2b:null 2c:["$","title",null,{"children":"Mean Goals per match have decreased ~40% since First FIFA World Cup | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:Tae4,### Example Insight 1: The average number of goals per match in international football has increased significantly over time, revealing trends that correspond with changes in rules, playing styles, and global events. **Considerations for Visualization:** - Temporal Aggregation: To observe long-term trends, the visualization should aggregate the data over different time units—such as by year, decade, or even quarter—to see how the average goals per match have changed. - Date Truncation: Utilize date truncation to group match data into these time units. For instance, truncating dates to the year allows aggregation of all matches within that year. - Iterative Analysis: Create multiple iterations of the visualization by aggregating the data at various temporal granularities (daily, monthly, yearly, decade) to determine which level of aggregation best highlights the trends. - Line Chart with Moving Average: A line chart plotting the average goals per match over time can reveal fluctuations. Applying a moving average can smooth out short-term variations and highlight longer-term trends. - Annotations and Events: Add annotations for significant rule changes (like the introduction of the offside rule), historical events (such as World Wars), or technological advancements (like better training methods) to provide context. - Interactive Filters: Allow users to filter the data by tournament type, region, or team strength to see if trends differ across these dimensions. ### Example Insight 2: European national teams exhibit better performance during colder seasons, suggesting that factors like climate and player availability impact their success rates during those periods. **Considerations for Visualization:** - Date Parts Analysis: Extract parts of the date, such as month or season, to analyze performance metrics like win rate or average goals scored in those periods. - Heatmaps or Seasonal Plots: Use a heatmap to display performance metrics across months and years, helping to identify any seasonal trends or patterns. - Iterative Visualization: Experiment with grouping data by different date parts—month, quarter, season—to find the most meaningful patterns. For example, plotting win rates by month over several years. - Calendar View: A calendar-based visualization can show daily or monthly performance, highlighting peak periods of success or decline. - Comparative Analysis: Compare teams from different climatic regions to see if, for example, teams from colder climates perform better in cooler months. - Contextual Information: Incorporate data on climate conditions, player injuries, or major tournaments occurring during certain times to explain performance variations. 32:T6c3,### Football Analysts and Statisticians: - Interests: Deep analysis of football trends, statistical modeling, and understanding the evolution of the game. - Needs: Detailed temporal data showcasing fluctuations in mean goals per match, with the ability to correlate these trends with specific time periods, rule changes, or global events. - Usage: To support research, publications, or presentations that require evidence-based insights into scoring trends over time. ### Sports Historians and Researchers: - Interests: The historical progression of international football, including how external factors have influenced the sport. - Needs: Visualizations that highlight significant changes in scoring patterns and allow for the identification of periods corresponding with historical events or shifts in playing styles. - Usage: To contextualize football within broader historical narratives, and to publish findings in academic journals or books. ### Coaches and Sports Strategists: - Interests: Understanding long-term trends to inform coaching methods and strategic decisions. - Needs: Insights into how the game's pace and scoring frequency have changed, potentially affecting current training and tactical approaches. - Usage: To adapt strategies that align with contemporary scoring trends or to revisit successful tactics from past eras. ### Football Enthusiasts and Fans: - Interests: General curiosity about the sport's evolution, enjoyment of engaging and informative content. - Needs: Accessible visualizations that tell a compelling story about how international football has changed over the years. - Usage: For personal education, sharing on social media, or enhancing their appreciation of