assessmentsmathreal-world data

Mock Exams from Real Data: Create Practice Questions from FPL Team News

ttestbook

2026-01-29 12:00:00

11 min read

Convert FPL team news into data-rich mock exams: probability, inference, simulation, and rubrics, plus 2026 tools and templates.

Turn FPL Team News into High-Value Mock Exams for Statistics & Math (2026)

Hook: Struggling to build realistic, engaging practice tests that students will actually care about? Use live Fantasy Premier League (FPL) team news and injury updates to create data-rich mock exams that teach probability, inference, and modeling — while keeping learners excited. In 2026, with richer public sports data and better classroom AI tools, this approach is both practical and high-impact.

Why FPL team news is perfect classroom fuel

FPL team news combines short, structured updates (injuries, doubts, suspensions) with quantitative outputs (minutes, expected points, ownership percentages). This mix maps perfectly to statistics and applied math learning goals: probability, conditional events, hypothesis testing, regression, time series, and simulation. Using real data tackles two major student pain points — dry textbook examples and the gap between theory and practice — while giving teachers low-cost, high-engagement assessment material.

What changed in 2025–2026: trends that make this approach timely

Expanded accessibility of sports metadata: By late 2025 many public and semi-public feeds (BBC team news, the official FPL API, and community-maintained datasets) provide more granular injury tags and fixture metadata that are classroom-ready.
AI-assisted question generation: In early 2026, educators commonly use LLMs and notebook templates to auto-generate multiple variations of the same problem, speeding up test creation while preserving academic integrity through randomized parameters.
Seamless LMS integration: Auto-graded Jupyter-compatible notebooks and CSV imports allow teachers to deliver computational exams and grade complex solutions quickly.

How to construct a mock exam from team news — step-by-step

Below is a repeatable workflow you can apply in minutes using public FPL team news and fixture data.

Step 1 — Choose a recent slate of team news

Select a single gameweek with a compact fixture list (4–6 matches) and clear injury updates. Example source: a BBC Sport team-news roundup from January 2026 that lists players "out", "doubtful", and "available" for matches like Manchester United v Manchester City. Extract the status tags and fixture pairings into a CSV.

Step 2 — Define variables and assumptions

Turn textual labels into quantitative variables. Example mapping:

Out = 0% chance of starting
Doubtful = 40% chance of starting (adjustable)
Available / back from AFCON = 90% chance of starting

Document these assumptions at the top of the exam so students can justify deviations when doing sensitivity analysis. When designing classroom logistics and micro-rituals around exam flow, refer to advanced study architectures to align questions with test-taking routines.

Step 3 — Create question types mapped to learning objectives

Mix short-answer probability items, multi-part inference questions, and computational simulation tasks. Below we provide a full mock exam blueprint using a sample dataset derived from team news.

Mock Exam: FPL Team News (Sample gameweek)

Setup: Use the following simplified dataset (students receive a CSV). This dataset is extracted from a team-news roundup and simplified for classroom use.

Sample dataset (provided to students)

Match A: Manchester United vs Manchester City
Manchester United: De Ligt (out), Lacey (suspended), Mazraoui (out), Mbeumo (available), Amad Diallo (available)
Manchester City: Bobb (out), Dias (out), Gvardiol (out), Kovacic (out), Marmoush (out), Savinho (out), Stones (out), Gonzalez (doubtful)
Assumptions: out = 0% start probability, doubtful = 40%, available = 90%.

Exam structure and scoring

Section A — Core probability questions (30 points)
Section B — Inference & hypothesis testing (30 points)
Section C — Modeling & simulation (30 points)
Section D — Short reflections & interpretation (10 points)

Representative questions (with worked approaches)

Section A — Probability fundamentals

Q1 (6 pts): What is the probability that Manchester City will field Nico Gonzalez in the starting XI? Explain your reasoning using the assumptions above.
Answer approach: Gonzalez is listed as "doubtful" so by assumption P(start) = 0.40 or 40%.
Q2 (8 pts): Given the dataset, what is the probability that neither Manchester United nor Manchester City starts any of the players listed as "out" or "suspended"? (Assume listed "out/suspended" players are never starting.)
Answer approach: If "out/suspended" players have 0% start probability, the probability that none of those players start is 1 (a trivial check). This is a trick question to test their reading of assumptions. Expected student answer: 100%.
Q3 (8 pts): Suppose City has 11 slots. If all listed available players (not out/doubtful) are candidates for automatic inclusion but City is missing many defenders, calculate the probability that Gonzalez starts and a) John Stones doesn't start (given Stones is out), b) Gonzalez is the only listed non-out defensive option.
Answer approach: a) Stones is out (0%), so probability Stones doesn't start is 1. The joint event probability = P(Gonzalez starts) * 1 = 0.40. b) If Gonzalez is the only non-out defensive option among listed names, then probability he is the only defensive starter from this set equals 0.40 * (1 - 0)^others; students should justify roster reasoning. This question tests conditional thinking and careful reading.

