Stats from the ASL/KSL era

by Jacob Stubbe Østergaard / JackyVSO

Introduction

The following is a set of insights gleaned from data on offline Starcraft 1 tournament games at the pro level between 2016 and 2023. It includes combined insights related to:

• Game duration
• Matchup dynamics
• Player winrates and ratings
• Map balance
• Spawn locations
• Player and matchup development over time

…and much more.

The dataset comprises all 16 seasons of ASL and all 4 seasons of KSL for a total of 1,906 games. This is enough to make lots of statistically significant inferences but also few enough that more fine-grained insights that build on a small subset of the games come with a lot of uncertainty.

Data for each game includes players, outcome, date, duration, spawn locations, map details, map selection and tournament context. I have personally compiled the data in a SQL database which I have then queried for the insights. Most of the data has been collected from Liquipedia, while game duration and spawn location has been collected from the AfreecaTV VODs.

It should be noted that since the dataset consists exclusively of top level games, the insights in this article apply only to Starcraft played at the very highest skill level. Different dynamics may be at play at other levels. For analysis of ladder game data instead of ASL/KSL, check out Kraekkling’s recent work.

Making these stats available is my attempt to give something back to the community. I hope you find them interesting. Let me know if you have further questions that may be answered from this dataset, and I’ll get back to you.

Table of contents

The article is divided into four main parts: Matchups, Maps, Players and Tournament Stats. For the casuals, I recommend using this menu to find the stats you’re interested in. For the nerds, I recommend diving right in and reading the article from end to end.

1. Matchups

• Frequency of each matchup
• Overall winrate for each race
• Overall winrates for each non-mirror matchup
• Matchup winrates by year
• Average duration of each matchup
• Longest games by matchup
• Matchup winrates by game duration
• Effect of cross spawns on game duration for each matchup
• Effect of cross spawns on winrate for each matchup

2. Maps

• Matchup winrates by number of spawn locations
• Balance ranking of popular maps
• Tileset frequency

3. Players

• Games played by each player
• Player winrates
• Elo ranking list
• Elo graphs of top players
• Player winrates by game duration
• Average game duration by player

4. Tournament Stats

• Longest and shortest games in ASL/KSL history
• Bo5/Bo7 win probabilities by game outcomes
• Map selection advantage
• Relative advantage of being a seeded player
• Group decider matches: winners’ loser vs. losers’ winner
  
  

1. Matchups

The distribution of matchups of games in the database is as follows:

We can see that mirror matchups are quite rare, which is only logical. The comparative scarcity of TvPs can be explained by a slight overrepresentation of Zergs in ASL and KSL in general (see the figures for total games played by each race in the table below) but that leaves no obvious explanation for why there have then been more TvTs than ZvZs. Maybe Zergs try to avoid each other in group selections. Maybe ZvZ series are generally more one-sided and therefore shorter than TvTs series. I don’t know. Now let’s move on to matchup winrates, about which this study indicates that

Artosis is technically correct (the best kind of correct)

This table shows the overall winrate for each race across both its non-mirror matchups:

Overall race winrates
Race	Total games	Overall winrate
Terran	1091	50.33
Protoss	1057	50.67
Zerg	1162	49.09

It seems to show that Protoss is the best race by a margin of 0.3 percentage points over Terran. It also seems to show that Terran is the 2nd best race. But since Artosis often points out that Terran only appears to be doing well because Flash is so good, I decided to check what the winrates would be without Flash. To make a fair comparison, I also removed the statistically best player for each of the other races (Rain for Protoss and Effort for Zerg), and this is what the updated figures look like:

Overall race winrates
Race	Total games	Overall winrate
Terran	973	47.97
Protoss	892	49.94
Zerg	1088	48.29

So Terran does seem to be the worst race without Flash, but only 0.3 percentage points below Zerg.

(While this was a fun exercise, I should really emphasize that it means very little. The Protoss winrate advantage of 50.67 vs 50.33 over Terran amounts to an advantage of no more than four games, which makes it statistically insignificant by any reasonable standards. There is really only one thing that these numbers show very clearly, and that is that Starcraft is a remarkably balanced game - at least when played on carefully designed maps)

Now let’s look at the winrates for the individual non-mirror matchups. The numbers confirm the well-known pattern of T > Z > P > T but suggest that Zerg’s advantage over Protoss is slightly smaller than Terran’s advantage over Zerg and Protoss’ advantage over Terran:

While these figures are very unsurprising, the story becomes a lot more interesting when we go into some more detail. For starters, let’s take a look at the development of the matchup winrates year on year:

These graphs tell quite a different story than the overall winrates. The power distributions have actually fluctuated greatly as the races have struggled for the upper hand in the metagame. The picture becomes still more nuanced when we take game duration and spawn locations into consideration.
First, let’s look at how game duration correlates to matchup winrates.

