I'm working on a project to determine the best rebounders since 2000. The NBA player tracking stats ( https://www.nba.com/stats/players/rebounding ) include a neat statistic called "Rebound Chances" dating back to 2013-14. From that season onward, I have been able to analyze the best and worst "rebounders above average" by dividing rebounds by rebound chances and comparing to the league average.

I'm trying to estimate rebound chances per game for players over the prior 13 seasons. I've developed a couple of regression models in R, but the errors, especially for the top rebounders, have been too large for my liking. My best regression models have used individual REB percentage and REB per game.

I appreciate any ideas, and I'm happy to share some of my results for the past 11 seasons!

Hi,

I am looking for courses related to football data science.
I know there is plenty of resources on Youtube,Github and etc.

I have already done soccermatics course.

Also maybe potentially you noticed some nice black friday deal?

Hey! I am tasked with doing the stats and seeding for my local softball league. And though I took stats in uni a decade ago I can't wrap my head around how to normalize the data when we have so many "non comparable" factors involved in how we set up the season.

Overview:

• There are 23 teams: Div A with 5 teams, and Div B, C, and D with 6 teams each.
• You don't necessarily play the other teams in your division an equal amount of times because we allow cross-divisional play, so the match-up matrix can get complicated.
  • We calculate "expected points" when making the matrix - as in if the team beats every team below them and loses to all teams above, how many points do they get - and we make sure if all teams perform as expected they would remain in their place at the end of the year.
• Points: you get 4 points for a win against the highest division (A) and 1 point for a win against the lowest division (D) and a for a tie you get half of the points you would get if you had won (so a B team playing A would get 2 points for a tie but that A team would get 1.5 points)
• There is a Finals Tournament at the end of the year with 4 games per team.
• Final standings for seeding is made of results from regular season (weighted 70%) and results from final tournament (weighted 30%)

Questions:

• Since each team has an "expected points" value, should I use that in some way to help normalize the overall points? i.e. if a team had easier match-ups should that matter if they simply got less points than a team above them?
• While regular season points include wins and ties, past stats people have given the Finals tournament results a value between 1 (= team placed last) and 6 (team placed first) in terms of how a team placed. Is that sufficient?
  • Ex. 6 teams in Div B, Team X finish 2nd so they get 5/21 points (where 21 equals total possible points in the tourney)

Overall I just can't quite figure out how to normalize for 1) different match-ups within a division across a season, and 2) adding regular season and finals tournament results together in a way that puts them on equal footing.

No the time it’s supposed to start - the actual time the event starts. Does anyone know where I can get a database of “actual” start times?

The intuitive answer seems to be yes but I’ve seen some analytics people suggest that there isn’t a relationship between FG distance and likelihood of being blocked. Was wondering if anyone has anything to add to this or data in either direction. Thanks!

Here is an example:

Man City: 36% | DRAW: 39% | Tottenham: 25%

https://preview.redd.it/m2bils9zxa2e1.png?width=1080&format=png&auto=webp&s=85ad60143fd18d502326f2396982684da418c38c

Its based on the stats:
- Points in the last 5 games: Weight 100%
- Goals Scored in the last 5 games: Weight 100%
- Goals Conceded in the last 5 games: Weight 100%
- Head to head Points in the last 3 games: Weight 100%
- Home/Away Points in the last 3 games: Weight 100%

What do you think?
In the app I have more stats to chose from, and the weight can of course be set as preferred to change the predictions

Does Longomatch/Nacsports/... have some blackfriday deals? In 2023 NacSports had a Black Friday deal. But, it seems they have no deal this year. Do you know a good alternatieve that have a blackfriday deal? I'm a scout/va for a lower league team in Belgium and want to tryout/learn a va program.

Anyone know how to get the documentation for an endpoint using the above in the nba_api library?

Hi,

I'm looking at creating a football statistical model to predict the score of a game.

Some people seem to use the negative binomial distribution for this, but I can seem to with it what the parameters are. Is it something like number of successes given a number of shots? But then how would you know how many shots would be in a game?

Anyone have any links on modelling football?

Hello everyone,

I would like to create my own database for the league in which my favorite club plays. This is a lower league where you can't find values like expected goals etc. at Sofascore or Fotmob.

I can access these values via a data provider, but I don't know how to get started.

On the one hand, I would like to have a dashboard for each game, like in Football manager in the photo. Is it possible to create something like this with Tableau, Looker Studio or another program?

https://preview.redd.it/kdxbfx8uo12e1.png?width=3292&format=png&auto=webp&s=d20e0d5f5edd2561371341c15903f11d9d43e6c8

On the other hand, I'm not sure how to manage the players and team data and add them after each game

For each team an Excel where I add the new values of the players? Or a database program?

Thanks to anyone who can help me

Hey everyone!

I am very interested in getting into the sports analytics industry. I have a bachelors in psychology, but sports is my passion, and I also love math. Do you have any recommendations on what route I should take?

Hey everyone,

I wanted to share a project I've been working on that I believe could benefit many in our community. A while back, a friend introduced me to his method of collecting GAA and Football match statistics using Python and Jupyter Notebook. While his approach was effective, I realized that not everyone has the coding skills or time to set up such a system. This sparked an idea: why not create a tool that allows anyone, regardless of technical background, to collect and analyze match stats easily?

