2b:null 2c:["$","title",null,{"children":"Visualizing the Evolution of Formula One Performance Metrics (1950-2023) | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:T139d,# Visualizing the Evolution of Formula One Performance Metrics (1950-2023) By [Aritra Saharay](https://ari-s-123.github.io/Personal-Site) - [Demo Website](https://ari-s-123.github.io/F1-Dataviz-Fall-2024) - [Demo Video](https://youtu.be/XQ68FJEpzv4) - [Class Video](https://youtu.be/M88hbqMdse0) - [Slides](https://docs.google.com/presentation/d/1izjWGaTZDQ1J8PJYd8L3g14OvUC2hLTqcBUds3DVUWU/edit?usp=sharing) Formula One (F1) stands as the pinnacle of motorsport, epitomizing the fusion of cutting-edge automotive technology, driver prowess, and strategic team management. Since its inception in 1950, the sport has undergone profound transformations influenced by technological advancements, regulatory changes, and evolving competitive dynamics. In recent years, significant developments such as the introduction of hybrid power units in 2014, the implementation of budget caps in 2021 to level the competitive field, and adaptations due to global events like the COVID-19 pandemic have further reshaped the landscape of F1. The primary objective of this project is to analyze and visualize the evolution of key performance metrics in Formula One (F1) over the period from 1950 to 2023. By examining metrics such as mean career win rates for drivers and points per race for constructors, the project aims to uncover trends, assess the impact of regulatory changes, and highlight significant shifts in team and driver performances. These visualizations will provide valuable insights for fans, analysts, and teams to understand the dynamics that have shaped the sport over the decades. ### Specific Aims: - Trend Analysis: Examine how drivers' and constructors' performance metrics have evolved over different eras of F1. - Impact Assessment: Determine the influence of major regulatory changes and technological advancements on performance outcomes. - Comparative Insights: Identify teams and drivers that have shown significant improvements or declines in performance. - Interactive Exploration: Develop interactive visualizations that allow users to explore the data in-depth through various analytical lenses. ### Main Insights: - Mercedes has the best (lowest) mean finishing position in a given season of all time of 3.17 in 2017. - Zakspeed has the worst (highest) mean finishing position in a given season of all time of 35.03 in 1989. Funnily enough, 1989 was also the last year they participated in F1... - Out of the drivers with at least 20 race starts, Juan Manuel Fangio appears to be the driver with the best mean career finishing position of all time with an average finishing place of 4.79. However, it must be noted that the next best driver according to this metric is Lewis Hamilton at 4.88, a driver with a far greater total number of race starts at 332 compared to Fangio's 58. - Out of the drivers with at least 20 race starts, Bernd Schneider appears to be the driver with the worst mean career finishing position of all time with an average finishing place of 28.5. Interestingly, he drove for Zakspeed in 1989, the same team that we previously identified as having the worst mean finishing position for a constructor in a single year... ### Data sources and abstraction **Dataset Type:** The project utilizes static multivariate tabular datasets in CSV format encompassing historical Formula One data from 1950 to 2023. These datasets include comprehensive information on drivers, constructors, race results, etc. This is an author-maintained dataset with a universal public domain license from Kaggle, sourced from the Ergast F1 API updated to July 2024 results. I downloaded a snapshot of this data in October of 2024. The incomplete 2024 season data has been filtered out, and results from sprint races and Indianapolis 500 have not been considered. **Data Description for Key Tables:** - Drivers Table: - Variables: driverId, driverRef, number, code, forename, surname, dob, nationality, url - Constructors Table: - Variables: constructorId, constructorRef, name, nationality, url - Results Table: - Variables: resultId, raceId, driverId, constructorId, number, grid, position, positionText, positionOrder, points - Races Table: - Variables: raceId, year, round, circuitId, name, date, time, url, fp1_date, fp1_time **References:** - Primary Dataset: [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) - Regulatory Changes: Compiled from official FIA publications and reputable news sources. ### Contents - [Idea 1: F1 Constructors performance over time with Regulatory Changes](#idea1) - [Idea 2: F1 Drivers' Career Winrate](#idea2) - [Idea 3: Impact of Budget Cap on F1 Constructor Performance](#idea3) - [Idea 4: F1 Constructor Consistency by Season](#idea4) - [Idea 5: F1 Driver Title Contributions](#idea5) - [Idea 6: Mean Finishing Position of F1 Drivers by Country](#idea6) - [Idea 7: Mean Career Finishing Position of F1 Drivers](#idea7) 32:T6c5,### Chart 1 Insights: - Mercedes in 2015 had the highest mean points per race in a given season of all time at 37. This is out of a maximum possible score of 43 in a single race at the time. That is roughly 86% of the highest possible value for that key performance indicator. - The introduction of V8 engines doubled Ferrari's mean points per race from ~5 in 2005 to ~11 in 2006 while weakening that same metric for McLaren and Williams. - The points system changes in 2010 severely inflated the amount of points constructors scored in the following years compared to the years preceding that rule change. ### Chart 2 Insights: - Mercedes has the best (lowest) mean finishing position in a given season of all time of 3.17 in 2017. This implies that it would have been a safe gamble to bet that they would be on the podium every race. I believe the reason why it is 2017 and not 2015 is because of the two DNFs that Nico Rosberg had in 2015 which for this metric hurt more than simply not scoring any points at all. - Zakspeed has the worst (highest) mean finishing position in a given season of all time of 35.03 in 1989. However, during that season they were officially classified as 2nd last instead of last as one would expect. The team that finished below them "EuroBrun" did not manage to qualify for a single race. It appears that the chosen performance indicator of mean finishing position for this specific dataset punishes DNFs more than simply not taking part in a race. Funnily enough, 1989 was also the last year they participated in F1... - Contrary to popular belief, Ferrari's performance in 2020 was not their worst of all time. That crown goes to 1992 where there mean finishing position was 14th...33:T143f,The first chart shows how many points per race a constructor or set of constructors have earned over a given season/year and also if any regulatory changes occurred for that season. Due to the evolution of the points system in F1 over time, comparing performance across decades can be challenging and not representative. Therefore the graph of mean finishing position was made. The second chart shows the mean finishing position a