This dataset provides detailed individual player statistics for India and Pakistan cricketers, covering both batting and bowling performances. It includes data from T20 internationals and IPL performances, which are useful indicators of form and skill in Asia Cup contexts.

The dataset contains: Batting stats: Matches, innings, runs, highest score, average, strike rate, boundaries (4s, 6s), 50s, 100s. Bowling stats: Overs, runs conceded, wickets, average, economy rate, strike rate, best figures, 5w/10w hauls. Player-level identifiers: Player name, role, and format.

olumn Descriptions General

Format → Type of tournament/match (e.g., IPL, T20 International).

Name → Player’s full name.

Team → Country (India or Pakistan). (Add this if you combine both datasets)

Batting Stats:

M_x → Matches played (batting).

Inn_x → Innings batted.

Runs_x → Total runs scored.

BF → Balls faced.

HS → Highest score in an innings.

Avg_x → Batting average (Runs/Innings dismissed).

SR_x → Batting strike rate (Runs scored per 100 balls).

NO → Number of times not out.

4s → Total number of fours hit.

6s → Total number of sixes hit.

50 → Number of half-centuries scored.

100 → Number of centuries scored.

200 → Number of double centuries scored. (rare in T20/IPL, but included for completeness)

Bowling Stats:

M_y → Matches played (bowling).

Inn_y → Innings bowled.

B → Balls bowled.

Runs_y → Runs conceded while bowling.

Wkts → Total wickets taken.

Avg_y → Bowling average (Runs conceded ÷ Wickets taken).

Econ → Economy rate (Runs conceded per over).

SR_y → Bowling strike rate (Balls bowled per wicket).

BBI → Best Bowling in an Innings (e.g., 4/20 = 4 wickets for 20 runs).

BBM → Best Bowling in a Match.

5w → Number of times the bowler took 5 wickets in an innings.

10w → Number of times the bowler took 10 wickets in a match.

🏏 IndoPak T20 Rivalry – Cricket Dataset 📌 About the Dataset

The India vs Pakistan T20 rivalry is one of the most intense and exciting battles in cricket history. This dataset contains match-level information such as:

✅ Year & Venue

✅ Tournament (World Cup / Asia Cup / Bilateral)

✅ Runs & Wickets of both teams

✅ Toss winner & batting order

✅ Match winner

✅ Pitch condition

It’s designed for data analysis, visualization, and machine learning projects.

📂 Dataset Structure Column Name Description Example match_id Unique ID for each match 1, 2, 3… year Year of the match 2021 venue Match location New York, Kolkata tournament Tournament name WorldCup, AsiaCup india_runs Runs scored by India 166 pak_runs Runs scored by Pakistan 184 toss_winner Team that won the toss Pakistan bat_first Team batting first India/Pakistan india_wickets Wickets lost by India 2, 6 pak_wickets Wickets lost by Pakistan 5, 7 winner Match winner India/Pakistan pitch_condition Pitch type (Batting/Bowling friendly etc.) Balanced 🎯 Possible Use Cases

📊 Data Visualization → Win % analysis, toss impact, venue stats

🤖 Machine Learning → Predicting match winners or runs

🏆 Sports Analytics Projects → Performance comparison across tournaments

📰 Storytelling with Data → Rivalry insights for blogs & articles

📈 Sample Insights to Explore

Does winning the toss impact the match result?

Which venues favor India or Pakistan more?

Are batting-friendly pitches leading to higher scores?

How have results changed over the years?

This dataset provides historical military expenditure data for India and Pakistan, obtained from the World Bank Open Data API. It includes figures in current U.S. dollars across multiple years, giving insight into defense budget trends for the two major South Asian countries.

This data is useful for:

• Comparing military spending trends between India and Pakistan

• Studying economic allocation to defense over time

• Time series analysis of South Asian defense policies

The groundwater systems of northwest India and central Pakistan are amongst the most heavily exploited in the world. Groundwater has been monitored in the region for more than a century resulting in a unique long-term record of groundwater level change. The BGS has compiled groundwater level data from northwest India (Haryana and Punjab) and Pakistan (Punjab) between 1884 and 2020. The dataset, presented here, was compiled from various sources between 2018 and 2020. The excel file consists of two tabs both containing groundwater level data (in metres below ground level) and location information. In the first tab (Full_dataset), which contains the full dataset, there are 68783 rows of observed groundwater level data from 4028 individual sites. In the second tab (LTS) there are 7547 rows of groundwater level observations from 130 individual sites, which have water level data available for a period of more than 40 years and from which at least two thirds of the annual observations are available.