That's when I went to work building Scorelect —a web application designed to simplify the process of recording and analyzing sports data, with a focus on GAA and Football matches. The platform enables users to:

• Record Game Events: Log various match events by selecting actions and marking their occurrence on a virtual field.
• Analyze Performance: Access tools to evaluate team and player performance through the collected data.
• Export Data: Easily export the data for further analysis or record-keeping.

Currently, it's just me working on this project, and I'm continuously adding features to enhance its value. My goal is to develop a tool that's genuinely useful for coaches, players, and enthusiasts alike. I'm currently trying to work on a Pro version with additional features to support the project's sustainability.

I would greatly appreciate any feedback or suggestions you might have. Whether it's about the user interface, functionality, or features you'd like to see, your input would be invaluable in helping me improve the app.

Thank you for taking the time to read this, and I look forward to hearing your thoughts!

Best regards,

Hi all,

As the title says, does anyone have experience or success in SaaS within the sports industry?

I’ve been in SaaS for 8 years, working across different areas with experience in growth, product, marketing, and data. While I’ve enjoyed it, I haven’t yet found a product I’m truly passionate about.

I’m really into sports, especially basketball, and I feel like my skills could fit well in sports tech. I focus on full-funnel growth - customer journeys, experiments, optimizing onboarding, improving retention, refining pricing strategies, driving user acquisition, and more

Has anyone worked in the sports space? Whether it’s analytics, fantasy, or something else, I’d love to hear your experiences or recommendations. Thanks!

Hey everyone, I’m currently a junior in high school and I’m really interested in sports and stats. Last year I found out about coding and how to code, but unfortunately it was only a one year class. I wanted to ask you guys what’s the best coding language to learn for sports data analytics, and what’s the best place to learn it by yourself? Thanks in advance!

When investigating injuries for a recent basketball project, I realized there aren't any great sources of injury data. So, I created one!

https://statsurge.substack.com/p/creating-an-nba-injury-database

This daily database updates with each morning's injury report. All of the data is available for free download! Hope this helps with your various projects, and let me know if you'd like to see anything added.

Hello everyone. I want to know where one can find IN-GAME live win probability CHARTS for MLB, NBA, NHL, and NFL games. I am trying to self learn analytics, but am desperately looking for live in-game win probability charts for all sports (kind of like what Baseball Savant or Fangraphs has for baseball.....but better if possible because the graph UIs for those suck). There are some "gambling" sites that charge and I don't even think they show live in game win probability charts. And I know some sites give the chart say 24 hours after a game. I'd love to find where one can find these LIVE in-game charts, not after the game. Extra points if the graph is log!!!! Thank you.

Hi all,

I'm working on a data analytics project focused on NFL ticket pricing and strategy, and I’m hoping to tap into this community for advice on finding good data sources. Specifically, I’m interested in historical and real-time ticket prices, attendance trends, sales data, and any relevant factors (e.g., game location, team performance, weather conditions) that might influence ticket pricing and demand.

Does anyone have recommendations for sources—free or paid—that provide this kind of data? I’ve come across sites like Ticketmaster and StubHub, but access to bulk data is limited. Are there APIs, datasets, or research tools that provide in-depth or historical ticketing data for NFL games?

Any guidance or tips would be appreciated. Thanks in advance!

Hey all, hoping someone can send me in the right direction. I’ve just purchased analyzing baseball data with R (second edition) by Jim Albert, and really struggling to get going. I have never used R and just new in general to the data science field, I’m trying to self teach all of this to pursue my dream of working in a sport organization. I am pretty advanced in SQL but that’s it. I am on chapter 1, downloaded the Lehman files and R on my laptop. I am going though the questions on 1.2.8, but struggling highly to get going at all.

I was wondering if anybody knows if there’s some sort of walkthrough/cheat sheet I can use online to get myself familiarized with the exercises? The book mentions GitHub has all of this, but when I check the actual exercise walk through, the questions seem to be completely different. Any help highly appreciated!

I do some work in women's sports, specifically the unpopular ones that don't have actual databases. I've tried scrapping using the importxml function in excel and I have tried a couple of methods on R, but nothing seems to actually pull the data. Does anyone have any advice so I don't have to copy and paste for 3,000+ players?

Example website for people unfamiliar with format: https://goheels.com/sports/womens-volleyball/roster/zoe-behrendt/25494

Hey all not sure if this is the right place to post this but figure someone might find this useful. I recently created a Sofifa Dataset CSV and thought that might be useful to some people here.

https://www.kaggle.com/datasets/jmacd745/sofifa-data-set

Hey everyone,

We’re offering an opportunity for sports video analysts to try out a new tagging and distribution tool designed to streamline video workflows.

Developed in the Nordics with a global focus, this software helps you:

•	Tag and organize key moments effortlessly
•	Distribute content efficiently to your team
•	Optimize video management for easy access

Interested? Leave a comment or PM for more details.

Requirements: have a Mac OSX device or iPad.

Best

Here's an example:

https://preview.redd.it/76wlmsibvc0e1.png?width=1080&format=png&auto=webp&s=ba820129ebdae288404805dae8089e90fc446e1d

There are a bunch of filters to and some other graphs below to view some trends and tendencies.

https://nbashotanalysis.streamlit.app/