constructor or set of constructors have achieved over a given season/year and also if any regulatory changes occurred for that season. #### Data (what) [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) Dataset type: Table Chart 1: Attributes: * Nominal * Constructor Name * Regulatory Change * Temporal * Year * Quantitative * Points Per Race (derived) Chart 2: Attributes: * Nominal * Constructor Name * Regulatory Change * Temporal * Year * Quantitative * Mean Finishing Position (derived) #### Tasks (why) for the first chart T1. Compare points per race between constructors over time ('Year'). (Compare, streamgraph) T1.1. Identify the constructor that performed (‘Points per Race’) the best during a given season('Year'). (Identify, streamgraph) T1.2. Identify the exact points per race a specific constructor scored during a given season ('Year'). (Identify, streamgraph) T1.3. Identify the highest points per race a specific constructor scored in their entire history ('Year'). (Identify, streamgraph) T1.4. Identify the constructor that had the highest points per race in a single season of the 21st Century ('Year'). (Identify, streamgraph) T1.5. Identify the constructor that had the highest points per race in a single season of the latest year ('Year'). (Identify, streamgraph) T1.6. Identify any changes in the technical regulations that took place in 2022 ('Year'). (Identify, streamgraph) T1.7. Identify any changes in the points system that took place between 2003 and 2010 ('Year'). (Identify, streamgraph) #### Tasks (why) for the second chart T2. Compare mean finishing position between constructors over time ('Year'). (Compare, multi-line chart) T2.1. Identify specific periods where Ferrari's mean finishing position significantly declined and explore potential external factors influencing these trends. (Identify, annotated line chart) T2.2. Identify the constructor that had the best (lowest) mean finishing position during a given season('Year'). (Identify, line chart) T2.3. Identify the exact mean finishing position a specific constructor achieved during a given season ('Year'). (Identify, line chart) T2.4. Identify the best (lowest) mean finishing position in a single season a specific constructor achieved in their entire history ('Year'). (Identify, line chart) T2.5. Identify the constructor that had the best (lowest) mean finishing position in a single season of all time ('Year'). (Identify, line chart) T2.6. Identify the constructor that had the the worst (highest) mean finishing position in a single season of all time ('Year'). (Identify, line chart) T2.7. Identify the constructor that had the best (lowest) mean finishing position in a single season of the 21st Century ('Year'). (Identify, line chart) T2.8. Identify the constructor that had the best (lowest) mean finishing position in a single season of the latest year ('Year'). (Identify, line chart) T2.9. Identify any changes in the technical regulations that took place in 2014 ('Year'). (Identify, line chart) #### How for Chart 1 Marks: Area representing each constructor. Channels: * Position: X-axis for Year * Area: Y-axis for Points Per Race. * Color Hue: Differentiates constructors. * Annotations: Indicate regulatory changes. Effectiveness: Use of area is effective for quickly identifying constructors with highest points per race over time. #### How for Chart 2 Marks: Circles representing individual data points with lines connecting them across years. Channels: * Position: X-axis for Year, Y-axis for Mean Finishing Position Per Race. * Color Hue: Differentiates constructors. * Annotations: Indicate regulatory changes. Effectiveness: Lines with circles are effective for identifying both individual data points and attribute evolution over time. #### Iterations - Added a filter for selecting constructors - Added a filter for selecting year range - Added annotations depicting regulation and points system changes - searchCheckbox component with all and none buttons and search bar - Renders main chart last (so it is "on-top" of the annotations) - Truncate annotations and show the full description on a hover tooltip - Changed Chart 1 to streamgraph - Added Chart 2 - Implemented data imputation - Added color hue separation to annotations (red for technical and blue for points) - Added toggle for showing annotated regulation changes - Made year range searchable - Added dynamic distribution statistics - Fixed tooltip time format - Removed data imputation and instead added circle mark to line graph to convey when a data point exists.34:T40d,### Insights: - Britain has the highest number of total wins at 309. Lewis Hamilton is responsible for roughly a third of those. This country also has the highest number of race starts so I believe this achievement can be attributed to the large size of this category. - The Netherlands has the highest mean career win rate of 10.55%, with Argentina following closely behind with a mean career win rate of 10.15%. I believe this is likely due to the efforts of Max Verstappen and Juan Manuel Fangio and the possibility that in general there probably haven't been too many drivers from these two countries compared to other countries with more total wins like Germany and Britain. - The cohort born in the 1980s has the highest total number of wins at 258. - The cohort born in the 1910s has the highest mean career win rate of 7.05%, with the 1980s and 1960s following at 6.57% and 5.16%. Interestingly, the three driver champions with highest number of titles (Schumacher, Hamilton, Fangio) are each from one of these cohorts.35:T7a6,The charts above are meant to compare total wins and mean win rate across drivers' nationalities and birth decade. The birth decade metric was chosen due to the absence of data for "debut season/year in F1". #### Data (what) [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) Dataset type: Table Chart 1: Attributes: - Nominal - Nationality - Quantitative - Wins (derived) Chart 2: Attributes: - Nominal - Nationality - Quantitative - Mean Career Win Rate (derived) Chart 3: Attributes: - Temporal - Birth Decade - Quantitative - Wins (derived) Chart 4: Attributes: - Temporal - Birth Decade - Quantitative - Mean Career Win Rate (derived) #### Tasks (why) for Chart 1 T3. Identify the nationality with the highest total number of wins. #### Tasks (why) for Chart 2 T4. Identify the nationality with the highest mean win rate. #### Tasks (why) for Chart 3 T5. Identify the decadal cohort with the highest total number of wins. #### Tasks (why) for Chart 4 T6. Identify the decadal cohort with the highest mean win rate. #### How for Chart 1 Marks: Bars representing each nationality. Channels: - Position: X-axis for Nationality, Y-axis for Total Wins. - Size: Height of bars indicates number of wins. #### How for Chart 2 Marks: Bars representing each nationality. Channels: - Position: X-axis for Nationality, Y-axis for Mean Win Rate. - Size: Height of bars indicates win rate %. #### How for Chart 3 Marks: Bars representing each decadal cohort. Channels: - Position: X-axis for Birth Decade, Y-axis for Total Wins. - Size: Height of bars indicates number of wins. #### How for Chart 4 Marks: Bars representing each