Trouncing in Dota 2: An Investigation of Blowout Matches
Bibliographic record
Abstract
compete against each other, such as Dota 2, play a major role in esports tournaments, attracting millions of spectators. Some matches (so-called blowout matches) end extremely quickly or have a very large difference in scores. Understanding which factors lead to a victory in a blowout match is useful knowledge for players who wish to improve their chances of winning and for improving the accuracy of recommendation systems for heroes. In this paper, we perform a comparative study between blowout and regular matches. We study 55,287 past professional Dota 2 matches to (1) investigate how accurately we can predict victory using only pre-match features and (2) explain the factors that are correlated with the victory. We investigate three machine learning algorithms and find that Gradient Boosting Machines (XGBoost) perform best with an Area Under the Curve (AUC) of up to 0.86. Our results show that the experience of the player with the picked hero has a different importance for blowout and regular matches. Also, hero attributes are more important for blowouts with a large score difference. Based on our results, we suggest that players (1) pick heroes with which they achieved a high performance in previous matches to increase their chances of winning and (2) focus on heroes’ attributes such as intelligence to win with a large score difference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".