Target companies in India, Pakistan, and Bangladesh with Success.ai’s verified datasets. Includes emails, phone numbers, and decision-maker profiles. Continuously updated datasets. Best price guaranteed.

Pakistan Exports of live animals to India was US$24.65 Thousand during 2010, according to the United Nations COMTRADE database on international trade. Pakistan Exports of live animals to India - data, historical chart and statistics - was last updated on December of 2025.

This dataset contains ball to ball information from India vs Pakistan t20 world cup 2024 match. Few of the attributes from the dataset are over(ball number), batsman, bowler, type of shot, runs and many more.

This dataset contains tracks generated using a bespoke tracking algorithm developed within the BITMAP (Better understanding of Interregional Teleconnections for prediction in the Monsoon And Poles) project, identifying and linking upper-tropospheric vortices (described in Hunt et al, 2018, QJRMS - see linked documentation). This utilised data derived from from various simulation output for the WCRP Coupled Model Intercomparison Project, Phase 5 (CMIP5) 'Historical' experiment. Similar datasets were produced using various model output from the WRCP CMIP5 'RCP45' and 'RCP85' experiments and the ECMWF ERA-Interim reanalysis model output, also available within the parent dataset collection.

Western disturbances (WDs) are upper-level vortices that can significantly impact the weather over Pakistan and north India. This is a catalogue of the tracks of WDs passing through the region (specifically 20-36.5N, 60-80E) on the 500 hPa layer. This differs from those tracks from the ECMWF Era-Interim data which were carried out on the 450-300 hPa layer. See linked documentation for details of the algorithms used.

BITMAP was an Indo-UK-German project (NERC grant award NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. For further details on the project itself see the linked Project record.

India Exports of iron and steel to Pakistan was US$1.57 Million during 2024, according to the United Nations COMTRADE database on international trade. India Exports of iron and steel to Pakistan - data, historical chart and statistics - was last updated on November of 2025.

Dataset of Pakistan Imports from India, including historical data, latest releases, and long-term trends from 2003-01-01 to 2024-12-31. Available for free download in CSV format.

India Exports of matches to Pakistan was US$21.29 Thousand during 2024, according to the United Nations COMTRADE database on international trade. India Exports of matches to Pakistan - data, historical chart and statistics - was last updated on November of 2025.

CODE.PY OUTPUT

Institutional Benchmarking and Regression Results

To contextualize institutional performance, we first compare India and Pakistan against the global mean of common-law economies on three governance indicators: Protecting Minority Investors (PMI), Enforcing Contracts (EC), and a composite index of the Legal-Political Environment. The global averages for common-law countries are:

• PMI: 18.6

• EC: 37.9

• Legal-Political Environment: 7.44 (on a 0–10 scale)

Relative to these benchmarks:

• India outperforms the PMI average by 5.6 ranks, but underperforms EC by 125.1 ranks and legal-political environment by 2.74 points.

• Pakistan underperforms on all three: +9.4 ranks in PMI (worse), +118.1 ranks in EC, and –4.24 points on legal-political environment.

These gaps suggest that formal investor protections (PMI) are stronger in India, but contract enforcement and broader institutional trust lag significantly in both countries.

Regression Analysis: Institutional Predictors of Governance Outcomes

We ran robust Ordinary Least Squares (OLS) regressions with Control of Corruption (cc), Rule of Law (rl), and Political Stability (pv) (from the Worldwide Governance Indicators) as predictors of country performance in both PMI and EC.

✅ Protecting Minority Investors (PMI)

• Model Fit: R² = 0.364; F(3, 182) = 46.91; p < 0.001

• Significant predictors:

  • Rule of Law (β = –62.74, p < 0.001): Strong negative relationship, consistent with countries with weaker rule of law having higher PMI ranks (i.e., worse protections).

  • Political Stability (β = +22.28, p < 0.001): Higher stability is associated with better (lower) PMI rank.

  • Control of Corruption (β = +15.91, p = 0.078): Marginally significant.

✅ Enforcing Contracts (EC)

• Model Fit: R² = 0.389; F(3, 182) = 52.32; p < 0.001

• Significant predictors:

  • Rule of Law (β = –47.55, p < 0.001): Again, weak rule of law predicts poor performance.