Note:because of the limited data available (only 400-500 tournament games played in each non-mirror matchup since 2016), I have created these graphs using 10 automatically clustered intervals, which means the intervals are of different length but each represent roughly the same amount of games (20-50 games per interval). Using any more intervals than this, let alone setting a point for every single minute, would leave some intervals with way too few data points, which would make the graphs noisy/random. The presented graphs represent a compromise between that and a very coarse version with only a few intervals but more certainty. Their minor details do not represent reality but their major trends are accurate.

Looking at winrates across game duration intervals, the trend is that

Zerg rules the early game but struggles in the midgame. The late game is balanced.

We can see that if the game ends quickly, that bodes well for Zerg. In TvZ, there is a clear trend of early victories being Zerg, midgame victories being Terran, and the late game being very even. Long games are quite evenly split in all three matchups. PvZ has a similar trend but with semi-long games being once again Zerg-favored.

Moving on to spawn locations, we come to one of the most unambiguous findings of this study:

Cross spawns is VERY BAD for Terran

… and amazing for Protoss.

Effect of cross spawns in each matchup
Matchup	Adjacent spawns winrate	Cross spawns winrate	Net effect
TvP	Terran +14	Protoss +7	Protoss +21
TvZ	Terran +14	Terran +2	Zerg +12
PvZ	Zerg +13	Protoss +4	Protoss +17

Such strong discrepancies across 200+ games in each matchup on 4-player maps is unlikely to have happened randomly. This table begins to reveal how the overall winrate of each matchup is really just an average of a range of varied winrates under specific conditions. TvP in general might be slightly Protoss-favored but TvP on a 4 player map with adjacent spawns is strongly Terran-favored.

If that table began to reveal how circumstance-dependent the power dynamics between the races are, these charts should drive the point home. They show how matchup winrates are affected by whether it’s a 2-player, 3-player or 4-player map:

The most notable thing here is just how lopsided TvP on 2-player maps is. Out of 116 games in the database, Terran has won 38 and Protoss has won 78. If TvP was never played on 2-player maps, it would be a Terran-favored matchup. Terran also fares better with more spawning locations against Zerg, although this is far less pronounced. This is consistent with another finding in the data, which was that the bigger the map (by size in tiles), the better for Terran. I have not included that figure here because it shows the same as the above but less markedly. PvZ, on the other hand, seems hardly to be affected by the number of spawning locations on the map at all. I’ll leave it to more accomplished players to explain this difference.

2. Maps

Now let’s keep our focus on the maps for a bit before we move on to stats about players. No less than 68 different maps have been used in ASL and KSL: 19 2-player maps (553 games), 14 3-player maps (391 games), and 35 4-player maps (962 games).

(For some reason, we almost never see maps on Ash World tileset. Why?)

Since most of these maps have been in use for only one or two seasons, it isn’t really possible to glean any statistical insights from them. However, for the most popular maps which have been played across many seasons, it should be possible to make meaningful comparisons. I thought it might be interesting to devise a metric to score these maps according to how balanced they are. The following balance ranking of the 13 most played maps was calculated using RMSE (root mean squared error) across the three matchups, with 50.0% as the target value.

Note: Using RMSE instead of MAE (mean absolute error) means that a strong bias in one matchup is punished more than a moderate bias across all matchups. Whether to use one or the other is a question of what one’s idea of balance really is. I’m using RMSE because I personally think a map that is perfectly balanced in two matchups but has a 75% bias in the third one is less balanced than one that has scores of, say, 57/40/58.