the sport.33:T7a0,### Dataset Type **Tabular Dataset:** The dataset is a relational table extracted from the results.csv file, which contains detailed records of international football matches from 1872 to 2024. ### Dataset Availability Static dataset found @ [Kaggle](https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017) ### Attribute Table | **Attribute** | **Type** | **Data Type** | **Orientation** | **Availability** | **Derived** | **Computation** | |:-----------------------:|:------------:|:-------------:|:---------------:|:----------------:|:-----------:|:-----------------------------------------:| | date | Temporal | Date | Continuous | Available | No | None | | home_score | Quantitative | Integer | Ratio Scale | Available | No | None | | away_score | Quantitative | Integer | Ratio Scale | Available | No | None | | total_goals | Quantitative | Integer | Ratio Scale | Derived | Yes | home_score + away_score | | time_period | Temporal | String | Discrete | Derived | Yes | Truncate date to desired granularity | | match_count | Quantitative | Integer | Ratio Scale | Derived | Yes | Count(total_goals) per time_period | | mean_goals_per_match | Quantitative | Float | Ratio Scale | Derived | Yes | SUM(total_goals) / match_count | | historical_events | Categorical | String | Nominal | External | Yes | Manually aligned with date or time_period |34:T94a,### Primary Idiom **Line Chart:** - A line chart is ideal for displaying quantitative data over a continuous temporal axis. - It effectively shows trends, patterns, and fluctuations in data over time. - It allows for easy comparison of values across different time periods. ### Secondary Idioms - **Annotations:** Adding markers or text annotations for historical_events to provide context. - **Confidence Intervals or Error Bands:** If variability within time periods is significant, shading areas around the line can represent variance. ### Marks & Channels Table | **Attribute** | **Type** | **Mark** | **Channel** | **Usage** | **Function** | **Expressiveness** | **Effectiveness** | |:--------------------:|:------------:|:------------:|:-------------------------:|:---------------------:|:------------------------------:|:----------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------:| | time_period | Temporal | Points/Lines | Horizontal Position (X) | Express, Order, Align | Represents progression of time | Position along a common scale is an accurate way to represent temporal data. | The human eye naturally interprets time from left to right on a horizontal axis, making it intuitive to follow trends over time. | | mean_goals_per_match | Quantitative | Points/Lines | Vertical Position (Y) | Express | Represents mean goals | Vertical position on a common scale accurately conveys quantitative differences. | Allows for precise comparisons of values and easy detection of trends, peaks, and troughs over time. | | historical_events | Categorical | Text/Markers | Position (X), Color/Shape | Align, Separate | Annotates events on timeline | Placing annotations directly on the timeline connects events with observed trends. | Helps users correlate fluctuations in mean_goals_per_match with external factors, enhancing understanding. |35:T420,annotations = [ { year: new Date(1891, 0, 1), event: "Football nets invented" }, { year: new Date(1907, 0, 1), event: "Offside in own half introduced" }, { year: new Date(1914, 0, 1), event: "World War I Begins" }, { year: new Date(1918, 0, 1), event: "World War I Ends" }, { year: new Date(1925, 0, 1), event: "Two-player offside rule introduced" }, { year: new Date(1930, 0, 1), event: "First FIFA World Cup" }, { year: new Date(1939, 0, 1), event: "World War II Begins" }, { year: new Date(1945, 0, 1), event: "World War II Ends" }, { year: new Date(1958, 0, 1), event: "Substitutions Introduced" }, { year: new Date(1970, 0, 1), event: "Introduction of Red and Yellow Cards" }, { year: new Date(1986, 0, 1), event: "Maradona's 'Hand of God' Goal" }, { year: new Date(1992, 0, 1), event: "Back-pass Rule Implemented" }, { year: new Date(2012, 0, 1), event: "Goal-Line Technology Approved" }, { year: new Date(2018, 0, 1), event: "VAR Introduced in World Cup" }, { year: new Date(2020, 0, 1), event: "COVID-19 Pandemic" }, ]36:[{"id":"f9499d7806cd4202c12a3b07f3c02ce7a4eeaf531bb384aa58c31b4df7f03e52f9011426f4325fcd4d17776a7ef28cdb5696fa5085d371badd6b8c63c805cb89","href":"https://static.observableusercontent.com/files/f9499d7806cd4202c12a3b07f3c02ce7a4eeaf531bb384aa58c31b4df7f03e52f9011426f4325fcd4d17776a7ef28cdb5696fa5085d371badd6b8c63c805cb89","name":"results.csv","type":"text/csv","lastModified":1730413032966.266,"size":3595861}] 