Stats for WCS AM/EU semifinals

Posted on April 12, 2014 by kevin

It’s past 3AM here, and over the past 6 hours or so, I have been cranking on a few minor features for Spawning Tool, but primarily machine learning to learn to label build orders. It’s not very well-trained at the moment, but it got to 61% on Reaper Expands, so it was above 50-50. More importantly, the code ran to completion! I’ll write more about that soon.

In the meantime, however, I think I might be sleeping in tomorrow, so I thought I would publish stats before heading to bed. Enjoy the semifinals tomorrow!

MC (P) v jjakji (T)
1. MC beat jjakji 3-1 at IEM Sao Paulo with surprises everywhere. He opened Blink, Phoenix, DTs, and Robo
http://spawningtool.com/research/?p=1&after_time=&before_time=&after_played_on=&before_played_on=&p1-race=&p1-tag=106&p2-race=&p2-tag=475
2. Out of 10 recent TvPs, jjakji went for a Bio Mine composition. Expect to see more of it
http://spawningtool.com/research/?p=1&after_time=&before_time=&after_played_on=1%2F1%2F14&before_played_on=&p1-race=&p1-tag=475&p1-tag=132&p2-race=1&p2-tag=

MMA (T) v San (P)
1. San loves Templar. Before the 25 minute mark, he casts ~2.6x as many Storms as all PvTs, and Ghost usage is also up to compensate
http://spawningtool.com/research/abilities/?after_time=&before_played_on=&p2-race=2&p1-race=&p1-tag=286&before_time=&after_played_on=&p2-tag=&el-after_time=&el-before_time=25
http://spawningtool.com/research/abilities/?after_time=&before_played_on=&p2-race=2&p1-race=1&p1-tag=&before_time=&after_played_on=&p2-tag=&el-after_time=&el-before_time=25

Alicia (P) v HyuN (Z)
1. In 18 games, Alicia has never played a PvZ shorter than 12 minutes.
http://spawningtool.com/research/winrates/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=285&p2-race=3&before_time=&after_played_on=
2. Unlike his ZvT, HyuN doesn’t care how long a ZvP lasts: his win rates are always about the same
http://spawningtool.com/research/winrates/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=82&p2-race=1&before_time=&after_played_on=

Revival (Z) v Oz (P)
1. Oz does a lot of Forge Fast Expands (which are less popular than Nexus First and 1 Gate Expand builds) and at a lot of different timings
http://spawningtool.com/research/tags/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=380&p2-race=3&before_time=&after_played_on=

Stats for WCS AM/EU quarterfinals day 2

Posted on April 11, 2014 by kevin

I’m a little late here, but here are some numbers:

Snute (Z) v MMA (T)
1. MMA beat Snute in the ATC Season 2 Finals http://spawningtool.com/7773/
2. Snute opened 15 Hatch, 16 Pool in all 10 ZvTs in 2014. However, he has also gone for Roach aggression, Swarm Hosts, and Ultralisks out of it http://spawningtool.com/research/tags/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=58&p2-race=2&before_time=&after_played_on=1%2F1%2F14

San (P) v Welmu (P)
1. I didn’t find much of interest for this matchup. In 2014, though, Welmu has at least 26 San replays to study
http://spawningtool.com/research/?p=1&after_time=&before_time=&after_played_on=1%2F1%2F14&before_played_on=&p1-race=&p1-tag=286&p2-race=1&p2-tag=

Polt (T) v Revival (Z)
1. Revival plays very long ZvTs. 8/11 (73%) went longer than 20 minutes compared to 45% of ZvTs globally
http://spawningtool.com/research/winrates/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=374&p2-race=2&before_time=&after_played_on=
2. Polt also tends to go long, playing 19 / 35 (54%) over 20 minutes. This series could take awhile
http://spawningtool.com/research/winrates/?after_time=&before_played_on=&p2-tag=&p1-race=&p1-tag=57&p2-race=3&before_time=&after_played_on=

Oz (P) v Arthur (P)
There aren’t many replays for these players other than Oz v sOs at IEM Katowice
http://spawningtool.com/research/?p=1&after_time=&before_time=&after_played_on=&before_played_on=&p1-race=&p1-tag=380&p2-race=1&p2-tag=

Stats for WCS AM/EU quarterfinals day 1

Posted on April 10, 2014 by kevin

Today, the WCS season finals begin for Europe and America. I compiled a few stats that I posted to Team Liquid and are reproduced below.

