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Unveiling Success Drivers in Gaming: A Machine Learning Study Across Steam, Twitch, and Metacritic

Ma, Jiesi·2025·International Journal of Computer Games Technology

Abstract

This study employs machine learning to assess the relative impact of major platforms—Steam, Twitch, and Metacritic—on video game revenue. Through an integrated analysis of three comprehensive datasets comprising commercially successful titles on Steam, key predictors of financial performance were identified. My analysis establishes Steam‐derived metrics as primary revenue drivers, with follower counts, peak concurrent users (peak CCU), and total reviews exhibiting the strongest predictive power and positive correlation with game earnings. Steam follower counts serve as a significant prelaunch indicator. Further analysis reveals game attributes like Early Access participation generate substantial revenue benefits, while supported languages, genres, and multiplayer features show comparatively weaker effects. Beyond Steam, both Twitch metrics (peak viewers/channels) and Metacritic critic scores exhibit significant positive revenue relationships. This association provides evidence that critical reception and livestreaming visibility collectively substantiate their roles in shaping player behavior to drive game revenue growth. These findings offer actionable insights for developers: prioritize Steam player engagement, supplemented by strategic Twitch marketing and Metacritic outreach. As the first integrated cross‐platform analysis in gaming economics, this research provides a foundation for exploring multiplatform dynamics.

