Best Examples of Statistics in Sports Performance Analysis for Science Fair Projects

Real-World Examples of Statistics in Sports Performance Analysis

Sports today are basically moving math labs. Teams don’t just watch games; they measure everything. When teachers ask for examples of statistics in sports performance analysis, they’re talking about exactly this kind of data-driven decision-making.

Here are several concrete directions you can take, each grounded in real methods that professional analysts use.

Shooting Efficiency in Basketball: A Classic Example of Sports Statistics

Basketball might be the easiest entry point if you want an example of statistics in sports performance analysis that’s rich but still manageable.

Analysts don’t just look at points per game anymore. They focus on efficiency:

• Field goal percentage (FG%) and three-point percentage (3P%)
• Effective field goal percentage (eFG%), which gives extra weight to three-pointers
• True shooting percentage (TS%), which also factors in free throws

A strong science fair project can ask: Does shot distance affect shooting efficiency for NBA players? You could:

• Collect shot data from a player or team (NBA.com’s stats pages are a gold mine)
• Group shots by distance ranges (0–3 ft, 3–10 ft, 10–16 ft, 16–23 ft, 3-point range)
• Calculate FG% and eFG% for each range
• Use bar charts and confidence intervals to compare them

This is one of the best examples of statistics in sports performance analysis because it mirrors what real NBA analytics departments do when they design offensive strategies.

Baseball and Sabermetrics: On-Base Percentage, WAR, and Beyond

Baseball basically invented modern sports analytics. If you want examples of statistics in sports performance analysis with deep historical roots, baseball is your sport.

Instead of just batting average, analysts use:

• On-base percentage (OBP)
• Slugging percentage (SLG)
• On-base plus slugging (OPS)
• Wins Above Replacement (WAR)

A realistic project angle: Which statistic better predicts team wins: batting average or on-base percentage?

You could:

• Pull team-level data for a recent MLB season from sources like Baseball-Reference or MLB’s stats
• Compute correlations between team wins and AVG, OBP, and OPS
• Show which stat has the strongest relationship with winning

This gives you a clear, statistically grounded argument and a strong narrative: some stats feel important, but the data tells a different story.

Soccer’s Expected Goals (xG): Probability Meets Performance

Soccer analytics exploded over the last decade, and expected goals (xG) is now a standard metric. It’s a perfect modern example of statistics in sports performance analysis, because it’s literally built on probability.

xG assigns a probability to each shot based on factors like:

• Distance from goal
• Angle
• Type of assist (through ball, cross, etc.)
• Body part used (head, foot)

A strong project question: Do teams that consistently outperform their xG over a season actually have better finishers, or are they just lucky?

You could:

• Take xG and goals scored for 5–10 teams from a recent Premier League or MLS season (sites like FBref, which uses StatsBomb data, are helpful)
• Compare goals vs. expected goals with scatter plots
• Calculate whether the difference is statistically significant using simple tests (even a basic t-test if you’re comfortable)

This lets you discuss randomness, variance, and regression to the mean—big ideas in statistics, wrapped in a sport lots of people care about.

American Football: Third-Down Conversion and Play-Calling

In American football, coaches constantly face tradeoffs: run or pass, go for it or punt, blitz or drop back. That makes it a rich source of examples of statistics in sports performance analysis tied to decision-making.

One project-ready angle is third-down performance:

• Compare a team’s third-down conversion rate by play type (run vs. pass)
• Examine conversion rates by distance (3rd-and-short vs. 3rd-and-long)

You might explore: Is passing actually better than running on 3rd-and-short for NFL teams?

Steps:

• Collect play-by-play data from a recent NFL season (the NFL’s own stats site or data mirrored on sports analytics blogs)
• Group plays by situation: distance to go and play type
• Calculate conversion percentages and confidence intervals
• Use chi-square tests to see if differences are statistically meaningful

This is one of the best examples because it shows how stats guide strategy, not just describe performance.

Volleyball or Tennis: Serve Success and Placement

If you want something you can measure yourself at school, volleyball and tennis are great. They give you hands-on examples of statistics in sports performance analysis without needing pro-level datasets.

For volleyball, a project could ask: Does serving to specific zones increase the chance of winning the point?

You could:

• Watch recorded matches (high school, college, or pro) and code each serve by zone
• Record whether the serving team wins or loses the rally
• Calculate point-win percentages by serve location

In tennis, try: Does first-serve percentage correlate with match outcome?

• Track first-serve percentage and points won on first serve for several players
• Compare stats in matches they win vs. matches they lose

These real examples show how even simple percentages and basic hypothesis testing can produce meaningful insights.

Running and Endurance Sports: Pace, Heart Rate, and Split Times

Endurance sports are fantastic for time-series data. Runners and cyclists already track:

• Pace per mile or kilometer
• Heart rate
• Elevation changes

A project question might be: How does temperature affect running pace in 5K races?