Most to least balanced maps
Rank	Map	Total games	TvP	TvZ	PvZ	Balance score
1	Sylphid	141	47.1	47.1	58.8	94.4
2	Eddy	55	54.5	58.8	46.7	94.0
3	Butter	41	44.4	37.5	50.0	92.1
4	Polypoid	77	53.3	64.7	54.5	90.9
5	Eclipse	94	36.0	52.4	43.3	90.9
6	Vermeer	46	62.5	64.3	50.0	89.0
7	Circuit Breaker	149	64.7	58.3	62.5	87.9
8	Fighting Spirit	105	40.0	60.9	34.6	87.7
9	Benzene	48	42.9	30.8	58.3	87.2
10	Heartbreak Ridge	48	30.0	57.9	55.6	87.2
11	Transistor	41	66.7	37.5	33.3	84.6
12	Overwatch	54	37.5	69.2	70.6	82.2
13	Gladiator	47	12.5	50.0	69.2	75.7

The winrates displayed are for the first race mentioned in the matchup (e.g. Sylphid has a 47.1% winrate for T in TvP, 47.1% for T in TvZ and 58.8% for P in PvZ)

So Sylphid appears to be the most balanced map yet made, closely followed by (In The Way of An) Eddy, whereas Gladiator is the least balanced - even though it’s somehow perfectly balanced in TvZ. Looking at the scores for Vermeer, Polypoid and Circuit Breaker, we can see that what is sometimes termed “standard maps” are actually clearly Terran-favored, whereas more unusual maps might be less comfortable for Terran.

3. Players

In this section, we’ll abandon the bird’s eye view and instead zoom in on how the individual Starcraft 1 pros have been faring against each other. A total of 76 players have qualified for ASL and/or KSL at least once (27 Zerg, 26 Terran, 23 Protoss). Here they are, listed according to how many tournament games they’ve played:

Total games played (ASL+KSL)
Rank	Player	Games
1	SoulKey	205
2	Mini	200
3	Light	170
4	Rain	165
5	Best	163
6	Sharp	157
7	Snow	157
8	hero	151
9	Queen	140
10	Rush	135
11	FlaSh	118
12	Last	117
13	Action	115
14	Larva	113
15	Bisu	111
16	Mind	108
17	Soma	101
18	Shuttle	91
19	Stork	91
20	Jaedong	88
21	JyJ	84
22	EffOrt	74
23	Shine	73
24	RoyaL	71
25	Mong	58
26	Sea	53
27	MIsO	47
28	sSak	42
29	Horang2	42
30	Sacsri	40
31	Modesty	40
32	Movie	38
33	Ample	34
34	BarrackS	34
35	Jaehoon	32
36	GuemChi	31
37	free	29
38	Killer	29
39	Leta	25
40	Calm	24
41	Piano	19
42	ggaemo	17
43	ForGG	17
44	Hyuk	15
45	beast	14
46	HyuN	12
47	nOOb	11
48	815	9
49	Brain	8
50	Ss1nz	8
51	ZeLoT	7
52	Ruin	7
53	HiyA	6
54	Pusan	5
55	Sky	5
56	Tyson	5
57	YSC	5
58	BishOp	5
59	Lazy	4
60	firebathero	4
61	ByuL	4
62	Iris	3
63	NaDa	3
64	Yoon	3
65	Hint	2
66	Maru	2
67	Force(Name)	2
68	Terror	2
69	PURPOSE	2
70	Tinkle	2
71	soso	2
72	Scan	2
73	Speed	2
74	JJabNewDa	2
75	ivOry	2
76	Yerim2	0

Notably, the two finalists of this last ASL are also the two players with the most total games played in ASL and KSL.

In terms of winrates, these are the overall standings:

Total
Rank	Name	Games	Winrate
1	FlaSh	118	74.58
2	Rain	165	64.24
3	Last	117	59.83
4	EffOrt	74	59.46
5	SoulKey	205	59.02
6	Mini	200	56.0
7	Light	170	55.88
8	Soma	101	55.45
9	Bisu	111	54.95
10	hero	151	54.3
11	Queen	140	53.57
12	Snow	157	52.23
13	Best	163	52.15
14	Larva	113	51.33
15	RoyaL	71	50.7
16	Rush	135	50.37
17	Mind	108	50.0
18	JyJ	84	50.0
19	Action	114	49.12
20	Sharp	156	48.08
21	Leta	25	48.0
22	Jaedong	88	47.73
23	Shuttle	91	47.25
24	Sea	53	47.17
25	Stork	91	45.05
26	Jaehoon	32	43.75
27	Shine	73	42.47
28	Calm	24	41.67
29	Ample	34	41.18
30	sSak	42	40.48
31	Horang2	42	40.48
32	GuemChi	31	38.71
33	free	29	37.93
34	Sacsri	40	37.5
35	Mong	58	36.21
36	BarrackS	34	35.29
37	Killer	29	34.48
38	Movie	38	34.21
39	Hyuk	15	33.33
40	Modesty	40	32.5
41	MIsO	47	31.91
42	Piano	19	31.58
43	ggaemo	17	29.41
44	ForGG	17	17.65

This leaves little room for doubt about who has been the best player in this era of Starcraft. However, FlaSh hasn’t played since 2020, and when, in a moment, we’ll check a ranking that privileges recent performance, the story will be more ambiguous.