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  1. 10.1371/journal.pone.0299819 · DOI
  2. 10.1109/CoG57401.2023.10333235 · doi:10.1109/cog57401.2023.10333235
  3. 10.5260/chara.13.3.26 · doi:10.5260/chara.13.3.26
  4. 10.1145/3359242 · DOI
  5. 10.1109/NetGames.2015.7382994 · DOI
  6. 10.1177/0163443718818363 · DOI · Paper page
  7. 10.7557/23.6150 · doi:10.7557/23.6150
  8. 10.1287/mksc.2022.0400 · DOI
  9. 10.2478/njms-2019-0007 · DOI
  10. 10.1109/CSCI46756.2018.00205 · DOI
  11. 10.1007/978-1-4471-4769-5 · DOI · Paper page
  12. Ma J., 2024, Navigating the Competitive Landscape of Paid Games: Insights From the Steam Tag System
  13. Ma J., 2024, Cultural Perspectives on User Review Disparities: A Comparative Study of East Asian and Western Players on Steam
  14. Johnson M. R., 2024, Twitch. In Digital Media and Society
  15. 10.2139/ssrn.4011595 · DOI
  16. 10.1080/1369118X.2017.1386229 · DOI · Paper page
  17. 10.1080/08997764.2022.2115503 · DOI
  18. 10.2307/j.ctvc77jqw · DOI · Paper page
  19. 10.1145/3402942.3402958 · DOI
  20. 10.1007/s10824-007-9041-z · doi:10.1007/s10824-007-9041-z
  21. 10.1007/s11747-019-00631-3 · DOI
  22. 10.1145/2658537.2658694 · DOI
  23. 10.1109/SNAMS.2019.8931866 · doi:10.1109/snams.2019.8931866
  24. 10.1386/jgvw.4.1.81_1 · doi:10.1386/jgvw.4.1.81_1
  25. 10.1109/CoG57401.2023.10333255 · doi:10.1109/cog57401.2023.10333255
  26. Joseph D., 2017, Distributing Productive Play: A Materialist Analysis of Steam
  27. 10.1177/1469540521993929 · DOI
  28. 10.1007/s10664-018-9627-4 · DOI
  29. 10.1145/3377290.3377300 · DOI
  30. 10.1177/1555412018756488 · DOI
  31. 10.1386/jgvw.8.1.63_1 · DOI
  32. 10.14257/ijmue.2016.11.12.22 · doi:10.14257/ijmue.2016.11.12.22
  33. 10.5220/0006898201550162 · doi:10.5220/0006898201550162
  34. UllmannG. PolitowskiC. GuéhéneucY.-G. andPetrilloF. What Makes a Game High-Rated? Towards Factors of Video Game Success May 2021 http://arxiv.org/abs/2105.14137. · arXiv
  35. 10.1109/CoG57401.2023.10333139 · doi:10.1109/cog57401.2023.10333139
  36. 10.54552/v82i3.101 · doi:10.54552/v82i3.101
  37. 10.1051/e3sconf/202338802001 · doi:10.1051/e3sconf/202338802001
  38. 10.1609/aiide.v11i1.12804 · DOI
  39. 10.1016/j.procs.2018.08.078 · DOI
  40. 10.1109/MLBDBI54094.2021.00037 · doi:10.1109/mlbdbi54094.2021.00037
  41. 10.5220/0012853800004547 · doi:10.5220/0012853800004547
  42. 10.1145/2987443.2987489 · DOI
  43. 10.1177/10949968241274693 · DOI
  44. 10.1093/oso/9780192897879.001.0001 · doi:10.1093/oso/9780192897879.001.0001
  45. Pedregosa F., 2011, Scikit-Learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825
  46. DorogushA. V. ErshovV. andGulinA. CatBoost: Gradient Boosting With Categorical Features Support 2018 https://arxiv.org/abs/1810.11363. · arXiv
  47. 10.1145/2939672.2939785 · DOI · Paper page
  48. Ke G., 2017, Lightgbm: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing Systems, 30
  49. 10.1016/j.sbspro.2013.12.027 · DOI · Paper page
  50. 10.1109/ICASSP.2019.8682358 · doi:10.1109/icassp.2019.8682358
  51. 10.1023/A:1010933404324 · DOI · Paper page
  52. 10.1007/978-1-4842-7150-6 · doi:10.1007/978-1-4842-7150-6
  53. 10.1007/s10462-020-09896-5 · DOI · Paper page
  54. 10.1007/s12205-023-2475-9 · doi:10.1007/s12205-023-2475-9
  55. 10.1016/j.atmosenv.2023.120111 · DOI
  56. 10.1186/s40537-020-00369-8 · DOI · Paper page
  57. 10.14569/IJACSA.2024.0151237 · doi:10.14569/ijacsa.2024.0151237
  58. 10.2991/aebmr.k.220307.137 · doi:10.2991/aebmr.k.220307.137
  59. 10.1007/978-3-030-44900-1_9 · doi:10.1007/978-3-030-44900-1_9
  60. 10.1177/14614448221081401 · DOI
  61. 10.3390/life14040502 · DOI
  62. 10.34010/jupiter.v2i2.8676 · doi:10.34010/jupiter.v2i2.8676
  63. 10.1007/s10664-017-9531-3 · DOI
  64. 10.1109/JEEIT.2019.8717501 · doi:10.1109/jeeit.2019.8717501
  65. Baumgartl C., 2021, Valheim: Der inoffizielle Guide
  66. CastleM. Baldur’s Gate 3 Early Access Review|Rock Paper Shotgun Rock Paper Shotgun accessed June 2025 https://www.rockpapershotgun.com/baldurs-gate-3-review-early-access.
  67. 10.3390/e24070860 · DOI
  68. 10.3390/info14020110 · DOI
  69. 10.1016/j.techfore.2022.121683 · doi:10.1016/j.techfore.2022.121683
  70. Eurich M., 2014, The Business-to-Consumer Lock-in Effect, Cambridge Service Alliance, 14
  71. 10.1016/j.entcom.2024.100802 · DOI
  72. Shneider F., 2020, The Value of Metacritic and its Relationship With Video Game Sales
  73. 10.3905/jod.2012.19.3.069 · doi:10.3905/jod.2012.19.3.069
  74. Ma J., 2025, Unveiling Success Drivers in Gaming: A Machine Learning Study Across Steam, Twitch, and Metacritic

Metadata

Authors
Ma, J.
Venue
International Journal of Computer Games Technology
Publisher
Wiley
Published
2025-01
Volume
2025
Issue
1
ISSN
  • 1687-7047
  • 1687-7055
Cited by
2
Language
en
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Ma, J. (2025). Unveiling Success Drivers in Gaming: A Machine Learning Study Across Steam, Twitch, and Metacritic. International Journal of Computer Games Technology. https://doi.org/10.1155/ijcg/5776632

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