A scorecard tells you what happened. IPL Matchdesk asks why it happened, whether the model saw it coming, and what changed next.
74 matches · 10 teams · 13 venues · 50,000 season simulations
Explore the live season archive →
IPL Matchdesk is a prediction-first analytics product for the 2026 Indian Premier League. It connects match results, player form, venue behavior, model probabilities, season simulations, and post-match evaluation in one interactive review room.
- Who has the edge—and how confident should we be?
- Is the venue changing the matchup more than recent form?
- Which player battle could flip the result?
- How did one match reshape the playoff race?
- When the model was wrong, what did it miss?
Those answers usually live across scorecards, spreadsheets, articles, and disconnected statistics. IPL Matchdesk brings them into the same frame, so a prediction can be inspected alongside the evidence behind it and the result that followed.
Most sports prediction projects end when they produce a winner. This one keeps going.
Every completed match becomes a test: the forecast is compared with the result, competing model families are scored, probability calibration is reviewed, and trust gates decide which model is safe to serve next. That turns prediction from a one-time guess into a visible learning loop.
For fans, the product explains the season beyond the points table. For analysts, it makes assumptions, uncertainty, model disagreement, and failure visible instead of hiding them behind a single percentage.
| Enter through | What you can explore |
|---|---|
| Matchdesk | Match context, projected XIs, win probability, key battles, risks, and turning points |
| Season Room | Fixtures, table swings, team form, qualification paths, and championship probability |
| Model Room | Accuracy, calibration, challenger performance, trust gates, and completed-match learning |
| Player Lab | Roles, ratings, comparisons, technical profiles, career context, and similar-player networks |
| Venue Files | Par scores, toss impact, chase bias, pitch character, and venue specialists |
| Network Room | Repeat battles, batting partnerships, player movement, and similarity graphs |
| Ask Archive | Conversational questions grounded in the synced IPL data layer |
Production, lineup-only, no-phase, venue-history, and sequence models examine the match from different angles. Their predictions can agree, split, or challenge the current serving model.
Completed-match audits track accuracy, probability quality, challenger performance, and ambiguity. A wrong call remains part of the product rather than disappearing from the record.
Consensus probabilities are calibrated and model disagreement is surfaced. Monte Carlo simulation turns the playoff and title race into distributions instead of presenting one fixed future.
Governance checks validate prediction schema, projected-lineup coverage, calibration, model split share, and schedule alignment. If a hard gate fails, the serving decision falls back to the production model.
SHAP explanations, venue context, lineup strength, player ratings, weather signals, and matchup features help connect a probability to the cricket behind it.
flowchart LR
subgraph input["Inputs"]
A["Ball-by-ball data"]
B["IPL schedule, squads, standings"]
C["Weather and live context"]
D["Curated reference data"]
end
subgraph pipeline["Python Pipeline"]
E["Ingest and clean"]
F["Enrich players, teams, venues"]
G["Build features"]
H["Train and compare models"]
I["Simulate season outcomes"]
J["Score completed predictions"]
end
subgraph data["App-Ready Data"]
K["Compact enrichment tables"]
L["Prediction and audit artifacts"]
M["Neon Postgres"]
end
subgraph app["Next.js on Vercel"]
N["Server data access"]
O["Interactive dashboard"]
P["Chat and SQL exploration"]
end
subgraph product["Product Views"]
Q["Match archive"]
R["Teams and players"]
S["Venues"]
T["Model audit"]
U["Simulations"]
V["Networks and comparisons"]
end
subgraph automation["Automation"]
W["GitHub Actions"]
X["Neon sync"]
Y["Vercel redeploy"]
end
A --> E
B --> E
C --> F
D --> F
E --> F --> G --> H
H --> I
H --> J
F --> K
I --> L
J --> L
K --> M
L --> M
M --> N
N --> O
N --> P
O --> Q
O --> R
O --> S
O --> T
O --> U
O --> V
W --> E
W --> X
X --> M
X --> Y
The heavy analytical work stays in Python. Compact, app-ready tables and artifacts are synchronized to Neon, allowing the public Next.js experience to remain fast without rebuilding models during a page request.
The project combines ball-by-ball match data, official schedule and standings feeds, squad and player reference data, venue history, weather and availability context, cricket news signals, and generated model artifacts. Automated workflows collect new data, retrain or refresh model families, run validation, synchronize the runtime layer, and redeploy the product.
- Modeling: Python, pandas, scikit-learn, XGBoost, LightGBM, TensorFlow/Keras, SHAP
- Product: Next.js, React, TypeScript, Recharts, force-directed network visualization
- Data and delivery: Neon Postgres, GitHub Actions, Vercel
The project began as a Python pipeline for IPL match prediction. It grew into a broader product question: can a cricket dashboard show not only the result and the forecast, but the reasoning, uncertainty, model competition, and learning that connect them?
IPL Matchdesk is the answer I built: a season archive where every prediction remains accountable to what happened next.
IPL Matchdesk is an independent fan analytics project. It is not affiliated with, endorsed by, or associated with the Indian Premier League, BCCI, or any IPL franchise. Team names and marks belong to their respective owners.