decadal cohort. Channels: - Position: X-axis for Birth Decade, Y-axis for Mean Win Rate. - Size: Height of bars indicates win rate %. #### Iterations - Added a filter for minimum number of race starts per driver36:T7a5,The first chart will compare the points distribution of constructors before and after the budget cap was introduced in 2021 using two box plots. Three-year long periods were chosen for comparison because that is largest amount of time that has passed since the budget cap was introduced 3 years ago. The second chart will compare the same points distribution separated by the participating constructors. #### Data (what) [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) Dataset type: Table Chart 1: Attributes: * Temporal * Period (Pre-2021, Post-2021, derived) * Quantitative * Points Per Race (derived) Chart 2: Attributes: * Temporal * Period (Pre-2021, Post-2021, derived) * Nominal * Constructor Name * Quantitative * Points Per Race (derived) #### Tasks (why) for Chart 1 T7. Analyze the distribution of points per race for the three year periods before and after the 2021 budget cap's implementation to evaluate its effect on competitiveness. (Analyze, box plot using boxes for results grouped by period, points per race for y-axis) #### Tasks (why) for Chart 2 T8. Analyze the distribution of points per race for individual constructors before and after the 2021 budget cap's implementation to evaluate its effect on constructor performance. (Analyze, box plot using boxes for results grouped by constructor, points per race for y-axis, color hue for period) #### How for Chart 1 Marks: Boxes representing the distribution of points per race. Channels: * Position: X-axis for Period, Y-axis for Points Per Race. * Box Size: Interquartile Range #### How for Chart 2 Marks: Boxes representing the distribution of points per race. Channels: * Position: X-axis for Constructor Name, Y-axis for Points Per Race. * Box Size: Interquartile Range * Color Hue: Differentiates periods. #### Iterations - Added Chart 237:Tca4,This chart visualizes the relationship between F1 driver champions and the constructor(s) they won their title(s) with. #### Data (what) [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) Dataset type: Network Attributes: * Nodes * Driver Name * Constructor Name * Edges/Links * [Driver Champion => Constructor they won the title with] #### Tasks (why) **T10.** _Identify_ drivers who have won World Drivers' Championships with multiple constructors to discover patterns of driver versatility and mobility across teams in Formula One history. (Identify, categorical relationship analysis using a Sankey Diagram) **T10.1.** _Analyze_ constructors that have secured championships with multiple drivers to understand patterns of team dominance and the effectiveness of team strategies in achieving success with different drivers. (Analyze, network exploration using a Sankey Diagram) #### How **Sankey Diagram:** - A Sankey Diagram is ideal for displaying node-link relationships where a target variable can have multiple sources and vice-versa. - It effectively reveals patterns in network data. - It allows for easy comparison of relationship/link values across different categories. ### Marks & Channels Table | **Attribute** | **Mark** | **Visual Channel** | **Channel Use** | **Effectiveness** | |:------------------------------------------------:|:--------:|:----------------------------------:|:-----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------:| | Driver | Node | Position (Left), Label | Express, Separate | Position allows users to quickly grasp patterns without conscious effort. Being sorted by size allows quick grasping of visual hierarchies. | | Constructor | Node | Position (Right), Color Hue, Label | Express, Separate | Position and color enable users to quickly grasp patterns without conscious effort. Being sorted by size allows quick grasping of visual hierarchies. | | Number of Championships Won (Driver-Constructor) | Link | Link Thickness (Width) + Tooltip | Express | Width is an effective channel for representing quantitative differences. Tooltip prevents clutter | | Total Championships Won by Driver | Node | Height (Size) + Tooltip | Express | Larger nodes stand out, indicating higher values. Tooltip prevents clutter | | Total Championships Won by Constructor | Node | Height (Size) + Tooltip | Express | Emphasizes constructors with greater historical success. Tooltip prevents clutter |38:T63d,### Insights - At first glance, Poland seems to have the best mean finishing position at 10.65 over 99 races. However, this is due to the fact they have only ever had a single driver compete under their flag, Robert Kubica, who appears to have been a great performer relative to the average F1 driver throughout the sport's history. Raising the minimum races per driver to 5 or 10 does not change this fact as Kubica has taken part in several seasons worth of F1 races. - Raising both the minimum number of drivers and minimum races per driver to 5 makes Mexico stand out with a mean finishing position of 13.01 across 6 drivers over 444 races. I believe that Sergio Perez is largely responsible for this as he is the most successful Mexican driver in F1 history. - Raising both the minimum number of drivers and minimum races per driver to 10 highlights Argentina as the most successful with a mean finishing position of 12.81 across 10 drivers over 343 races. I suspect that Juan Manuel Fangio's era of domination is the likely culprit behind this stat. - There are only 5 countries in the whole world who have had at least 20 drivers drive at least 20 races each in F1: Brazil, Germany, Italy, France, and the United Kingdom. Out of this, the UK appears to have the best mean finishing position of 12.49 across 42 drivers over 3814 races. This doesn't feel like a surprise, given that the UK has historically produced the highest number of F1 drivers' champions. However, it does show that at this point in time F1 has overall still been largely historically populated by European drivers.39:Td96,This chart visualizes the mean finishing positions of drivers aggregated by the country they are from and displays it on a choropleth heatmap. #### Data (what) [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020) Dataset type: Geospatial Attributes: * Nominal * Country Name * Quantitative * Mean Finishing Position #### Tasks (why) **T11.** _Identify_ countries with the lowest average finishing positions of their Formula One drivers to discover patterns in driver performance based on nationality and highlight countries with comparatively lower driver performance. (Identify, geospatial analysis using a choropleth map) **T11.1.