Now let’s take a look at the top players in each matchup:

Note: Only players with at least 10 games in the matchup have been included

A few things I found interesting in those rankings:

• While Light is often praised for his TvZ, it is statistically his worst matchup
• Mong has the most lopsided record, being #2 behind FlaSh in TvP but dead last in TvT and TvZ
• No one has a 55%+ winrate in ZvZ. It really seems to be the most random matchup
• ZvZ is often listed as SoulKey’s “weakness” but he still has a 54% winrate, ranking 3rd
• FlaSh is #1 in every Terran matchup
• Rain is the absolute monarch of Aiur…

…which brings us to the next table: best and worst matchup scores across all matchups:

Note: Only players with at least 15 games in the database have been included

FlaSh once again asserting dominance by occupying two spots on the top 5, while Mong has the dubious honor of posting two entries to the bottom 5. RoyaL notably manages to be an ASL champion while his TvP is the 5th worst matchup anyone has (with more than 10 ASL/KSL games in the matchup).

Elo rankings

Now, these winrates have all been historical, spanning the entire period from the 1st ASL in 2016 to the last one in 2023. If we want to get a better idea of who are the strongest players right now, it might be interesting to look at how their Elo rating might look like. Calculating Elo with k = 30 and starting rating = 1600, we get the following ranking as of October 19th, 2023:

ASL+KSL Elo rankings, Oct 19th 2023
Rank	Name	Games	Rating
1	SoulKey	205	1849
2	FlaSh	118	1848
3	Rain	165	1809
4	Rush	135	1763
5	Mini	200	1759
6	JyJ	84	1751
7	EffOrt	74	1742
8	Light	170	1742
9	Last	117	1716
10	Best	163	1706
11	Larva	113	1702
12	hero	151	1699
13	Mind	108	1678
14	Soma	101	1673
15	RoyaL	71	1668
16	Action	114	1666
17	Bisu	111	1663
18	Queen	140	1658
19	Snow	157	1651
20	Sharp	156	1633
21	Leta	25	1609
22	Calm	24	1603
23	Ample	34	1586
24	Horang2	42	1586
25	Jaedong	88	1585
26	Stork	91	1574
27	Sacsri	40	1569
28	Jaehoon	32	1566
29	Shine	73	1562
30	Sea	53	1557
31	free	29	1556
32	Killer	29	1541
33	sSak	42	1539
34	Shuttle	91	1525
35	BarrackS	34	1525
36	Movie	38	1523
37	MIsO	47	1520
38	GuemChi	31	1519
39	Modesty	40	1518
40	Mong	58	1475

SoulKey’s recent streak has in fact put him ahead of FlaSh by a single point. It will be interesting to hopefully see them battle it out in the next ASL! We should also note that FlaSh dropped some rating during his last recorded games, when he was knocked out by Soma in ASL season 10 while playing Random. One can certainly speculate that he might still have been at #1 if he had stuck to his main race.

Having access to the exact date each game in the database was played, we are also able to draw a graph of how the top players’ ratings have developed over the years:

What this graph shows most clearly is just how dominant FlaSh was in 2017-18. 2018-19 brought a sea change with Rain, Last, SoulKey and Light rising to prominence. Later on, in 2021, we saw the rise to greatness of Larva, Mini and Rush.

Player winrates and game duration

Having access to the each game’s duration, I drew up a script to search for players with high variance between winrates for different game duration intervals. Here’s what I found:

If you're playing Snow or JyJ, finish them off quickly!

Snow and JyJ both show a clear trend. If the game is short, they likely lose. If it’s long, they likely win.

Mind Mind in the midgame

Mind is vulnerable in the early game and average in the late game but, for some reason, deadly in the 11-13 minute interval!

If you're playing Shine, kill him in the midgame

Shine is the opposite of Mind: dangerous in the early and late game but vulnerable in the midgame.