  • Political Stability (β = +11.27, p = 0.029): More stable environments enforce contracts more efficiently.

  • Control of Corruption was not significant (p = 0.61).

These results underscore the salience of Rule of Law and Political Stability in explaining variation in corporate governance effectiveness across countries.

Peer-Adjusted Z-Scores: Rule of Law and Judicial Independence

To further refine the comparison, we standardized India and Pakistan’s scores relative to global peers using z-scores:

• India: Judicial Independence z = +1.39, Rule of Law z = +1.28

• Pakistan: Judicial Independence z = +0.25, Rule of Law z = –0.95

This highlights India’s relative institutional strength in legal capacity, while Pakistan falls below global norms, particularly on rule of law.

CODE2.PY OUTPUT

Summary Statistics and Cross-National Comparison

We begin by comparing key governance indicators for India and Pakistan over the 1996–2020 period using data from the V-Dem dataset. Table X reports descriptive statistics for six core institutional quality variables:

• v2x_rule: Rule of Law

• v2x_jucon: Judicial Constraints on the Executive

• v2xlg_legcon: Legislative Constraints

• v2x_freexp: Freedom of Expression

• v2x_polyarchy: Electoral Democracy Index

• v2x_corr: Control of Corruption

Key Observations from the 23-year panel:

• Rule of Law (v2x_rule): India displays a high mean score of 0.579 (SD = 0.029), while Pakistan lags significantly behind at 0.237 (SD = 0.023).

• Judicial Constraints (v2x_jucon): India again leads with a mean of 0.814, compared to 0.537 for Pakistan.

• Control of Corruption (v2x_corr): Interestingly, Pakistan scores higher (0.868) than India (0.566), suggesting a potential data artifact or performative anti-corruption signaling.

These descriptive statistics show a consistent pattern of stronger rule-of-law institutions in India. However, India’s governance edge does not hold across all indicators—especially corruption control, which exhibits counterintuitive results.

T-Test Results: India vs. Pakistan

We formally test whether the differences in means between India and Pakistan are statistically significant using two-sample t-tests:

The highly significant differences in nearly all variables confirm that India and Pakistan follow distinct institutional trajectories—though India’s relative weakness in corruption control invites further scrutiny under the CMF framework.

Structural Breaks in India’s Democratic Governance

Using the ruptures package and a rolling t-test approach, we detect structural breakpoints in India’s democratic trajectory:

• Based on v2x_polyarchy, break years are identified at 2011, 2016, and 2021.

• The rolling t-test method suggests more granular shifts starting as early as 2001, with notable accelerations around 2011–2019.

These breakpoints align with major political and constitutional developments in India and support the CMF argument that formal continuity in legal benchmarks may obscure deeper institutional volatility.

CODE3.PY OUTPUT

Summary Statistics and Institutional Quality Comparison: India vs. Pakistan

We begin by reporting descriptive statistics for two core institutional variables—Control of Corruption (v2x_corr) and Judicial Constraints on the Executive (v2x_jucon)—drawn from the V-Dem dataset for the years 1996–2020:

• Control of Corruption (v2x_corr):

  • India: Mean = 0.566, SD = 0.027, indicating relatively consistent performance with moderate corruption control.

  • Pakistan: Mean = 0.868, SD = 0.051, suggesting surprisingly strong corruption scores, but with greater variability. This may reflect methodological distortions or performative anti-corruption institutions that lack substantive checks—a key focus of our Critical Macro-Finance (CMF) interpretation.

• Judicial Constraints (v2x_jucon):

  • India: Mean = 0.814, SD = 0.013, indicating strong and stable judicial oversight over executive actions.

  • Pakistan: Mean = 0.537, SD = 0.140, reflecting weaker, more volatile institutional constraints.

T-Test Results: Are India and Pakistan Statistically Different?

Two-sample t-tests confirm that these differences are highly statistically significant:

These results validate the hypothesis that India and Pakistan exhibit substantially divergent institutional trajectories—though not always in expected directions. India shows stronger judicial oversight, while Pakistan appears to outperform in corruption metrics, warranting

Exports to Pakistan in India increased to 59.48 INR Billion in January from 2.97 INR Billion in December of 2023. This dataset includes a chart with historical data for India Exports to Pakistan.