** _Compare_ the average finishing positions of Formula One drivers across different countries to understand regional differences in driver performance and identify factors that may contribute to these variations. (Compare, quantitative comparison using a choropleth map) #### How | **Attribute** | **Type** | **Orientation** | **Derived** | **Source** | **Description** | |:---------------------------:|:--------:|:---------------:|:-----------:|:------------------------:|:---------------------------------------------------------------------------------------:| | driverId | Integer | Nominal | No | drivers.csv, results.csv | Unique identifier for each driver | | forename | String | Nominal | No | drivers.csv | Driver's first name | | surname | String | Nominal | No | drivers.csv | Driver's last name | | nationality | String | Nominal | No | drivers.csv | Driver's nationality | | positionOrder | Integer | Ordinal | No | results.csv | Finishing position in a race (1st, 2nd, etc.) | | Country | String | Nominal | Yes | Derived | Standardized country name using a map | | DriverName | String | Nominal | Yes | Derived | Full name of the driver | | AvgFinishingPosition | Float | Quantitative | Yes | Derived | Average finishing position per driver | | RaceCount | Integer | Quantitative | Yes | Derived | Number of races a country finished (calculated for drivers and then grouped by country) | | CountryAvgFinishingPosition | Float | Quantitative | Yes | Derived | Average of drivers' AvgFinishingPosition per country | | DriverCount | Integer | Quantitative | Yes | Derived | Number of drivers from each country 3a:T512,### Insights: - At first glance, Dorino Serafini appears to be the driver with the best mean career finishing position of all time with an average finish of 2nd place... However, this driver has only driven a single F1 race in their entire career. If we filter out everyone with less than 20 race starts, Juan Manuel Fangio appears to be the driver with the best mean career finishing position of all time with an average finishing place of 4.79. However, it must be noted that the next best driver according to this metric is Lewis Hamilton at 4.88, a driver with a far greater total number of race starts at 332 compared to Fangio's 58. - On the surface, Enrico Bertaggia appears to be the driver with the worst mean career finishing position of all time of 38.83. Keep in mind that 39 is the worst possible finishing position possible in the dataset... However, this driver has only driven 6 F1 races in their entire career. If we filter out everyone with less than 20 race starts, Bernd Schneider appears to be the driver with the worst mean career finishing position of all time with an average finishing place of 28.5. Interestingly, he drove for Zakspeed in 1989, the same team that we previously identified as having the worst mean finishing position for a constructor in a single year...3b:Tf81,regulatoryChanges = [ { year: new Date(1952, 0, 1), description: "Crash helmets made mandatory for all drivers", type: regChangeType.TECHNICAL }, { year: new Date(1954, 0, 1), description: "Best five results counted instead of four", type: regChangeType.POINTS }, { year: new Date(1958, 0, 1), description: "Commercial petrol made compulsory, alcohol-based racing fuels banned", type: regChangeType.TECHNICAL }, { year: new Date(1960, 0, 1), description: "6th place now gets 1 point instead of 0", type: regChangeType.POINTS }, { year: new Date(1961, 0, 1), description: "Introduction of rear-engine cars (1.5L), minimum car weight of 450kg, double braking system and roll bar made mandatory, standardized seatbelt anchorage introduced", type: regChangeType.TECHNICAL }, { year: new Date(1962, 0, 1), description: "Winner now gets 9 points instead of 8", type: regChangeType.POINTS }, { year: new Date(1966, 0, 1), description: "Increase of engine capacity from 1.5L to 3.0L and minimum car weight to 500kg", type: regChangeType.TECHNICAL }, { year: new Date(1967, 0, 1), description: "Best nine results counted instead of five", type: regChangeType.POINTS }, { year: new Date(1973, 0, 1), description: "575kg minimum weight, driver numbering system introduced with #1 for World Champion, crushable structure around fuel tanks mandatory", type: regChangeType.TECHNICAL }, { year: new Date(1981, 0, 1), description: "Permanent medical facilities required at all tracks", type: regChangeType.TECHNICAL }, { year: new Date(1989, 0, 1), description: "Ban on turbochargers, engine volume increased to 3.5L", type: regChangeType.TECHNICAL }, { year: new Date(1990, 0, 1), description: "Large mirrors and quick-release steering wheel made mandatory", type: regChangeType.TECHNICAL }, { year: new Date(1991, 0, 1), description: "All races counted towards championship from now, Winner now gets 10 points instead of 9", type: regChangeType.POINTS }, { year: new Date(1993, 0, 1), description: "Safety car introduced, mandatory pit lane entry chicane, rear impact testing requirements", type: regChangeType.TECHNICAL }, { year: new Date(1994, 0, 1), description: "Electronic driver aids banned", type: regChangeType.TECHNICAL }, { year: new Date(1995, 0, 1), description: "Engines limited back to 3.0L", type: regChangeType.TECHNICAL }, // { // year: new Date(2003, 0, 1), // description: "HANS (Head And Neck Safety) device made mandatory", // type: regChangeType.TECHNICAL // }, { year: new Date(2003, 0, 1), description: "Points extended to 8th place", type: regChangeType.POINTS }, { year: new Date(2006, 0, 1), description: "Introduction of 2.4L V8 engines and a single tire supplier (Bridgestone)", type: regChangeType.TECHNICAL }, { year: new Date(2010, 0, 1), description: "Winner now gets 25 points, points extended to 10th place", type: regChangeType.POINTS }, { year: new Date(2014, 0, 1), description: "Transition to 1.6L V6 turbo-hybrid power units", type: regChangeType.TECHNICAL }, { year: new Date(2018, 0, 1), description: "Addition of Halo as driver protection", type: regChangeType.TECHNICAL }, { year: new Date(2019, 0, 1), description: "Fastest lap during race now gets 1 points", type: regChangeType.POINTS }, { year: new Date(2021, 0, 1), description: "Enforcement of strict budget caps (starting at $145 million)", type: regChangeType.TECHNICAL }, { year: new Date(2022, 0, 1), description: "Overhaul of aerodynamic regulations and introduction of sustainable fuels", type: regChangeType.TECHNICAL } 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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":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Visualizing the Evolution of Formula One Performance Metrics (1950-2023)","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":862,"value":"## Note: Sprint Races and Indianapolis 500 have _NOT_ been counted throughout this project","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":71,"value":"idea1 = md`### Idea 1: F1 Constructors' performance over time with Regulatory Changes\n\nIdiom: __Multi-line Chart__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1211,"value":"filtersNote1 = md`### The following filters affect both the graphs in this section`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":450,"value":"viewof constructorFilter = searchCheckbox(uniqueConstructors, {\n label: \"Select Constructor(s)\",\n value: [\n \"Sauber\",\n \"Williams\",\n \"Red Bull\",\n \"Mercedes\",\n \"Ferrari\",\n \"McLaren\",\n \"Alpine F1 Team\",\n \"Aston Martin\",\n \"Haas F1 Team\",\n \"Toro Rosso\",\n \"AlphaTauri\",\n \"Alfa Romeo\",\n \"Renault\",\n \"Brawn\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1275,"value":"viewof minYear = Inputs.range([1950, 2023], {\n value: 1950,\n step: 