If you're playing Jaedong, ...errhh... ??

This is Jaedong’s graph.

Note: The intervals in the above graphs have been automatically generated to each represent an equal share (roughly 20%) of the player’s games, and only players with a lot of games in the database have been included. The winrate in each duration interval for each player in the graphs above is thus calculated from somewhere between 15 and 30 games. This does mean there’s a lot of uncertainty but likely most of the trends are not random.

Lastly, before we move on to some general tournament stats, here’s a ranking of the pro players from “fastest” to “slowest” based on their average game duration:

Average game duration by player
Rank	Name	Race	Avg. game duration
1	HyuN	Zerg	9:50
2	beast	Zerg	9:55
3	Hyuk	Zerg	11:09
4	Calm	Zerg	12:09
5	Soma	Zerg	12:22
6	Queen	Zerg	12:36
7	MIsO	Zerg	12:49
8	Shine	Zerg	12:58
9	Killer	Zerg	13:01
10	hero	Zerg	13:15
11	Sacsri	Zerg	13:20
12	Jaedong	Zerg	13:24
13	SoulKey	Zerg	13:25
14	Bisu	Protoss	13:37
15	ggaemo	Zerg	13:40
16	Mini	Protoss	13:43
17	Modesty	Zerg	13:49
18	Movie	Protoss	13:56
19	Larva	Zerg	13:58
20	EffOrt	Zerg	14:00
21	BarrackS	Terran	14:00
22	nOOb	Protoss	14:08
23	Action	Zerg	14:13
24	Horang2	Protoss	14:41
25	Jaehoon	Protoss	14:46
26	Rain	Protoss	14:47
27	FlaSh	Terran	14:48
28	RoyaL	Terran	14:50
29	Stork	Protoss	14:53
30	Best	Protoss	15:02
31	Ample	Terran	15:08
32	ForGG	Terran	15:22
33	Rush	Terran	15:24
34	GuemChi	Protoss	15:30
35	Snow	Protoss	15:40
36	Light	Terran	15:42
37	Piano	Terran	15:55
38	Shuttle	Protoss	16:02
39	Leta	Terran	16:05
40	Last	Terran	16:07
41	Mind	Terran	16:36
42	sSak	Terran	16:45
43	JyJ	Terran	16:52
44	Sea	Terran	17:11
45	free	Protoss	17:30
46	Sharp	Terran	17:42
47	Mong	Terran	18:22

Note: Only players with at least 10 games in the database have been included

Some findings here:

• As the “slowest” Zerg player, Action’s games are slightly longer than those of the “fastest” Terran player, BarrackS
• free is practically a Terran trapped in a Protoss’ body
• FlaSh is notably the second fastest Terran, but this may in part be because a few of his matches were not actually played in Terran matchups. This will only have had a minor effect though.
  
  

4. Tournament Stats

In this section, we’ll look at some overall stats. First we’ll look at the longest and shortest games, game duration by matchup, and game duration by spawn locations. Then we’ll take a look at how Bo5 and Bo7 series normally play out, and then finally the advantage of being seeded and the probability of winning a group decider match.

Longest and shortest games in ASL/KSL history

Just for curiosity’s sake, these were the longest games ever played in ASL or KSL:

Longest games
#	M.	Winner	Loser	Map	Date	Tmnt.	S.	Duration
1	TvT	Last	Sharp	Sylphid	2019-7-21	ASL	8	48 minutes
2	TvT	Mind	Rush	Fighting Spirit	2017-9-12	ASL	4	47 minutes
3-4	TvT	Sea	FlaSh	Benzene	2017-1-22	ASL	2	41 minutes
3-4	PvZ	Best	Action	Nemesis	2023-4-3	ASL	15	41 minutes
5	TvP	Light	Rain	Fighting Spirit	2018-10-18	KSL	2	40 minutes
6-9	PvZ	Shuttle	EffOrt	Transistor	2018-4-15	ASL	5	39 minutes
6-9	PvZ	hero	Mini	Third World	2018-5-13	ASL	5	39 minutes
6-9	TvZ	SoulKey	Mong	Vermeer	2022-2-17	ASL	13	39 minutes
6-9	TvP	Best	Light	Seventy-six	2023-3-28	ASL	15	39 minutes
10	PvZ	Larva	Rain	Gold Rush	2017-10-15	ASL	4	38 minutes