Section B — Inference & hypothesis testing

Q4 (15 pts): Using minimum 10 preceding gameweeks (instructor supplies CSV), test the hypothesis that teams missing more than two defenders score fewer goals on average. State null and alternative hypotheses, choose an appropriate test, compute test statistic, and conclude at alpha = 0.05.
Answer approach: H0: mu_groupA - mu_groupB = 0 where groupA = matches with >2 defenders missing, groupB = matches with ≤2 defenders missing. Use two-sample t-test (unequal variances recommended). Provide test stat, p-value, and conclusion. Also require reporting effect size (Cohen's d) and 95% CI. Instructor should provide the dataset; expected teaching points: checking normality, sample sizes, robustness of t-test, possible use of non-parametric Mann–Whitney if assumptions fail.
Q5 (15 pts): Construct a logistic regression predicting whether a doubtful player (like Gonzalez) starts (1) or not (0) using predictors: days since last match, press conference "fitness" score (0–10), and whether the match is a derbies (1/0). Interpret coefficients and compute predicted probability for a hypothetical input.
Answer approach: Fit logit: logit(p) = beta0 + beta1*days + beta2*fitness + beta3*derby. Students should interpret beta2 as the log-odds increase per unit fitness. Provide numerical example: if beta0 = -2, beta1 = 0.03, beta2 = 0.4, beta3 = 0.8 and inputs days=4, fitness=6, derby=1 -> logit = -2 + 0.12 + 2.4 + 0.8 = 1.32 -> p = exp(1.32)/(1+exp(1.32)) ≈ 0.79 or 79%.

Section C — Modeling & simulation

Q6 (15 pts): Monte Carlo simulate the distribution of Manchester City's total expected points (FPL scoring) for the match, given the uncertain start for Gonzalez (40% start) and a simplified points model: starter expected points = 6, bench expected points = 1, captaincy ignored. Run 10,000 simulations and report mean, median, and 90% prediction interval. Provide code in a Jupyter cell or pseudocode.
Answer approach: Students produce simulation steps: for i in 1..10000: draw U~Uniform(0,1); if U <= 0.4 then points = 6 else points = 1. Collect distribution and compute summary stats. Expected mean = 0.4*6 + 0.6*1 = 2.4 + 0.6 = 3.0 points. Simulation should confirm analytically derived mean. For instructors building simulation infrastructure or pipelines, see guides on AI-driven forecasting and backtest stacks for reproducible simulation practices.
Q7 (15 pts): Use a Poisson model to forecast goals for Manchester United given their recent mean goals per match = 1.6 and Manchester City mean conceded per match = 0.9 (instructor-provided). Compute probability United scores 0, 1, and ≥2 goals. Also compute the expected number of goals and variance.
Answer approach: For lambda = 1.6, P(0) = e^-1.6*(1.6^0)/0! = e^-1.6 ≈ 0.201; P(1) = e^-1.6*1.6 ≈ 0.322; P(≥2) = 1 - P(0) - P(1) ≈ 0.477. For Poisson, mean = variance = 1.6. These probabilistic forecasts parallel methods used in practical forecasting stacks and can be compared to Bayesian priors and hierarchical models discussed in forecasting playbooks.

Section D — Interpretation & communication

Q8 (10 pts): Short answer: Explain one limitation of using press-conference team news to parameterize start probabilities, and propose one statistical method to partly correct for it.
Model answer: Limitation: team news is noisy and biased (managers may under/over-report fitness). Correction: use Bayesian updating combining prior predictive models (based on historical start rates by injury label) with observed press-conference signals to produce posterior start probabilities.

Worked solution highlights and instructor notes

Provide students with a model solution set and code snippets. For Q6 (Monte Carlo) instructors can include this Python snippet:

import numpy as np
n=10000
starts = np.random.rand(n) < 0.4
points = np.where(starts, 6, 1)
np.mean(points), np.median(points), np.percentile(points,[5,95])

For Q4: emphasize effect-size reporting, and show how to run the two-sample t-test in R or Python (scipy.stats.ttest_ind). When assembling instructor tooling and grading workflows, think about orchestration and reproducibility—use cloud-native orchestration and environment choices (serverless vs containers) to keep auto-grading reliable.

