Crick Genie XI

yashhvyass — Tue, 01 Sep 2026 06:52:58 GMT

Crick Genie XI

Track: Creative Thinking

Project story

Crick Genie XI started with a simple idea: what if a Crick scout could explore IPL history as naturally as they would talk to another analyst?

The first version of the app was much more structured. A scout chose a type of decision, configured it on another page, and then moved to Genie to investigate it. It worked technically, but while testing the product I realized that it did not feel like a real scouting workflow. There was too much movement between pages, and every investigation felt isolated from the next.

That led to the current version: a single Scouting Workspace where analysts can organize the questions that matter, investigate them with Databricks Genie, review whether the evidence changes their thinking, save useful research, and carry those findings into a Scouting Game Plan.

The product is built around one principle:

Genie investigates the evidence. The scout makes the decision.

What problem or creative idea does the app address?

Sports scouting rarely comes down to one question. Before a match, an analyst might want to understand a batter-bowler matchup, compare players, identify phase specialists, study season trends, evaluate venue tendencies, or see who has historically performed under extreme chase pressure.

A dashboard can show predefined statistics, and a chatbot can answer individual questions. What interested me was what happens after the answer.

If a scout investigates ten questions, which ones changed their thinking? Which findings are worth saving? Which questions are still unresolved? What could the historical data not answer at all? And how do those pieces eventually become something useful for match preparation?

Crick Genie XI turns those isolated analytical interactions into a scouting workflow. A question can move from an initial tactical instinct to evidence review and then become Held, Revised, Unresolved, or a Data Gap. Useful exploratory findings can be saved separately as Research Notes.

Instead of only storing answers, the app keeps track of what the scout believed, what the evidence showed, what changed, and what is still unknown.

Who is it designed for?

The primary user is a Crick scouting or performance analyst preparing for an opponent, evaluating players, or researching tactical situations.

The experience is designed so the scout does not need to know SQL or understand which table contains the answer. They can think in Crick language:

“How has Virat Kohli performed against Jasprit Bumrah?”

“Who are the strongest batters in the final overs (16–20)?”

“How has a bowler’s economy changed across seasons?”

“Who performs well when 15+ runs per over are required with 12 balls or fewer remaining?”

Coaches and other members of a Crick operations team could consume the final findings, but the workflow is primarily modeled around the analyst doing the research and deciding what evidence should be carried forward.

How the scouting workflow works

The main experience is the Scouting Workspace.

A scout starts by adding questions they want to investigate. The app provides templates for common scouting problems such as Player Battle, Phase Role, Venue Call, Pressure Test, Player Comparison, Season Trend, Breakout / Decline, Bowling Threat, Bowler Trend, and Role Fit. These templates make common investigations easier, but they are not a restriction—the analyst can also create a custom question.

Genie lives directly inside the same workspace. Once a question is selected, the analyst can investigate it, inspect the answer and supporting evidence, and continue to the next question without constantly navigating between separate parts of the application.

For tactical questions, the scout can record an initial call before seeing the evidence. After reviewing Genie's result, that question can end in four states. Held means the original call still stands. Revised means the evidence changed the scout's thinking. Unresolved means evidence was available, but the analyst is not ready to commit. Data Gap means the available historical record cannot reliably answer the exact question.

The workspace organizes the session into To Investigate, Reviewed Calls, Research Notes, and Open Questions, so uncertainty remains visible rather than disappearing into chat history.

Open research and the Game Plan

Not every useful investigation begins with a hypothesis. Sometimes the scout simply wants to explore, for example:

“Who are the strongest wicket-taking bowlers historically?”

Those questions can be asked directly through Genie. If the result is useful, the scout can save it as a Research Note. I intentionally kept Research Notes separate from Reviewed Calls because a useful discovery is not necessarily a tactical decision.

The workspace eventually produces a separate Scouting Game Plan that brings together the work the analyst chose to carry forward: reviewed calls, saved research, and open questions or data gaps.

The Game Plan is not presented as an AI-generated winning strategy. It is an evidence-reviewed record of the scout's own work. Genie provides the historical analysis; the human analyst owns the tactical interpretation.

Application architecture and data flow

The app is powered by historical IPL ball-by-ball data from Cricsheet, covering 1,243 matches and more than 295,000 deliveries.

I used a medallion-style architecture to turn the raw match files into data that Genie could reason over reliably.

The Bronze layer contains the raw Cricsheet JSON files. In the Silver layer, Spark transforms those files into cleaned match- and delivery-level Delta tables with normalized seasons, teams, venues, delivery sequencing, dismissals, and Crick-specific scoring logic.

The Gold layer is where the data becomes scouting-ready. Rather than asking Genie to reason directly over raw ball-by-ball records for every question, I created purpose-built analytical tables for different types of investigations.

These include batter-bowler matchups, batting and bowling by innings phase, team performance by venue, batting under chase pressure, and overall and season-level batter and bowler statistics.

