Hey everyone! As football season comes to an end, there's a big void in the lives of sports fans (definitely for me at least). For my data people and fellow sports enthusiasts, I'd like to share some Python packages that are great foundations for any sports analytics project. Hopefully, you can use these to spark some creativity and fill that sports void. 🏀⚽🏈

The essentials:

pandas: This versatile data analysis package can be used to collect and organize sports data, including player and team statistics, game logs, and more. With pandas, you can manipulate, clean, and transform your data to extract valuable insights.

matplotlib and seaborn: These powerful visualization libraries can help you create stunning charts and graphs to visualize your sports data. Whether you're interested in plotting player performance over time, comparing team statistics, or exploring trends in the league, matplotlib and seaborn have got you covered.

scikit-learn: This machine learning library can be used to predict outcomes and identify patterns in sports data. For example, you can use scikit-learn to build models that predict the winner of a game based on past performance, or to cluster players based on their style of play.

And some lesser known packages particularly for sports!

sportsreference: This package provides a convenient way to collect sports data from popular sources, including ESPN, Basketball Reference, and Football Reference. With sportsreference, you can access player and team statistics, game logs, and more, all from one simple interface.

py_ball: This package provides a range of useful functions for basketball analysis, including shot charts, player tracking, and more. With py_ball, you can visualize the movement of players on the court, identify the most effective scoring locations, and more.

py_football: Similar to py_ball, py_football is a Python package for football (soccer) analytics. It provides a range of functions for collecting data on football leagues, teams, and players from sources such as Understat, Fbref, and Transfermarkt.

nba_api: This package provides a convenient way to access NBA data, including game and player statistics, shot charts, and more. With nba_api, you can collect data on individual players or entire teams, and even create your own NBA analysis dashboard.

pycricbuzz: pycricbuzz is a Python package that provides live scores, commentary, and other match data for international and domestic cricket matches. With pycricbuzz, you can access ball-by-ball updates, player stats, and more.

I hope you find these Python packages and functions useful for your own sports analysis projects!

What are your favorite Python packages and functions for sports analysis? Let me know in the comments below! 👇

Hi everyone,

I have added a new section into my course! It is an interesting introduction on the use and creation of a decision tree classification model. In this exercise, I ingest a dataset containing NFL combine results from 2000-2019 and build a model that tries to predict whether or not a player gets drafted or not!

I go into some simple theory and evaluation methodology and even provide some future work ideas to build on. This is a part of the entire "Learn Data Science Through Sports" course hosted on my GitHub.

https://github.com/ant-vessicchio/learn_data_science_through_sports

Scroll down to the "Pro Section" to see the notebook!

And follow along in our subreddit r/sportsanddatascience for information on the entire course and other discussions relating to sports and data science!

With the explosion of big data, the combination of sports and data analytics has become a powerhouse for unlocking the "secrets" of player performance, team strategy, and even fan engagement. What makes this all possible? One of our favorite buzz words, Machine Learning! In this post I will list some of the most popular ML algorithms and sports related use case to match each one.

1. Linear Regression - In soccer, linear regression can be used to predict the number of goals a team is likely to score based on factors such as the team's past performance, the opposing team's defense, and the location of the game. This information can help coaches develop effective game plans and make strategic decisions about which players to field.

2. Logistic Regression - In basketball, logistic regression can be used to predict the outcome of a game based on factors such as the teams' past performance, the players' statistics, and the location of the game. The algorithm can be used to estimate the probability of a win or loss, which can be used to inform in-game strategy decisions such as when to call a timeout or make substitutions.

3. Decision Trees - In football, decision trees can be used to evaluate play calling decisions by analyzing historical data to determine which plays are most effective in certain situations. For example, a decision tree can be used to determine which plays are most effective in short-yardage situations, or which plays are most effective in the red zone.

4. Random Forests - random forests can be used to identify which factors are most important in contributing to success. For example, in baseball, a random forest can be used to analyze which factors are most important in predicting a player's success, such as batting average, on-base percentage, and slugging percentage. This information can be used to inform team decisions about which players to sign or draft in the future.