India Exports of watch cases and to Pakistan was US$34 during 2015, according to the United Nations COMTRADE database on international trade. India Exports of watch cases and to Pakistan - data, historical chart and statistics - was last updated on November of 2025.

India Imports from Pakistan of Ash and Residues-containing Metals or Metallic Compounds was US$91.16 Thousand during 2019, according to the United Nations COMTRADE database on international trade. India Imports from Pakistan of Ash and Residues-containing Metals or Metallic Compounds - data, historical chart and statistics - was last updated on November of 2025.

Context The ICC Champions Trophy 2025 is an international One Day International (ODI) cricket tournament organized by the International Cricket Council (ICC), marking the return of this prestigious event after its last edition in 2017. Scheduled to be co-hosted by Pakistan and Dubai, it will feature the top eight teams in the ICC ODI rankings competing in a round-robin and knockout format. Known as the "mini World Cup," the tournament holds significant historical and cultural importance, especially for Pakistan, hosting its first major ICC event since 1996. With matches set in iconic venues across Pakistan and the UAE, the Champions Trophy 2025 promises thrilling encounters and intense rivalries, serving as a prelude to the Cricket World Cup 2027.

Content The dataset includes ODI matches played between 2020 and 2024, featuring teams such as Pakistan, India, Australia, England, South Africa, New Zealand, Sri Lanka, and Bangladesh. It focuses on head-to-head matchups and provides detailed match-level data, including dates, venues, scores, results, batsman averages against specific opponents, and team bowling performance against opposing sides. This dataset offers valuable insights into individual and team performances during this period.

The dataset provides:

Mat: Match HS: Highest Score NO: Not out Ave: Average of Batsmen BF: Balls Faced SR: Strike rate 4s: Fours 6s: Sixes Mdns : Maidens over bowled BBI: Best bowling in Innings Econ: The average number of runs they concede per over they bowl SR: a measure of how quickly a bowler takes wickets, or gets batters out

File Format: Pak vs Ind(Batting) means Pakistan batting vs India,Ind vs Pak (Bowling) means India bowling vs Pakistan and vice verca.

Acknowlegements This dataset belongs to me.I'm sharing it here for free.You may do with it as you wish.

India Imports from Pakistan of Dates, figs, pineapples, avocados, fresh or dried was US$233.13 Thousand during 2022, according to the United Nations COMTRADE database on international trade. India Imports from Pakistan of Dates, figs, pineapples, avocados, fresh or dried - data, historical chart and statistics - was last updated on November of 2025.

Pakistan Imports from India was US$304.93 Million during 2024, according to the United Nations COMTRADE database on international trade. Pakistan Imports from India - data, historical chart and statistics - was last updated on December of 2025.

Dataset Description

This dataset is sourced from FAOSTAT, the comprehensive statistical database maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides detailed and reliable data on global agriculture, food security, nutrition, and related topics. The dataset covers the period from 1971 to 2022, offering a 50-year perspective on trends and changes in agricultural production, trade, resource use, and environmental impacts.

Visit the FAOSTAT website: https://www.fao.org/faostat/.

Variables :

• Year: The year for which the data is recorded (e.g., 1971, 2022).
• China: A metric (likely percentage change or growth rate) for China in the given year.
• India: A metric for India in the given year.
• Pakistan: A metric for Pakistan in the given year.
• United Arab Emirates: A metric for the UAE in the given year.
• United Kingdom: A metric for the UK in the given year.
• United States of America: A metric for the USA in the given year.

Each column (except Year) represents a country and contains numerical values, possibly indicating growth rates, percentage changes, or other metrics over time.

Possible Sources International Organizations: FAOSTAT (Food and Agriculture Organization): Provides data on agriculture, food security, and related metrics. World Bank: Offers economic, demographic, and environmental data. United Nations (UN): Publishes data on global development indicators. IMF (International Monetary Fund): Provides financial and economic data. Government Agencies: National statistical offices (e.g., Census Bureau, Ministry of Agriculture). Central banks or economic departments. Research Institutions: Universities or think tanks that collect and analyze data for specific studies

Pakistan Imports from India of Watch Cases and Parts was US$3.24 Thousand during 2012, according to the United Nations COMTRADE database on international trade. Pakistan Imports from India of Watch Cases and Parts - data, historical chart and statistics - was last updated on November of 2025.