1,\n label: \"Minimum Year\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"viewof maxYear = Inputs.range([1950, 2023], {\n value: 2023,\n step: 1,\n label: \"Maximum Year\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1291,"value":"viewof showsRegChanges = Inputs.toggle({\n label: \"Show Regulation Changes\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1134,"value":"chart1Notes = md`**Note:** Higher mean points per race per season = better performance`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1591,"value":"chart1Stats = md`### Statistics for current selection`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1589,"value":"idea1Chart1SelectionStats = getStats(conPointsPerRaceFiltered)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":431,"value":"conPointsPerRace = vl\n .layer(\n showsRegChanges ? [...annotationLayers, ...annotationTextLayers] : [],\n conPerformanceChart\n .markArea({ opacity: 0.6, interpolate: \"basis\", tooltip: true })\n .encode(\n vl\n .y()\n .fieldQ(\"pointsPerRace\")\n .title(\"Mean Points Per Race\")\n .stack(\"center\")\n .axis(false)\n )\n )\n .resolve({ scale: { x: \"shared\", y: \"shared\" } })\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1132,"value":"chart2Notes = md`**Note:** Lower mean finishing position per race per season = better performance`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1595,"value":"chart2Stats = md`### Statistics for current selection`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1648,"value":"idea1Chart2SelectionStats = getStats(conMeanPosFiltered)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1108,"value":"conMeanFinishingPos = vl\n .layer(\n showsRegChanges ? [...annotationLayers, ...annotationTextLayers] : [],\n meanFinishingPosition\n )\n .resolve({ scale: { x: \"shared\", y: \"shared\" } })\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1224,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":111,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":230,"value":"idea2 = md`### Idea 2: F1 Drivers' Career Winrate\n\nIdiom: __Bar Chart__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1214,"value":"### The following filter affects all four graphs in this section","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":737,"value":"viewof minRaces = Inputs.range([0, 380], {\n value: 0,\n step: 1,\n label: \"Minimum number of race starts per driver\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"natCareerWins = vl\n .markBar()\n .data(meanWinRateByNationality)\n .encode(\n vl.x().fieldN(\"nationality\").title(\"Nationality\").sort(\"-y\"),\n vl.y().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip([\n vl.tooltip().fieldN(\"nationality\").title(\"Nationality\"),\n vl.tooltip().fieldQ(\"meanWinRate\").format(\".2%\").title(\"Mean Win Rate\"),\n vl.tooltip().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip().fieldQ(\"races\").title(\"Race Starts\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Total Wins by Nationality\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"natMeanCareerWinrate = vl\n .markBar()\n .data(meanWinRateByNationality)\n .encode(\n vl.x().fieldN(\"nationality\").title(\"Nationality\").sort(\"-y\"),\n vl\n .y()\n .fieldQ(\"meanWinRate\")\n .title(\"Mean Win Rate\")\n .axis({ format: \".2%\" })\n .scale({ domain: [0, 1] }),\n vl.tooltip([\n vl.tooltip().fieldN(\"nationality\").title(\"Nationality\"),\n vl.tooltip().fieldQ(\"meanWinRate\").format(\".2%\").title(\"Mean Win Rate\"),\n vl.tooltip().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip().fieldQ(\"races\").title(\"Race Starts\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Mean Win Rate by Nationality\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":797,"value":"bdCareerWins = vl\n .markBar()\n .data(meanWinRateByBirthDecade)\n .encode(\n vl.x().fieldN(\"birthDecade\").title(\"Birth Decade\"),\n vl.y().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip([\n vl.tooltip().fieldN(\"birthDecade\").title(\"Birth Decade\"),\n vl.tooltip().fieldQ(\"meanWinRate\").format(\".2%\").title(\"Mean Win Rate\"),\n vl.tooltip().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip().fieldQ(\"races\").title(\"Race Starts\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Total Wins by Birth Decade\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":770,"value":"bdMeanCareerWinRate = vl\n .markBar()\n .data(meanWinRateByBirthDecade)\n .encode(\n vl.x().fieldN(\"birthDecade\").title(\"Birth Decade\"),\n vl\n .y()\n .fieldQ(\"meanWinRate\")\n .title(\"Mean Win Rate\")\n .axis({ format: \".2%\" })\n .scale({ domain: [0, 1] }),\n vl.tooltip([\n vl.tooltip().fieldN(\"birthDecade\").title(\"Birth Decade\"),\n vl.tooltip().fieldQ(\"meanWinRate\").format(\".2%\").title(\"Mean Win Rate\"),\n vl.tooltip().fieldQ(\"wins\").title(\"Wins\"),\n vl.tooltip().fieldQ(\"races\").title(\"Race Starts\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Mean Win Rate by Birth Decade\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1253,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":246,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":238,"value":"idea3 = md`### Idea 3: Impact of Budget Cap on F1 Constructor Performance\n\nIdiom: __Multi-Box Plot__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"budgetCapPointsDist = vl\n .markBoxplot()\n .data(budgetCapData)\n .encode(\n vl\n .x()\n .fieldN(\"period\")\n .title(\"Period\")\n .sort([\"2018-2020\", \"2021-2023\"])\n .axis({ labelAngle: 0 }),\n vl.y().fieldQ(\"pointsPerRace\").title(\"Points Per Race\")\n )\n .width(800)\n .height(400)\n .title(\"Constructors' Points Distribution Before and After 2021 Budget Cap\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":929,"value":"budgetCapPointsCon = vl\n .markBoxplot()\n .data(budgetCapData)\n .encode(\n vl\n .x()\n .fieldN(\"constructorName\")\n .title(\"Constructor\")\n .sort(\"-y\")\n .axis({ labelAngle: -45 }),\n vl.y().fieldQ(\"pointsPerRace\").title(\"Points Per Race\"),\n vl.color().fieldN(\"period\").title(\"Period\")\n )\n .width(800)\n .height(400)\n .title(\n \"Individual Constructors' Points Distribution Before and After 2021 Budget Cap\"\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1265,"value":"### Insights\n\n- When spread out across three years, the budget cap seems to have increased the median points per race by 1 but also decreased the lower and upper quartiles by 1. So, in terms of gap in points per race the budget cap seems to not have had a meaningful impact. However, while this dataset does not have details on qualifying pace gaps, that is one potential area for exploration.\n \n- The budget cap does not appear to have been completely effective in reducing the domination of all the \"top\" teams, namely Mercedes, Ferrari, and Red Bull. Mercedes and Ferrari did experience a drop in means points per race after the introduction of the budget, but Red Bull surged ahead in the same comparison. Coincidentally, there was a major technical regulation change in aerodynamics in 2022. This implies that technical regulations play a more impactful role in affecting the mean points per race of constructors than the budget cap has so far. This obviously has the potential to change for future seasons.