2f:{"id":"f13a4d1ffabf55a0","slug":null,"title":"Mean Goals per match have decreased ~40% since First FIFA World Cup","thumbnail":"59b31e043531410b9d1d4f16a7ffe4d8b909aff8bf1a2dbdb38145920edab5ba","access_level":"public","create_time":"$D2024-10-31T03:52:17.611Z","update_time":"$D2025-05-24T17:38:58.499Z","view_time":null,"first_public_time":"$D2024-11-02T06:33:09.990Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":395,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"613ce9d861a8bc3b","login":"ari-s-123","name":"Aritra Saharay","avatar_url":"https://avatars-next.observableusercontent.com/1da9e61c3f8fc3970bbf95defa0acaffca78064cddc0cddbb20beb8cec3e419f","tier":null,"bio":"I'm an aspiring SWE and a student pursuing a Master's Degree in CS at Northeastern University. My present interests lie at the intersection of Web/Mobile Development, Data Visualization, A11y, and AI."}],"members":[{"type":"individual","id":"613ce9d861a8bc3b","login":"ari-s-123","name":"Aritra Saharay","avatar_url":"https://avatars-next.observableusercontent.com/1da9e61c3f8fc3970bbf95defa0acaffca78064cddc0cddbb20beb8cec3e419f","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Mean Goals per match have decreased ~40% since First FIFA World Cup","cells":[{"id":0,"value":"# Mean Goals per match have decreased ~40% since First FIFA World Cup","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":300,"value":"vl\n .layer(yearChart, ...annotationLayers, ...annotationTextLayers)\n .resolve({ scale: { x: \"shared\", y: \"shared\" } })\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"## Real Insight\n\n\n\nThe main insight that I discovered from this visualization was that the mean goals per match seems to have decreased by a noticeable margin over the last century. In 1930, when the First FIFA World Cup took place, the mean goals per match for that year was ~4.70. In 2024, that same figure is ~2.60. This represents a decrease of ~45% in the span of 94 years of rule changes and technological improvements. This could be explained by two possible causes: either international football teams' offensive prowess has become weaker over the century or that the average level of defensive ability in international football teams has drastically improved over the past century.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":30,"value":"## Example Insights","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":36,"value":"## Chosen Insight","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"### Example Insight 1: The average number of goals per match in international football has increased significantly over time, revealing trends that correspond with changes in rules, playing styles, and global events.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":160,"value":"## Task Abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"**T1.** _Analyze_ the mean number of goals per match ('Mean Goals/Match') over time ('Time') to observe trends and fluctuations in scoring rates. (Analyze, time-series analysis using date truncation)\n\n**T1.1.** _Compare_ the mean goals per match ('Mean Goals/Match') across different decades ('Decade') to highlight shifts in playing styles and strategies. (Compare, temporal comparison using date truncation)\n\n**T1.2.** _Identify_ significant changes in scoring trends ('Mean Goals/Match') to correlate with historical events or rule changes ('Historical Events' annotations) (Identify, trend detection with contextual correlation)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"## Target Audience","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":48,"value":"## Data Abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":162,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":52,"value":"## Idiom Abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":320,"value":"annotationTextLayers = annotations.map((ann) =>\n vl\n .markText({ align: \"left\", dx: 5, dy: -5, fontSize: 12, angle: -45 })\n .data([ann])\n .encode(vl.x().fieldT(\"year\"), vl.y().value(0), vl.text().fieldN(\"event\"))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"annotationLayers = annotations.map((ann) =>\n vl\n .markRule({ color: \"red\" })\n .data([ann])\n .encode(vl.x().fieldT(\"year\"), vl.tooltip().fieldN(\"event\"))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"yearChart = vl\n .markLine({ tooltip: true })\n .data(results_cleaned)\n .transform(vl.timeUnit(\"year\").field(\"date\").as(\"year\"))\n .encode(\n vl.x().fieldT(\"year\").title(\"Year\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(1000)\n .height(500)\n .title(\"Yearly Aggregated Mean Goals\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":294,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":260,"value":"## Date Parts Exploration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":280,"value":"// Winter - Dec to February\n// Spring - March to May\n// Summer - June to August\n// Autumn - September to November\nvl\n .markBar()\n .data(results_cleaned)\n .transform(\n vl\n .calculate(\n `month(datum.date) == 12 || month(datum.date) <= 2 ? 