Drowning in Liquid’s Data – Champions of Starladder I-Leauge StarSeries S3

This is my third published article where I analyse the drafting patterns of professional teams using machine learning and data analysis techniques based on my skills I learnt in my degree. This time I’m tackling the drafts of Team Liquid’s very successful run at Starladder I-League StarSeries S3 this last weekend.

Straight into it, this was some seriously hard data to find patterns in. In 11 games liquid played 32 heroes out of a possible 55, with 19 of those only being played once (and 8 only being played twice). So in 11 games Liquid only played 5 heroes more than twice.

This makes my favourite algorithm for this type of analysis, apriori, pretty bad, as it relies on finding common pairs of attributes in a data set to find patterns. However, I still have a fair share of information about Liquid’s drafts to share.

Continue reading “Drowning in Liquid’s Data – Champions of Starladder I-Leauge StarSeries S3”


Digital Chaos’ Journey Through the TI6 Meta

A few days ago I posted some analysis of Wing Gamings’ draft from The International, where I used a rule mining algorithm to pull interesting stats as an exploratory project into applying data analysis techniques to Dota 2. You can read this blog here if you missed it. Today, as requested, I’m going to be doing a similar sort of thing, but with the second place team at this years TI, Digital Chaos.

Wings were an ‘unpredictable’ team, and what we’re looking for in Digital Choas is something a little different. I’m expecting to be able to make sense of the data a bit easier for DC. Their drafts weren’t as crazy as wings, but there is a few interesting stories. In the twilight games of TI4, most agree that VG perfected the meta, but NewBee knew how to counter it. In this article I’m going to convince you that TI6 did have a meta-game, and not only did DC know how to play it, but they knew how to play against it, and that’s why it took a meta-breaking team to bring them down.

Continue reading “Digital Chaos’ Journey Through the TI6 Meta”

Dota 2 – Wings Gaming’s “Unpredictable” Drafting Style

In 1994 a married couple were invited to a meeting with the board of directors at the UK’s second largest Supermarket. Edwina Dunn and Clive Humby presented to Tesco, and following an awkward silence, Lord MacLaurin uttered data sciences’ version of  ‘one small step’

“You know more about my customers after three months, than I know after 30 years”

Tesco launched Tesco Clubcard, the world’s first supermarket loyalty card, and unleashed the power of analysing shoppers’ baskets into the world.

Association rule mining is one algorithm, it analyses the probability that sets of items will appear together in a shoppers basket, when you buy eggs and bacon, you might by sausages. When you buy Nachos you might buy salsa. Hell, when you pick Shadow Demon you might pick Mirana.

Over the past few weeks i’ve been pulling data from Valve’s API and analysing it, using a couple of different methods and algorithms. In this first blog post I’m going to discuss this year’s international champions’ “unconventional & unpredictable” style of drafting. Continue reading “Dota 2 – Wings Gaming’s “Unpredictable” Drafting Style”