Data Visualization Lab: Visualize Fantasy Football and Travel Trends Together

Hook: Turn exam stress into creative mastery — build a dashboard that blends Fantasy Premier League with 2026 travel trends

Students, teachers, and lifelong learners often face two problems: too many datasets and no clear story. If your class is tired of abstract exercises, run a lab that teaches data visualization and data storytelling by combining Fantasy Premier League (FPL) stats with travel destination popularity from 2026. This lab turns messy tables into a compelling dashboard that explains how sports schedules, player popularity, and travel buzz interact — the exact kind of project hiring managers want to see.

Why this lab matters in 2026

In late 2025 and early 2026, two trends made this cross-domain project especially timely. First, travel demand has rebounded worldwide and shifted toward experience-first and sustainable destinations. Industry roundups such as The Points Guy's list of the 17 best places to visit in 2026 reflect these patterns. Second, sports analytics — and FPL in particular — remained a top source of public data that tracks fan attention and leisure behavior in near realtime. Teaching students to fuse these sources trains them in modern visual analytics and interdisciplinary thinking.

Learning outcomes

• Design interactive dashboards that reveal correlations between match schedules and travel interest.
• Perform data cleaning and transformation across sport and travel datasets.
• Choose visual encodings to communicate comparisons, trends, and anomalies.
• Construct a reproducible lab workflow suitable for portfolios and class assessments.

Overview of the lab: From concept to dashboard story

This lab is structured as a 4-session module for a semester course or intensive workshop. Each session is actionable and resource-efficient. Students will end with a multi-tab interactive dashboard and a short video pitch that explains their insight.

Session 1: Problem framing and hypotheses

• Introduce the datasets: FPL statistics (player minutes, ownership percentage, expected goals, total points) and travel trend lists (The Points Guy 2026 destination list for qualitative ranking, Google Trends time series for each destination query, and open flight search aggregates where available).
• Prompt sample hypotheses: Do home derby weekends increase searches for city trips? Does a marquee player injury dampen travel interest to that city? Are rising travel destinations more likely to host promotional matches or events that spike FPL attention?
• Assign groups and a one-page hypothesis card that lists dependent and independent variables and the primary visual that will test the hypothesis.

Session 2: Data collection and cleaning

Students will collect and harmonize data. Emphasize reproducibility: record API calls, dataset versions, and dates.

1. FPL data sources: official FPL APIs, public scrapes, or curated CSVs that include player points per gameweek, ownership, minutes, and team affiliation. Use reliable outlets for team news and injuries to annotate dates — the BBC's FPL roundups in early 2026 are a good reference for weekly team news and absences.
2. Travel data sources: The Points Guy 2026 destination list for qualitative ranking, Google Trends time series for each destination query, and open flight search aggregates where available.
3. Geographic mapping: Map Premier League clubs to cities and travel destinations to cities or regions to allow joins. Use standard country and city codes to avoid mismatches.

Data cleaning checklist

• Standardize date formats to ISO 8601.
• Normalize location names using a lookup table (example mapping: Manchester United to Manchester city boundary).
• Fill or flag missing values rather than silently dropping rows; retention of sparsity can be an insight.
• Aggregate gameweek-level FPL stats to the city-date level to match travel weekly search volumes.

Designing the dashboard: visuals and narrative arcs

Good dashboards follow a story arc: context, key evidence, and action. For this lab, guide students to produce a dashboard with three tabs: Overview, Match-Trip Correlations, and Deep Dives.

Overview tab

• Topline metrics: number of gameweeks analyzed, total travel search volume for the 17 destinations, mean FPL points per team, and number of marquee matches.
• Destination sparkline grid: 17 small multiples showing normalized search interest over time, annotated with major matches occurring in that destination's city.
• Map layer: an interactive map plotting stadiums and destinations; size indicates travel interest, color indicates FPL activity.

Match-Trip Correlations tab

Here the goal is to test hypotheses and quantify associations.

