{"id":491,"date":"2016-06-13T12:11:50","date_gmt":"2016-06-13T11:11:50","guid":{"rendered":"http:\/\/ubiquis.co.uk\/?p=406"},"modified":"2020-04-24T12:23:43","modified_gmt":"2020-04-24T11:23:43","slug":"using-pdi-and-c-tools-to-display-real-time-scores-analysis-for-uefa-euro-2016","status":"publish","type":"post","link":"https:\/\/ubiquis.co.uk\/using-pdi-and-c-tools-to-display-real-time-scores-analysis-for-uefa-euro-2016\/","title":{"rendered":"Using PDI and C-tools to display real time scores analysis for UEFA Euro 2016"},"content":{"rendered":"
So, the Euro 2016 started this past Friday. We’ll have matches almost everyday until July 10th.<\/p>\n
And here at Ubiquis we decided to start a little project around the Euro. The basic idea is: suppose England gets to half time level with Slovakia. Given both teams’ past history, what’s the most likely scenario? A win for England? A draw? A win for Slovakia? We got score change information from all Euro finals matches since 1960 from Wikipedia and parsed it with PDI. And using a real time scores API called xmlscores.com, built a couple queries that answer one basic question: “out of all matches in which team A was leading\/trailing by X goals or more, at the same point in time, how many ended up in wins, losses and draws?”.<\/p>\n
So, for example, when yesterday Germany was leading Ukraine by 1 goal at Half time, the dashboard would give us some idea of how often Germany managed to keep a lead and win the match, or how often Ukraine managed to turn the tables around when losing by 1 goal or more and ended up drawing or winning the match.<\/p>\n
The dataset is quite small and the data model very simple, but it serves to show how Pentaho C-tools and PDI can be leveraged to create a real time information system, fed by external data sources, in a reliable maner.<\/p>\n