'Winter' :\n month(datum.date) >=3 && month(datum.date) <= 5 ? 'Spring' :\n month(datum.date) >=6 && month(datum.date) <= 8 ? 'Summer' :\n 'Autumn'`\n )\n .as(\"season\")\n )\n .encode(\n vl.x().fieldN(\"season\").title(\"Season\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Mean Goals by Weather Season\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":274,"value":"vl\n .markBar()\n .data(results_cleaned)\n .transform(vl.calculate(\"'Q' + quarter(datum.date)\").as(\"quarter\"))\n .encode(\n vl.x().fieldO(\"quarter\").title(\"Quarter\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Mean Goals by Quarter\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":264,"value":"vl\n .markBar()\n .data(results_cleaned)\n .transform(vl.timeUnit(\"month\").field(\"date\").as(\"month\"))\n .encode(\n vl\n .x()\n .fieldO(\"month\")\n .timeUnit(\"month\")\n .title(\"Month\")\n .axis({ format: \"%B\" }),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Mean Goals by Month (Over All Years)\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"vl\n .markBar()\n .data(results_cleaned)\n .transform(vl.timeUnit(\"day\").field(\"date\").as(\"day_of_week\"))\n .encode(\n vl.x().fieldO(\"day_of_week\").timeUnit(\"day\").title(\"Day of Week\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Mean Goals by Day of Week\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":256,"value":"## Date Truncation Exploration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":249,"value":"## Selected Variation: Yearly Aggregation\n\n- Balance between noise and detail","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":233,"value":"vl\n .markLine()\n .data(results_cleaned)\n .transform(\n vl.calculate(\"floor(year(datum.date) / 10) * 10 + 's'\").as(\"decade\")\n )\n .encode(\n vl.x().fieldO(\"decade\").title(\"Decade\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Decadal Aggregation - Noise eliminated\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":224,"value":"vl\n .markLine()\n .data(results_cleaned)\n .transform(vl.timeUnit(\"year\").field(\"date\").as(\"year\"))\n .encode(\n vl.x().fieldT(\"year\").title(\"Year\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(500)\n .title(\"Yearly Aggregation - Much less noise\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"vl\n .markLine()\n .data(results_cleaned)\n .transform(vl.timeUnit(\"yearquarter\").field(\"date\").as(\"year_quarter\"))\n .encode(\n vl.x().fieldT(\"year_quarter\").timeUnit(\"yearquarter\").title(\"Quarter\"),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Quarterly Aggregation - Insignificant decrease in noise\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":212,"value":"vl\n .markLine()\n .data(results_cleaned)\n .transform(vl.timeUnit(\"yearmonth\").field(\"date\").as(\"year_month\"))\n .encode(\n vl.x().fieldT(\"year_month\").title(\"Month\").axis({ format: \"%b %Y\" }),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Monthly Aggregation - Less noise\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"vl\n .markLine()\n .data(results_cleaned)\n .encode(\n vl.x().fieldT(\"date\").title(\"Date\").axis({\"format\": \"%d %b %Y\"}),\n vl.y().fieldQ(\"total_goals\").aggregate(\"mean\").title(\"Mean Goals per Match\")\n )\n .width(750)\n .height(400)\n .title(\"Daily Aggregation - Too much noise\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"results_cleaned = results\n .filter((d) => d.home_score !== null && d.away_score !== null)\n .map((d) => ({ ...d, total_goals: d.home_score + d.away_score }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"results = FileAttachment(\"results.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"[Data Source](https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"import { vl } from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":368,"value":"style = html`\n\n\n\n`","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W36","stdlib":"1"}} 30:"$2f:body"