You could:

• Collect public race results and note temperature on race day (weather archives from sources like the National Weather Service)
• Group results into temperature bands (cool, mild, hot)
• Compare average finishing times or pace by temperature group

This connects sports performance to physiology. For background on how heat affects the body, you can reference research from sites like NIH or CDC, which discuss performance and heat stress.

Injury Risk and Workload: When Sports Meets Health Data

One of the more advanced examples of statistics in sports performance analysis focuses on how workload relates to injury risk. This is where sports data intersects with health and medical research.

Analysts look at variables like:

• Minutes played per week
• Number of high-intensity sprints
• Back-to-back games or travel distance

A project idea: Do NBA players who play more minutes per game miss more games due to injury?

You could:

• Gather minutes per game and games missed for a season
• Use scatter plots to visualize the relationship
• Compute correlation coefficients

You can also frame it more like a health study and connect it to overuse injuries and recovery, using background information from sources like Mayo Clinic on overuse injuries and rest.

Using Statistics to Evaluate Clutch Performance

Every fan argues about “clutch” players. Statisticians ask: Is clutch performance real, or just random hot streaks? That question alone can power an entire science fair project.

A project-ready example of statistics in sports performance analysis is comparing a player’s performance:

• In regular situations vs. high-pressure situations
• For example, early in the game vs. final minutes, or low-leverage vs. high-leverage moments

You might:

• Define what counts as a “clutch” situation (final 2 minutes, one-possession game, etc.)
• Pull shooting or hitting stats for those situations vs. normal situations
• Use hypothesis testing to see if the differences are larger than you’d expect from random variation

This lets you talk about sample size, variance, and why small samples can be misleading—core statistical ideas illustrated with players your classmates actually know.

Turning Sports Statistics into a Science Fair Project

Knowing a few examples of statistics in sports performance analysis is only step one. The key is turning those examples into a clear, testable project.

Step 1: Start with a Sharp Question

Good questions are narrow and measurable. Instead of:

• “How do stats affect sports?”

Try things like:

• “Do NBA players shoot a higher three-point percentage from the corners than from above the break?”
• “Does a higher on-base percentage predict more wins than batting average in MLB?”
• “Are soccer teams with higher xG but fewer goals likely to improve in the second half of the season?”

Each of these is tied directly to real examples of statistics in sports performance analysis that professionals already care about.

Step 2: Find Reliable Data Sources

You don’t need insider access. Many leagues publish detailed stats for free. For background or health-related angles, these sources are useful:

• NIH – for general research on exercise, performance, and physiology
• CDC – for physical activity guidelines and health context
• Mayo Clinic – for exercise and injury information

Combine these with sports-specific databases (league stat pages, reference sites, or open-source datasets) and you have everything you need for a solid analysis.

Step 3: Choose the Right Statistical Tools

You don’t have to use advanced methods. Many of the best examples of statistics in sports performance analysis rely on tools you already learn in middle or high school:

• Percentages and rates (shooting percentages, conversion rates)
• Averages and medians (points per game, average pace)
• Standard deviation (how consistent a player or team is)
• Correlation (do two variables move together?)
• Simple hypothesis tests (are two percentages meaningfully different?)

If you want to stretch a bit, regression analysis lets you predict one variable (like wins) from others (like OBP, home runs, and ERA).

Step 4: Tell a Clear Story with Your Results

Judges care less about fancy formulas and more about whether your question, method, and conclusion line up.

A strong presentation:

• States the question clearly in plain language
• Explains your data source and why it’s reliable
• Shows charts that are easy to read and labeled clearly
• Interprets the stats: What do these numbers mean for how the sport is played?

Use your chosen example of statistics in sports performance analysis to make a point: maybe teams should shoot more corner threes, or maybe batting average really is overrated.

FAQ: Examples of Statistics in Sports Performance Analysis

Q: What are some simple examples of statistics in sports performance analysis for beginners?
Simple examples include tracking basketball free-throw percentages before and after practice, comparing first-serve percentages in tennis between wins and losses, or measuring how often a volleyball team wins points when serving to different zones.

Q: What is an example of a more advanced sports statistics project for high school?
A strong advanced example is using expected goals (xG) data in soccer to test whether teams that underperform their xG in the first half of a season tend to improve in the second half. This uses correlation, regression, and concepts like regression to the mean.

Q: Where can I find data for real examples of statistics in sports performance analysis?
League stat pages (NBA, NFL, MLB, MLS, Premier League) and reference sites often publish detailed, downloadable stats. For health- or physiology-related angles, you can use background information from NIH, CDC, or Mayo Clinic.

Q: How much math do I need to use real examples of sports statistics in a project?
For most middle and high school projects, basic algebra, percentages, averages, and simple graphs are enough. If you’re comfortable with correlation or basic hypothesis tests, you can push your analysis further, but you don’t need college-level math to work with meaningful data.

Q: Can I use my own team’s stats as an example of sports performance analysis?
Absolutely. That can be one of the best examples because you control the data collection. You can track your team’s shooting, serving, or running times, then analyze patterns across practices or games. Just be clear about your sample size and any limitations.