…and these were the shortest:

Shortest games
#	M.	Winner	Loser	Map	Date	Tmnt.	S.	Duration
1-4	TvZ	SoulKey	FlaSh	Camelot	2017-5-28	ASL	3	2 minutes
1-4	TvZ	SoulKey	Sharp	Gladiator	2018-8-24	KSL	1	2 minutes
1-4	TvZ	Mind	SoulKey	Heartbreak Ridge	2018-11-9	KSL	2	2 minutes
1-4	TvZ	Shine	BarrackS	Nemesis	2022-8-30	ASL	14	2 minutes

Note: Since game duration is only registered by whole minutes completed, games with the same duration in minutes cannot be ranked among each other. There are 27 games in the database that ended between 3:00 and 4:00 but only the four shown above ended between 2:00 and 3:00.

SoulKey is known as a macrozerg but that apparently doesn’t stop him from having been involved in 3 of the 4 shortest ever games in ASL and KSL.

Unsurprisingly, the three longest ever games were all TvTs. These are the average game durations by matchup:

Average duration of each matchup
Rank	Matchup	Average duration
1	TvT	17m58s
2	TvP	16m54s
3	PvZ	14m34s
4	TvZ	14m20s
5	PvP	12m25s
6	ZvZ	9m16s

And these have been the longest games in each matchup respectively:

Longest game of each matchup
Matchup	Winner	Loser	Map	Date	Tournament	Season	Duration
TvT	Last	Sharp	Sylphid	2019-7-21	ASL	8	48 minutes
PvZ	Best	Action	Nemesis	2023-4-3	ASL	15	41 minutes
TvP	Light	Rain	Fighting Spirit	2018-10-18	KSL	2	40 minutes
TvZ	SoulKey	Mong	Vermeer	2022-2-17	ASL	13	39 minutes
PvP	Snow	free	Optimizer	2020-10-11	ASL	10	31 minutes
ZvZ	Action	EffOrt	Apocalypse	2023-8-21	ASL	16	23 minutes

Effect of cross spawns on game duration

It’s well known that cross spawns makes games longer, but this effect is drastically stronger in some matchups than others. Here’s how cross spawns affects game duration:

Matchup duration by spawns
Matchup	Adjacent spawns	Cross spawns	Difference
PvZ	13m38s	16m8s	19.03
ZvZ	9m20s	10m32s	13.53
Overall	14m21s	15m45s	10.04
TvP	16m34s	17m53s	8.25
PvP	11m58s	12m51s	7.82
TvT	16m58s	17m51s	5.38
TvZ	14m34s	15m13s	4.63

Cross spawns has by far the strongest effect on the duration of PvZs, whereas it has little effect on TvT and TvZ. Again, I’ll leave it to the experts to interpret this difference but I’m curious about the answer.

Series stats

The first game myth

In almost every single pre-series interview ever, one or both players states that they “just need to win the first map” and then they’ll probably take the whole series. It’s really remarkable how ubiquitous this belief is. (And by the way, why are those pre-series interviews always so boring?! Is it something about Korean culture? Whenever they’re on, I keep thinking of all the stupid nonsense I’d be saying if I had the floor instead.) Anyway, that was actually what prompted me to start gathering all this data in the first place. This incessantly repeated claim just sounded spurious to me, so I wanted to check it. So I did, and I found that

• The winner of game 1 ended up winning the Best-of-5 73.6% of the time.

That might look like a lot at first glance but we have to remember that obviously winning any game in a best of 5 is going to greatly increase your chances of winning the whole series. To find out if the first game is really so important, what we can do is compare this figure to the same figure for games 2 and 3. Plugging those into the same query, we find that

• The winner of game 2 ended up winning the Best-of-5 71.6% of the time, whereas
• The winner of game 3 ended up winning the Best-of-5 73.1% of the time.

So that’s what the game 1 advantage amounts to. It’s more important than game 3 by 0,5pp and more important than game 2 by 2pp. This is based on 197 Best-of-5s in the database. Running the same calculations for Best-of-7s actually indicates that game 2 is more important than any other game but that’s based on only 35 recorded Best-of-7s and therefore tells us almost nothing.

Incredibly, in ASL (but not KSL), the game 1 myth has held true for all 16 finals series. I have no explanation for this. On the flip side, this means that if you discount finals (only the first 7 of which were Best-of-5 in ASL), game 1 has been less important than game 3 across 190 recorded non-final Best-of-5s.

