold of. I mined data about old games from every different source and API I could find.