Architecture:

Cricsheet IPL JSON
↓
Bronze
Raw match files
↓
Silver
Cleaned match + delivery tables
↓
Gold
Scouting-ready analytical tables
↓
Unity Catalog
↓
Databricks Genie
↓
Crick Genie XI
↓
Scouting Workspace
↓
Scouting Game Plan

Unity Catalog provides the governed data layer, while Genie sits between the curated tables and the custom Databricks App.

Why the Gold layer mattered

One of the most important lessons from the project was that good conversational analytics starts with good data modeling.

For example, a batter-vs-bowler question should use a matchup table, while a season-trend question should use season-level statistics. A final-overs ranking should use phase-specific data rather than combining unrelated aggregates.

The transformation logic also handles Crick-specific details such as legal deliveries, wides and no-balls, credited dismissals, bowler-conceded runs, dot balls, powerplay, middle overs, and final overs, franchise and venue normalization, and minimum sample-size thresholds.

One of the more specialized Gold tables models chase pressure using both required run rate and balls remaining. That makes it possible to ask questions such as:

“Who performs best when 15+ runs per over are required with 12 balls or fewer remaining?”

The scout does not need to know how that scenario is represented in the underlying data. Genie handles that translation.

What can users ask the Genie Agent?

Users can ask both structured scouting questions and open-ended questions supported by the historical IPL data.

For example:

“Compare Virat Kohli and Rohit Sharma as IPL batters.”

“Who are the most economical bowlers in the final overs (16–20) with a meaningful sample?”

“How has Jasprit Bumrah’s economy changed across seasons?”

“How has Mumbai Indians performed while chasing at Wankhede?”

“Who performs best under extreme required-rate pressure?”

The structured templates simply give the scout useful starting points. The main goal is to let the user think in terms of the Crick question they are trying to answer, rather than the schema underneath it.

How does Genie power the app's main experience?

Genie is the analytical engine of Crick Genie XI, not an extra chatbot placed beside the application.

When a scout investigates a question, Genie interprets the natural-language request, generates SQL against the curated IPL tables, executes the analysis, and returns a grounded answer. The app also allows the analyst to inspect the generated SQL and returned table or visualization, making the evidence behind the answer visible.

The custom application then handles what happens next: whether the scout holds or revises their original call, leaves the question unresolved, identifies a data gap, saves useful exploratory research, or carries the finding into the Game Plan.

Without Genie, users could still create a list of scouting questions. But the central capability—conversationally investigating those questions against governed IPL data—would disappear.

The core flow is:

Question → Evidence → Human review → Scouting memory → Game Plan

Trust, transparency, and data gaps

I did not want the application to sound more certain than the data allows.

Historical IPL data cannot automatically tell a scout about future lineups, injuries, live weather, current tactical intent, or what will happen in the next match. That is why Unresolved and Data Gap became first-class outcomes instead of failure states.

If evidence exists but is not convincing enough, the scout can leave the question unresolved. If the underlying data cannot support the requested analysis, the question can be preserved as a data gap instead of forcing an answer.

For a scouting analyst, knowing what we do not know can be just as valuable as another statistic.

What did I learn while building and testing the app?

The biggest lesson was that adding more features did not automatically make the product better.

The first design separated the experience into a Decision Room, Call Room, and Ask Genie page. Each component worked, but testing the full journey made it obvious that the scout was spending too much time managing the interface.

I redesigned the product around one Scouting Workspace, bringing the question queue and Genie investigation together. That made the workflow much more natural.

I also learned that better data preparation mattered more than adding more AI components. Carefully curated Gold tables, Crick-specific metric definitions, consistent dimensions, sample-size rules, and clear Genie instructions had a direct impact on the quality of the answers.

Finally, I learned to treat uncertainty as useful information. Adding Unresolved and Data Gap made the app feel much closer to how a real analyst would work.

What makes Crick Genie XI different?

Most conversational analytics experiences stop at:

Ask → Answer

Crick Genie XI continues:

Ask → Investigate → Review → Hold / Revise / Leave Open → Remember → Build the Plan

That is the creative idea behind the project.

The goal is not to build an AI Crick coach. It is to make historical IPL evidence easier to investigate, easier to trust, and easier to incorporate into a real human scouting process.

Final takeaway

Crick Genie XI turns conversational IPL analytics into a human-in-the-loop scouting workflow.

It helps analysts challenge tactical assumptions, investigate historical evidence transparently, preserve useful research, acknowledge uncertainty, and turn a collection of individual questions into an evidence-reviewed scouting plan.

Video:
https://drive.google.com/file/d/1jcEucaSWKRN2qX7Y1Z_M-xX4vRvamxjL/view?usp=drive_link

Re: Crick Genie XI

yashhvyass — Tue, 01 Sep 2026 12:33:39 GMT

Crick here is Cricket_. I was not allowed to write Cricket_ because of community guidelines so I changed it to Crick

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Crick Genie XI

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