Unlock hundreds more features
Save your Quiz to the Dashboard
View and Export Results
Use AI to Create Quizzes and Analyse Results

OR
Sign inSign in with Facebook
Sign inSign in with Google
Quizzes > Quizzes for Business > Sports

Take the Premier League Statistics Quiz

Challenge Your Football Data Analysis Skills

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art displaying Premier League Statistics Quiz

As a football fan and data enthusiast, this football stats quiz provides the perfect challenge to test your knowledge of goals, assists, and possession metrics. Suitable for students and educators exploring sports analytics, it sharpens practical skills with real Premier League data. The interactive format can be tailored freely within our editor to suit any learning goal. Take inspiration from the Premier League Trivia Quiz or delve deeper with a Statistics Research Methods Quiz. Explore more engaging quizzes to broaden your statistical insight.

Which metric is most commonly used to rank teams in the Premier League standings?
Total points
Fair play ranking
Goals scored
Goal difference
Teams in the Premier League are ordered by total points, which directly reflects their win and draw outcomes. Goal difference and goals scored serve as tiebreakers if points are equal.
How many points does a team earn for a draw in a Premier League match?
1
3
0
2
A draw awards each team one point in the Premier League points system. Wins award three points and losses award none.
Which club has won the most Premier League titles to date?
Manchester United
Manchester City
Arsenal
Chelsea
Manchester United leads the Premier League era with 13 titles. Other clubs have won fewer since the league's inception in 1992.
If the league records 1,024 total goals over 380 matches, what is the average goals per game?
3.0
2.5
2.7
3.2
Average goals per game = total goals ÷ matches, so 1024 ÷ 380 ≈ 2.694, which rounds to 2.7. This metric helps evaluate scoring trends.
A team records 70% possession in a match. What does this primarily indicate?
They committed more fouls
They controlled the ball for the majority of the match
They had more shots on target
They won by a large goal margin
Possession percentage measures ball control time. A 70% value means that team held the ball substantially longer than their opponent.
Team A has 80 points from 38 games, Team B has 78 points from 37 games. Which has the higher points per game (PPG)?
Cannot determine without goal difference
Team A
They are equal
Team B
Team A's PPG = 80/38 ≈2.105, Team B's = 78/37 ≈2.108. Team B has the slightly higher average points per game.
Which Premier League season between 2010 - 11 and 2019 - 20 had the highest average goals per match?
2012 - 13
2015 - 16
2016 - 17
2018 - 19
The 2018 - 19 season set a record with 1,072 goals over 380 games, averaging about 2.82 goals per match, the highest in that decade.
If a team averages 55 passes per defensive action (PPDA) compared to a league average of 65, what does this indicate?
They press more intensively than average
They have better attacking transition
They concede more goals
They commit more fouls
Lower PPDA means fewer passes allowed before attempting to win the ball, indicating a higher pressing intensity compared to the league average.
Which advanced metric estimates the probability that a given shot will result in a goal?
Expected assists (xA)
Progressive passes
Expected goals (xG)
Pass success rate
Expected Goals (xG) quantifies chance quality by assigning a probability to each shot based on historical data and shot characteristics.
A correlation coefficient of 0.85 between possession percentage and match wins indicates what?
A strong positive relationship
A moderate negative relationship
A weak negative relationship
No relationship
A coefficient near +1 indicates a strong positive relationship, meaning higher possession percentages are associated with more wins.
A player has attempted 50 passes with a completion rate of 92%. How many passes did they complete?
48
46
45
47
92% of 50 passes = 0.92 × 50 = 46 successful passes, which is an important measure of passing efficiency.
To compare the distribution of goals scored across all Premier League teams, which chart type is most suitable?
Pie chart
Line chart
Box plot
Bar chart
A box plot displays the distribution, median, and variability of goals scored, highlighting outliers and spread effectively.
A team's expected goals against (xGA) per match is 1.1, but they concede 0.9 on average. What does this suggest?
They concede too many chances
They are underperforming defensively
Their offense is weaker
They are performing better than expected defensively
Conceding fewer goals than expected (0.9 vs 1.1 xGA) indicates the defense is outperforming the statistical model's prediction.
Which analysis method would you use to test if possession percentage significantly predicts goals scored?
Chi-square test
ANOVA
Linear regression
Kaplan - Meier estimate
Linear regression assesses the predictive relationship between a continuous independent variable (possession %) and continuous dependent variable (goals scored).
Using a Poisson model with λ = 1.5 goals per match, what is the probability of scoring exactly 2 goals?
0.25
0.35
0.45
0.15
Poisson probability = e❻¹·❵ * (1.5²/2!) ≈ 0.2231 × (2.25/2) ≈ 0.25. This models goal counts per match.
In a multiple regression predicting match points, the coefficient for possession % is 0.04 with p = 0.02. What does this imply?
Each 1% increase in possession increases expected points by 0.04, statistically significant
The model is invalid because p < 0.05
Each 1% increase in possession reduces expected points by 0.04
Possession % has no impact on points
A positive coefficient means each additional percentage point of possession increases expected points by 0.04. A p-value of 0.02 indicates this effect is statistically significant.
An ANOVA test on goals scored across the top five teams returns p = 0.03. What conclusion is correct at α = 0.05?
No significant difference in means
ANOVA is inappropriate for means comparison
Significant differences exist among team goal means
The sample size is too small
A p-value below 0.05 leads to rejecting the null hypothesis, indicating at least one team's mean goals scored differs significantly from the others.
To analyze goal trends over ten seasons and identify upward or downward patterns, which method is most appropriate?
Principal component analysis
Time series decomposition
Cross-sectional analysis
Chi-square test
Time series decomposition separates trend, seasonality, and noise in longitudinal data, making it ideal for multi-season goal trend analysis.
A coach uses k-means clustering on player data including passing accuracy and progressive passes. What is the primary goal of this method?
Analyze causal relationships
Predict future match outcomes
Test for statistical significance
Group players into clusters with similar passing profiles
K-means clustering partitions players into groups based on similarity in specified features like passing metrics, helping identify player types.
In a Poisson regression modeling goals, the xG coefficient is 0.8. How do you interpret this value?
xG has no effect on goal count
A one-unit increase in xG multiplies expected goals by e❰·❸ ≈ 2.23
Expected goals increase by 0.8 goals per match directly
The model cannot include xG as a predictor
In Poisson regression, coefficients are on the log scale. A 0.8 coefficient means e❰·❸ ≈ 2.23 multiplier in expected goals for each unit increase in xG.
0
{"name":"Which metric is most commonly used to rank teams in the Premier League standings?", "url":"https://www.quiz-maker.com/QPREVIEW","txt":"Which metric is most commonly used to rank teams in the Premier League standings?, How many points does a team earn for a draw in a Premier League match?, Which club has won the most Premier League titles to date?","img":"https://www.quiz-maker.com/3012/images/ogquiz.png"}

