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Sports Innovation Institute IU Indianapolis

Sports Innovation Podcast: Introducing the SII Director, Dr. Liz Wanless

Posted on by Geoff Sherman

by Dr. Liz Wanless, director of the Sports Innovation Institute

My First Semester: A Thrill Ride in Indianapolis Sport Innovation
Reflecting on my first semester as Director of the Sports Innovation Institute and Associate Professor at Indiana University Indianapolis, I am struck by the dynamic opportunities, inspiring connections, and the extraordinary support that have defined this new chapter in my career. From day one, the fast-paced energy of this sports-obsessed city swept me up and set the stage for me to join the richly woven fabric of Indiana University Indianapolis and Indianapolis sports.

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Sports Innovation Podcast: High-Impact Practices in Higher Education – Capstone

Posted on by Geoff Sherman

Dr. David Pierce, Scott Spillman, and Bryan Kryder after the capstone presentations at Mudsock Youth Athletics in Spring 2024.


by The SII Team

As part of an initiative undertaken by the IU Indianapolis Institute for Engaged Learning, the Sports Innovation Podcast developed a special episode focused on High-Impact Practices in Higher Education.

Department of TESM chair Dr. David Pierce and Right Hand co-founder and CEO Bryan Kryder sat down and discussed the high-impact practice of Capstone courses in the sports management curriculum at IU Indianapolis. They talked about the 11 taxonomies that exist at IU Indianapolis through the IEL and how they have seen iteration after iteration of the course come to pass with success.

External partnerships are vital to a Capstone course and the duo has partnered with entities like Grand Park Sports Campus, Lucas Oil Raceway, and Indiana Sports Corporation to solve problems and answer pressing questions in the communities they serve. They discussed the partnerships, their methodologies during the class, and how instrumental the capstone can be in graduates getting positions in the job market.

#Indy4Sports #SportsInnovation #HighImpactPractice

Predicting Men’s College Basketball Win Percentage and Close Game Percentage

Posted on by Geoff Sherman

by Chin-yu Wu, School of Informatics graduate student

As a spectator watching a basketball game, I always want to know how close my team is to winning the game when the team is having the lead. On the other hand, when the team is trailing I want to know the probability of coming back under this situation, or even in a game that is not that close I always wonder whether the game will come down to the wire or not. These numbers and information sometimes could be estimated by some experienced fan’s intuition, but for a spectator that does not have that much experience watching the game would see this information very valuable.

The main product of this project is a dashboard that could let the users interact. The dashboard could let the users choose the margin of points and time left on the play clock (20:00, 16:00, 12:00, 8:00, 4:00), then the probability of winning and the game becoming a close game would come out. The reason I choose these time periods is because these are the college media timeouts. In this research, the definition of a close game is under 4 minutes and the margin of points is within four. The probability of the game winning by the home team is based on logistic regression and the probability of it becoming a close game is based on a 3-layer neural network. The data I used is all the conference games from the 2023-2024, 2022-2023, and 2021-2022 seasons. The reason I only used conference games is because these games are more evenly matched. It will have fewer games that are won by 20 or more points.  so the result of the model is more valuable and accurate.

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Soccermetrics: Analyzing Division I Men’s Soccer Data in 2023

Posted on by Geoff Sherman

by Will Emhardt, School of Informatics graduate student

The dataset I decided to use for this project was the previous season of Division 1 Soccer, ending in December of 2023. Using college soccer data, I only pulled the list of contests for the 2023 season and then the play-by-play for every contest in 2023. Initially I set out to create a heatmap displaying all goals and shots with filters like time in the game, location on the field, and many more. As I dug into this objective I garnered curiosity to explore more realms within the soccer game.

First, I created the heatmap on a soccer pitch to show high and low density areas for shots and goals, then I decided I should have an option to show the same data as a scatterplot. Second, I created a set piece metric to show the attacking capability of teams off of set pieces and when defending set pieces. Third, I created a corner kick metric to also show the propensity to score immediately after a corner kick, or on the other side the capability of defending a corner kick. Lastly, I included a more general scatterplot with the ability to track individual athlete’s statistics. The general idea of the individual performance scatterplot was to display the certain strengths of players throughout Division 1.