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":265,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"idea4 = md`### Idea 4: F1 Constructor Consistency by Season\n\nIdiom: __Multi-Box Plot__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":970,"value":"viewof selectedSeason = Inputs.range([1950, 2023], {\n value: 2023,\n step: 1,\n label: \"Selected Season\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":994,"value":"seasonalPoinstDist = vl\n .markBoxplot()\n .data(yearlyResults)\n .encode(\n vl\n .x()\n .fieldN(\"constructorName\")\n .title(\"Constructor\")\n .sort(\"-y\")\n .axis({ labelAngle: -45 }),\n vl.y().fieldQ(\"points\").title(\"Points\"),\n vl.tooltip([\n vl.tooltip().fieldN(\"constructorName\").title(\"Constructor\"),\n vl.tooltip().fieldN(\"raceName\").title(\"Race\"),\n vl.tooltip().fieldQ(\"year\").title(\"Year\"),\n vl.tooltip().fieldQ(\"points\").title(\"Points\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Seasonal Points Distribution for \" + selectedSeason)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1304,"value":"### Insights:\n\n- Alfa Romeo and Alpha Tauri had the highest number of outlier results in 2023 at 3.\n \n- Aston Martin, Ferrari, McLaren, and Mercedes were the only constructors in 2023 that did not have any outlier results.\n \n- Haas had the smallest interquartile range in 2023 for points per race at 0. They were the most consistently performing constructor for that season, even if they were consistently poor-performing.\n \n- McLaren had the largest interquartile range in 2023 for points per race at 16.75. They were the least consistently performing constructor for that season. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":277,"value":"This chart will show the distribution in points per race for every constructor during a given season using a box plot to represent every constructor.\n\n#### Data (what)\n\n[Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020)\n\nDataset type: Table\nAttributes:\n * Nominal\n * Constructor Name \n * Quantitative\n * Points Per Race (derived)\n \n#### Tasks (why)\n\nT9. Analyze the consistency of constructors' performance in a selected season by comparing the distribution of their points per race. (Analyze, box plot showing each team's points per race for a chosen season, position for y-axis)\n\n#### How\n\nMarks: Boxes representing each constructor's distribution of points per race for the selected season.\n\nChannels: \n * Position: X-axis for Constructor Name, Y-axis for Points Per Race.\n * Box Size: Interquartile Range\n\n#### Iterations\n\n- Added a filter for selecting the season.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1036,"value":"idea5 = md`### Idea 5: F1 Driver Title Contributions\n\nIdiom: __Sankey Diagram__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1029,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1324,"value":"### Insights:\n\n- Hamilton is responsible for more of Mercedes' driver titles than any other driver that has driven for them at 67% of Mercedes' WDCs. He also has the highest number of titles with a single team (6).\n \n- Ferrari has the highest number of distinct champions that have won with them (9).\n \n- Ferrari also has the highest number of WDCs when you filter out drivers that have not won with a title the same team more than once.\n \n- McLaren has the largest number of WDCs from drivers who went on to win more than one title in their career.\n \n- McLaren and Ferrari both share the top spot for having the highest number of drivers (3) that have won with them more than once.\n \n- Williams and Alfa Romeo are the only teams with multiple WDCs to not have a single driver that has won more than a single title with them. Of these, Williams has the highest number (7).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1327,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1043,"value":"idea6 = md`### Idea 6: Mean Finishing Position of F1 Drivers by Country\n\nIdiom: __Choropleth Map__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1040,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1335,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1337,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1365,"value":"idea7 = md`### Idea 7: Mean Career Finishing Position of F1 Drivers\n\nIdiom: __Scatter Plot__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1405,"value":"viewof nationalityFilter = searchCheckbox(uniqueNats, {\n label: \"Select Nationalities\",\n value: uniqueNats\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1412,"value":"viewof minRaces2 = Inputs.range([0, 380], {\n value: 0,\n step: 1,\n label: \"Minimum number of race starts per driver\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1500,"value":"idea7Stats = md`### Statistics for current selection`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1571,"value":"idea7SelectionStats = getStats(driverMeanPosFiltered)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1433,"value":"idea7Note = md`**Note:** Lower mean career finishing position = better performance`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1419,"value":"driverMeanCareerPos = vl\n .markCircle({ stroke: \"black\", strokeWidth: 1 })\n .data(driverPerformanceMeanPosFiltered)\n .encode(\n vl.x().fieldQ(\"totalRaces\").title(\"Total Career Race Starts\"),\n vl\n .y()\n .fieldQ(\"meanFinishingPosition\")\n .title(\"Mean Career Finishing Position\")\n .scale({ domain: [0, 40] }),\n vl\n .color()\n .fieldN(\"nationality\")\n .title(\"Nationality\")\n .scale({ scheme: \"spectral\" }),\n vl.tooltip([\n vl.tooltip().fieldN(\"driverName\").title(\"Driver\"),\n vl.tooltip().fieldN(\"nationality\").title(\"Nationality\"),\n vl\n .tooltip()\n .fieldQ(\"meanFinishingPosition\")\n .format(\".2f\")\n .title(\"Mean Career Finishing Position\"),\n vl.tooltip().fieldQ(\"totalRaces\").title(\"Total Career Race Starts\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Mean Career Finishing Position vs Total Career Race Starts\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1652,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1668,"value":"The scatter plot above shows the mean finishing position that F1 drivers have achieved over their whole career.\n\n#### Data (what)\n\n[Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020)\n\nDataset type: Table\n\nAttributes:\n * Nominal\n * Driver Name\n * Nationality\n * Quantitative\n * Mean Career Finishing Position (derived)\n\n#### Tasks (why)\n\nT12. Identify the driver with the best (lowest) mean finishing position. (Identify, scatter plot)\n\nT12.1. Identify the driver with the worst (highest) mean finishing position. (Identify, scatter plot)\n\n#### How\n\nMarks: Circles representing each driver.\n\nChannels: \n * Position: X-axis for Total Races, Y-axis for Mean Career Finishing Position.\n * Color Hue: Differentiates drivers by their nationality.