• Time-series overlays: overlay team popularity metrics (ownership %) with Google Trends for the city, aligned by week. Use normalization and a shared index to compare different scales.
• Lag analysis: provide controls for week lags. For example, does a peak in travel searches precede a big match or follow a surprise result?
• Scatter matrix: compare variables like FPL ownership, player minutes, travel search growth, and flight price changes to surface correlations.

Deep Dives tab

• Event story: pick a case study such as a city on The Points Guy 2026 list and a simultaneous high-profile derby to tell a micro-story. Annotate the timeline with injury announcements and travel advisories.
• What-if simulator: allow students to toggle player participation or ticket promotion to project travel interest changes using simple elasticity assumptions. Consider lightweight on-device forecasting approaches described in on-device AI primers for fast prototyping.

Visual encodings and accessibility

Choose encodings that match data types. Use color to encode categories and position for quantitative comparisons. In 2026, accessibility and inclusive design are non-negotiable: ensure color contrast, provide text alternatives, and include keyboard navigation in interactive dashboards.

Guidelines

• Use sequential color palettes for single-variable intensity (e.g., search volume) and diverging palettes for metrics that center around a neutral value (e.g., change in ownership).
• Prefer position and length over area or color when showing precise comparisons.
• Provide dynamic tooltips that explain metrics and sources. A tooltip can show how many FPL points a player scored, why a week is annotated, and the travel source (for example The Points Guy ranking or Google Trends).

Analytical techniques to teach

Beyond chart selection, students should learn analysis patterns used in real-world visual analytics.

• Normalization: convert metrics to z-scores or index them to a baseline week so travel searches and FPL ownership are comparable visually.
• Rolling windows: use 3-week or 4-week rolling averages to smooth out matchweek noise.
• Lagged correlation: compute cross-correlations to find leading or lagging relationships between matches and travel signals.
• Segmentation: split audiences by domestic vs international searches to detect inbound tourism trends around high-profile matches.
• A/B annotations: annotate outcomes when the same stadium hosts promotional events or when roster changes are publicized, using news sources to establish causality evidence.

Practical workflow and tools (2026-ready)

Choose a stack that balances teaching value with practicality. In 2026, browser-based tools and lightweight notebooks are mainstream in classrooms.

• Data wrangling: pandas or Polars in Python, or R tidyverse for students familiar with R. Polars is gaining traction in 2025–2026 for speed on large tables.
• Visualization libraries: Altair or Vega-Lite for declarative plots, Plotly Dash or Streamlit for interactive dashboards, and Observable for JavaScript-first interactive work. Teach students to export static images and embed interactive versions in a portfolio; for teams storing large media, see creative media vault approaches in Creative Teams in 2026.
• Mapping: use lightweight tile providers and GeoJSON for stadium boundaries. Mapbox and Leaflet integrations remain standard for interactive maps.
• Version control and reproducibility: keep datasets in a data folder with a readme, and use notebooks or scripts with clear dependencies. For secure long-term storage of dataset snapshots, consider reviews such as the KeptSafe Cloud Storage Review. Encourage a single figure-making script to re-run for grading or presentations.

Case study walkthrough: Manchester matchweek and Manchester city travel searches (worked example)

Walkthrough with a simplified hypothesis: home derbies in Manchester produce a measurable local bump in travel search interest for Manchester city searches on Google Trends.

1. Aggregate FPL weekly gameweek data for both Manchester clubs into a city-week table. Include player ownership changes and marquee player availability flags.
2. Pull Google Trends series for the query Manchester travel or Manchester vacation for the same weeks.
3. Normalize both series to week 1 and compute 3-week rolling averages.
4. Visualize with aligned sparklines: annotate the sparkline at weeks with derby fixtures, and use a bar chart below to show match attendance or TV viewership as context.
5. Calculate cross-correlation for lags -2 to +2 weeks to identify whether travel interest leads or follows match events.

In our illustrative result, you might find a short-lived increase in local search interest in the week leading to a derby, suggesting fans plan short trips or match packages. This kind of evidence supports student recommendations to travel marketers or club partnership teams.

Assessment and rubric

Grade on clarity of question, quality of data pipeline, visualization choices, storytelling, and reproducibility. Provide a rubric with explicit thresholds for each dimension so students know the expectations.

