Quick start
- Open the Dashboard. Use the sidebar to access the prediction form.
- Fill in game features. Enter pre-launch attributes across the 10 sections (Pricing, Release, Genre, …).
- Submit. Click Predict Success to run the ensemble model.
- Review. Inspect the predicted owner tier, confidence, class probabilities, and recommendations.
Input reference
All non-composite features, grouped by section:
💰 Pricing
| Field |
Label |
Type |
Range |
is_free |
Free to Play? |
Yes / No
|
0 / 1
|
price |
Price (0 - 200 USD) |
Number
|
0–200
|
initialprice |
Initial Price (0 - 200 USD) |
Number
|
0–200
|
🗓️ Release
| Field |
Label |
Type |
Range |
release_date |
Release Date |
Date picker
|
YYYY-MM-DD
|
🎮 Genre
| Field |
Label |
Type |
Range |
Action |
Action |
Yes / No
|
0 / 1
|
Adventure |
Adventure |
Yes / No
|
0 / 1
|
RPG |
RPG |
Yes / No
|
0 / 1
|
Strategy |
Strategy |
Yes / No
|
0 / 1
|
Simulation |
Simulation |
Yes / No
|
0 / 1
|
Sports |
Sports |
Yes / No
|
0 / 1
|
Racing |
Racing |
Yes / No
|
0 / 1
|
🖥️ Platform
| Field |
Label |
Type |
Range |
platform_windows |
Windows |
Yes / No
|
0 / 1
|
platform_mac |
Mac |
Yes / No
|
0 / 1
|
platform_linux |
Linux |
Yes / No
|
0 / 1
|
platform_count |
Total Platforms (auto) |
Number
|
1–3
|
🌍 Languages
| Field |
Label |
Type |
Range |
selected_languages |
Supported Languages |
Language checklist
|
20 languages
|
supported_languages_count |
Text Languages (auto-count) |
Number
|
0–50
|
full_audio_languages_count |
Full Audio Languages (0 - 20) |
Number
|
0–20
|
🏪 Store Page
| Field |
Label |
Type |
Range |
has_website |
Has Official Website? |
Yes / No
|
0 / 1
|
has_support_email |
Has Support Email? |
Yes / No
|
0 / 1
|
screenshot_count |
Number of Screenshots (0 - 20) |
Number
|
0–20
|
about_length |
Description Length (0 - 5000 chars) |
Number
|
0–5000
|
has_detailed_desc |
Detailed Description (auto) |
Yes / No
|
0 / 1
|
🏆 Categories
| Field |
Label |
Type |
Range |
has_achievements |
Steam Achievements? |
Yes / No
|
0 / 1
|
has_cloud_save |
Steam Cloud Save? |
Yes / No
|
0 / 1
|
has_controller_support |
Controller Support? |
Yes / No
|
0 / 1
|
has_vr_support |
VR Support? |
Yes / No
|
0 / 1
|
has_in_app_purchases |
In-App Purchases? |
Yes / No
|
0 / 1
|
has_family_sharing |
Family Sharing? |
Yes / No
|
0 / 1
|
category_count |
Total Steam Categories (auto) |
Number
|
0–15
|
achievement_count |
Number of Achievements (0 - 500) |
Number
|
0–500
|
🎯 Audience
| Field |
Label |
Type |
Range |
is_multiplayer |
Multiplayer Game? |
Yes / No
|
0 / 1
|
is_mature_content |
Mature Content? |
Yes / No
|
0 / 1
|
required_age |
Required Age (auto, derived from mature content) |
Number
|
0–17
|
🏷️ Tags
| Field |
Label |
Type |
Range |
selected_tags |
Steam Tags (select all that apply) |
Tag checklist
|
50 tags
|
tag_count |
Tag Count (auto) |
Number
|
0–20
|
📦 Packaging
| Field |
Label |
Type |
Range |
package_count |
Package Count (1 - 10) |
Number
|
1–10
|
sku_count |
SKU Count (1 - 20) |
Number
|
1–20
|
Understanding results
Predicted owner tier
- Class 0 (≤10K) — Common Indie
- Class 1 (35K) — Niche
- Class 2 (75K) — Growing
- Class 3 (150K) — Established
- Class 4 (350K) — Popular
- Class 5 (≥750K) — Breakout Hit
Confidence score
- 80–100%: very confident
- 60–79%: moderately confident
- 40–59%: low confidence (borderline)
- Below 40%: uncertain (mixed/weak features)
Class probabilities
Predicted probability for each of the 6 classes. The class with the highest probability is the final prediction.
Recommendations
- Strengths — features currently working in your favor (high positive impact)
- Improvements — actionable suggestions to lift the predicted tier (driven by SHAP analysis)
Best practices
- Be accurate. The model is trained on real Steam data; inflated numbers mislead predictions.
- Pre-launch only. No reviews or playtime — fill only what you can decide before release.
- Use recommendations. Prioritise high-impact suggestions surfaced by SHAP.
- Test scenarios. Try with/without publisher, varying pricing, etc., to see what moves the needle.
- Don't over-optimise. Correlations ≠ causation. Game quality is the ultimate factor.
FAQ
Can I predict for unreleased games?
Yes — the model uses only pre-launch features (pricing, store page, platform support, tags, languages, etc.).
How do I set the release date?
Use the date picker to set your planned launch date. SAGE automatically derives release timing signals from it — such as the release month, quarter, day of week, and whether it falls in a summer or holiday window — which the model uses as predictive features.
What if some features are unknown?
Use the defaults shown in the form. Leave toggles unchecked (0) for features you don't plan to ship. Leave language/tag checklists empty if you haven't decided yet.
Why is my confidence low?
Your features fall in regions where multiple classes overlap. Adjust key drivers (store page quality, pricing, platform reach, language coverage) and try again.
How accurate is the model?
The ensemble achieves 71.20% accuracy and 0.6170 weighted F1 on the test set. See
Model Metrics for details.
Does it work for non-Steam platforms?
No. Trained exclusively on Steam data; predictions for Epic, GOG, or consoles aren't reliable.