MC (P) v StarDust (P)
1. StarDust beat MC 2-0 at IEM Cologne. The games lasted 6:56 and 8:38 with MC attacking early in both games (ref)

2. MC is 3-9 (25%) in PvPs lasting 20-25 minutes, but 6-1 (86%) in PvP >25 minutes (ref)

3. MC is diligent in using his Phoenixes. Although he makes slightly fewer (.54 v .65) Phoenixes per game than his opponents, he uses Graviton Beam more often (2.81 v. 1.61) (ref 1) (ref 2)

VortiX (Z) v. jjakji (T)
1. VortiX opened Hatch First in 18/20 (90%) of ZvTs. It’s usually a 15 Hatch followed by a 16 Extractor or Pool. (ref)

2. VortiX is 4-0 in games 8-12 minutes long, all of which were Roach Baneling all-ins. However, he’s only 4-4 in games going Roach Baneling overall (ref)

Alicia (P) v. Bomber (T)
1. Alicia is 19-6 (76%) in games longer than 16 minutes, while only 69% overall. (ref)

2. Don’t play Bomber straight up in TvP. He’s 2-7 in games <16 minutes and 11-4 in games >16 minutes. Blink Stalkers and Dark Templar are good bets (ref 1) (ref 2)

TaeJa (T) v. HyuN (Z)
1. At ASUS ROG Summer 2013, TaeJa beat HyuN 3-2 (ref)

2. HyuN can be deadly in the early game. He’s 9-0 before 12 minutes and 13-2 before 16 minutes (ref)

Methodology
I used the data from Spawning Tool to generate all of these statistics. Notably, replays for WCS 2013 season 3 and the current WCS season have not been released, so those are not included in the current sample. Some players have more data from other recent tournaments, whereas others may be based on older data with different play styles.

If you get chance, please poke around with the data on Spawning Tool and share any other interesting trends you find!

IEM Katowice PvT Blink: 64%. PvT Overall: 61%, a game theoretic analysis

Posted on March 29, 2014 by kevin

2 weeks ago, IEM Katowice showed off some sick games. My favorite was sOs’s Phoenix into Colossi into Carriers on Alterzim Stronghold, but the biggest news apparently was Blink Stalkers in PvT. After several crushing games by HerO and sOs, it really looked like Blink was imbalanced in this matchup. The most insightful analysis I saw was from bwindley, and now I have had a chance to label data and crunch numbers, here’s my take on the situation with a few numbers, a little analysis, and some game theory.

The easiest point to make here is the raw win rates. In 33 PvTs, Protoss went Blink Stalkers in 11 of them. The overall PvT win rate was 20-13 for 61% (ref). In games where Protoss went Blink, the win rate was 7-4 for 64% (ref).

For comparison, in Spawning Tool, the overall PvT win rate is 804-767 for 51% (ref). In games where Protoss researched Blink before 6:00, the win rate is 71-58 for 55% (ref).

So Blink wins slightly more than normal, but it’s pretty dang close. One would hope that different strategies would have different win rates, or else the meta-game had stagnated as no strategy would confer an advantage over any other (more on this below). Continue reading →

Spawning Tool and the philosophy of FiveThirtyEight

Posted on March 25, 2014 by kevin

This past week, ESPN launched FiveThirtyEight, a website dedicated to data journalism. The lead for the site is Nate Silver, a statistician and writer, who most famously correctly predicted the winner of all 50 states in the 2012 US presidential elections. Roughly, the site is dedicated to to the use of quantitative methods in journalism across politics, economics, science, life, and sports.

Silver outlines a manifesto for the site, and I want to draw attention to a few points he makes. First, he leads with the point that his presidential prediction was not impressive by comparing it to other models, but instead by comparing it to pundits. I think the framing of his argument is very important here because it points out the type of thinking that we’re used to. Second, he points out the spectrum along quantitative and qualitative approaches to journalism. Both types of analysis are important, but he sees quantitative as being under-represented, hence the creation of FiveThirtyEight. Third, he outlines an approach to journalism as collection, organization, explanation, and generalization. Particularly, he criticizes the last two steps in in conventional journalism. Explanation in often missing as journalists fail to properly attribute causation, and predictions (as part of generalization) are under-scrutinized and often inaccurate. Continue reading →

In-game scorekeeping for StarCraft?

