Tag Archives: WESG 2016

The WESG 2016-that-actually-took-place-in-2017 wrap-up.

Non-standard calendars and their year counting madness. How about those crazy things?

World Electronic Sports Games 2016, also known as Alibaba Spent 3.7M USD This Year To Virtue Signal Its Success To A New Vertical, is probably the closest thing esports has to a world final for multiple games. Well, at least a multi-title event that’s mildly relevant in this era of inflating prize pools not run by the usual suspects.

I keep up with events like these by going back after the fact and gathering information about the results, as well as some general statistics about the games and prize awards for each tournament. Here’s my entries regarding the WESG 2016 results (using my goto source, Liquipedia) and a little blurb about why I record the statistics I have.

  • CSGO
    • 1ST: ENVYUS, 800K USD
      • GROUP A WINNER (11PTS, 8-2)
      • 2-1 TYLOO (16-9 CACHE, 8-16 MIRAGE, 16-14 DUST2)
      • 2-1 SPACE SOLDIERS (8-16 CACHE, 22-19 CBBLE, 16-14 DUST2)
      • 2-0 KINGUIN (2ND: 400K USD) (16-5 TRAIN, 16-6 DUST2)
    • VIRTUS.PRO (3RD: 200K USD) 2-0 SPACE SOLDIERS (4TH: 60K USD) (16-8 CBBLE, 16-6 NUKE)

First: I usually don’t write anything in my notebook in lowercase unless I need to actually remember the case of what I’m writing down. Pretty ironic since the sub-title for the blog and prominent name for the podcast contains the word lowercase.

As for not recording game wins/losses and only recording map wins/losses in CSGO, the former is the only base statistic that matters without listing round wins/losses in series for group games. It’s the most basic representation of a team’s performance in a group stage without also stating the rosters’ cumulative kill-death-assist ration. In a perfect world, if you’re gathering KDA statistics, you might as well be gathering average economy statistics, too.

Knowing round scores against certain matchups, however, is a perfectly sane thing to remember. Especially when we’ve moved beyond the mundane

And let us not forget the real metric that matters, here, winnings. Yeah, I could be lazy and just write out $800K, but sometimes the currency of the award isn’t USD. Using symbols seems lame in a notebook that only I’m going to read. Might as well be pedantic if I’m going to do whatever in my magic book of personal records and notes and so on.

  • DOTA 2
    • 1ST: TNC, 800K USD
      • GROUP D WINNER (10PTS, 7-3 IN 361M46S)
      • 2-1 DILECOM (IN 114M43S)
      • 2-0 ALLIANCE (IN 75M50S)
      • 2-1 CLOUD9 (2ND: 400K USD) (IN 132M22S)
    • ALLIANCE (3RD: 200K USD) 2-1 INFAMOUS (4TH: 60K USD) (IN 126M52S)

Dota 2 is a grand ol’ game of strategy, tactics and fatigue. Typically, you could also say that League of Legends is the same thing, along with many other Dota-clones, however not all Dota-clones receive near-complete makeovers of their end-games as recently as Data 2 has. 7.00’s mid-to-late game changes revolving around its implementation of a skill tree is a huge change in game mechanics.

Match length might begin to tell us if the teams have adapted their strategies to the new mechanics in the patch and in turn optimize all heroes’ viability for all situations—which we’d see if games trended towards longer times.

OR… a trend for shorter match times could mean that matches are more often decided by the magic of a player hitting 25th level followed by a blatant, drastic steamrolling.

  • SC2
    • 1ST: TY (T), 200K USD
      • GROUPD D WINNER (8-4)
      • 3-0 STEPHANO (Z)
      • 3-0 NEEB (P)
      • 4-3 MARU (T) (2ND: 100K USD)
    • NEEB (P) (3RD: 50K USD) 3-1 SHOWTIME (P) (4TH: 20K USD)

When it comes to the top tiers of StarCraft 2 professional play, map selection, player race, and starting position might be more important statistics to track here, but I’m not a living, breathing statistics machine that is obsessed over identifying trends like this.

Maybe if SC2 was more of a major esport and not in the rut that it is.

  • RNGSTONE
    • 1ST: STAZ, 150K USD (25-16)
    • 2ND: ORANGE, 70K USD (28-15)
    • 3RD: BUNNYHOPPER, 40K USD (26-18)
    • 4TH: XIXO, 20K USD (21-18)

The only things that matter in RNGstone, since every player uses all classes of decks in a tournament setting, are game wins and… losses. Since the game is practically decided by a randomized, predetermined deck, there’s not really a reason to bother associating most of the statistics that one could reasonably derive from a game.

That statistic is average turns taken to win. With that statistic, you can identify those with the super-optimized decks and those with decks that might require longer to set up a victory condition.

Actually, yeah, sure. That statistic doesn’t help as much as I thought it might.