Sample rubric components

• Data integrity: documented sources and cleaned tables (25%).
• Analytical rigor: correct normalization and correlation checks (25%).
• Visual clarity: appropriate chart types and accessible colors (20%).
• Storytelling: a clear headline insight and supporting evidence (20%).
• Reproducibility: scripts or notebooks that re-run end-to-end (10%).

Examples of compelling student stories

Use example prompts to inspire students' narratives. Provide short proven story templates they can adapt.

• Proof of concept: Show how a marquee signing increased ownership and local travel searches the next month.
• Contrarian insight: A destination on The Points Guy 2026 list had flat travel interest during a season due to match scheduling conflicts; suggest timing strategies for travel marketers.
• Policy angle: Compare sustainability-minded destinations with their event calendars to recommend greener matchday tourism practices.

Common pitfalls and how to teach around them

• Avoid overclaiming causation. Teach students to present correlations with contextual news annotations and to propose plausible mechanisms rather than definitive claims. For advice on designing assessments and exam-adjacent feedback loops, see Adaptive Feedback Loops for Exams in 2026.
• Watch for scale mismatches. If flight searches are global and FPL metrics are local, limit analysis to comparable geographies or stratify by origin country.
• Guard against selection bias by documenting why specific destinations from The Points Guy 2026 list were chosen and by testing alternate search queries.

Evaluation: extending the lab beyond class

Encourage portfolio-ready deliverables: a hosted interactive dashboard, a one-page PDF summary, and a 90-second video pitch. Suggest students publish their non-sensitive work to a class gallery or GitHub Pages with clear data licenses. For instructors scaling classes, the 2026 growth playbook for tutors has useful course packaging ideas.

Advanced extensions for capstone or research projects

• Machine learning: forecast travel interest using match schedules, roster announcements, and historical travel seasonality. Consider lightweight, privacy-forward architectures discussed in on-device AI.
• Network analytics: build a bipartite graph linking players or teams with destinations to identify travel hubs and influencer nodes.
• Monetization analysis: estimate economic impact of match-related travel by combining ticket price distributions and average trip spend.

Instructor notes and resources

Provide starter datasets, a notebook template, and a list of trusted references. Cite timely sources so students learn to attribute responsibly. Example references worth bookmarking:

• The Points Guy 2026 best places to travel for destination signals and qualitative context.
• FPL roundups and team news pages for injury and lineup context that affect player ownership and interest.
• Google Trends and public flight meta-aggregators for search and price signals.

Always record dataset snapshots with dates. In 2026, reproducibility and provenance are the strongest signals of trustworthiness in student projects.

Actionable takeaways for students and teachers

• Start small: pick 3–5 destinations and two teams to pilot the pipeline before scaling. If you need help choosing platforms to promote your final project, see our benchmark of social platforms.
• Document everything: keep a one-page data provenance log for each dataset. For secure snapshots and storage options, review cloud storage guides such as KeptSafe.
• Design to the story: pick one headline insight and select visuals that directly support it.
• Make it interactive: filters for week ranges, city, and player let users explore alternate explanations.
• Be transparent: state assumptions in simulations and show confidence intervals for forecasts.

Final classroom assignment example

Deliver a dashboard, a two-slide summary, and a 2-minute spoken explanation. The prompt: Use FPL and 2026 destination data to answer one question about leisure trends and propose an evidence-based recommendation for a stakeholder (travel marketer, club partnership manager, or fan travel operator).

Closing: why this lab prepares students for real-world visual analytics

By combining FPL stats with The Points Guy's 2026 travel signals, students practice interdisciplinary data fusion, learn to surface timely insights, and craft narratives that matter to stakeholders. This kind of work trains the exact mix of technical skill, domain knowledge, and communication expertise that employers ask for in 2026.

Call to action

Ready to run this lab? Download the starter dataset, the instructor guide, and a notebook template from our class repository. Then assign the project, run two feedback rounds, and have students publish a one-slide elevator pitch. If you want a turnkey syllabus or a grading rubric tailored to your course, request our free instructor pack and sample dataset snapshot from January 2026.

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