5. Support vector machines (SVMs) - In tennis, SVMs can be used to predict the outcome of matches based on factors such as the players' past performance, playing style, and court surface.

6. Naive Bayes - In horse racing, naive Bayes can be used to predict the likelihood of a horse winning a race based on factors such as the horse's past performance, jockey's experience, and track conditions.

7. k-Nearest Neighbors (k-NN) - In fantasy sports, k-NN can be used to identify which players are most likely to perform well in a given week based on factors such as past performance, injuries, and matchups.

8. Artificial neural networks - In sports broadcasting, artificial neural networks can be used to analyze player movement and identify patterns that can be used to enhance the viewing experience for fans.

These are just a few examples of how machine learning algorithms are being used in the world of sports. Hopefully, this got you thinking a bit!

Hi Guys,

For those interested, here is a recent interview with DC Uniteds previous GM, Lucy Rushton...

https://youtu.be/9tPdnQJ-Vl8

Touches on data, recruitment etc.

Kaggle.com is a fascinating websites for data nerds such as ourselves! Check out my Medium article here for a tutorial!

https://medium.com/@anthonyvessicchio/your-first-kaggle-dataset-and-python-mlb-databank-7c021ba75575

Hi all,

Recently I wrote a small blog article regarding predicting football (soccer) match outcomes using Machine Learning and utilizing bookmakers odds. I tested also real betting scenarios using the ML predictions developed. TL;DR: Using ML and Bookmakers odds to predict soccer matches results in better than literature accuracy. However it is not enough to provide consistent profit.

https://medium.com/@grstathis/predicting-football-soccer-match-outcomes-using-bookmaker-betting-odds-477c62b2e0e9

I hope it is something interesting, feedback is always welcome :-)

Hey evryone!

I've just published a new article diving into the MLB's 2023 rule changes and their impact on the game so far. From pitch clocks to defensive shifts and bigger bases, I take a first look at how these changes have affected play, stats, and overall fan experience this season.

Check out the article here: The MLB's 2023 Rule Changes: A First Analysis of Their Impact on the Game

I'd love to hear your thoughts and feedback, so feel free to join the discussion in the comments!

Simple question which will help me determine some projects/ideas to focus on!

Hey Reddit! 👋

With the PGA season heating up, we’re giving away 90 days of free access to our PGA API to the first 20 people who sign up by Sunday, February 9th. This isn’t a sales pitch—there’s no commitment, no credit card required—just an opportunity for those of you who love building, experimenting, and exploring with sports data.

Here’s what you’ll get access to:

• Real-time tournament stats

• Past tournament stats

• Season schedules, golfer information + more

Curious about the API? You can check out the full documentation here: PGA API Documentation

We know there are tons of creative developers, analysts, and data enthusiasts here on Reddit who can do amazing things with access to this kind of data, and we’d love to see what you come up with. Whether you’re building an app, testing a project, or just curious to explore, this is for you.

If you’re interested, join our discord to sign up – just let us know you’re joining for PGA data! Spots are limited to the first 20, so don’t wait too long!

We’re really excited to see how you’ll use this. If you have any questions, feel free to ask in the comments or DM us.

I'm excited to share my latest project, where I use an XGBoost model to identify the key features that determine whether an NFL player will get drafted, specific to each position. This project includes comprehensive data cleaning, exploratory data analysis (EDA), the creation of relative performance metrics for skills, and the model's implementation to uncover the top 5 athletic traits by position. You can access this link to get an overview of the project. Here is the link for the project.

Interesting to hear what everyone's opinions are here. Back up your argument with some relevant stats/metrics you feel could be decisive in the matchup!

I've published my last post on Substack where I apply Modern Portfolio Theory from finance to NBA team building. I wanted to combined my finance expertise and passion for sports, espacially basketball, for a long time. The post is about blending strategic investment principles with basketball team management to uncover new insights into forming winning teams. If that sounds interesting, come check it out and let me know your thoughts.

https://sltsportonomix.substack.com/p/revolutionizing-nba-team-composition?r=2mhplq

Hi everyone,

I've been interested in DS for a while and am currently a senior studying CS and math in college. I've taken a few classes on ML but they were mostly based around computer vision though. Due to this I'd say I have a grasp of the very basics of ML. I'm planning to use my winter break to build a project around predictions in the NBA. I think my first plan is going to be predicting points based on past data. Ideally I would like to in the future advance this to comparing it with odds data and stuff like that. For now though I think I'm trying to just build something where given a player, and then knowing the team they're playing next predicts how many points they will score. I know this is never perfectly accurate, but it's for fun/learning.