All of these visuals and metrics are hosted on a Streamlit app where the user can filter the data however they please.

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Estimating Win Probabilities of Division I FBS Football Plays

Posted on by Geoff Sherman

by Elliott Scott, School of Informatics graduate student

This report analyzes football game states using college sports data to predict win probabilities for various game situations. The dataset comprises 1.6 million rows of play-by-play data from Division I FBS football games, focusing on play types like punts, field goals, passes, and rushes. Each play type is independently modeled using a generalized linear model to estimate win percentages, considering variables such as yards to the end zone, down number, yards to go, period number, and score difference. Results are presented through an interactive Shiny app, enabling users to adjust game states and calculate win percentages. This tool offers strategic insights to enhance decision-making in football. The study recommends incorporating time data and leveraging unused spatial data columns to improve model accuracy and predictive capabilities.

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Analyzing Division One Hockey Soccer Performance Metrics

Posted on by Geoff Sherman

by Mark Ronad, School of Informatics graduate student

This summer, I had the unique opportunity to dive deep into the dynamic and ever-evolving world of sports analytics, with a particular focus on Division One hockey and soccer teams. My project, aptly named “Performance Metrics Analysis for Division One Teams,” was an intensive exploration of performance data from the 2023-2024 season. By leveraging advanced data science techniques, I aimed to uncover insights that could provide valuable guidance to coaches, players, and analysts, ultimately enhancing their strategic decision-making and performance evaluation processes.

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IceScout Pro: Using Collegiate Hockey Data to Establish a Player Scouting Platform

Posted on by Geoff Sherman

by Anurag Reddeddy, School of Informatics graduate student

This project was inspired by my exploration of various ice hockey platforms. While the leaderboards on these platforms were informative, I noticed a lack of detailed player shot maps, on-ice tendencies, and shot conversion rates. IceScout Pro was created to address this gap by presenting these insights in a creative and comprehensive manner, highlighting individual player performance and team dynamics. IceScout Pro aims to develop an advanced analytical tool to enhance the evaluation of team and player performance in Division I men’s and women’s ice hockey.

By leveraging detailed play-by-play and game-by-game data from the 2022, 2023, and 2024 seasons, the project provides coaches, scouts, and analysts with comprehensive insights into player behavior and team dynamics. I have developed a Shiny web application and a Tableau dashboard specifically for coaches and analysts to facilitate team analysis, opposition analysis, and scouting. The application and dashboard include team and player leaderboards, player performance profiles, which feature player shot charts, on-ice tendencies, and shot conversion rates.

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The Crumbs: Examining Offensive Rebound and Forced Turnover Success Rate in Men’s College Basketball

Posted on by Geoff Sherman


by Kael Ecord, School of Informatics graduate student

My project was titled “The Crumbs: Examining Offensive Rebound and Forced Turnover Success Rate”. The goal of my project was to use college basketball data from all games played by Big 12 Conference teams from 2018-19 through 2023-24 to create two new metrics. The first being offensive rebound success rate and the second being forced turnover success rate. The goal of creating these new statistics was to create a way for teams, fans, and media members to quantify the value of a team’s offensive rebound. This was accomplished by analyzing how often a team was successful after securing an offensive rebound or forcing a turnover.

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Building a Healthy Ecosystem for Volunteer Youth Sports Coaching

Posted on by SII Team

by David Pierce, Ph.D.

During the spring 2024 semester, our senior capstone course in Sports Innovation at Indiana University Indianapolis partnered with Mudsock Youth Athletics (MYA) in Fishers, IN, and the Center for Sports Transformation (CST) to examine recruitment, retention, and quality of volunteer youth sports coaching and present ideas that would help MYA to continue to create a healthy ecosystem for coaching. The students interviewed 45 youth sports coaches in Mudsock, and the CST held three four-hour zoom meetings with members of the Mudsock Board. Students watched these board meetings as part of their research process.

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