Posted on March 22, 2014 by kevin

So far, Blizzard has given the community amazing tools for doing replay analysis, and the community has compiled a lot of stats and built services to work with that data. Even so, I think there’s a gap in levels of analysis. At the top, we have aligulac, which compiles win-loss data into accessible predictions but doesn’t give much insight into a playstyle or why the data is the way it is. At the bottom, we have ggtracker, which provides graphs and data for individual games and can compile stats from low-level mechanics in games, but is missing higher-level analysis.

In-between is where I think theorycrafting reigns: strategy. What build orders work against what? Which players are the best at worker harass? Who has the best forcefield placement? These details lie below win-loss analysis, and although these may be extractable from the replay data, this analysis requires some qualitative analysis to determine what constitutes specific tactics or plays. Even the best machine learning techniques (supervised learning) require labeled examples to learn from. Continue reading →

“How important is having a supply advantage?” continued

Posted on February 27, 2014 by kevin

A week and a half ago, I slapped together some graphs about the importance of supply differences and posted it to reddit. There were a ton of very thoughtful comments from the community, some of which I riffed on to create more graphs and investigate more of the data. This post is a summary of the rest of what I found, with all data available in this spreadsheet.

The first graph shows the global win rates and win rates in mirror matchups given certain supplies. The most direct way to use the data is to observe a game and point back to the graph at any time to figure out how much of an edge someone has. Continue reading →

Explanation of win rates by supply difference

Posted on February 16, 2014 by kevin

Casters use a lot of different information to make predictions and analyze the state of a game of StarCraft 2. One often used stat is supply difference: if player A has 120 supply and player B has 100 supply, we might say that player A has a 20 supply advantage. I wanted to determine how important a supply advantage is by looking a lot of data and seeing what a player’s win percentage is given a certain supply advantage.

Results and Discussion*

You can view the original data at https://docs.google.com/spreadsheet/ccc?key=0AjlUNdJN-kiedFc5QkZxRVRoUHBWNGRCUUVjOGFmVUE&usp=sharing Continue reading →

Analysis of ByuL’s reactive ZvP in Proleague

Posted on December 29, 2013 by kevin

Proleague (SPL) is back for 2014. I have become a bit of a StarCraft snob and think that Proleague is by far the most exciting and interesting StarCraft out there. It’s a bummer that they have changed the time this year so that all matches start at 2AM (at the earliest) for me, but I’ll happily follow along with Fantasy Proleague and by watching VODs.

Having just rewritten my Zerg versus Protoss strategy guide, I’m very excited about a game I just watched between IM ByuL and Remember Prime. Played on the new map Sejong Science Base, I think it perfectly illustrates with the type of safe, reactive Zerg style that I had seen before and was recommending for you to try.

Link to the VOD

ByuL opens with a 15 Hatchery, 16 Spawning Pool. He takes his gas at 3:35 (17 supply, after the Overlord), which allows him to get Metabolic Boost for his Zerglings early. He makes 4 Zerglings as soon as the Pool finishes and sends them to scout his opponent.

Continue reading →

Zerg versus Protoss Strategy Updated

Posted on December 27, 2013 by kevin

After playing primarily team games with my friends over the past few months, I am getting back into playing 1v1. This time around, I think I’ll be playing Zerg. I admittedly was somewhat lost in how to play out my games, then I remembered that I had a guide on how to do it. Then I read it and realized that some of the strategies were painfully outdated. Here’s an update. As usual, feel free to comment if you have any thoughts or feedback.

Zerg versus Protoss (ZvP)

Since the release of Heart of the Swarm, ZvP has become much more interesting. Previously, Protoss only fast expanded into 2 base timing attacks, but recently, Protoss have mixed in more 1 Gate Expand builds and early pressure. There might also be 2 base all-ins, Stargate openings, or just macro into deathballs. All of this means that Zerg must be more flexible. Continue reading →

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