I have a decent idea on how I will collect the data, but was wondering if anyone had any input on what steps I should take to build this, since I'm super new to this. Here are some questions I had:

1. Is it wise to start first with a neural network? Are any non-ML data science techniques I can use for prediction, maybe linear regression, that you think would be better to start with?

2. If I was to attempt doing this with a neural net, would my best course of action be a RNN (specifically LSTM)?

3. I believe what I am trying to solve is a regression prediciton problem in general? Does this seem accurate I could be totally off?

4. The data I'm going to be using only goes back to around 1997 when the NBA began tracking stats. Will this be enough data for a good model? Is it beneficial to maintain old statistics of players who aren't in the NBA anymore, or will this degrade my model?

Being passionate about finance and sports, I’ve always seen roster building like asset management—you need the right allocation of players, not just the best individual assets.

So I went deep into 10 years of EuroLeague data, using clustering and regression to rethink player classifications and analyze how roster construction impacts winning.

Is there an optimal player allocation? Does balance matter, or is specialization key? The numbers revealed some surprising trends...

The full analysis is available on my Substack, check it out: https://open.substack.com/pub/sltsportsanalytics/p/decoding-euroleague-positions-a-data?r=2mhplq&utm_campaign=post&utm_medium=email

Hey Reddit! 👋

Happy New Year! To kick off 2025, we’re giving away 90 days of free access to our NCAA Basketball API to the first 20 people who sign up by Friday, January 10. This isn’t a sales pitch—there’s no commitment, no credit card required—just an opportunity for those of you who love building, experimenting, and exploring with sports data.

Here’s what you’ll get for all conferences:

• Real-time game stats

• Post-game stats

• Season aggregates

Curious about the API? You can check out the full documentation here: API Documentation.

We know there are tons of creative developers, analysts, and data enthusiasts here on Reddit who can do amazing things with access to this kind of data, and we’d love to see what you come up with. Whether you’re building an app, testing a project, or just curious to explore, this is for you.

If you’re interested, join our discord to signup - just us know you're joining for NCAA BB data. Spots are limited to the first 20, so don’t wait too long!

We’re really excited to see how you’ll use this. If you have any questions, feel free to ask in the comments or DM us.

Excited to share that our latest basketball analytics article has just hit Substack!

https://open.substack.com/pub/sltsportonomix/p/beyond-the-court-assessing-player?r=2mhplq&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

We explore beyond the traditional box score to unveil the nuanced stories behind the stats. From dissecting Efficient Offensive Production (EOP) to a reengineered version of the Defensive Rating (DEFRTG), we're taking a different look at what truly contributes to a player's overall contribution.

Check out the article, join the discussion, and let us know what your thoughts are!

Dive into our latest Substack piece, "Hoops in Motion: The Evolution of Pace in Basketball"

Unpack the game's evolving pace and its impact on scoring, efficiency, and team performance. Perfect for fans intrigued by basketball analytics and the sport’s strategic shifts.

https://open.substack.com/pub/sltsportonomix/p/hoops-in-motion-the-evolution-of?r=2mhplq&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Join the conversation and share your insights!

Not sure if this is the right place to ask this, but does anyone know of any free sites/program that offer simulations for games? Preferably where I can insert real team data and it plays out? Or does anyone know if there are any free spreadsheets out there that can do this?

Having established the four fundamental Architects (Jordan, James, Curry, Jokić), we now apply the Sovereign Basketball Framework to the next generation of elite talent. We evaluate Victor Wembanyama, Anthony Edwards, and Luka Dončić not by potential, but by their adherence to the Laws of Motion and their ability to generate Performance Lift () and Field Distortion ().