\n \n#### Iterations\n\n- Added a filter for nationality\n- Added a filter for minimum race starts\n- Added dynamic distribution statistics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1051,"value":"## Data Processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1604,"value":"conMeanPosFiltered = meanFinishingPositionConstructorFiltered.map(\n (d) => d.meanFinishingPosition\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1575,"value":"conPointsPerRaceFiltered = flattenedPointsPerRaceFilteredByYear.map(\n (d) => d.pointsPerRace\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1583,"value":"getStats = (data) => ({\n mean: twoDecF(d3.mean(data)),\n median: twoDecF(d3.median(data)),\n mode: getMode(data),\n lowerQuartile: twoDecF(d3.quantile(data, 0.25)),\n upperQuartile: twoDecF(d3.quantile(data, 0.75))\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1549,"value":"getMode = (data) => {\n const freq = d3.rollup(\n data,\n (v) => v.length,\n (d) => d\n );\n let mode;\n let maxCount = 0;\n for (const [val, count] of freq) {\n if (count > maxCount) {\n maxCount = count;\n mode = val;\n }\n }\n return twoDecF(mode);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1513,"value":"twoDecF = d3.format(\".2f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1495,"value":"driverMeanPosFiltered = driverPerformanceMeanPosFiltered.map(\n (d) => d.meanFinishingPosition\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1409,"value":"driverPerformanceMeanPosFiltered = driverPerformanceMeanPos.filter((d) => {\n return d.totalRaces >= minRaces2 && nationalityFilter.includes(d.nationality);\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1401,"value":"uniqueNats = Array.from(uniqueNatSet).sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1395,"value":"uniqueNatSet = new Set(allNationalities)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1393,"value":"allNationalities = fullResultsMinus2024.map((d) => d.Driver.nationality)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1369,"value":"driverPerformanceMeanPos = Array.from(\n d3.group(fullResultsMinus2024, (d) => d.driverId),\n ([driverId, entries]) => {\n return {\n driverId,\n driverName: entries[0].Driver.DriverName,\n nationality: entries[0].Driver.nationality,\n meanFinishingPosition: d3.mean(entries, (d) => d.positionOrder),\n totalRaces: entries.length\n };\n }\n).sort((a, b) => a.meanFinishingPosition - b.meanFinishingPosition)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":610,"value":"conPerformanceChart = vl\n .markLine({ point: true })\n .data(flattenedPointsPerRaceFilteredByYear)\n .encode(\n vl.x().fieldT(\"year\").title(\"Year\"),\n vl\n .y()\n .fieldQ(\"pointsPerRace\")\n .title(\"Points Per Race\"),\n vl\n .color()\n .fieldN(\"constructorName\")\n .scale({ scheme: \"spectral\" })\n .title(\"Constructor\"),\n vl.tooltip([\n vl.tooltip().fieldT(\"year\").format('%Y').title(\"Year\"),\n vl.tooltip().fieldN(\"constructorName\").title(\"Constructor\"),\n vl.tooltip().fieldQ(\"pointsPerRace\").format(\".2f\").title(\"Points Per Race\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Constructors' Points Per Race from \" + minYear + \" to \" + maxYear)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":619,"value":"annotationTextLayers = regulatoryChangesFiltered.map((ann) =>\n vl\n .markText({\n align: \"left\",\n dx: 5,\n dy: -5,\n fontSize: 12,\n angle: -45,\n limit: 140,\n ellipsis: \"...\"\n })\n .data([ann])\n .encode(\n vl.x().fieldT(\"year\"),\n vl.y().value(0),\n vl.text().fieldN(\"description\"),\n vl.tooltip().fieldN(\"description\")\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"annotationLayers = regulatoryChangesFiltered.map((ann) =>\n vl\n .markRule({ color: ann.type === regChangeType.TECHNICAL ? \"red\" : \"blue\" })\n .data([ann])\n .encode(vl.x().fieldT(\"year\"), vl.tooltip().fieldN(\"description\"))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"regulatoryChangesFiltered = regulatoryChanges.filter((change) => {\n const year = change.year.getFullYear();\n return year >= minYear && year <= maxYear;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1194,"value":"regChangeType = Object.freeze({\n TECHNICAL: \"technical\",\n POINTS: \"points\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":457,"value":"uniqueConstructors = Array.from(uniqueConstructorsSet).sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":455,"value":"uniqueConstructorsSet = new Set(allConstructors)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"allConstructors = fullResultsMinus2024.map((d) => d.Constructor.name)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"flattenedPointsPerRaceFilteredByYear = flattenedPointsPerRaceFiltered.filter(\n (d) => {\n const year = d.year.getFullYear();\n return year >= minYear && year <= maxYear;\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"flattenedPointsPerRaceFiltered = meanPointsPerRaceByYearConstructorFiltered.flatMap((d) =>\n d.constructors.map((c) => ({\n year: new Date(d.year, 0, 1),\n constructorName: c.constructorName,\n pointsPerRace: c.pointsPerRace\n }))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":909,"value":"budgetCapData = flattenedPointsPerRaceConstructor\n .filter((d) => {\n return d.year.getFullYear() >= 2018;\n })\n .map((d) => ({\n ...d,\n period: d.year < new Date(2021, 0, 1) ? \"2018-2020\" : \"2021-2023\"\n }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"yearlyResults = constructorRacePoints.filter((d) => {\n return d.year === selectedSeason;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1000,"value":"constructorRacePoints = Array.from(\n d3.group(\n fullResultsMinus2024,\n (d) => d.Race.year,\n (d) => d.Race.raceId,\n (d) => d.constructorId\n ),\n ([year, races]) =>\n Array.from(races, ([raceId, constructors]) => {\n return Array.from(constructors, ([constructorId, entries]) => {\n return {\n year: year,\n raceId: raceId,\n raceName: constructors.values().next().value[0].Race.name,\n constructorId: constructorId,\n constructorName:\n constructorLookup[constructorId]?.name ||\n entries[0].ConstructorName ||\n constructorId,\n points: d3.sum(entries, (d) => d.points)\n };\n });\n }).flat()\n).flat()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":980,"value":"allYears = Array.from(\n new Set(flattenedPointsPerRaceConstructor.map((d) => d.year.getFullYear()))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":907,"value":"flattenedPointsPerRaceConstructor = meanPointsPerRaceConstructor.flatMap((d) =>\n d.constructors.map((c) => ({\n year: new Date(d.year, 0, 1),\n constructorName: c.constructorName,\n pointsPerRace: c.pointsPerRace\n }))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":566,"value":"meanPointsPerRaceByYearConstructorFiltered = Array.from(\n groupedByYearConstructorsFiltered,\n ([year, constructors]) => {\n return {\n year: year,\n constructors: Array.from(constructors, ([constructorId, races]) => {\n const groupedByRace = d3.group(races, (d) => d.Race.raceId);\n const totalPointsPerRace = Array.from(\n groupedByRace,\n ([raceId, drivers]) => {\n return d3.sum(drivers, (d) => d.points);\n }\n );\n const meanPointsPerRace = d3.mean(totalPointsPerRace);\n return {\n constructorName: constructorLookup[constructorId].name,\n pointsPerRace: meanPointsPerRace\n };\n })\n };\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"meanPointsPerRaceConstructor = Array.from(\n