Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →A StarCraft bot’s win rate is not an unconditional measure of its strength: it describes how that bot performed against particular opponents, under a particular game version, map and matchup mix, rules, scoring system, and time limit. To judge whether the rate means anything beyond that sample, check the protocol, inspect the denominator and breakdowns, preserve replays, and pair full-game results with focused tests.
Start by defining the experiment
Before comparing percentages, record enough detail for someone else to understand what counted as a match and a win. At minimum, report:
| # | Preview | Product | Price | |
|---|---|---|---|---|
| 1 |
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StarCraft II: Wings of Liberty | $13.12 | Buy on Amazon |
| 2 |
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Starcraft Gold - Includes Expansion Pack (PC CD) | $29.99 | Buy on Amazon |
| 3 |
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Starcraft II: Legacy of the Void - Standard Edition | $38.95 | Buy on Amazon |
| 4 |
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StarCraft: Brood War Expansion (Boxed) | $3.59 | Buy on Amazon |
- Game title and edition, client or patch, and API version.
- Game mode, rules, map list, map-selection method, and starting-position policy.
- Player races, bot build or commit, opponent identities and strength range, and whether opponents were fixed or sampled.
- Information available to each player, including any restrictions or permitted cheats, plus game speed and runtime limits.
- How draws, adjudications, disconnects, crashes, slowdowns, and timeouts were handled.
- Evaluation dates, total games, wins, losses, draws or adjudications, and the exact denominator used for the percentage.
Include an uncertainty summary, such as a confidence interval, when available. There is no universal game count established by these sources that guarantees a reliable estimate; state the actual sample size and uncertainty rather than presenting a threshold as a rule.
Why tournament rules change the meaning of a win
SSCAIT’s official rules illustrate how specific a protocol can be: the tournament uses 1v1 Melee on StarCraft: Brood War 1.16.1, randomly selects games from its map pool, and prohibits complete map vision and other cheats. A game can end after 90 in-game minutes or after five real-world minutes without a unit death; in those cases, the result is determined by kills plus razings score. Crashes and excessive slowdown count as losses. SSCAIT retains replays and makes them available after the tournament. A percentage from these rules should be identified as an SSCAIT result, not treated as interchangeable with a ladder or another event’s record. SSCAIT tournament rules
#1 Best Overall
- Fast-paced, hard-hitting, tightly balanced competitive real-time strategy gameplay that recaptures and improves on the original game
- Three completely distinct races: Protoss, Terran, and Zerg
- Units and gameplay mechanics distinguish each race
- 3D-graphics engine with support for visual effects and massive unit and army sizes
- Full multiplayer support, with competitive features and matchmaking utilities available through Battle.net
Check what went into the aggregate
A pooled percentage can rise or fall because the sample contains a different mix of maps, matchups, positions, opponents, or dates—even if the bot itself has not changed. Where the records allow it, break results out by each of those categories and show the number of games in every group.
- Report the race matchup and map for each result group.
- Include starting position or side when it can affect play.
- Separate opponent identities or strength bands rather than hiding them in a single pool.
- Show bot version and evaluation period so changes over time are visible.
If practical, give each bot the same opponent and map mix, and rotate starting positions. If the samples are not balanced, disclose the difference and avoid claiming that it caused a change in performance.
Blizzard Entertainment’s historical StarCraft II balance report shows why map and matchup context matter: it gave examples such as a 70% PvT win ratio on Cloud Kingdom, a 62% PvZ ratio on Korhal Compound, and a 37% TvZ ratio on Metalopolis. These are historical examples from that report, not current balance figures or bot results. Blizzard also said those rates changed from week to week and day to day, and discussed how map-veto systems affect ladder and tournament pools. The report’s examples are a reason to expose map, region, skill composition, and time in a rate report—not a basis for inferring present-day balance. Blizzard’s historical balance report
Rank #2
- Build new units
- construct Lurkers, Medics, Valkyries, Corsairs, Dark Archons and more
- Explore new worlds
- storm frozen wastes, scour arid deserts and navigate the twilight worlds of the Dark Templar
- Command new missions
Preserve matches so the result can be checked
Keep the replays and the configuration needed to reproduce or audit them: rules, bot builds, opponent versions, map files, and logs. A replay can clarify whether a bot crashed, stalled, received unintended information, or faced an unusual opening. It can also reveal whether a score or tiebreak was applied correctly; SSCAIT allows administrators to correct a disputed result after reviewing a replay under its tournament rules.
Use full-game results and focused tests for different questions
A competition or ladder record measures complete-agent performance under that event’s conditions. It does not, by itself, explain why a bot won or lost. In their 2015 benchmark paper, Alberto Uriarte and Santiago Ontañón argue that competition results alone are insufficient for understanding the strengths and weaknesses of specific techniques or agents. Their proposed scenario tests complement, rather than replace, competition results. Uriarte and Ontañón’s 2015 benchmark paper
The StarCraft AI benchmark reference describes normalized measures for targeted scenarios. These can diagnose capabilities, but they should not be reported as full-game win rates.
Rank #3
- This is a standalone product. It does not require any other version of StarCraft II to play
- Internet Connection Required
- Battle.net registration and Battle.net Desktop Application required
| Measure | What it captures |
|---|---|
| Survivor life | Remaining unit hit points summarized in relation to scenario duration. |
| Time survived | Survival duration relative to a set timeout. |
| Time needed | Time to complete an event or reach a specified condition. |
| Units lost | Relative unit losses between players. |
Its scenarios probe reactive control and kiting, symmetric-army combat, navigation around dynamic obstacles, building placement under a rush, and recovery after an opponent disrupts a plan. A weak scenario score can help locate a specific problem that an overall match percentage cannot isolate. StarCraft AI benchmarks
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Read headline wins within their stated conditions
AlphaStar’s matches against MaNa
DeepMind reported that AlphaStar defeated Team Liquid professional player Grzegorz “MaNa” Komincz 5–0 in test matches held on December 19, 2018, following a benchmark match against Dario “TLO” Wünsch. DeepMind described the MaNa matches as taking place under professional match conditions, on a competitive ladder map, and without game restrictions. This is a clearly described demonstration, but it is one event—not evidence of a universal rate across races, maps, player pools, or time. DeepMind’s AlphaStar account
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteReinforcement-learning results against built-in AI
A 2019 paper by Pang and coauthors reported a win rate above 99% against built-in AI difficulty level 1 and above 93% against the hardest non-cheating built-in level 7. The paper’s setup included a 64×64 map and restrictive units. Those results describe performance against those built-in opponents under that study’s conditions; they are not an unrestricted ladder record or a result against professional human players. Pang et al., “On Reinforcement Learning for Full-Length Game of StarCraft”
Compare bots on matched protocols
For a fair comparison, use the same conditions for each bot or clearly identify where they differ. These are the main comparison axes:
- Version and ruleset: game and client version, APIs, restrictions, and adjudication rules.
- Opponent set: identities, skill range, races, and whether every bot faced the same opponents.
- Maps and starts: map pool, selection and veto policy, matchup coverage, and starting positions.
- Sample and uncertainty: games in each group, evaluation period, and uncertainty around each rate.
- Information and compute: legal observations and actions, game speed, runtime allowance, and timeout treatment.
- Outcome versus diagnosis: full-game results alongside scenario-specific measures, kept distinct.
When a source does not establish a current map pool, leaderboard, or sample-size rule, do not infer one from an older report or study. Tie each percentage to the dated event or study that produced it and report the evidence available for that specific comparison.
Quick Recap
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