Statistics and Probability · Data Analysis
Predict cricket scores with a simple model
Use the score at 10 overs to predict the final total and measure how wrong you are.
- DifficultyIntermediate
- Estimated time1–2 weeks
- Budget₹0
- ClassClass 11 to college
- TeamSolo or up to 2
- You needComputer only
Prerequisites: None beyond your class work
Original · by TechnifyedData AnalysisPortfolio ProjectFree to doComputer onlyResearch orientedPortfolio focused
Overview
Use the score at 10 overs to predict the final total and measure how wrong you are.
Skills you will use
Materials and tools
- Public scorecards for at least 100 limited-overs innings
- Spreadsheet
Tools and software
Step-by-step roadmap
Start
You will finish with: a prediction formula and its average error on unseen matches.
- 1
Prerequisites
Nothing special to know first. Then collect the 2 items in the materials list.
- 2
Learn
Read up on data analysis: public-data, regression, sports. Your textbook chapter and one short video are enough.
- 3
Plan
Write the research question, who or what you will study, how many, and the exact questions or measures.
- 4
Build
- Collect the score and wickets at 10 overs and the final score.
- Plot final score against the 10-over score.
- Fit a line.
- Add wickets lost as a second factor.
- Test on 20 innings you held back.
- 5
Test
Check a sample of your data by hand, and ask whether another explanation fits your findings equally well.
- 6
Document
Keep the raw responses or data safe, anonymise names, and build the tables and charts. Report structure
- 7
Present
State the question, the method in one line, the two or three main findings and their limits. Presentation structure
- 8
Publish
Share a short paper or poster; offer a summary to the people who took part.
Expected outcome
A prediction formula and its average error on unseen matches.
Other versions of this project
Advanced version
Compare your model with the simple run-rate projection shown on TV.
Ways to do this project
Text marked "You write this" is a prompt for your own work; everything else is specific to this project.
- Research question
- You write thisOne question you can answer with data, narrow enough to finish in the time you have.
- Hypothesis
- You write thisWhat you expect to find, and why.
- Variables
- You write thisWhat you change or compare, what you measure, and what you hold constant.
- Methodology
- You write thisDescribe the method in enough detail that someone else could repeat it: sample, tools, procedure.
- Data collection
- You write thisSay what you will record, how many times, and how you will keep the records safe.
- Analysis
- You write thisChoose the table, chart or test that answers the question; report averages with their spread.
- Limitations
- You write thisList what could have affected the result: small sample, instrument limits, things you could not control.
Literature review
Find five to eight sources (your textbook, review articles, reports) and note what each says about your question. Group them by idea, not one after another, and end with what is still not known.
References
List every source you used in one style throughout (author, year, title, where it was published, link and the date you opened it).
Use this as the outline of your README or case study.
- Problem
- You write thisThe problem in one or two sentences.
- Why it matters
- You write thisWho has this problem and what it costs them.
- What was built
- A prediction formula and its average error on unseen matches.
- Technology
- Spreadsheet
- Architecture
- You write thisOne diagram: the parts and how data moves between them.
- Implementation
- You write thisThe 5 build steps in your own words, with one code or design decision you are proud of.
- Challenges
- You write thisThe hardest bug or decision, and how you got past it.
- Results
- You write thisNumbers: accuracy, speed, users, survey answers.
- Demo and code
- You write thisA link to a live demo or a short video, and to the GitHub repository.
- Lessons learned
- You write thisWhat you would do differently next time.
- Future improvements
- Compare your model with the simple run-rate projection shown on TV.
Working as a team
Solo or up to 2. Suggested roles:
Report and presentation
Report structure
- Title
- Abstract
- Introduction and research question
- Review of what is already known
- Method: sample, tools, procedure
- Findings (tables and charts)
- Discussion
- Limitations
- Conclusion and suggestions
- References
- Annexure: questionnaire or data
Presentation structure
- Research question
- Why it matters
- Method and sample
- Finding 1
- Finding 2
- Finding 3
- What it means
- Limitations
- Conclusion
Viva questions
Prepare your own answers to these before you present.
- What is the aim of your project, in one sentence?
- What is the principle behind it?
- Why did you choose these materials or tools?
- What result did you get, and was it what you expected?
- What went wrong and how did you fix it?
- How could this be improved or used in real life?
Resources
Decision making with spreadsheetIIT Roorkee · Free course course
33 Spreadsheet Projects Course for Beginners – Excel and Google SheetsfreeCodeCamp · Free video course
Analyzing Spreadsheet Data with PythonIBM · Free course courseLibreOfficelibreoffice.orgOpen Government Data Platform Indiadata.gov.inKaggle datasetskaggle.comKhan Academykhanacademy.orgSource and attribution
Original
Written by Technifyed. Free to use for your own school or college project; write the report in your own words. Added 1 Oct 2026.
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