groupedByYearConstructors,\n ([year, constructors]) => {\n return {\n year: year,\n constructors: Array.from(constructors, ([constructorId, races]) => {\n const groupedByRace = d3.group(races, (d) => d.Race.raceId);\n const totalPointsPerRace = Array.from(\n groupedByRace,\n ([raceId, drivers]) => {\n return d3.sum(drivers, (d) => d.points);\n }\n );\n const meanPointsPerRace = d3.mean(totalPointsPerRace);\n return {\n constructorName: constructorLookup[constructorId].name,\n pointsPerRace: meanPointsPerRace\n };\n })\n };\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1102,"value":"meanFinishingPosition = vl\n .markLine({ point: true })\n .data(meanFinishingPositionConstructorFiltered)\n .encode(\n vl.x().fieldT(\"year\").title(\"Year\"),\n vl\n .y()\n .fieldQ(\"meanFinishingPosition\")\n .title(\"Mean Finishing Position Per Race\"),\n vl\n .color()\n .fieldN(\"constructorName\")\n .scale({ scheme: \"spectral\" })\n .title(\"Constructor\"),\n vl.tooltip([\n vl.tooltip().fieldT(\"year\").format('%Y').title(\"Year\"),\n vl.tooltip().fieldN(\"constructorName\").title(\"Constructor\"),\n vl.tooltip().fieldQ(\"meanFinishingPosition\").format(\".2f\").title(\"Mean Finishing Position\")\n ])\n )\n .width(800)\n .height(400)\n .title(\"Mean Finishing Position by Constructor Over the Years from \" + minYear + \" to \" + maxYear)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1118,"value":"meanFinishingPositionConstructorFiltered = meanFinishingPositionByYearConstructor.filter(\n (d) => {\n const year = d.year.getFullYear();\n return year >= minYear && year <= maxYear;\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1099,"value":"meanFinishingPositionByYearConstructor = Array.from(\n groupedByYearConstructorsFiltered,\n ([year, constructors]) => {\n return Array.from(constructors, ([constructorId, entries]) => {\n const meanPos = d3.mean(entries, (d) => d.positionOrder);\n const constructorName =\n entries[0].ConstructorName ||\n constructorLookup[constructorId]?.name ||\n constructorId;\n return {\n year: new Date(year, 0, 1),\n constructorName: constructorName,\n meanFinishingPosition: meanPos\n };\n });\n }\n).flat()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":563,"value":"groupedByYearConstructorsFiltered = d3.group(\n fullResultsFilteredByConstructor,\n (d) => d.Race.year,\n (d) => d.constructorId\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":939,"value":"groupedByYearConstructors = d3.group(\n fullResultsMinus2024,\n (d) => d.Race.year,\n (d) => d.constructorId\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":755,"value":"meanWinRateByNationality = winsByNationality.map((country) => ({\n ...country,\n meanWinRate: country.wins / country.races\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":715,"value":"winsByNationality = Array.from(\n d3.group(driverPerformanceMerged, (d) => d.nationality),\n ([nationality, drivers]) => ({\n nationality,\n wins: d3.sum(drivers, (d) => d.wins),\n races: d3.sum(drivers, (d) => d.totalRaces)\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":767,"value":"meanWinRateByBirthDecade = winRateByBirthDecade.map((d) => ({\n ...d,\n meanWinRate: d.wins / d.races\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":773,"value":"winRateByBirthDecade = Array.from(\n d3.group(driverPerformanceBDs, (d) => d.birthDecade),\n ([birthDecade, drivers]) => ({\n birthDecade,\n wins: d3.sum(drivers, (d) => d.wins),\n races: d3.sum(drivers, (d) => d.totalRaces)\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":764,"value":"driverPerformanceBDs = driverPerformanceMerged.map((d) => {\n const year = d.dob.getFullYear();\n const birthDecade = Math.floor(year / 10) * 10 + \"s\";\n return {\n ...d,\n birthDecade: birthDecade\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"driverPerformanceMerged = driverPerformanceFiltered.map((dp) => ({\n ...driverLookup[dp.driverId],\n ...dp\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"driverPerformanceFiltered = driverPerformance.filter((d) => {\n return d.totalRaces >= minRaces;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":335,"value":"driverPerformance = d3\n .rollups(\n fullResultsMinus2024,\n (v) => ({\n wins: v.filter((d) => d.positionOrder === 1).length,\n totalRaces: v.length\n }),\n (d) => d.driverId\n )\n .map(([driverId, perf]) => ({\n driverId,\n wins: perf.wins,\n totalRaces: perf.totalRaces,\n meanWinRate: perf.wins / perf.totalRaces\n }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"fullResultsFilteredByConstructor = fullResultsMinus2024.filter((result) =>\n constructorFilter.includes(result.Constructor.name)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"fullResultsMinus2024 = fullResultsNoIndy.filter((r) => {\n return r.Race.year < 2024;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1147,"value":"fullResultsNoIndy = fullResults.filter((r) => {\n return r.Race.name !== \"Indianapolis 500\";\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"fullResults = resultsWithDriversConstructors.map((result) => ({\n ...result,\n Race: raceLookup[result.raceId]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"raceLookup = Object.fromEntries(races.map((r) => [r.raceId, r]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":326,"value":"resultsWithDriversConstructors = resultsWithDrivers.map((result) => ({\n ...result,\n Constructor: constructorLookup[result.constructorId]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":324,"value":"constructorLookup = Object.fromEntries(\n constructors.map((c) => [c.constructorId, c])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":322,"value":"resultsWithDrivers = mergedResults.map((result) => ({\n ...result,\n Driver: driverLookup[result.driverId]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":319,"value":"driverLookup = Object.fromEntries(\n standardizedDrivers.map((d) => [d.driverId, d])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":317,"value":"standardizedDrivers = drivers.map((driver) => ({\n ...driver,\n DriverName: `${driver.forename} ${driver.surname}`\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"mergedResults = results.map((result) => ({\n ...result,\n status: statusLookup[result.statusId] || \"Unknown\"\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":311,"value":"statusLookup = Object.fromEntries(status.map((d) => [d.statusId, d.status]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"status = FileAttachment(\"status.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":307,"value":"results = FileAttachment(\"results.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":303,"value":"races = FileAttachment(\"races.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":301,"value":"constructors = FileAttachment(\"constructors.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"drivers = FileAttachment(\"drivers.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":291,"value":"import { vl } from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"import { searchCheckbox } from \"@john-guerra/search-checkbox\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"style = html`\n\n\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3c","stdlib":"1"}} 30:"$2f:body"