Learning Outcomes

  1. Analyse match statistics to identify top-performing teams
  2. Evaluate goal-scoring trends across Premier League seasons
  3. Interpret possession and passing metrics in real matches
  4. Identify key players using advanced performance data
  5. Apply statistical concepts to real-world football scenarios

Cheat Sheet

  1. Poisson Distribution for Goal Predictions - Imagine goals popping up like fireworks: the Poisson distribution lets you predict how many strikes each team might land by analyzing average goal frequencies. It's perfect for sharpening your match forecasts and impressing your friends with data-driven insights. Poisson Modeling and Predicting English Premier League Goal Scoring
  2. Understanding Expected Goals (xG) - Dive into the magic of xG, which assigns each shot a probability of finding the net based on distance, angle, and context. This metric turns raw goal counts into a fairer measure of performance, revealing hidden brilliance or missed chances. Expect the (Un)Expected: A Beginner's Guide to Advanced Football Metrics
  3. Possession Statistics and Control - Possession numbers and passing accuracy tell a story of which team ruled the turf, dictating play and creating chances. By tracking these stats, you'll spot who really owned the midfield battleground. Premier League Possession Stats | FBref.com
  4. Analyzing Passing Sequences - Ever wonder how teams build those silky moves? Metrics like "10+ pass sequences" and "build-up attacks" shine a light on orchestration behind each goal-scoring opportunity. Premier League club styles revealed in advanced Opta stats | Football News | Sky Sports
  5. Pressing Intensity with PPDA - PPDA (Passes Per Defensive Action) measures how hungry a squad is to snatch the ball back, with lower values indicating relentless pressure. It's like a stamina test for defenders and midfielders in the heat of battle. Expect the (Un)Expected: A Beginner's Guide to Advanced Football Metrics
  6. Advanced Metrics: xG and xA - Combine xG (Expected Goals) with xA (Expected Assists) to gauge a player's full creative and finishing impact. These numbers separate true playmakers from mere bystanders on the pitch. Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach
  7. Shot Quality vs. Quantity - Not all shots are created equal: analyzing where and how chances are crafted shows which teams focus on high-quality opportunities instead of firing wildly. This deep dive reveals true attacking efficiency. Poisson Modeling and Predicting English Premier League Goal Scoring
  8. Positional Impact on xG - Strikers, wingers, and midfield maestros rack up different xG values based on their hot zones. Understanding these positional effects adds nuance to your player comparisons and tactical breakdowns. Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach
  9. Identifying Team Styles - Are they direct counter-attackers or patient builders? Metrics like "direct attacks" versus "build-up attacks" help you decode a team's preferred style and predict how they'll approach each match. Premier League club styles revealed in advanced Opta stats | Football News | Sky Sports
  10. Applying Stats to Real Matches - Turn theory into practice by diving into live match data, interpreting key metrics, and drawing your own conclusions. Platforms like FBref offer a treasure trove of stats - perfect for your next analysis project or fantasy league edge. Premier League Possession Stats | FBref.com
Make a Quiz (FREE) Copy & Edit This Quiz Create a Quiz with AI

Sports Quizzes

Powered by: Quiz Maker