In conclusion:

Map selection advantage

Players and casters often place some emphasis on the advantage of map selection. I wanted to test this assumption also and in doing so, I found something surprising:

Being the one who selects the map is just as important in mirror matchups as in non-mirror matchups

Generally speaking;

• the player who chose the map went on to win the game in 54.9% of cases,
• whereas for mirror matchups only, the map chooser won 54.4% of the time

This is surprising because the advantage of choosing a map is often chalked up to being about whether this map is generally good for your race in the given matchup or not.

The relatively low overall winrate for the map chooser seems partially to be due to the limited average range of choices. Once we get to the last few maps of the series, there might only be two or three maps left to choose from, which might not be much of an advantage. As such, we can see that when counting only the first map choice:

• the player who chose the first map won that game in 61.1% of cases,
• whereas in mirror matchups only, the first map chooser won 60.5% of the time

Although non-mirror matchups appear to show a very slightly greater map choice advantage, this is well within the bracket of uncertainty.

Reverse sweeps

The last series stat I’ve registered is the probability of reverse sweeps. In 197 Best-of-5s, we have seen six reverse sweeps. The probability of making a reverse sweep in a Best-of-5 (i.e. the probability that if you’re down 0-2, you go on to win the series) stands at 6%.

In 35 Best-of-7s across ASL and KSL, there has never been a reverse sweep.

Seeds

Having dealt with the first game myth, let’s try to take on another pre-game-interview cliché: “My goal is to make it to the Round of 4 and get seeded for the next season”. Further investigation shows that this cliché has somewhat more merit. Comparing the average season results of seeded players and non-seeded players, we get the following:

• Average result of seeded player: 8.8
• Average result of non-seeded player: 14.6

This means that the average result for a seeded player was roughly getting eliminated in the Round of 8, whereas the average result of a non-seeded player was roughly getting eliminated in the Round of 16. This is a really considerable advantage, and it should be noted I am comparing the same set of players, meaning the average result for non-seeded players listed here is the average result for players who have been seeded in at least one season, in seasons where they were not seeded. So the figures can’t be explained by arguing that players who earn a seed are obviously just better than those who don’t. More likely the explanation is that the ASL’s Round of 24 with its Best-of-1 format is a real slaughterhouse that even the best players struggle to make it out of alive.

To further galvanize this point, becoming a seeded player greatly increases your chance of becoming a seeded player again in the next season:

• Probability of gaining seed as already seeded player: 36.7%
• Probability of gaining seed as non-seeded player: 18.4%
  

Decider matches: Winners' loser vs losers' winner

That leaves just one more thing I wanted to check. Something has always bothered me about the double elimination format employed in starleague group stages. It seems unfair when there’s a rematch in the decider match and the winner of the first match gets eliminated. Those players came out 1-1 on the night and although the advancing player did win the intervening match, that may only have been because he was facing easier opposition in the losers’ match than the eliminated player was facing in the winners’ match.

In any case, I found that across 176 decider matches,

• Winners’ loser advanced 53% of the time, while
• Losers’ winner advanced 47% of the time

5. (You must construct) Additional notes

Please get in touch if you have found any errors in these charts, if you feel there’s something important I forgot to mention, or if you have any requests for insights you’d like me to pull from the database. Anything pertaining to game duration, map details, tournament results, series outcomes or temporal developments should be possible to draw from there. The database also includes some columns that I have not touched upon here, such as birth year of the players, year of publication for maps, ASL tiers, and Ro. 24 and Ro. 16 group affiliations. I have also categorized maps as island or standard but since so few games have been played on island maps, I can’t really make any inferences about that. What I don’t have is any information about build orders. That would be the next level. But it would be challenging to categorize the build orders in useful ways and very time-consuming to gather the data.

I would like to stress that I am not a statistician. I’m just a layperson with a basic understanding, doing my best. I haven’t bothered to calculate p-values for all the charts, and my uncertainty assessments are just estimates. I apologize if you’re a professional statistician and reading any of this has caused you to facepalm.

Feel free to copy these charts and tables but please link here if you publish them.

Thanks to Liquipedia for providing most of the data and to everyone who’s keeping Starcraft alive during its very first century as the greatest game ever!

How to get in touch:

Reddit: u/jacobvso
TL.net: JackyVSO

If you’re interested in a detailed explanation of how I did this, that is available here.