The gamification of sports: what can the Hungarian Hackathon uncover?
The world of spots has been deploying analytics to improve both fan engagement and athlete performance. The Hungarian State Lottery believes there is a third use case; to improve engagement with gamers. Which is why the organization has partnered with SAS on the upcoming Hungarian Hackathon.
Their online sports betting website includes a huge amount of interesting data; contestants have to solve the task by developing an analytical model based on the available dataset. Endre Vad, Analyst at State Lottery, and Gergely Gálfi, Data Integration Consultant at SAS told us about the background of the Hackathon and their role in the competition.
- Endre, what made you want to get involved in the SAS Data Science Hackathon? In other words, what’s in it for you?
Our biggest motivation to participate in the hackathon is stepping out of the routine. We can all get so caught up in a daily work, relying firmly on already well-known paths, that we can easily miss opportunities that would come with innovation. And that is truth for any business. Therefore, competition like this is a great opportunity to give us fresh perspective, and maybe open a brand-new approach to some aspects of our business. Innovation and out of the box thinking are key expectations from such data science contests, and it can also be a great inspiration to those less familiar with data science.
- Has State Lottery ever taken part in or sponsored a hackathon before?
No, this is the first time. In case the dataset includes relevant questions we haven’t been focusing on so far, the innovative way of presenting the information hidden in data can even set exciting new directions for all of us.
- Gergely, please tell us a bit about the data that you are providing.
The dataset we are providing for this competition is live betting data containing live score data of around 500 football events. The collected information for each event is very detailed. Basically, it contains everything that happened during the match, such as ball possession, kick-offs, throw ins, yellow and red cards, penalties, etc. But that’s not all. We also provide information about the exact position of the ball on the field.
- What sort of issues will the participants need to be aware of with the data? For example, are there lots of missing data, or is data quality an issue?
The goal of the competition is to build an analytical model to predict events that depend on many factors and the least predictable component of them is luck. We think this is a difficult task by itself, so we try to help the job of participants with as much data as we can provide. Data quality is not an issue, but the participants will need to perform some data transformation steps in order to prepare data for analytical functions.
- What kinds of problem are you hoping the participants will help you to solve and why?
We have no doubt that in such a short time it is not a realistic expectation from participants to come up with something we can use directly that helps solve challenges we face during our daily routine. As mentioned before, innovation is the key expectation and beyond this we are very excited to see how advanced analytics could be leveraged in our “unpredictable” world.
We believe future users of advanced analytics tools will come from not only the the data science community, but also from the students in higher education. Therefore, in September-October this year a SAS Data Science Hackathon competition for students will be organized where participants have to solve real-world business problems using advanced analytical tools. The event series will take place at many venues within the MEEE region, including Athens, Warsaw, Istanbul and Budapest.
Click here for registration and further information!
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