Advanced problems for higher-level courses

Bayesian hierarchical models: Treat player-level start probabilities as draws from a team-level Beta distribution. Use hierarchical Bayes to borrow strength across players in the same squad.
Survival analysis for injuries: Model time-to-return using Cox proportional hazards models when you have timestamped injury reports.
Network analysis: Use pass networks or lineup co-occurrence networks to test whether injury to a single central defender changes team connectivity metrics.

Designing rubrics and auto-grading strategies (2026 best practice)

Use explicit rubrics for computational and written parts. Example allocation for a 15-point problem:

Correct model selection and assumptions stated: 4 points
Computation / code correctness: 7 points
Interpretation and limitations: 4 points

Auto-grade numeric outputs by checking within tolerances (e.g., ±1% for simulation-based means). For written interpretation, pair automated keyword checks with a quick human spot-check sample (5–10% of submissions) — a standard practice as of 2026 to maintain reliability while scaling assessment. Operationally, consider micro-edge and observability playbooks when scaling grading pipelines across clusters.

Academic integrity and creating multiple exam forms

In 2026, instructors avoid reuse by programmatically randomizing parameters (e.g., changing start probabilities 30–70%) and rotating player names. Use seeded PRNGs to reproduce results for grading, and keep the seed secret until after submission. For scheduling many randomized forms and calendar-driven delivery, see guides on scaling calendar-driven micro-events for cadence and delivery considerations.

Classroom implementation: 90-minute mock exam blueprint

0–10 min: Read dataset and assumptions.
10–35 min: Section A (probability questions).
35–65 min: Section B (inference & regression) — allows time for calculations or coding.
65–85 min: Section C (simulation & modeling) — computational environment recommended.
85–90 min: Section D and quick reflection.

Case study: Class of 2025 pilot

At a mid-sized university in late 2025, an instructor piloted three FPL-based mocks across undergraduate statistics sections. Key outcomes:

Student engagement rose by 28% vs traditional datasets (measured by end-of-class surveys).
Average problem-solving time decreased after two iterations as students became familiar with sports-data conventions.
AI-assisted item variants reduced instructor prep time by ~60%.

These results align with the 2026 trend toward domain-relevant assessment: context matters for motivation.

Tips for instructors — quick checklist

Document assumptions clearly; students should always rationalize them.
Provide a small, clean CSV and a short data dictionary.
Include both pen-and-paper and computational questions for variety.
Offer starter code (Python/R) for simulation tasks to remove technical barriers.
Use randomized parameters to create multiple exam forms and reduce cheating.

Future predictions: FPL-style datasets in education (2026–2030)

Over the next four years we expect:

Growing adoption of real-time sports feeds in curricula, as APIs become more standardized and licensing more permissive for educational use.
Better integration of human-in-the-loop evaluation: instructors will use AI to propose model solutions, but final grading will include instructor review for higher-order reasoning.
Expanded use cases beyond sports — any regular, high-frequency public event feed (weather, elections, market microdata) will be used similarly to train statistical thinking.

Key takeaway: Using FPL team news to design mock exams creates meaningful, data-rich assessments that teach core statistical skills while increasing student motivation — a best practice in 2026 pedagogy.

Resources & starter templates

Public team news feeds: BBC Sport team news, club press releases
FPL data: official FPL API and community-maintained CSVs (check license)
Tooling: Jupyter notebooks, Google Colab, RStudio Server
Auto-grading: nbgrader (Jupyter), Gradescope (for PDFs and short answers)

Final actionable takeaways

Start small: Convert one team-news article into 4–6 exam items for your next quiz. If you plan to productize templates or starter banks, review strategies for creator monetization and micro-subscription distribution.
Use assumptions: Declare mapping from text labels to probabilities — this is a teachable decision point.
Mix modalities: Combine analytic, computational, and interpretation tasks to assess the full skillset.
Leverage 2026 tools: Auto-generate variants and auto-grade numeric output to scale while preserving quality. For operational choices and sustainable infra, consult micro-edge and observability playbooks and debates on serverless vs containers.

Call to action

Ready to build your first FPL-based mock exam? Download our free starter CSVs, Jupyter templates, and a 90-minute exam blueprint from testbook.top. Subscribe for weekly item banks that update with the latest team news so you can deliver fresh, high-engagement practice problems every week.

testbook

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.

Up Next

From Page to Screen: A Workshop for Adapting Graphic Novels

reviews•8 min read

Review: BudgetMax 6T for Students — Is It Still the Best Value Phone in 2026?

media literacy•10 min read

Deepfakes, Platform Shifts and Critical Thinking: A Media Literacy Lesson Plan

2026-01-24T05:01:55.905Z