Smartphone-enabled Football and Basketball Game Analytics

Photo of the UB bull mascot.

Are you interested in sports and in particular, Football (US) and Basketball? Are you familiar with voice recognition, and speech-to-text (STT)? If so, we have an interesting project for you.

Project description

Currently, coaches manually take notes about game statistics (including players performance) during practices and games for future evaluation and analysis. There are a few automated systems which rely on wearable and infrastructure sensors, as well as advanced computer technologies such as wireless sensing and communications and computer vision to gather the performance and statistics. However, such systems are costly and thus not feasible for most teams.

The objective of this project is to develop an effective system that relies on coaches, smartphones and cloud, speech-to-text (STT) or voice-text (V2T), and natural language processing (NLP) technologies to gather statistics. The basic idea is to ask (assistant) coaches to vocally comment during practices/games using their smartphones, and use the V2T/STT and NLP technologies to automatically generate reports. Each coach on the team may focus on some aspects of the play (defense or offense side), or certain players, but the system will automatically fuse the reports together to generate a complete report.

Ideally, the game statistics can be made available and updated in near real-time, so the coaches can adjust their strategies during time out periods, intermissions or half-time.

Application Instructions -
We are looking for highly motivated and skilled students (with knowledge about the games and strong coding skills) who are willing to work in the Fall’22 semester for credits, or as a volunteer (please note that there is no pay and if you register for a course, you can earn course credits by working on this project without having to do other course work but you are responsible for paying the tuition as well).

Interested candidates should send an email with “Subj: V2T/STT for Sports Analytics” and include their resume to,,
In the email, the candidates should also mention-
1. Position(s) they are interested in (mobile, backend, web)
2. Details of related experience in software development, esp in terms of V2T/STT and NLP.
3. The number of hours per week they are available to work on the project, and
4. whether they are interested in registering a relevant course (e.g., independent study or supervised research or a project course such as CSE 611) to earn credits.
The applications will be reviewed on a rolling basis until the positions are filled, so please apply asap.

Project outcome

A smartphone app that takes voice input, parses it and generates a game report, along with analytics results that can be accessed through smartphones or a web portal.

Project details

Timing, eligibility and other details
Length of commitment Longer than a semester (6-9 months)
Start time Fall  
In-person, remote, or hybrid? Hybrid
Level of collaboration Small group project (2-3 students)
Benefits Academic credit
Who is eligible Juniors and seniors with some knowledge of Football and Basketball; V2T/STT, Natural Language Processing; Mobile App; Web development

Core partners

  • Select local high school and college teams and coaches

Project mentor

Chunming Qiao

SUNY Distinguished Professor

Computer Science and Engineering

Phone: (716) 645-3180


Start the project

  1. Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
  2. After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.) 

Preparation activities

The specific preparation activities for this project will be customized through discussions between you and your project mentor. Please be sure to ask them for the instructions to complete the required preparation activities.


sports, analytics, AI/ML, mobile app, computer science and engineering