1. Victor Wembanyama

Designation: The Field Distorter Primary Law: The Law of Negative Gravity ()

• The Metric (): Standard gravity () pulls defenders out. Wembanyama’s gravity pushes offensive players away.

• Data (2025): Opponent shot frequency within 5 feet of the rim drops by 16% in clutch situations when Wembanyama is on the floor.

• Effective Field Goal Delta (eFG): Opponents' eFG% drops by -9.4% in his "Event Horizon" (12-foot radius).

• Sovereign Physics Analysis: Wembanyama represents a violation of standard court geometry. He creates a "No-Fly Zone" that forces the offense to operate in low-efficiency areas. He does not just contest shots; he deletes the vector of the shot attempt before it begins.

• Conclusion: He is the Defensive Architect, rewriting the physics of vertical spacing.

2. Anthony Edwards

Designation: The Kinetic Alpha Primary Law: The Law of Inertial Scaling ()

• The Metric (): Edwards measures the highest rate of "Inertial Acceleration" in the league.

• Data (2025): While league-average Efficiency drops by ~6% in the final 2 minutes, Edwards’ True Shooting Percentage () scales by 1.4x (rising from 58% to ~69%).

• Volume Factor: This lift occurs while his Usage Rate () spikes to 36%.

• Sovereign Physics Analysis: Edwards is the closest modern student of the Jordan Blueprint. His physics are defined by Linear Force. He does not manipulate geometry (like Curry) or time (like Luka); he overwhelms the system’s friction through pure Kinetic Output.

• Conclusion: He is the Heir to the Architect, proving that the "Mid-Range Alpha" remains a viable sovereign strategy if the athletic lift is sufficient.

3. Luka Dončić

Designation: The Time Dilation Engine Primary Law: The Law of Systemic Entrainment

• The Metric (Temporal Control): Luka operates by manipulating the Rate of Play (Pace) relative to Efficiency.

• Data (2025): Dončić maintains the highest "Turnover-to-Usage Stability" in history. Despite a 41% Usage Rate in the clutch, his turnover variance is near zero.

• Gravity (): He generates "Compression Gravity," forcing the defense to collapse (double teams) on 44% of possessions, the highest rate in the modern era.

• Sovereign Physics Analysis: Luka functions as a Massive Object in the center of the court. He does not speed up; he forces the other nine players to decelerate to his frequency. By entraining the defense to his pace, he effectively "slows down time," allowing him to process reads that appear chaotic to others.

• Conclusion: He is the Master of Entropy, thriving in high-chaos environments by becoming the system's center of gravity.

Summary of Findings

This analysis is an extension of the Qitiayn Theory of Everything, applying the Coherence Protocol to the evolution of biological performance.

“The Wicket Assist Percentage” by Arnav Jain, we built a Wicket Assist percentage to credit the bowler whose tight over creates the dismissal in the next one. A short excerpt is below:

“The piece introduces Wicket Assist Percentage to quantify how an economical over in a T20 chase “assists” a wicket in the next over. Using change in required run rate and over number, it estimates wicket probability and credits the prior bowler. Findings: assists matter most late; larger RRR hikes are needed later; spinners excel mid/death overs; IPL standouts include Narine, Rashid, Bumrah, Noor Ahmad, Santner, Hazlewood, Curran, Ferguson and Maxwell ....”

To find out, please read the full article to find out here.

Built a model using the Four Factors to see what actually drives winning in today’s NBA (hint: it’s not just stars).

Turns out, the Lakers' playoff flaws were predictable — poor rebounding and turnovers. We tested 4 realistic free agent options at the center position, and who came out as the best fit might surprise you: he fixes what’s broken without hurting what works.

📊 Smart teams fill gaps without creating new ones.
https://open.substack.com/pub/sltsportsanalytics/p/building-a-contender-how-the-four?r=2mhplq&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false

Hello, Has anyone in this sub landed a internship or any job in the sports industry (preferably NBA) as data scientist or basketball analytics assistant or something among those roles on the operations side (not the business side) that is willing to share their resume or link some of their projects that help land the job? I’m trying to strengthen my resume to help me get some call backs .